POPULATION AND HEALTH IN DEVELOPING COUNTRIES

POPULATION AND HEALTH IN

DEVELOPING COUNTRIES

V O L U M E 1

POPULATION AND HEALTH IN

DEVELOPING COUNTRIES

V O L U M E 1

Population, Health, and Survival

at INDEPTH Sites

INTERNATIONAL DEVELOPMENT RESEARCH CENTREOttawa • Cairo • Dakar • Montevideo • Nairobi • New Delhi • Singapore

Published by the International Development Research CentrePO Box 8500, Ottawa, ON, Canada K1G 3H9http://www.idrc.ca

© INDEPTH Network 2002

National Library of Canada cataloguing in publication data

Main entry under title :Population and health in developing countries. Volume 1. Population, health, and survival atINDEPTH sites

Includes bibliographical references.INDEPTH : International Network for the continuous Demographic Evaluation ofPopulations and their Health.ISBN 0-88936-948-8

1. Public health surveillance — Developing countries.2. Public health surveillance — Africa.3. Health planning — Developing countries.4. Public health — Developing countries — Statistics.5. Public health — Africa — Statistics.6. Developing countries — Population — Statistics.7. Health status indicators.I. INDEPTH Network.II. International Development Research Centre (Canada)

RA652.2.P82.P66 2001 614.4’22724 C2001-980345-1

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted,in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior permissionof the International Development Research Centre. Mention of a proprietary name does not constitute endorse-ment of the product and is given only for information.

IDRC Books endeavours to produce environmentally friendly publications. All paper used is recycled as wellas recyclable. All inks and coatings are vegetable-based products. The full catalogue of IDRC Books is availableat http://www.idrc.ca/booktique.

CONTENTS

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ixPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xiiiIntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

PART I. DSS CONCEPTS AND METHODS

Chapter 1. Core Concepts of DSS

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7Demographic surveillance systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7Demographic surveillance area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8Longitudinality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8Primary DSS subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9Eligibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11Residency and membership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12Core DSS events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12Episodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14Other events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Chapter 2. DSS-generated Mortality Rates and Measures

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17Rates and ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17Standardization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20Confidence intervals for rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

Chapter 3. DSS Methods of Data Collection

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21Establishing the monitored population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22Planning for data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23Initial census . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23Update rounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23Recording demographic events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26Monitoring mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27Tracking migrants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28

v

Additional rounds of data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29Geographic information systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

Chapter 4. Processing DSS Data

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32The INDEPTH concept of a data core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33The reference data model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35The role of the reference data model in maintaining data integrity . . . . . . . . . . . . . . . . . .39Extending the core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

Chapter 5. Assessing the Quality of DSS Data

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43Assessing data quality in the field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43Assessing data quality at the data centre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47

PART II. MORTALITY AT INDEPTH SITES

Chapter 6. Comparing Mortality Patterns at INDEPTH Sites

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51Age-specific mortality rates and life tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .52Crude death rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53Child mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57Adult mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61Annex: Life tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .63

Chapter 7. INDEPTH Mortality Patterns for Africa

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83Mortality models and Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83Principal-components analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87Principal components of INDEPTH mortality data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .89INDEPTH mortality patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .96Demonstration of the HIV–AIDS model life-table system . . . . . . . . . . . . . . . . . . . . . . . . . .111Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .114Annex: AIDS-decremented model life tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .115

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PART III. INDEPTH DSS SITE PROFILES

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .129

Chapter 8. Butajira DSS, Ethiopia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .135

Chapter 9. Dar es Salaam DSS, Tanzania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .143

Chapter 10. Hai DSS, Tanzania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151

Chapter 11. Ifakara DSS, Tanzania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .159

Chapter 12. Morogoro DSS, Tanzania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .165

Chapter 13. Rufiji DSS, Tanzania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .173

Chapter 14. Gwembe DSS, Zambia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .183

Chapter 15. Manhiça DSS, Mozambique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .189

Chapter 16. Agincourt DSS, South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .197

Chapter 17. Dikgale DSS, South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .207

Chapter 18. Hlabisa DSS, South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .213

Chapter 19. Nouna DSS, Burkina Faso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .221

Chapter 20. Oubritenga DSS, Burkina Faso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .227

Chapter 21. Farafenni DSS, The Gambia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .235

Chapter 22. Navrongo DSS, Ghana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .247

Chapter 23. Bandim DSS, Guinea-Bissau . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .257

Chapter 24. Bandafassi DSS, Senegal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .263

Chapter 25. Mlomp DSS, Senegal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .271

Chapter 26. Niakhar DSS, Senegal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .279

Chapter 27. Matlab DSS, Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .287

Chapter 28. ORP DSS, Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .297

Chapter 29. FilaBavi DSS, Viet Nam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .305

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Appendix 1. Working Examples of DSS Forms

Example 1. DSS Baseline Form (Rufiji DSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .312Example 2. Household Registration Book (HRB) (Rufiji DSS) . . . . . . . . . . . . . . . . . . . .313Example 3. Pregnancy Outcome / Birth Form (Rufiji DSS) . . . . . . . . . . . . . . . . . . . . . . .315Example 4. Death Registration Form (Navrongo DSS) . . . . . . . . . . . . . . . . . . . . . . . . . . .316Example 5. Marital Status Form (Butajira DSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .316Example 6. VA Form: Deaths of Children from Day 31 to 5 Years (Morogoro DSS) . . . .318Example 7. In-migration Form (Navrongo DSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .320Example 8. Out-migration Form (Navrongo DSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .321

Appendix 2. Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .323

Appendix 3. Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .327

Appendix 4. Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .333

ix

FOREWORD

Traditional sources of health information collected from health facilities often serve asthe basis for health-services planning and allocation of resources in many parts of thedeveloping world. Yet, health-facility-based data provide only fragmentary and biasedinformation. Not all population groups have geographic or economic access to healthfacilities. Those that do have such access are usually self-selected and are often thosewho visit health-care centres only when they suffer from a serious illness. A greatmajority of poor people may have less access to health-care facilities than those whoare better off, and poor people often treat themselves or use nontraditional healthcare. Women may suffer gender disparities as well, with time and cultural constraintson the use of health-care facilities, particularly in rural settings. Services for childrenare also severely constrained. Thus, health-facility-based data are not representative ofthe health problems of all rural and urban communities and do not therefore reflecttheir health status.

This void of valid health information for a large segment of the world’s popula-tion makes it difficult for policymakers to formulate rational health policies toimprove the health of these people. As the authors of this book argue, “the need toestablish a reliable information base to support health development has never beengreater” (INDEPTH Coordinating Committee, this volume, p. 1). Ideally, reliablehealth information should be population and community based, inclusive of allgroups, and collected prospectively and continuously. Such an ideal is best metthrough demographic and health surveillance systems collecting demographic andhealth data on selected population samples. Often, randomly selected cross-sectionalhousehold surveys every few years complement these methods of research.

Demographic and health surveillance systems serve a number of functions:

• They provide health information that more accurately reflects the prevailingdisease burden of populations;

• They assist in monitoring and tracking new health threats, such as emergingand reemerging infectious disease and drug resistance, and alert the healthcommunity to prepare a response; and

• They can serve as a platform for action-oriented research to test and evaluatehealth interventions, such as new vaccines or drugs, health-education messages,and the cost-effectiveness of initiatives.

The premier example of such a system is the Health and DemographicSurveillance System (formerly known as the Demographic Surveillance System) ofMatlab, Bangladesh, which started operations in 1963 as a major component of the

x ✦ Foreword

field research program of the International Centre for Diarrhoeal Disease Research,Bangladesh. It is recognized as the largest and longest sustained prospective longitudi-nal demographic and health surveillance of any population in the world. It has madesignificant contributions to health development in both Bangladesh and the rest ofthe world. The high cost of running such a system has delayed replication in otherparts of the developing world. However, thanks to the fast-paced development of user-friendly computers, this constraint has been partially overcome.

Over the last decade, a growing number of community-based field stations haveevolved in Asia and sub-Saharan Africa and started to generate reliable longitudinalpopulation-based health and demographic data. This bodes well for countries withsuch stations, as it marks the first step toward rational health planning and meaningfulhealth programs for the people of these countries. Recently, these stations joined toform a network called the International Network for the continuous DemographicEvaluation of Populations and Their Health in developing countries (INDEPTH), cre-ating “a trans-continental resource of robust, longitudinal, health and demographicdata in some of the most information deprived settings in the world” (INDEPTHFounding Document; http://www.indepth-network.org). In the span of a few years,INDEPTH has matured rapidly, succeeding in strengthening the capabilities of mem-ber sites and developing strategies to harness their potential to redress long-standinginequities in health. This development has been possible because of the dedicationand hard work of a few individuals, and this monograph is clearly an indication of thehigh quality of the network’s work.

The emergence of INDEPTH should be welcome news to the donor commu-nity, where people often, and rightly, complain that the programs they fund in low-income countries are not usually based on the real needs of the people. By the sametoken, donors should come out strongly in support of INDEPTH, because they will beinvesting in an initiative that directly addresses one of the major constraints of devel-opment assistance. Researchers in program countries should also take advantage ofthe INDEPTH sites to promote essential national health research. The domination ofhealth-facility-based biomedical research should give way to policy-relevant researchwith the likelihood of a more immediate effect on the health of the people in thecountries in the program.

Demissie HabteWorld BankWashington, DC1 June 2001

PREFACE

This monograph is the first in a series from the International Network for the continu-ous Demographic Evaluation of Populations and Their Health in developing countries(INDEPTH). It seeks to do several things. First, it seeks to compile, for both easy refer-ence and comparative purposes, and in detailed and summary formats, the essentialcharacteristics of each participating demographic surveillance system (DSS) site.Second, it seeks to present, for the first time, the mortality structure of each of thesesites in a coherent and comparative format. Third, based on a network-wide analysis ofthe African site data, it proposes a methodology to generate, again for the first time,African model life tables that are based on objective empirical data.

The focus of this volume is the structures of populations at INDEPTH sites andthe characteristics of their health and survival. The monograph is divided into threeparts: Part I discusses core concepts and methods used in DSSs; Part II provides a com-parison of mortality patterns in INDEPTH sites; and Part III presents profiles ofINDEPTH sites.

As this is the first publication of its kind on DSSs in Africa and Asia, we thoughtit would be expedient to discuss core concepts and methods commonly used in mostof the sites. Among the concepts discussed in Chapter 1 are the DSS area, longitudi-nality, DSS subjects, residency and membership, and core DSS events. Rates and mea-sures generated using DSS are discussed in Chapter 2, with specific emphasis on theuse of person–years lived in calculating rates. Chapter 3 discusses the DSS methods ofdata collection, starting with the initial census to establish the DSS population. Thischapter discusses initial censuses, update rounds, and the vital events-registration sys-tem. It also puts emphasis on mortality monitoring and the tracking of migrants. Theprocessing of DSS data is the main focus of Chapter 4. This chapter treats the impor-tant issues of quality assurance and control at the data-processing level. In Chapter 5,Part I ends with a discussion of the quality of DSS data, both in the field and at thedata centre. This chapter then provides a detailed discussion of statistical and demo-graphic techniques for analysis of DSS data.

Part II presents a comparison of mortality patterns of INDEPTH sites for the1995–99 period. Chapter 6 starts with a discussion of crude overall mortality atINDEPTH sites. This chapter presents an INDEPTH population-age standard for sub-Saharan Africa (SSA) for the standardization of mortality rates, and it gives the reasonfor using this new standard instead of the United Nations models.

The INDEPTH age standard for SSA typifies the population in developingcountries, with its very young age structure. INDEPTH sites have used this standard tocompare mortality in SSA. This comparison highlights age-specific mortality, consider-ing mortality in infancy, childhood, and adulthood. This discussion compares theINDEPTH standard for SSA with the Segi population and the new World Health

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Organization standard population. The chapter ends with a presentation of basic life-table indicators for INDEPTH sites, based on their age-specific mortality rates over the1995–99 period. Part II ends with Chapter 7, which analyzes more than 6.4 millionperson–years of observation at the African INDEPTH sites to identify mortality pat-terns. The emergent patterns are demonstrated to be substantially different from con-ventionally used model mortality patterns applied in Africa.

Part III presents profiles of 22 INDEPTH sites. The profiles are listed in alpha-betical order, first according to region, and then according to country. These profilesare expected to stand for some time as the main reference source for basic detailsabout INDEPTH sites and their DSS operations. Based on a structured template, eachprofile provides a site description, including the physical geography and populationcharacteristics. It discusses DSS procedures at the site, including data collection andprocessing. Finally, each profile presents basic outputs, including demographic indica-tors. A summary matrix of all the DSS sites, presented in the introduction to Part III,provides the core details for each site.

INDEPTH monograph editorial team for Volume 1:

Osman A. Sankoh (University of Heidelberg, Germany, and Nouna DSS,Burkina Faso)

Kathleen Kahn (Agincourt DSS, South Africa)Eleuther Mwageni (Rufiji DSS, Tanzania)Pierre Ngom (Nairobi DSS, Kenya)Philomena Nyarko (Navrongo DSS, Ghana)

1 June 2001

ACKNOWLEDGMENTS

This volume is an outgrowth of the efforts of many people, both INDEPTH membersand its collaborators, who gave of their time and expertise to writing these chapters.We would like to particularly thank the following for their invaluable contributions tothe corresponding chapters:

• Pierre Ngom, Justus Benzler, Geoff Solarsh, and Vicky Hosegood (Chapter 1);

• Rose Nathan, Heiko Becher, and Abdur Razzaque (Chapter 2);

• Eleuther Mwageni and Robert Mswia (Chapter 3);

• Peter Wontuo, Noah Kiwanuka, and Jim Phillips (Chapter 4)1;

• Philomena Nyarko, Fred Binka, and Mark Collinson (Chapter 5);

• Sam Clark and Pierre Ngom (Chapter 6);

• Sam Clark (Chapter 7); and

• DSS site teams (Chapters 8–29).

We would also like to thank INDEPTH site members, whose names are men-tioned in the site profiles, for coordinating the writing of their site’s profile. Specialthanks go to Rose Lusinde and Don de Savigny for producing the map panels for thesite locations and particularly to Kathleen Kahn and Don de Savigny for coordinatingthe formatting and editing of the 22 site-profile chapters making up Part III of themonograph.

The INDEPTH coordinators would like to express their gratitude to theINDEPTH editorial committee, led by Osman A. Sankoh, for its outstanding work incompiling this first monograph. We acknowledge with pleasure the willingness of indi-vidual site teams and their leaders to collaborate in sharing such rich data sets andexperiences. We also recognize the contributions of all our investment partners —local communities, public-sector services, academic and research institutions, anddonors — all of whom, often over prolonged periods, continue to support and sustainour efforts. We express particular thanks and appreciation to the many sponsors ofINDEPTH, including the Rockefeller Foundation, the Navrongo Health ResearchCentre, the Population Council, the World Health Organization, and the Andrew W.

xiii

1 Based on Benzler, J.; Herbst, A.J.; MacLeod, B. (in alphabetical order): A reference data model for demographic surveillance systems.INDEPTH 1999, http://www.indepth-network.org.

xiv ✦ Acknowledgements

Mellon Foundation, for providing the funds needed to enable INDEPTH networkingactivities to function. We look forward to attracting new partners to join with us inadvancing our mission, goals, activities, and products.

Finally, we thank internal and external reviewers for their invaluable com-ments, which increased the validity and clarity of many sections of the monograph.

INDEPTH Coordinating Committee

Fred Binka, Chair (Ghana, 1998–2001)Steve Tollman, Deputy Chair (South Africa, 1998–2001)Pedro Alonso, Member (Mozambique, 1998–2000)Yemane Berhane, Member (Ethiopia, 1998–2001)Chuc N.T.K., Member (Viet Nam, 2000–)Don de Savigny, Member (Tanzania, 1998–2001)Bocar Kouyaté, Member (Burkina Faso, 2000–)Boubakar Sow, Member (Mali, 1998–1999) Siswanto Wilopo, Member (Indonesia, 1998–2001)

1 June 2001

INTRODUCTION

As we enter the new millennium, with the revolution of the information age still gain-ing speed, it seems inconceivable that large parts of the Earth’s population remaindevoid of vital health information. For 1 billion people living in the world’s poorestcountries, where the burden of disease is highest, no one registers those who are bornor who die or ascertains the causes of their deaths. From the limited data available, thehealth profile of these populations can be likened to an iceberg: the bulk of reliabledata on trends in age, gender, geographic variations, and burden of disease remainshidden. This great void in population-based information constitutes a major and long-standing constraint on the articulation of effective policies and programs to improvethe health of the poor and thus perpetuates profound inequities in health. The needto establish a reliable information base to support health development has never beengreater.

Recently, experience has emerged from a growing number of community-basedfield stations that have continuous monitoring systems for geographically defined pop-ulations. These field stations generate high-quality, population-based, longitudinalhealth and demographic data with the potential to fill this information void in thedeveloping world. Since 1997 a number of organizations have made a systematic effortto harness and make more readily available the products of these disparate initiatives.A series of meetings were convened by the University of Witwatersrand (South Africa)(Agincourt Health and Population Programme); Department of Tropical Hygiene andPublic Health, University of Heidelberg (Germany); the Rockefeller Foundation(Bellagio, Italy); and the Ministry of Health (Navrongo, Ghana) to examine the poten-tial for harnessing these sites through a network. These activities culminated in ameeting convened in Dar es Salaam, Tanzania, 9–12 November 1998, to establish sucha network.

Seventeen field sites drawn from 13 countries in Africa and Asia participated inthis founding meeting. The name adopted for the network was the InternationalNetwork for the continuous Demographic Evaluation of Populations and Their Healthin developing countries (INDEPTH). Network membership has increased steadilysince then and currently stands at 29 health and demographic evaluation sites in16 countries (the 13 countries whose sites are profiled in this volume are shown inFigure I.1). The network’s founding document and constitution are available on theINDEPTH website (www.INDEPTH-network.org).

1

Figure I.1 Countries with DSS field sites participating in the INDEPTH network.

The defining characteristics of an INDEPTH field site are the following:

• A geographically defined population is under continuous demographic moni-toring, with timely production of data on all births, deaths, and migrations —sometimes called a demographic surveillance system (DSS); and

• This monitoring system provides a platform for a wide range of health-systeminnovations, as well as social, economic, behavioural, and health interventions,all closely associated with research activities.

The vision and goals of the network are

• To enhance substantially the capabilities of INDEPTH sites through technicalstrengthening, methodological development, widened applications to policyand practice, and increased interaction of site leaders, researchers, and man-agers; and thus

• To realize their potential to generate the information needed to– Set health priorities,– Allocate resources more efficiently and equitably,– Inform the development, implementation, and evaluation of health inter-

ventions and other social-sector programs,– Strengthen the decision-making capability of information systems,– Define a highly relevant research and development agenda,– Augment national research capacity, and thereby– Fulfill developing-country potential to redress long-standing inequities in

health.

2 ✦ Introduction

To achieve these goals and facilitate the effective interaction of INDEPTH sites,the network has identified the concept of flexible working groups focused on specificscientific issues or topics as a key mechanism. Seven working groups were initiallyestablished, with a focus on

• Comparative assessments of mortality;

• Analysis and capacity-strengthening;

• Technical support for field sites;

• Reproductive health;

• Malaria;

• Information and publications; or

• Applications to policy and practice.

Two further working groups have since been formed, focusing on adult healthand ethical practice. Thus, through active and concerted efforts, the network is encom-passing a critical agenda founded on traditional strengths in research on infectiousdiseases and nutrition, with a growing emphasis on reproductive health, and the net-work is extending this emphasis to chronic disease, injury, and related social phenom-ena such as rapid urbanization. A central objective is to use network sites to train localscientists in research and research management.

This monograph is the foundation for an INDEPTH series on various themes,including model life tables for Africa and Asia; cause-specific mortality in developingcountries; migration patterns; trends in fertility; reproductive health (includingHIV–AIDS); and health equity.

INDEPTH Coordinating CommitteeAccra, GhanaJune 2001

Introduction ✦ 3

P A R T I

DSS CONCEPTS AND METHODS

Chapter 1

CORE CONCEPTS OF DSS

Introduction

During the past 30 years, demographic surveillance systems (DSSs) have been estab-lished in a number of field research sites in various parts of the developing worldwhere routine vital-registration systems were poorly developed or nonexistent.Although these systems may have been developed differently in terms of their initialrationale, they are all required to track a limited and common set of key variablesdetermining population dynamics and demographic trends. DSSs have similarapproaches to defining key variables and their relationships and to developing systemsfor collection, storage, and analysis of these data. The core concepts presented heredraw directly from the ideas and experiences emerging from INDEPTH DSS sites inAfrica and Asia. It should be emphasized, however, that even though an effort hasbeen made to standardize the definitions, many DSS sites still define some of the con-cepts differently.

Demographic surveillance systems

A DSS is a set of field and computing operations to handle the longitudinal follow-upof well-defined entities or primary subjects (individuals, households, and residentialunits) and all related demographic and health outcomes within a clearly circum-scribed geographic area. Unlike a cohort study, a DSS follows up the entire populationof such a geographic area.

In such a system, an initial census defines and registers the target population.Regular subsequent rounds of data collection at prescribed intervals make it possibleto register all new individuals, households, and residential units and to update keyvariables and attributes of existing subjects. The core system provides for monitoringof population dynamics through routine collection and processing of information onbirths, deaths, and migrations — the only demographic events leading to any changein the initial size of the resident population. This core system is often complementedby various other data sets that provide important social and economic correlates ofpopulation and health dynamics. These may include information on events such ashousehold formation and dissolution, acquisition and loss of economic assets, andgrowth or depletion of income.

7

8 ✦ DSS Concepts and Methods

In many population sites, the DSS may also provide a platform for other studieswithin the same geographic area. This support varies from one study to another andmay include the provision of an initial sampling frame, adjustment for confoundingvariables, provision of additional explanatory variables, and measurement of thedemographic impact of interventions.

Demographic surveillance area

The demographic surveillance area (DSA) is an area with clearly and fairly permanentdelineated boundaries, preferably recognizable on the ground (for example, rivers,roads, and clearly demarcated administrative boundaries). The clear delineation ofboundaries enables an unambiguous distinction to be made between individuals,households, and residential units to include in the DSS and those to exclude.

The area of a DSS site depends mainly on the size of the population requiredfor demographic surveillance and related research activities (for a typical example, see“Establishing the monitored population” in Chapter 3). The size is also influenced bypragmatic considerations, such as the cost to the research centre and its capacity tomanage the associated logistics and human resources. The DSA may expand or shrinkover time in response to changing research needs or sources of funding. Thesechanges usually introduce additional complexity, as they alter eligibility criteria andmay make it difficult to maintain consistent definitions of internal and external migra-tions over the period of transition.

Longitudinality

Longitudinal measurement of demographic and health variables is one of the keycharacteristics of a DSS. This is achieved through repeated visits at more or less regu-lar intervals to all residential units in the DSA to collect a prescribed set of attributedata on registered subjects, who are consistently and uniquely identified. This andrecording events affecting these subjects during the interval between visits allow one toconstruct their history and differentiate DSS data from data collected in multiroundsurveys and other prospective studies that allow comparison over time only on anaggregated level.

Visits

DSSs collect data during rounds, or cycles, of visits to registered residential units in theDSA. The interval between visits depends on the frequency of the changes in the phe-nomena under study and on the length of recall intervals for the collected data, andthus on the research focus of each field site. However, like the size of the DSA andobserved population, it also depends on funding and logistics. This interval variesfrom one site to another, ranging from 1 week to 1 year. However, for the majority ofDSSs, observations are made at 3- or 4-month intervals. This is widely considered anappropriate interval to ensure comprehensive recording of births, deaths, and migra-tions, which is the minimum requirement for maintaining the coherence of any DSS .

Core Concepts of DSS ✦ 9

When intervals between visits are long (a year or more), researchers commonlyignore migration events and instead conduct a full census at each new round. In- andout-migration flows are then inferred through reconciliation of unlinked censusrecords after account is taken of births and deaths between censuses.

Data collected during each fieldwork round are not restricted to key demo-graphic events but may also include the various attributes of the primary subjects.These attributes may be fixed (for example, ethnicity, gender) or changing over time(for example, marital or residential status).

Unique identifiers

Unique identifiers for primary subjects are an indispensable element of DSSs. All sys-tems invariably formulate rules for assigning unique identifiers at the start of the DSS,but their methods for assigning these identifiers to DSS subjects may vary from onesite to another. There are two main approaches. One common strategy is to transpar-ently link the subjects in a single residential unit through a hierarchical system ofunique numbers. These are built up from a unique number for the residential unit,followed by serial numbers for each of the households within it (where the notion ofhouseholds applies) and then for each of the enumerated individuals within eachhousehold. In this system, the unique number for each individual in the DSS is a com-posite of the numbers for the residential unit, household, and household member.This may involve creating complex hierarchies, in which the unique number of theresidential unit itself is a composite reflecting allocation to regions, areas, and villages(where they exist). This system requires thorough mapping of the DSA beforeenumeration. It also requires proper training of enumerators to avoid confusion inassigning identifiers. When mapping of the DSA is coupled with georeferencing of res-idential units, using geographic information system (GIS) technology, global position-ing system (GPS) coordinates are assigned as location attributes of the residentialunits within the database.

The other strategy for assigning identifiers to individuals is to avoid any fixedlink to residential units and households. In this system, identifiers for each subject aresimply serial numbers incremented each time a new DSS subject is registered. This sys-tem requires providing field staff with block allocations of ID numbers with enoughlatitude to register new subjects. This approach should be coupled with computergeneration of the identifiers to safeguard against the assignment of the same ID to mul-tiple subjects on the ground. This strategy helps to preserve people’s anonymity outsidetheir residential units, or when their attribute data are accessed through the database.

Primary DSS subjects

DSSs are typically structured around three main subjects (Figure 1.1) within the DSA.These subjects have both a conceptual and a logistical rationale. From a logisticalpoint of view, it is not feasible to interview all individuals directly, and for this reasonindividuals are put in groups with physical and social meaning, and information is col-lected from credible and informed respondents within these groups. The reasons todistinguish between these subjects from a conceptual point of view will be dealt with ingreater detail in the following subsections. The three main subjects are (Figure 1.1) asfollows:

Figure 1.1. The three main DSS subjects.

• Residential units — These are the places where individuals live. They are definedin physical and geographic terms.

• Households — These are the groups to which individual members belong. Theyare often defined as social subunits of the residential unit.

• Individuals — These are the people who are living in the residential units andhouseholds. They are the subject of main interest in any DSS.

Residential units

All DSSs identify residential units as a primary subject of interest, although they vary inthe terms they use for these units (for example, compounds or homesteads) and may alsodiffer slightly in their definition of them. Residency, or physical presence within a DSAat a fixed place of abode and for a sufficiently long period, is an essential prerequisitefor the enumeration of individuals at risk for demographic events or disease exposure.

In most systems a distinction is made between places of residence and otherstructures, such as clinics, schools, churches, and stores. Identifying a unifying termfor all these structural units may have conceptual merit, and some systems haveattempted to do this, as these structural units share many characteristics and thisapproach simplifies the database hierarchy for handling this concept. In this system aninclusive term such as bounded structure may be used at a higher level and compounds(or homesteads) and facilities at the more specific level.

Households

Households may be variably defined in one or more of the following ways:

• A group of people who consume or make some contribution to food and othershared resources;


Residential Unit

part of / resident at resident at

member ofHousehold Individual

• A group of people who have a common allegiance to an acknowledged head ofa household;

• A group of people, each of whom is recognized by other members of the house-hold as belonging to a social group; or

• A group of people linked through ties of kinship.

The definition of household and its applicability both as a concept and as a sepa-rate DSS subject may vary greatly from one DSS to another. Households may simply beseen as fixed social subunits within residential units. In more complex systems, theymay be seen as independent subjects able to change their place of residence while pre-serving their social identity, and they may have members who are resident elsewhere.In such a system, a clear distinction would be needed between residency, whichdefines the state of being physically present in a given residential unit for a definedthreshold of time, and membership, which defines the state of belonging to a socialgroup irrespective of physical presence. These concepts have a clear overlap with therelated concepts of de facto population (persons who are physically present in a place)and de jure population (persons who usually reside in a given place), respectively. Theconcepts of residency and membership are discussed later in this chapter.

Individuals

The individuals are people of various ages, sex, and other personal characteristics whoare residents or members of the DSS residential units or households, respectively.Their personal characteristics may be fixed (sex, date of birth) or change over time(age, marital status). Unless their changes are predictable (like the yearly incrementof age), changing characteristics will need to be recorded repeatedly — or theirchanges will need to be recorded as events — to produce longitudinal trends.

Eligibility

Every DSS is required to define the population under surveillance. As most individualswithin any population have places of residence and attachments to social groups, thetask of defining the population begins with the identification of the residential units,households (where applicable), and individuals that will be visited and observed.Thereafter, a set of inclusion criteria must be applied to distinguish eligible from ineli-gible individuals or subjects within each subject category.

As residential units have fixed geographical positions in all DSSs, there are con-sistent and simple rules for their inclusion: they are included if they are situated in theDSA. In DSSs that deal with households as distinct (and potentially mobile) subjects,these households are eligible if (and while) they are situated in the DSA. This is whatis referred to as household residency.

Rules for individuals, particularly in highly mobile populations, are more com-plex. The most typical approach is to simply base their eligibility on residence, that is,physical presence. Individuals are eligible if (and while) they are resident at eligibleresidential units. This is what is referred to as individual residency. Another approach,


based on social linkages, rules that individuals are eligible if (and while) they aremembers of eligible households. This requires careful and consistent definitions ofhousehold and membership and can allow individuals who are not resident to remain asmembers of the household and therefore to qualify for observation.

Residency and membership

Clear geographical boundaries for the DSA and well-defined physical boundaries forresidential units are minimal prerequisites for following up DSS subjects consistentlyand arriving at numerators and denominators for rate calculations. In systems whereresidential units and households are separate subjects and there is a separate relation-ship between individuals and each of those subjects — expressed as residency andmembership, respectively — these concepts become substantially more complex.

Observing an individual’s presence in, or absence from, a specific residentialunit requires clear rules for residency status. The physical presence of an individualfor a very short time may not be taken into account when the amount of time spent inthe residential unit is computed. Conversely, the noncontinuous presence of an indi-vidual, with short periods of absence, may be considered continuous residency if he orshe meets a threshold for inclusion.

Residency and membership statuses are assigned at the start of the DSS, basedon prescribed eligibility rules. Thereafter, new residency episodes may commence as aresult of births or in-migrations exceeding a prescribed threshold of duration, andcurrent residency may end because of deaths or out-migrations, again exceeding a pre-scribed threshold of duration. New membership episodes may commence as a resultof events that initiate a social relationship with a household, such as birth, marriage,adoption, or household formation, and may be terminated by events that end such arelationship, such as death, divorce, or household dissolution.

Core DSS events

To know the size of the registered resident population at any time, a DSS collectsinformation about three core events that alter this size, namely, births, deaths, andmigrations. These events are described by the following fundamental demographicequation:

Pt1= Pt0

+ Bt0,t1

– D t0,t1

+ I t0,t1

– O t0,t1

[1.1]

where P is the population; B is the number of births; D is the number of deaths; I isthe number of in-migrants; O is the number of out-migrants; and t0, t1 is the timeinterval of their occurrence.

An underlying principle for recording events in a DSS is that of a populationat risk. Mortality, fertility, and migration rates are calculated by counting the numberof deaths, births, or migrations occurring within a registered population exposed tothe risk. For example, an individual who is not resident within the DSA is not consid-ered at risk of dying within the area. Consequently, most DSSs do not observe non-resident individuals or households and do not record their events.


Births and fertility

Pregnancies and their outcomes for all women registered in the DSS are recordedregardless of the place of occurrence of such events. The recording of births has twopurposes: for estimating fertility and for identifying a criterion for registering an indi-vidual. To estimate fertility, a DSS should record all pregnancy outcomes, includingmiscarriages (<28 weeks), induced abortions, stillbirths (≥28 weeks), and live births.All live births are then registered as individual members of the DSS, independent ofsubsequent survival. In some DSSs, fieldworkers take note of live births to visitors tothe DSA to alert the data collector in the next round to register the mother (if shebecomes eligible) and her child. This procedure is very helpful, as it greatly improvesthe accuracy of dates of birth of newly born babies and increases reporting of birthsfrom eligible mothers with frequent in- and out-migration.

Although most DSSs will report their estimates of the fertility of a specific agegroup of women, usually 15–49 years, they should also record births to women outsidethis age group.

The underreporting of pregnancies and their outcomes is a major problemacross all DSSs. Some DSSs have used the recording of pregnancies during routineupdate visits to improve birth coverage. Pregnancy observation has also been used toincrease the reporting of other pregnancy outcomes, particularly miscarriages,induced abortions, and stillbirths. However, this requires an update-visit interval of<5 months so that a notification of pregnancy can be obtained in one round, followedby the recording of the pregnancy outcome in the next visit.

Deaths and mortality

Deaths of all registered and eligible individuals are recorded, regardless of the placeof death. It may be impossible to record the deaths of previously eligible individualswho then out-migrated. In this case, observation of their survival is censored at thetime of migration. Information about the death of visitors to the DSA is sometimes col-lected, but it is only used in mortality estimates if a de facto population estimate is avail-able for each day.

Underreporting of deaths is typically less of a problem than that of births,because a death is widely known and remembered. Exceptions are the deaths of young(and yet unregistered) infants, particularly perinatal deaths, if cultural beliefs or griefhinders reporting.

Some DSSs collect more detailed information about deaths to establish thecause of death, generally through the so-called verbal autopsies (VAs).

Migrations and mobility

Two types of migration events occur:

• External migration — where residence changes between a residential unit in theDSA and one outside it; and

• Internal migration — where residence changes from one residential unit toanother in the same DSA.


Where nonresident household members are ignored, only external migrationaffects the size of the population, resulting in either the registration of a new in-migrant or the termination of follow-up of an out-migrant. However, recording inter-nal migration is very important to ensure the accuracy and validity of DSS data. TheDSS needs to identify internal migrations and migrants and collect supporting infor-mation to avoid double counting of individuals and to ensure that their exposure tothe social and physical environment is correctly apportioned. Migrations influence theregistration of births and deaths; for example, a death would not be recorded for anindividual who out-migrated before his or her death.

Defining the circumstances under which a migration is acknowledged to haveoccurred is notoriously difficult, not only for DSSs, but even for vital-registration sys-tems and censuses. Different DSSs have different criteria. One approach, generallyknown as the “50% rule,” considers individuals resident if they have spent most of thetime between two data-collection visits within the DSA. Any former resident who hasnot spent at least 50% of the time in the DSA would be recorded as having out-migrated.

However, many rural communities have individuals who regularly and pre-dictably change residence for seasonal work, employment, or educational opportuni-ties. The terms circular and pendular migration are often used. In the Hlabisa DSS, anewly established system in an area of very high population mobility, individual resi-dency has been replaced with household residency as a registration criterion.Consequently, although out-migrations are recorded, the fieldworkers do not auto-matically terminate follow-up observations.

Migration is a repeatable event — an individual may make several migrationsover time, both internally and externally. To maintain longitudinal integrity of dataconcerning individuals, a DSS should establish whether an external in-migrant haspreviously been registered in the DSS. The individual’s current and previous recordsshould be matched so that he or she is not handled as a new individual in the systembut as an individual under observation for several periods.

Episodes

Episodes are a logical complement to events. They are meaningful and identifiablesegments of time started and ended by events. The life of an individual, for instance,can be understood as an episode that started with the individual’s birth and endedwith his or her death. In the same way, residential units or households can be said tobe episodes that start when they are formed and end when they are dissolved.

The usefulness of the concept of episodes is not limited to primary subjects. Itapplies equally to associations between them and therefore provides a useful frame-work for handling residency, membership, marital status, and many other concepts.Episodes also make it much easier to formulate and implement validation rulesregarding events.


Other events

In addition to births, deaths, and migrations, other events are of interest for ourunderstanding of demographic, health, and social dynamics. One event on which dataare commonly collected relates to nuptiality or marital status. Most DSSs collect infor-mation about events such as marriage, defined as an event that starts a marital rela-tionship, and divorce, that is, an event that ends a marital union. Other eventsrecorded by DSSs depend on their complexity and research interests but may includethe change of a head of household, a household’s formation or dissolution, or theconstruction or destruction of building structures.

Nuptiality and conjugal relationships

DSSs collect data on nuptiality primarily because of the important influence of maritalpatterns on fertility. Marriage as a start of an episode is easily identified, although aperiod of sexual union may have preceded marriage. The ending of a conjugalrelationship can be less clearly marked, because it may not always be the death of oneof the partners or a divorce, but a period of separation. In DSAs where the nonmaritalfertility rate is high, other conjugal relationships become important, and the systemsrecord informal relationships as well as formal marriages. However, in taking on thisbroader approach to sexual relationships, the DSSs must overcome two hurdles:

• The difficulty of establishing the starting and ending events of conjugal rela-tionships that are not marked by official ceremonies; and

• The difficulty of establishing the link between two or more partners (in poly-gamous relationships, for example). For nonmarital conjugal relationships,where the partners often do not cohabit, greater efforts are needed to establishthis link in a database than is the case for marital unions.

Construction and disintegration of residential units

At any given time, new residential units may be under construction and other residen-tial units may be at various stages of disrepair following natural disasters or abandon-ment. The physical state may be distinct from the functionality of the residential unit;that is, it is possible that a residential unit is physically intact but long abandoned, andapparently broken-down units may still have households and individuals living inthem. It is also possible that broken-down or destroyed units may subsequently berebuilt, when the owner returns.

As the state of the residential unit is often — if not always — a good indicationof its functionality, a DSS should make provision to track both its physical state andfunction.


Events occurring in households

Similarly, households can go through important changes affecting their compositionand socioeconomic and health conditions. New households may form within an exist-ing residential unit when, for example, a son takes a wife and establishes a family of hisown or when a polygynous man takes another wife. Separate households may merge toform a new household, or a complete household may move to settle at another resi-dential unit. Households may lose one or more members over time and decrease insize, or they may completely dissolve through a process of slow attrition or a majorenvironmental or social disaster.

In environments with substantial social flux and instability, it is important tokeep track of these events and their effects on the formation and dissolution of house-holds. This is essential if DSSs have conceptualized households as subjects in their ownright. Because they also influence patterns of individual presence at a residential unit,these household changes have important implications for the composition of the resi-dential unit as a whole.


Chapter 2

DSS-GENERATED MORTALITY RATES

AND MEASURES

Introduction

This chapter provides definitions and explanations of key DSS-generated mortalityrates and measures, as well as describing the methodology employed in calculatingthem. It is intended for readers unfamiliar with these rates and measures. Their calcu-lation is basic, and the various formulas can be found in standard textbooks (see forexample, Shryock and Siegel 1976; Kpedekpo 1982; Newell 1994). These measureshave been briefly discussed in this chapter for quick reference, as they form the basisfor standardizing the results across DSS sites. Perhaps the most important reason fordiscussing them is the opportunity it affords to discuss the classic controversy overwhether to define some of them as rates or ratios (for example, infant mortality,under-five mortality, and maternal mortality). Furthermore, this chapter provides anexplanation of the need for a standard population and introduces the INDEPTH stan-dard population for Africa south of the Sahara, discussed in greater detail in Part II.

Rates and ratios

Rates and ratios are frequently used in measuring demographic events. Rate refers tothe frequency of events. A rate is estimated by taking the number of events in a givenperiod and dividing it by the population at risk during that period. Pressat (1985,p. 194) stated that the term rate

is also used more loosely to refer to the ratio between a sub-populationand the total. … In many other uses of rate, the measure in questionwould be better termed a ratio, proportion, or probability. The term canbe justified only when a dynamic process is being measured, not a staticdescription of a population at a given date, although its use in the lattersense is widespread. In general the word ratio is preferable to rate whenthe measure is not one relating events to a population at risk.

A ratio is the proportion between a numerator and a denominator that are related(for example, under-five child deaths per 1000 under-five person–years lived in a givenyear).

17

Crude death rate

The crude death rate (CDR) is defined as the number of deaths in a given perioddivided by the total population. Although the CDR can be computed for any segmentof time, the period usually used is a year, and the denominator used in the rate calcu-lation is the midyear population. The midyear population is the size of the population(or any specified group within the population) at the midpoint of a calendar year.This midpoint is often calculated as the arithmetic mean of the size of the populationat the beginning and end of the year. Conventionally, the rate is expressed as a num-ber per 1000 individuals.

In the case of a population under continuous surveillance, with possibly highin- and out-migration rates that may yield a strong variation in population size, the useof exact person–years lived is preferred. Person–years is the sum, expressed in years, ofthe time spent by all individuals in a given category of the population (Pressat 1985).Specifically, these years express the periods that eligible individuals spent in the DSA.Times or periods spent outside the DSA due to migration or death are excluded.

Age-specific death rate and ratio

Because of the differentials in exposure to the risk of dying, epidemiologists anddemographers often use age-specific death rates (ASDRs) and sex-specific death rates,instead of the CDR. ASDRs are the most commonly used. The ASDR for an age groupis defined as the number of deaths in the age group in a specific period divided by thetotal number of person–years lived in that age group during that period and multi-plied by 1000. Demographers often use a slightly different notation. They express theASDR of a particular age group as the deaths among individuals in that age group inthe year, divided by the mid-year population of that age group and then multiplied by1000. Five-year age groups are common, although age categories vary according to thepurpose of study.

The following discussion of infant, under-five, and maternal mortality measureshighlights the classic controversy over whether to define these measures as rates orratios. The denominator used in calculating a measure determines whether it is a rateor a ratio. As stated earlier, the measure is a rate when the total number of individualsat risk is used as the denominator, and it is a ratio when some other event is used asthe denominator.

Infant mortality

It is usually difficult to estimate the number of person–years lived for children <1 yearold (infants). Consequently, the total number of live births is often used as thedenominator to calculate the infant mortality rate. The total number of deaths amongchildren <1 year old in a calendar year is divided by the live births in the same year,multiplied by 1000. Calculating the infant mortality rate in this way makes it moreappropriately referred to as a ratio.

Infant deaths are unevenly distributed through the first year of life. A high pro-portion of infant deaths usually occurs in the first month of life. Of these deaths, ahigh proportion occurs during the first week of life; and of these, a high proportion


occurs during the first day. The conventional infant mortality rate or ratio may use-fully be broken up into rates or ratios covering the early stages of life and a rate orratio for the remainder of the year. The one for the first period is called the neonatalmortality rate or ratio, and that for the second period is called the postneonatal mor-tality rate or ratio. These concepts are briefly defined in the following paragraphs.

Neonatal mortality is defined as the number of deaths of infants <4 weeks old(or <1 month old) during a year. It is calculated by dividing the deaths of infants<28 days old during a year by the live births in the same year and multiplying by 1000.Early neonatal mortality is calculated by dividing the deaths of infants <7 days old dur-ing a year by live births in the same year and multiplying by 1000. Late neonatal mor-tality is calculated by dividing the deaths of infants 7–28 days old in a year by live birthsin the same year and multiplying by 1000. Postneonatal mortality is calculated by divid-ing the deaths of infants 4–51 weeks old during a year by live births in the same yearand multiplying by 1000.

Infant mortality can also be expressed as a probability of dying before reachingthe age of 1 year. Perinatal mortality is calculated by dividing the sum of stillbirths inthe year and the deaths of infants <7 days old during the year by the sum of stillbirthsin the year and live births in the same year.

Under-five mortality

Some consider the under-five mortality as a ratio expressing the number of deaths ofchildren <5 years old divided by the number of live births in a year and then multi-plied by 1000. Others treat it as a rate, calculating it by dividing the number of deathsof children <5 years old by the total number of person–years of children <5 years oldand multiplying by 1000. When under-five mortality is presented as a probability ofdying before age 5, it is expressed as 5q0.

Maternal mortality rate and ratio

Most DSSs record all pregnancies and their outcomes as well as deaths. As such, theyhave the potential to provide accurate, up-to-date estimates of maternal mortality ratesand ratios. The maternal mortality ratio is conventionally defined as the number ofdeaths due to puerperal (pregnancy-related) factors per 100 000 live births. But strictlyspeaking, this is referred to as a ratio because the denominator is not the persons atrisk of experiencing the event. In view of this, the following are the methods for esti-mating maternal mortality ratios and rates. The maternal mortality ratio is calculatedby dividing the number of pregnancy-related deaths in a specified period by that oflive births in the same period and multiplying by 100 000. The maternal mortality rateis calculated by dividing the number of pregancy-related deaths in a specified periodby person–years lived by women of childbearing age and multiplying by 1000.

Maternal mortality can also be estimated by relating maternal deaths to womenof reproductive age or to all pregnancies, including stillbirths and abortions.

DSS-generated Mortality Rates and Measures ✦ 19

Standardization

Age-standardized death rate

Crude mortality rates are inappropriate for comparing different populations withinthe DSS sites because of the different age structures within the sites. On the otherhand, a single parameter is required for simple comparison. Therefore, standardizedrates are used, in which the age-specific mortality rates are combined using a standardpopulation. An INDEPTH standard population for sub-Saharan Africa (SSA) has beendeveloped (see Table 6.2). More details on the INDEPTH standard population areprovided in Chapter 6. The Segi (1960) and the new World Health Organization(WHO) standard age distributions are also shown in Table 6.2.

Age-specific rates are weighted averages of rates, where the weights areobtained as a proportion of the standard population in the respective age group. Thesummation goes over all age groups.

Confidence intervals for rates

Estimates of the mean and standard deviation of a population are usually needed if itis impossible to deal with the entire population. The standard deviation of a distribu-tion of sample means is referred to as the standard error of the sample. It measureshow precisely the sample mean estimates the population mean. For example, with a95% confidence interval, about 95% of the sample means obtained by repeated sam-pling would lie within two standard errors below or above the population mean. Basedon the sample mean and its standard error, a range of likely values can be constructedfor a population mean that is not known. This range is referred to as a confidenceinterval. More precisely, there is a 95% probability that a particular sample mean lieswithin 1.96 standard errors above or below the population mean.

Confidence intervals can be calculated for the ASDRs. The variance of theCDRs or the ASDRs is used instead of the means. Estève et al. (1994) discussed themethod in detail. For a small number of deaths or for small populations, however,confidence intervals for ASDRs are not reliable, because the formula used to calculatethem is too imprecise. The question is then one of how large the numbers of deathsand populations must be to give reliable results. It is difficult to supply a rule ofthumb, and as Estève et al. (1994, p. 58) noted,

It is however difficult to tell what “sufficiently large” means in the pres-ent context because the numerator of a standardised rate is no longer aPoisson variable. Its variance depends not only on the total number ofobserved cases but also weighting scheme and the accuracy of the age-specific rates.


Chapter 3

DSS METHODS OF DATA COLLECTION

Introduction

Knowledge of the methods for collecting or compiling data at the DSS sites is essentialbecause these methods influence the ways that data are processed, analyzed, and inter-preted. The most common demographic methods used in data collection are cen-suses, sample surveys, and vital-events registration systems. The last method, however,is nonexistent or only partially applied in many developing countries. Given thepaucity of vital-events registration and knowledge on population or health-statustrends in such settings, demographic and health surveys have been introduced forhealth planning, practice, evaluation, and allocation of resources. Demographic esti-mates undertaken in developing countries have employed both indirect and directmethods, using retrospective single-round surveys and prospective multiround ones(Tablin 1984).

Indirect estimation methods rely on information obtained from subjects notdirectly at risk of a particular demographic phenomenon. The indirect methods canbe used to estimate levels and trends of fertility, mortality, and migration where datasources are defective or incomplete. An example of an indirect method is the estima-tion of infant and child mortality from proportions of surviving children or the estima-tion of adult mortality from those orphaned. Indirect estimation methods are alsoused to assess data collected using conventional methods. Such data are comparedwith other information to infer a certain pattern, on the basis of certain assumptions.If this pattern is reproduced then data can be further inferred. Indirect estimationmay, in addition, involve fitting of demographic models to fragmentary and incom-plete data (Pressat 1985). The results obtained are used to estimate a particularparameter.

Direct methods use data on the people at risk to establish a demographic meas-ure and pattern. These methods rely on data obtained from censuses, surveys, andrecorded data on the components of change — that is, births, deaths, and migration.Data obtained from these methods are used directly to provide estimates of demo-graphic phenomena, such as fertility, mortality, and migration. An example of a directmethod is the use of the number of children born to women of a particular age groupto estimate age-specific fertility rates.

In single-round surveys, a population is enumerated once during a survey, andretrospective data are gathered on past events (Kpedekpo 1982; Tablin 1984; Newell

21

1994), such as a birth or death that occurred in the last year (or a life and maternityhistory). This method may result in overestimation or underestimation of events, as aresult of memory lapse. Respondents may exclude events from the reference period. Ithas been argued that an underestimation of 30–40% is likely using this method(Tablin 1984). Some examples of single-round surveys are the World Fertility Surveyand the Demographic and Health Surveys.

Prospective surveys involve repeat visits (longitudinal data collection) to thesame respondents or the same study area (Pressat 1985). All DSS sites employ thismethod of data collection. This does not mean, however, that the methodologicalapproach is the same across all sites. Sites each have unique features, as shown in thevarious site chapters of this monograph. The purpose of this chapter is therefore toprovide a general description of the data-collection methods used by the DSS sites.The data-collection methods are described to provide a quick reference for thereader, rather than describing experiences with data collection. Periodically, specificexamples are provided from sites for clarification.

Establishing the monitored population

Selection and establishment of the DSA are prerequisites of any DSS site, but no spe-cific sampling method has to be employed in the selection of an area. Depending onthe nature of the study, sites employ probability or nonprobability sampling methods,or both, in drawing their sample population. Once an area has been selected the com-munity has to be mobilized to prepare it to participate in the research and ensure itscompliance. Mobilization activities involve conducting sensitization meetings withinfluential opinion leaders, such as councillors and village, hamlet, or religious lead-ers. During these meetings, the DSS staff presents and clarifies the project’s objectivesand expected output and outlines its anticipated activities. Other sensitization meth-ods include drama and sports activities involving the project staff and the community.

As DSSs are longitudinal studies, staff also have to maintain the community’scompliance with DSS activities longitudinally, and this means that mobilization of thecommunity is not limited to the initial stages but has to be a continuous process.Compliance is maintained in a variety of ways across sites, including giving feedback tothe community through presentation of results in simple tables or graphics, produc-tion and circulation of a newsletter, meetings with the key informants at regular inter-vals, and presentations of findings to health-management teams.

In terms of the minimum and maximum population size under DSS, there isno consensus. DSS sites can have a variety of population sizes under surveillance. Forexample, Butajira DSS (Ethiopia) began with a sample of 28 616 people (Berhane etal. 1999), whereas Navrongo DSS (Ghana) and Rufiji DSS (Tanzania) had, respec-tively, 124 857 and 85 102 people 1 year after they began operations (Binka et al. 1999;Mwageni and Irema 1999). The Adult Morbidity and Mortality Project (AMMP,Tanzania) has three sites and more than 300 000 people under surveillance (TMH1997). The site chapters give more details on the sample sizes of the various DSS sites.


Planning for data collection

Any data-collection exercise requires advance planning and recruitment and trainingof field staff, such as enumerators and supervisors. It also involves the designing andprinting of DSS forms and the preparation of field or training manuals. DSS enumera-tors are normally recruited from among those local individuals who meet minimumqualifications set for specific projects. Training focuses on proper ways to use DSSforms, conduct interviews, and handle various field forms. Field or interview manualsare used for training and are eventually provided to all field staff as reference materi-als during data collection. The training manuals clearly indicate the duties andresponsibilities of the field staff. In addition, the staff may receive training on how touse or operate field equipment, such as motorcycles. The field staff are given periodictraining on field operations to keep up to date on data-collection techniques.

Initial census

Data collection to establish the baseline population begins with a census, conductedby trained enumerators living in the study area. As stated earlier, they are trained onhow to use DSS forms and conduct interviews. The initial census establishes the foun-dation for a longitudinal surveillance system and helps obtain background data on thesubjects. Data are collected using standard questionnaires, with closed- or open-endedquestions, or both. Separate questionnaires are used to collect household and individ-ual data. The structured questionnaires comprise at least two sections: the header, forrecording the unit of interest; and the main part, for recording basic information (seeexample 1 in Appendix 1).

The type of data collected during the initial censuses depends on the specificobjectives of the site. In many sites, data are collected on variables such as householdcomposition (household head, relation to household head, etc.), culture (religionand ethnicity), demographic data (age, sex, marital status), and socioeconomic data(education, occupation, etc.). In addition, the DSS can collect data on behaviouralissues (alcohol consumption, smoking, etc.), housing, health-care use, and environ-mental conditions (source of drinking water, sanitation facility, etc.).

For identification purposes, each household and individual registered isassigned a unique number within its village and his or her household, respectively. Aseries of numbers for each individual may be used to identify the village, the house-hold, and the individual within the household. The number allocated to the individ-ual is permanent. In some systems, if an individual moves to a new area, the number isstill used to identify that person. In this way, it is possible to monitor migrants, as willbe shown.

Update rounds

The longitudinal system of data collection continues then with periodic visits to regis-tered households. The purpose of the visits is to record vital changes or events sincethe previous visit. These may include births or other pregnancy outcomes, marital sta-tus (marriages, divorces, separations, reconciliations), deaths, and migrations. Regulardata collection is undertaken to maintain accurate denominators for estimation of

DSS Methods of Data Collection ✦ 23

age-, sex-, and cause-specific death rates. The DSS approach has no specific interval forperiodic visits to the registered households (Indome et al. 1995). Yet, it is important toensure that the interval chosen between interview rounds is consistent for any givenhousehold or area. Provided they are consistent, periodic-visit cycles may range from 1to 12 months.

During the periodic visits or updates, the status of each individual is verifiedusing the household-registration or -record books (see example 2 in Appendix 1) orforms. The registration books are computer printouts of information on householdsand their members collected in the initial census. They are systematically arranged byhousehold to facilitate further visits or household contacts. These books can beprinted in rows and columns to maintain several rounds of data collection. The infor-mation on rows may correspond to individual members, as well as details of a house-hold, whereas the columns have spaces for filling in vital events detected in each DSSround. However, all vital events have to be registered on specific event forms. Theseforms may include observation of pregnancies, births, deaths, and marital changes(see examples 3–5 in Appendix 1). These are forms used in the Butajira, Navrongo,and Rufiji DSSs.

All errors that the interviewers note during update rounds they correctaccordingly in the respective book, along with filling out the changes form. Thechanges form requires the unique number of the household or individual, the changeto be made, the original information, and the correction. Corrections that mayrequire filling in the changes form include those for age, name, sex, missed membersof a household, and relationship to the head of household. Eventually, these forms aretaken to the data centre for correction of databases. This means that in DSS sites dataare collected in conjunction with data-management operations (details on data man-agement are provided later in this monograph). In most cases, the fieldwork and com-puter cycles coincide. Figure 3.1 summarizes the linkage between field and computeroperations in Rufiji DSS. This linkage aims at maintaining the integrity of data, as wellas ensuring timely reporting of findings. Upon completion of interviews in the house-hold (during the initial census or updates), the forms are taken to the computer cen-tre for data entry. Errors noted during quality control (for details, see Chapter 5) ordata entry are verified, reported to the field staff for diagnosis, and later corrected inboth the household-registration book and the computer databases.

Updating of vital events is not the only activity carried out during these peri-odic visits. During update rounds, enumerators register new people or households.These include the migrants, the newly married, and any individuals missed during theinitial census. The longitudinal system allows individuals to enter or exit the DSS atany time. They enter through births or in-migration and exit through deaths or out-migration (Figure 3.2). As these individuals are under surveillance, it is possible toestimate the total time spent by each individual in the study population. This timecontribution is called person–years of observation and is used as a denominator to esti-mate rates of events (such as fertility, mortality, and migration). Details on the uses ofperson–years of observation appear elsewhere in this monograph.

The periodic visits to registered households make DSS self-checking, allowingdata collected in one round to be checked and corrected in successive rounds. Thisreduces the risk of omitting, forgetting, or misreporting variables or events. Duringthe rounds it is also possible to select subsamples (nested studies) on which to collect


Figure 3.2. Prospective monitoring of demographic events.

Source: After Berhane et al. (1999).


A. Household visitation and

updating of HRB

F. Field verification and

correction of the HRB

G. Correction of database

H. Archiving of database

I. Printing of HRB and

report generation

B. Quality control

(5% sample)

DEATH OUT-MIGRATION

BIRTH IN-MIGRATION

C. Data entry with

on-line editingD. Error printing

E. Errors reported to

the supervisor

Field

Operations

Computer

Operations

EXIT

ENTER

INITIAL

CENSUSDYNAMIC COHORT (updated through cycles of enumeration)

Figure 3.1. The linkage between field and computer operations at the Rufiji DSS site, Tanzania.

Source: After Binka et al. (1999). Note: HRB, household-registration book.

data on specific items at marginal extra cost and without disturbing the originalpurpose of the surveillance. However, where the population is very mobile, a majorproblem of multiround surveillance is tracking subjects.

Recording demographic events

Monitoring of births and deaths in developing countries is very crucial, as these twoevents are easily omitted from routine statistical records and systems (Binka et al.1999). This can lead researchers to underestimate their occurrence. A good recordingsystem is needed to capture such events. Key informants can do this. Key informantsare usually senior or respected members of the community (such as village or hamletleaders) within the DSA. Key informants fill in their registers whenever an event hasoccurred, and they report this to the supervisors who visit them on regular basis.Ideally, being part of the community themselves, these people should not be individu-als who have to find out about these pregnancies, births, and deaths but those whowould hear about them in their course of normal life. As an incentive, a commonpractice is to pay key informants token fees for reporting such events, once they areconfirmed by the system. An example of the system for recording events, as practicedin the Rufiji DSS, is summarized in Figure 3.3.


Figure 3.3. Vital-events reporting system at the Rufiji DSS site, Tanzania. Source: After TEHIP (1996).

KEY INFORMANT

(Monitors births and deaths)

KEY-INFORMANT

SUPERVISOR

CONDUCTS INTERVIEW

VERIFIES EVENT IN

HOUSEHOLD

ENUMERATOR

(Visits each household every

120 days; records pregnan-

cies, births, and deaths)

COMPUTER CENTRE

(Checks on status of all

respondents for pregnancies,

births, and deaths)

CONFIRMS WITH

COMPUTER CENTRE

(Checks residence status)

DATABASE UPDATED

(Adds births and deaths)

DEATHS COUNTED

Key:

Primary

Secondary

In the vital-events reporting system of the Rufiji DSS, key informants observeand record any birth or death occurring in the study area. This information is passedon to the DSS key-informant supervisor (or enumerator, who informs the key-informant supervisor). Within 2 weeks, the key-informant supervisor visits thehouseholds where a birth or death has been reported and contacts the data centre forverification of the event. If the information is correct, the key informant is paid atoken fee. The key-informant supervisor then administers a verbal autopsy (VA) withone of the deceased’s relatives (who is well informed of the trend of illness of thedeceased) for all reported deaths. Enumerators also check births and deaths duringfixed enumeration rounds.

Monitoring mortality

Documentation of causes of death has contributed to progress in knowledge of epi-demiology and public health. Such documentation allows researchers and policymak-ers to assess the health status of a population, assign health priorities, study timetrends in mortality from specific causes, and evaluate health interventions.Documenting deaths is a common practice in developed countries, where most deathsoccur in a medical environment, postmortem autopsies are both feasible and cultur-ally accepted, and vital-events registration is mandatory and complete. In developingcountries, however, many deaths occur in the home, with limited or no medical atten-dance, and postmortem autopsies are rarely possible or complete and vital-events reg-istration is impractical. To assess the cause of death, one must rely on an alternativesource of information, that is, an attending relative’s description of symptoms andevents preceding death.

The VA is an indirect method employed in DSS sites to ascertain the causes ofdeath from close associates whom the DSS interviewers question regarding theirknowledge of the symptoms, signs, and circumstances leading to the death.Retrospective interviews of individuals who were there and can describe what hap-pened during the hours, days, or months preceding a death are done, and then a mostlikely cause of death is inferred from the sequence and combination of symptoms andevents. Specially designed forms (questionnaires) are used to suit the population ofinterest (TMH 1997). For example, if the study of interest is the mortality patterns ofchildren <5 years old, then a form is designed and structured to cover all signs andsymptoms of illnesses that affect mostly children of this age (see example 6 inAppendix 1). There are also special interview forms for deaths of children <31 daysold and for deaths of those ≥5 years old. The DSSs use trained medical personnel orlaypeople to conduct VAs.

VAs are used in health-care projects involved in research and evaluation ofhealth services. As earlier described, key informants record deaths that occur in theirarea in a mortality register; this is reported to the interviewers who will conduct theVA. The interviewers make appointments to visit the houses of the bereaved families.On the appointment day, an interviewer visits the house and administers a VA with thecaretaker or a close family member of the deceased. The VA questionnaires aredesigned to suit the settings of the area under surveillance (TMH 1997). Such infor-mation as name, age, sex, occupation, and other risk factors is usually collected, inaddition to an open history of events leading to the death, previously diagnosed med-ical conditions, and signs and symptoms that appeared before death. The interviewer


can use the questionnaires to record information on use of health facilities before thedeath, reasons for using or not using a particular health facility, the caretaker’s per-ception of cause of death, and confirmatory evidence of a cause of death (if available).The cause of death is determined from a combination of these signs and symptoms.

Causes of death from the VA questionnaires can be reached by either askingphysicians or using computer algorithms, depending on the design and structure ofthe questions. If physicians are asked to do this, then usually two physicians independ-ently code the VA forms and determine the cause of death, using some kind of agreedclassification (for example, the WHO International Classification of Diseases [ICD]for causes of diseases). In the case of discrepancies, a third physician is asked to codethe forms. Computer algorithms are based strongly on the checklist of signs and symp-toms recorded on the form. If discrepancies are noted at this level, then the cause ofdeath is categorized as unknown. Discrepant VA forms produced by the algorithm aretaken to physicians for diagnosis and coding. Usually, forms with discrepancies arefewer than others.

Tracking migrants

Migration is a complex subject, with a variety of definitions (Pressat 1985; Newell1994). As such, the definition relies more on the way data are collected and the pur-pose for which they are collected. Generally, migration refers to movement of people(groups or individuals) that involves a permanent or temporal change of their usualplace of residence (Pressat 1985). Migrants are therefore people who change theirusual place of residence. According to Kpedekpo (1982), classification of migrants canbe based on the following criteria:

• Those who are enumerated in a place different from where they were born;

• Those who resided in the place of enumeration for a period less than their ownage; and

• Those who resided (for a fixed period) in a place different from their resi-dence at the time of data collection.

Data on migration can be collected in several ways. Censuses, sample surveys,and continuous population registers are the most common (Shryock and Siegel 1976).Censuses and surveys can provide migration data directly (by asking questions about,for example, the number of moves, duration of residence, date of exit or entry, andprevious residence) or indirectly (by estimating migration from total counts of popula-tion and natural increase of two censuses or counts). The problem with these methodsis their failure to detect multiple moves or those that people cannot remember. Inaddition, past migrants are grouped together with most recent ones. Also, the indirectmethod requires very accurate data for the two censuses.

A migration history is another way to collect data on migrants. DSS sites collect-ing migration data employ this method. This is a continuous way of giving data on pre-vious residence of individuals with dates of their moving out and in. In this way,migrants are linked to the database. Special in- or out-migration forms are used totrack down migrants (see examples 7 and 8 in Appendix 1). The in-migration formrequires more details than that for out-migration. In addition to personal particulars


of an in-migrant (sex, date of birth, education, occupation, etc.), information on thedate of and reasons for the migration and the place of origin are also gathered. If in-migration involves a household, a household questionnaire is also used to recordhousehold characteristics. On the out-migration form, information is recorded on thedate of and reasons for the migration and the destination.

DSS sites do not record all the moves but only those within a certain period.For example, the Navrongo DSS considers an individual an in-migrant if this person isin the same place of residence for 3 months (Binka et al. 1994), whereas Rufiji DSSuses a 4-month criterion for the same purpose (TEHIP 1996). The opposite applies toan out-migrant. The purpose of setting these criteria is to find a proxy to determinethe residency status of individuals. This status enables estimation of the individual’soverall time contribution to supply denominators for calculation of other demo-graphic measures, such as mortality and fertility.

Additional rounds of data collection

The previous sections have focused on collection of data for demographic variables —mainly, births, deaths, and migrations. All these can be considered extradynamicevents, as they change frequently within a year. Other variables are constant or changeslowly, such as socioeconomic aspects like education, occupation, housing conditions(floor, roofing material), health-care use (like vaccination), and environmental condi-tions (like source of drinking water and sanitation facilities). Such information can becollected once in a year, preferably at the beginning of each calendar year.

A DSS can have other nested studies to capitalize on its population databaseand organizational infrastructure. Such studies employ a variety of designs, such ascohort, cross-sectional, and case referent, depending on the specific primary purposeof each study, and these studies are usually linked to the longitudinal surveillance sys-tem. The Butajira DSS, for example, used its database as a sampling frame for a studypopulation and used the routine surveillance to follow subjects in various studies ofacute respiratory infections (Berhane et al. 1999). In Tanzania, a new study aimed atmonitoring a program for antimalarial combination therapy uses the Ifakara,Morogoro (AMMP), and Rufiji DSAs.

Such nested studies in the DSS sites take advantage of the existing infrastruc-ture and field organization for data collection. Sometimes these new studies mayemploy supplementary personnel trained to collect information specific to each study.As a result, many DSS sites become pools of trained field staff.

Geographic information systems

A GIS is a computer-assisted information-management system for geographically refer-enced data. It integrates the management (that is, acquisition, storage), analysis, anddisplay (mapping) of geographic data (Loslier 1995). The GIS contains two integrateddatabases, namely, spatial (location information) and attribute (characteristics of thespatial features). The spatial database comprises digital coordinates obtained frommaps, using GPS. These coordinates can take a variety of forms, such as points (dis-pensaries, hospitals, schools, households), lines (roads, railways, rivers), or polygons(wards, towns, villages, hamlets). The attribute database can include information such


as population size or density and number of health facilities or personnel. The GIScan create a link between spatial data and their associated descriptive information. Itsstrength lies in its capacity for integration and analysis of data from many sources,such as population, topography, climate, vegetation, transportation network, socialservices, and epidemiological characteristics.

Many DSS sites use GPS to determine locations and boundaries of phenomenaof interest, including boundaries of settlements, households, and villages, and to maphealth services in terms of access and coverage. Thus, Navrongo DSS used GPS coordi-nates to assess the child-mortality impact of insecticide-treated bednets in 96 clustersof contiguous compounds (Binka et al. 1996). The data collected using GPS arejoined to spatial imagery with GIS. In this way, it is possible to combine and analyzethe occurrence of features with various locations. Nouna DSS in Burkina Faso has aGIS with data on all households in 49 villages and information on such features ashealth facilities, sources of water, roads, schools, and religious places (Sauerborn andKouyaté 20001).

Conclusion

This chapter has presented a general picture of the major data-collection activities atthe DSS sites. The data-collection process has been presented in terms of sequence ofevents carried out in DSS sites. It discussed the people involved in data collection andthe tools used in obtaining information. (Part III will describe specific data-collectionmethods the DSS sites employ, including sampling procedures, type of informationgathered, and key functions and responsibilities of the staff.) This chapter has alsoshown the potential of DSS sites to contribute reliable demographic and health-related data. Given developing countries’ lack of complete vital-events registration sys-tems and the costs of and long intervals between national censuses, the DSS approachis probably one of the best options for improving the quality of data. The DSS data-collection procedures are linked to data-management and quality-control procedures,which are the items discussed in detail in the next two chapters.

1 Sauerborn, R.; Kouyaté, B., ed. 2000. Nouna Research Centre, a platform for interdisciplinary field research in Burkina Faso, WestAfrica. Internal report.


Chapter 4

PROCESSING DSS DATA

Introduction

Compiling longitudinal population information poses unique data-management chal-lenges. Projects must maintain changing individual-level information on the composi-tion and household structure of a large, geographically defined population. Eventsthat arise — births, deaths, migrations, etc. — must be linked to individuals and otherentities at risk of these events. These events affect not only demographic rates, forinstance, but also relationships within and between households. As event historiesgrow, records of new events must be logically consistent with those of events in thepast. Seemingly obvious checks on data to meet minimal standards of integrity canresult in hundreds of lines of code.

Relating critically needed auxiliary data to dynamic population registers posesfurther challenges. Morbidity and cause-of-death data must be entered, linked, andstored. Most DSS projects also maintain socioeconomic data such as on marriage,family relationships, and economic conditions, owing to the strong correlationbetween health and socioeconomic status. These must be logically consistent withother longitudinal data on the population at risk and relationships among individualsunder surveillance. Moreover, projects are often launched to assess the impacts ofhealth technologies, service strategies, or policies, and this necessitates data entry,management, and checking procedures for the internal consistency of service infor-mation, as well as procedures to link this information to demographic histories.Variance in exposure to interventions must be monitored at the individual level, inconjunction with precise registration of demographic events and individual risk.Maintaining a detailed record of demographic events, relationships, and exposure torisks or interventions requires complex data-management operations, with a carefullycontrolled field-operation infrastructure to oversee and support data collection andentry, and a comprehensive computer system for the data-management operation.

Data-management systems required for this operation typically encompassthousands of lines of computer code. A key contribution of the INDEPTH network hasbeen technology-sharing to offset the complexity of developing a data system andcreating a reference data model for storage of DSS data. This generic model for datastorage facilitates cross-site comparative analyses of the type described in this volume,as it standardizes data rules and concepts across sites. Future work of the network willaddress the need for generic analytical and data-management software compatiblewith the reference data model.

31

This chapter outlines features of this reference data model that pertain to theINDEPTH DSSs. In the not-too-distant past, developing DSS software was difficult,time-consuming, and prone to conceptual and programmatic errors. Software genera-tors and object-oriented tools for software development greatly simply the task ofdeveloping a complex system, once common principles of software structure areinstantiated in a common applications framework. The mechanisms of INDEPTHhave marshalled these software innovations to meet the collective needs of memberstations. The reference data model will facilitate exchange of information, swift formu-lation of site-specific data management software and common software for data analy-sis, and simplified technical assistance and capacity-building operations.

Background

The work of the INDEPTH Technical Working Group (TWG) has been informed bythe achievements, limitations, and future needs of projects in Bangladesh, BurkinaFaso, Ghana, Indonesia, Mali, Senegal, South Africa, Tanzania, and Uganda. One ofthe earlier systems, the Bangladesh DSS in Matlab District, was developed in the 1960sand has since been used for a wide range of studies of demographic dynamics, familyplanning, epidemiology, health-services research, and other issues (Rahman andD’Souza 1981; D’Souza 1984). Although the Bangladesh DSS has redeveloped its com-puter operations several times, its field operations have provided a model for a widerange of DSS applications in developing countries. The Bangladesh DSS preciselydefined eligibility rules for members of a population under study; this, combined witha data system with rigorous logical-consistency checks, has provided high-quality datafor many research papers. A number of software systems have been written, based onexperiences with the Bangladesh DSS, including the Sample Registration System(Leon 1986a, b, 1987; Phillips et al. 1988; Mozumdar et al. 1990) and the IndramayuChild Survival Project of the University of Indonesia (Utomo et al. 1990). The DSS inNiakhar, Senegal, most recently described in Garenne (1997), has also influenced thetechnical design of a number of systems, including those of PRAPASS in Nouna,Burkina Faso (Sauerborn et al. 1996), and Agincourt, South Africa (Tollman et al.1995). Garenne (1997) described the concept of entry–exit files (similar to the con-cept of “episodes” described here) as a means of modeling both intervals of residenceat a location and intervals of relationships. Garenne also provided useful observationsregarding the implementation of field and software systems for longitudinal popula-tion studies.

To develop its data model, TWG synthesized the experience of these disparateapplications. The model specifies a demographic “core” common to field stationsdoing longitudinal research on populations (MacLeod et al. 1991; Phillips et al. 1991).Sites have developed software systems to manage this demographic core, maintain aconsistent record of significant demographic events in the population of a fixed geo-graphic region, generate registration books that the fieldworkers use, and computebasic demographic rates, such as birth, mortality, and total fertility. These core capa-bilities establish a computational framework to which projects add their site-specificdata and consistency specifications. The concept of a core also entails some genericprinciples of data collection and management that apply to all INDEPTH sites.


The INDEPTH concept of a data core

All participating sites in INDEPTH collect and maintain a common core of data.Attempts to standardize data processing have led to the concept of a “core system” thatprovides many of the common software requirements of field research laboratoriesand can be extended and modified to tailor software to various specifications. Thisconcept is based on the principle that certain characteristics of households, householdmembers, relationships, and demographic events are common to all longitudinal stud-ies of human populations, and software required to collect, enter, and manage datacan therefore be generic to a family of applications. TWG has identified these featuresof a core system common to all DSS operations. In this framework, the core systemmaintains a consistent record of baseline and longitudinal data on all households,household members, and their relationships in a geographically defined population,including births, deaths, migrations, and marriages. The core system maintains infor-mation on events and observation dates to give each entity in the study corresponding“person-day” counts of risk for demographic events. Core computer operations struc-ture data and maintain logical integrity on the following basic elements of a house-hold unit:

• All households have defined members at any given point in time (rules unam-biguously exclude nonmembers);

• All households have a single head at a given point in time, and members relateto one another and to the head in definable ways;

• Members have names, dates of birth, and other characteristics that do notchange over time;

• Events can occur to members, such as death, birth, in- and out-migration, andmarital-status change (attempts to enter event data on nonmembers arerejected at the point of data entry);

• Events change household membership and relationships according to fixedrules; and

• Episodes (such as pregnancies, conjugal relationships, or residencies) are asso-ciated with individuals at risk (that is, active members) and must follow simplelogical rules.

Although these are seemingly trivial items, mundane relationships tend tobecome complex and unwieldy when arrayed as a logical system of longitudinal popu-lation data; and portraying even simple relationships requires rigorous standards toavoid error. For example, to be counted as a death in a resident population, a con-cerned household member must be resident in the study area at the time of death; alive birth to a woman 5 months after she gave birth to another child would be aninconsistent event. A central contribution of TWG has been to clarify such minimalsystem logic so that the system prevents errors resulting in violation of business rulesand rendering data useless.

Processing DSS Data ✦ 33

All INDEPTH computer systems maintain standard DSS-processing operations:

• Data entry — Software allows for entry, deletion, and editing of the baseline andlongitudinal data. Baseline household information includes the householdlocation, individuals within the household, relationships between individuals,and familial social groups. Longitudinal information includes basic informa-tion on pregnancies and their outcomes, deaths, migrations in and out of thestudy area, marriages, and any other measures the investigators specify.

• Validation — Software checks for the logical consistency of data.

Most INDEPTH sites have also developed software for reporting outcomes andmanaging data:

• Reports and output — Routine software calculates and displays demographicrates and life tables and can compute age-specific and overall rates.

• Visitation register — Software prints the household-registration book, which isused by the fieldworkers to update and record information during householdinterviews.

• Utilities — This option is primarily used by the system administrator. It includescapabilities for adding new user IDs, setting interview-round information, andgenerating reconciliation reports to help track down unreported pregnancyoutcomes and unmatched internal migrants.

Tailoring the core system

Given the basic core model for data structure, each site has developed site-specificapplications using building blocks of the core framework, which allow software devel-opers to construct additional modules for project-specific data. At nine INDEPTHsites, standard tools of database-management packages have been used for anINDEPTH product known as the household-registration system (HRS) for the corespecification.1 Other INDEPTH sites have developed project-specific core capabilitiesto maintain the logical integrity of birth, death, migration, and marriage data overtime and in a format consistent with the reference data model. Each site modifies thecore to accommodate new cross-sectional data, special longitudinal modules, or vari-able classes or labels investigators want to add to field registers, along with logic tomaintain the integrity of new variables.

The tools of commercially available database packages greatly facilitate theprocess of core modification. Standard features of commercially available database sys-tems include those for easily adding data to the core system. For example, the HRS isbuilt from the form menu (data-entry screen) and database builders of the MicrosoftFoxPro system. These builders encourage and facilitate an object-oriented software-development approach through easily understandable mouse and menu procedures.To make changes to the core, a programmer locates the database table, menu, or form


1 The HRS formed the basis for INDEPTH software systems in The Gambia, Ghana (Binka et al. 1995), Indonesia, Mali, Mozambique,Tanzania (three sites), and Uganda. Applications involve a wide range of INDEPTH studies, including family-planning research,malaria interventions, child and maternal health, and correlates of HIV transmission. The current INDEPTH data model improves onthe original HRS and other INDEPTH systems by allowing investigators to track nonresident individuals; include more generalrelationships, rather than just marital relationships; and separate membership in social groups (such as the household or family) fromthe location.

object to be changed, then works with the small pieces of code, called code snippets,which are “attached” to the object. Some code snippets control the timing of the entryof data for a variable; others enforce rules of consistency. Some INDEPTH sites, suchas Hlabisa, are developing similar capabilities, using systems in SQL Server and Access.

The reference data model

As explained in Chapter 3, a DSS tracks the presence of individuals in a defined studyarea. These individuals can enter and leave the study area in a small set of well-definedways (for example, entering through birth or in-migration and leaving through deathor out-migration). The INDEPTH reference model uses events to record the waysindividuals enter (or return to) and leave the study area over time. Thus, eventsbracket the residency of any individual in the study area. In general, they occur inpairs, with one event (such as presence in the study area) initiating a state andanother event (such as migrating out or death) terminating that state. Use of episodesin the reference model makes this pairing of initiating and terminating eventsexplicit. The concept of episodes is diagramed in the centre section of Figure 4.1.

When a DSS tracks episodes, the concept of the “time resolution” of this track-ing is very important. Below a certain time threshold, movements into or out of a par-ticular place are not recorded. If a person leaves the physical location in the morningto go to the market and returns in the afternoon, this is not reflected in the DSS. Ifthis period of absence increases beyond a certain threshold (6 weeks, 3 months, orsome other period), it turns into an episode to be recorded in the DSS. This thresholdvaries from project to project, but the project always makes it explicit. The time resolu-tion for “in” episodes should be consistent with the time resolution for “out” episodes,that is, the time before a visit becomes residency or the time after which an absencebecomes an out-migration.

DSSs are concerned not only with the physical location or residence of individ-uals but also with their membership in social groups (such as households) and theirrelationships with other individuals (such as marital unions or parenthood). ManyDSSs also need to reconstruct genealogies and to record isolated events, such as preg-nancy outcomes or births and deaths external to the study area.

To support field operations and routine cleaning of data, a DSS must also keeptrack of where, when, and by whom a particular event was recorded. In this respect,the reference model provides a number of fields to facilitate construction of a good-quality data set. Another challenge for demographic field operations is to correctlyidentify migrating individuals. To resolve this problem, the reference data modelincludes fields to designate the place a migrant is moving to or coming from.

The INDEPTH reference model meets these requirements through its use ofthe following entities and the relationships between them (see Figure 4.1):

• Physical location — This entity records the physical locations where individualscan stay, either singly or in groups, such as a homestead, stand, or plot. At sev-eral INDEPTH sites, it is possible to pinpoint this location by using coordi-nates, such as latitudes and longitudes. This feature is easily linked to a GIS.External IDs, such as stand number or address, can be stored in addition to theunique location ID value. An individual is associated with a physical location ata given time through a “resident episode.”



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• Individual — This entity contains a record for every individual who has everresided in the study area. Optionally, this entity may record individuals whoseresidence in the study area has not been recorded but is required to complete agenealogy or relationship record. Records are uniquely identified through anindividual ID value. Genealogical linkages can be established by storing the IDsof the individual’s father and mother. This information (mother’s and father’sID) can also be useful for identification purposes, especially where name anddate of birth are not clearly defined, as is often the case in SSA.

• Social group — This entity stores information on a defined social group, such asa household. An individual is associated with one or more social groups,through one or more membership episodes.

• Observation — The observation entity stores the information that a particularphysical location has been observed at a given time. This entity can also storeinformation on the person making the observation and optional information,such as the census round. The observation entity is linked to all the eventsrecorded during the observation.

• Events — The events entity may indicate a change in the state of an individual(for example, from resident to nonresident, in the case of an out-migration).Events that initiate and terminate a particular state of interest (for example, res-idency) are combined and recorded as an episode (for example, residentepisode). These types of events are known as “paired events.” Events that donot record the start or end of a particular state are known as “point events.”The information common to all events (such as date of occurrence, type ofevent, and ID of the observation during which the event was recorded) is storedas part of the episode that this event initiates or ends (in the case of pairedevents) or as part of the point-event table (in the case of point events).Additional data associated with an event are stored in a separate entity. The fol-lowing event types are noted in Figure 4.1:– Birth — This event type records all live births to residents (stillbirths are

recorded as a pregnancy-outcome event). The event is linked to the resi-dent episode it initiates — it also initiates social-group membership andrelationship episodes.

– Death — This event type records all deaths of residents. A death event willterminate all open episodes belonging to the individual. The death-eventrecord is linked to the resident episode that the event terminates and con-tains additional data, such as the location and cause of death.

– Relationship start — This event type records the start of a relationship of oneindividual to another. By convention, relationship events are linked to thefemale in cases of heterosexual relationships and to the younger individualin cases of same-sex relationships. In the case of caretaking relationships,the relationship events are linked to the person receiving care.

– Relationship end — This event type records the end of a relationship betweentwo individuals.

– Membership start — This event type records the start of an individual’s mem-bership in a social group.

– Membership end — This event type records the end of an individual’s mem-bership in a social group.


– In-migration — An in-migration event initiates a new or changed physicallocation for an individual. It records the start of a new residence episode foran individual and can originate within or outside the study area. Additionaldata, such as origin, are usually stored in a separate entity linked to theepisode via the episode ID.

– Out-migration — An out-migration event terminates a residence episode at aphysical location for an individual. The destination of an out-migration canbe within or outside the study area. Additional data, such as destination, areusually stored in a separate entity linked to the episode via the episode ID.

– Status observation — Any number of optional events can be defined to recordstatus information observed for individuals, such as socioeconomic, nutri-tional, educational, or immunization status. Repeated status observationsmake no assumptions about the value of observed attributes during theobservation interval, even if subsequent observations measure the samevalues.

• Episodes — As Figure 4.1 shows, episodes can occur to residents, relationships,pregnancies, and memberships in social groups:– Resident episode — A resident episode records the stay of an individual at a

physical location. A resident episode can be initiated only by a DSS entry, abirth, or an in-migration event. It can be terminated only by a DSS exit, adeath, or an out-migration event.

– Relationship episode — A relationship episode records a time-dependent rela-tionship, such as a marital union, between two individuals. The episode isstarted by a relationship-start event and concluded by a relationship-endevent, a death, or a DSS exit. The relationship episode records the IDs ofthe two individuals involved in the relationship, but the events initiatingand terminating the episode are linked to only one of the individuals, asdescribed above.

– Pregnancy episode — Pregnancy is recorded as an episode, with certain attri-butes recorded on the first observation of the pregnancy and othersrecorded when the outcome of the pregnancy is known. One lesson we havelearned is that if you want to do a good job in child registration, you have toregister pregnancies first. However, if a pregnancy is not observed, but onlythe outcome, the start of the pregnancy episode is still recorded as the dateof the last menstrual period before the pregnancy. In this case the start andlast observation IDs will point to the same observation instances. If a preg-nancy is terminated by the woman’s death or out-migration, the reason fortermination is recorded as the terminating-event type, and the episode isconcluded. In the normal course of events, the pregnancy outcome couldbe recorded in the terminating-event type as spontaneous abortion,induced abortion, normal delivery, assisted delivery, or caesarean section.The “birth location” field refers to the delivery environment (for example,the name of a hospital or clinic where the delivery took place).

– Membership episode — A membership episode records the membership of anindividual in a particular social group. A membership episode can be initi-ated only by a DSS entry, a birth, or a membership start event. It can be ter-minated only by a DSS exit, a death, or a membership end event.


In summary, Figure 4.1 illustrates the entities and relationships of theINDEPTH reference data model. Mandatory fields and entities are displayed in boldtype, whereas optional fields and entities are displayed in normal (nonbold) type.

The role of the reference data model in

maintaining data integrity

As explained in Chapter 3, any DSS must maintain a large volume of data over anextended period. Unless specific measures are taken, the integrity of the data will suf-fer, along with the accuracy and reliability of the information in the system. INDEPTHhas taken steps to foster common standards for data integrity, based on a well-definedrelational model. Although not all systems have the same measures to protect dataquality, the following have been proposed or used at one or more INDEPTH site:

• “Audit stamp” — The audit stamp is part of every record in the database. Theaudit stamp records the operator and the date and time of the last update tothe record. In addition, a quality-check indicator may record whether therecord has been verified (for example, through a double-entry process).

• Standard values — Standard values should be used consistently throughout thedatabase to indicate the status of a particular data value. The following stan-dard values (and their meanings) are proposed:– “Never entered” — This is the default value for all data fields in a newly cre-

ated record.– “To be confirmed” — This indicates a need to query the value as it appears on

the input document and to take follow-up action.– “Not applicable” — Given the data in related fields or records, a value for this

data field is not applicable. – “Out of range” — The value on the input document is out of range and

could not be entered. Follow-up action yielded no better information or isnot applicable.

– “Unknown” — The value is not known. Follow-up action yielded no betterinformation or is not applicable.

(The actual values used to indicate the standard values depend on the data typeof the field and the natural value range for the data item. Care should be takento exclude these values from quantitative analysis of the data.)

• Date values — Date values are of particular importance in a DSS, and it is prefer-able to record the precision of date values in addition to the dates themselves.Each date or duration field should have an associated precision field forrecording the precision of the date value (for example, minutes, hours, days,weeks, months, quarters, semesters, years, decades).


Extending the core

Although the INDEPTH reference data model covers aspects common to allINDEPTH DSSs, it makes no attempt to specify all site-specific needs. However, it isdesigned to accommodate new components to meet the needs of a wide spectrum oflongitudinal studies, without losing its clear overall structure. Several ways are pre-sented in this section:

• Adding fields to existing entities — The simplest core extension is to add a datafield for a fixed-in-time attribute of objects, events, or episodes already imple-mented. Examples of this type of core modification are inauguration date of aphysical location, membership in an ethnic group, an individual’s Rh factor,the weaning age of a registered or member child, or the presence of a supervi-sor during an observation.

• Defining new types of social groups and relationships — Whenever the interaction ofindividuals can be formalized to permit specification of a start and an endpoint of this interaction, it can be expressed in terms of social-group member-ship (interaction with all other individuals being members of the same socialgroup at the same time) or of a relationship to just one other individual.INDEPTH data systems have specified a wide variety of such relationships andepisodes. For example, membership might be in a social group (such as a lin-eage), in a health-insurance scheme, or in any other type of group that suits theneeds of a study. A relationship can also be of a patient to a health-careprovider or of a tenant to a landowner. Membership is not always limited tosocial groups but sometimes involves a “membership” in a category of chroni-cally ill individuals or “membership” in a nested cohort study (where fulfill-ment of some predefined criteria might be the start events; and of others, theend events).

• Adding new types of episodes or events — As illustrated in Figure 4.1, the systemrecords four minimal, “predefined” types of episodes, and these can be adaptedto various purposes. New event types are sometimes specified to facilitate stor-age of supplemental information (applicable only under specific conditions)while keeping the corresponding episode record as parsimonious as possible.

• Defining events and episodes for physical locations and social groups — Althoughevents and episodes always refer to individuals, they sometimes relate individualsto other operations. An extended model can define additional events andepisodes with reference to physical locations or social groups. Point events andstatus observations can be defined to record information collected or observa-tions made about physical locations (such as housing type, water supply, numberof rooms), social groups (such as ID of household chief, monthly householdincome, agricultural production), or other nonconstant attributes.


Social groups can be related to other social groups, or “first-level” social groupslike households can be members of “second-level” social groups like clans or othertypes of networks. DSSs designed to track the interaction of households might definerelationship and membership episodes for social groups, to store information aboutthis topic.

Households are normally associated with only one homestead, even if the mem-bers of the household reside in more than one physical location. When social groupsare used to record households, this association can be depicted by an episode thatrecords the start and termination of occupation at a physical location. Householdsalso normally have a head of household. This head may change with time, but thehousehold will still retain its identity, and head of household can be recorded eitheras an updatable attribute (“Current head of household”) or as a member of the socialgroup. If the temporal dimension is important, the extension can be specified as anepisode linking the household to an acting head of household.

In summary, the reference data model provides a structure to accommodategreat flexibility in the design of longitudinal studies, and for this reason, INDEPTHincludes sites engaged in various study designs, with a wide range of data-managementneeds. Despite this diversity, the model has a core of logic and structure lendingintegrity to operations and providing a crucial foundation for technical collaborationamong sites.

Conclusion

This chapter has described the data model that INDEPTH has developed as the guid-ing framework for processing data at member sites. It makes attributes common tomost health and family-planning studies explicit. As well, it serves as a structural frame-work for the addition of project-specific data. Much work still needs to be done todevelop this model and a common data-processing system for INDEPTH core opera-tions. However, the common framework for data management has already facilitateddata sharing within the network, and nearly one-half of all INDEPTH sites use a com-mon software system for core operations. If this use of generic software is morebroadly accepted, the INDEPTH data model could serve as the basis for sharing systemdevelopment, capacity-building, and collaborative research.


Chapter 5

ASSESSING THE QUALITY OF DSS DATA

Introduction

In a DSS, errors occur at all stages of the operation. These may take the form of cover-age errors, resulting from omission or repeated counting of persons, or contenterrors, arising from incorrect reporting of the characteristics of respondents. To estab-lish whether the data are of reasonable quality, INDEPTH sites use a variety of evalua-tion procedures at the field, data-processing, and analysis stages.

Assessing data quality in the field

It is important to note, at the outset, that however comprehensive the data-checkingprocedure is, it cannot substitute for careful, methodological, and conscientious inter-viewing (Shackleton 1998). During training, fieldworkers are made to understand thatit is their primary responsibility to ensure accuracy and completeness of data. In addi-tion, field monitoring of data quality is ensured through regular supervisory visits,form checking, and reinterviews.

Supervised visits

The field supervisor’s role is to ensure that each fieldworker conducts interviews ofoptimum quality. An effective way for a field supervisor to do this is to join up with thefieldworker and observe one or more of the fieldworker’s interviews. The frequency ofthe supervisory visits varies from site to site and may be daily, weekly, or fortnightly.Such visits are normally unannounced. They are intended to help monitor the per-formance of the fieldworker from several perspectives. The first is to check whetherthe fieldworker is actually making the field visits. The supervisor then observes inter-views and discusses defects in interviewing techniques. Where necessary, a supervisormakes an effort to help resolve any problems the fieldworker may have. Supervisorspay particular attention to the sequence of the interview process, to prevent omissionof questions and make sure fieldworkers follow a logical and systematic format forinterviews.

43

Form checking

First and foremost, fieldworkers are expected to check their own work as part of theirdaily routine. Ideally, they should do this before leaving the site of an interview so thatthey can correct errors immediately. Key checks at this stage are to verify the numberof event forms, ensure no omission of questions, and provide valid codes for ques-tions. At some sites, each fieldworker gives completed forms to another team memberto check before handing them over to the supervisor at the data centre. In addition toobserving field interviews during visits, supervisors review samples of completed fieldquestionnaires to identify inconsistencies and assess their completeness. They pointout any obvious error for correction. In DSS activities, there is generally a high proba-bility of obtaining missing information on a revisit. Therefore, to maximize the chanceof identifying errors before the form leaves the field and minimize the effort requiredto do the revisit, the team supervisor carefully checks each form again. Here, thechecking is more comprehensive and includes validity of dates, consistency of house-hold relationships, and sensibleness of linked fields. Any error detected is returned tothe fieldworker for correction, and if needed the fieldworker does a revisit to makecorrections.

Duplicate visits

To further check on the reliability of the information, supervisors also carry out ran-dom field checks on compounds or households. On these visits, they re-administerportions of the questionnaires. The responses are compared with those obtained bythe fieldworker, to provide an idea of the degree of accuracy of the data. At some sites,those responsible for the spot checks also ensure that all neighbouring households areregistered. In addition to making random field visits, quality-control supervisors rein-terview a 3–15% sample of all compounds or households at the site. They compare thedata obtained from the re-enumeration with those of the fieldworker to determinewhether the original interview was actually conducted. This also helps to reveal any sys-tematic errors made by the interviewer and provides data for calculating error rates. Itmust be emphasized, however, that not all errors are completely attributable to thefieldworker, as they may arise if, for example, a different member of the compound orhousehold serves as the respondent. At some DSS sites, efforts to improve on coverageinclude an independent annual listing of all households, which is then cross-checkedagainst DSS households.

Assessing data quality at the data centre

General procedures

At the data centre, some sites have a second level of supervision: field headquarters’staff (senior supervisors) thoroughly examine completed questionnaires to identifyerrors missed by both interviewers and supervisors and ensure that data for individualrespondents are consistent. The next stage involves computer editing, using computerprograms with built-in checks to assess the validity of responses, either during or fol-lowing data entry. These built-in consistency checks help to flush out illogical


responses, invalid codes, double entries, and items with missing values. Verification ofdata is also carried out to detect systematic data-entry errors. This procedure helps toassess the performance of individual data-entry clerks and determine whether the gen-eral error rate of data entry is within acceptable limits. At the beginning of each data-collection and data-processing cycle, a verifier repeats the work of a data-entry clerk,until the clerk is qualified in terms of the maximum allowable error rate. Thereafter,only a sample of the work is verified to ensure that the clerk keeps up an acceptablelevel of accuracy.

Statistical techniques

Matching of records

The statistical procedure to determine the completeness of coverage and reliability ofthe data is to reinterview and to match individual records case-by-case from two datasources. To evaluate net coverage error, events from the DSS are matched one-on-onewith corresponding records from the re-enumeration of 3–15% of the original popula-tion. The proportion of records in the re-enumerated sample that were missed in theregistration process provides an estimate of the overall coverage error. To assess theaccuracy of the data, records from the two data sources are matched, based on a cen-tral variable, such as age. By matching individual records from the reinterview withthose from the DSS, it is possible to determine the number of individuals omittedfrom, or erroneously included in, each age group in the DSS. The assumption is thatthe probability of event omission from the quality-control sample is much lower than,and independent of, the probability of omission from the DSS, although surveys dohave correlation biases. Another statistical approach for evaluating coverage and con-tent errors is to compare both absolute and relative numbers from successive periodsof the DSS to identify deviations from expected patterns. Occasionally, aggregate fig-ures from the DSS are also compared with those from an independent source to testfor consistency.

Population pyramid

The population pyramid is a graphic representation of a population’s age–sex distri-bution. It is another method to assess the quality of age reporting and is used to give adetailed picture of the age–sex structure of the population. The basic form shows barscorresponding to age groups or single-year age distributions in ascending order, fromyoungest to oldest. These distributions may be in either absolute numbers or percent-ages calculated from the grand total for the population. In growing populations, thepyramid is expected to be triangular, with concave sides (that is, it narrows rapidlyfrom the base up). Thus, the shape of the pyramid helps to reveal irregularities, suchas age shifting and age heaping, in the age–sex structure of the population.

Alternative techniques

Undercounts and misplacements of events are very often encountered in DSS activi-ties. Other errors resulting in the misclassification of population characteristics alsooccur. Even with the best quality assurance, it is impossible to overcome all these

Assessing the Quality of DSS Data ✦ 45

errors in the field and at the data centre. Several standard statistical and demographicmethods are available to DSS sites for evaluating the accuracy of data.

Age preference

The degree of age preference can be used to test for deficiencies in the DSS data.Although age is the most important variable in demographic analysis, it is typicallyprone to errors of recall and other types of biases. Age misreporting takes two basicforms: “heaping,” or digit preference, and “shifting.” In less literate populations, thereporting of events, especially births, is usually clustered at certain preferred digits, asa result of ignorance, genuine reporting errors, or deliberate misreporting. Thus, it iscommon to find concentrations of people at ages with numbers ending in digits 0 and5 and, to a lesser extent, 4, 6, or 9. Indexes such as Whipple’s index and Myers’blended index have been developed to statistically assess the extent of age preference,based on the assumption that the population is rectangularly distributed over someage range (Shryock and Siegel 1976). Whereas Whipple’s index is a measure of prefer-ence for ages ending in 0 and 5, Myers’ index provides an overall measure of ageheaping, as well as an index of preference for other terminal digits.

To measure the extent of heaping on digits 0 and 5, Whipple’s index employsthe assumption of rectangularity over a 10-year range and compares the populationreporting ages ending in 0 and 5 in the range 23–62 years. The index varies between100, indicating no preference for digits ending in 0 or 5, and 500, indicating that onlydigits ending in 0 or 5 were reported. A United Nations-developed scale can be usedto evaluate the reliability of any data set based on the estimated Whipple’s index, asfollows: <105 = highly accurate; 105–109 = fairly accurate; 110–124 = approximate;125–174 = rough; 175� = very rough.

The Myers’ blended index involves determining 10 times the proportion of thepopulation reporting in each terminal digit for any 10-year age group. This yields anindex of preference for each terminal digit representing the deviation from 10% ofthe total population reporting the particular digit. The overall index is derived as halfthe sum of the absolute deviations from 10% and is interpreted as the minimum pro-portion of individuals for whom an age with an incorrect final digit is reported. Theindex is 0 when no age heaping occurs and 90 when all age reports have the same ter-minal digit.

Sex ratios

Another way to appraise the accuracy of data is to examine the general and the age-specific sex composition of the population. The measure usually examined is the sexor masculinity ratio, which is expressed by the following equation:

Sex ratio = Pm � 100 [5.1]Pf

where Pm and Pf are the number of males and females, respectively. The point of bal-ance for this measure is 100 and is interpreted as the number of males per 100females. In real life, however, most vital events can be predictably proportionedbetween males and females. Generally, males outnumber females at birth, but higherrates of male mortality with advancing age offset this pattern. A sex ratio at birth,


therefore, usually ranges between 95 and 102. Thus, failure to observe these typical sexdistributions may signify either errors in the data or unusual population characteris-tics. To obtain a more accurate assessment, researchers normally compare the sexratio estimated from the data with that obtained in previous years.

Age ratios

Another way to evaluate DSS data is to compare age ratios with expected or standardvalues. Age ratios are defined here as the ratio of the population in a given age groupto one-third the sum of the populations in that age group and in the preceding andfollowing groups, multiplied by 100. The age ratio is expressed for a 5-year age groupas follows:

Age ratio = 5 Pa � 100 [5.2]1/3(5 Pa�5 + 5 Pa + 5 Pa+5)

where 5Pa is the population in the given age group; 5Pa�5 is the population in the pre-ceding age group; and 5Pa+5 is the population in the following age group. In theabsence of extreme fluctuations in the past vital events, the age ratios should be aboutequal to 100, based on the assumption that coverage errors are about the same for allage groups and that complementary errors in adjacent age groups offset age-reportingerrors. The average absolute deviation from 100 of the age ratios, over all ages, givesthe age-accuracy index, or overall measure of the accuracy of the age distribution: thelower the age-accuracy index, the more accurate the age data.

Comparison with population models

Yet another way to assess DSS data is to compare the actual percentage distribution ofthe population by age with an expected age distribution corresponding to a popula-tion model, such as that of the “stable population.” With negligible migration andfairly constant fertility and mortality, the age distribution of a population will assume adefinite, unchanging form. Thus, the percentage age distribution of a population witha fairly stable structure can be used to evaluate the accuracy of the reported age distri-butions. For each age group, an index may be calculated by dividing the percentage inthe age group in a given country by the corresponding percentage in the stable popu-lation. Deviations from 100 signify under- or over-enumeration of the relative agegroups. The stable-population model (with zero population growth) and the quasi-stable population model (similar to the stable-population model but with moderatelydeclining mortality) may also be used to assess DSS population age–sex structures.

Conclusion

Right from the start of data collection, the DSS sites use various procedures to ensuresound data, including thorough, manual editing of the questionnaires in the field andat the data centre, partial or complete reinterviewing of a sample of respondents, andcomputer checks. At the analysis stage, depending on data requirements, specific tech-niques are applied to assess whether the data conform to an acceptable pattern. It isworth noting here that not all DSS sites have daily work routines. A few sites carry outonly annual censuses. However, evaluations of DSS data at many sites suggest that thedata are of reasonable quality and that they indicate an improvement over time.

Assessing the Quality of DSS Data ✦ 47

P A R T I I

MORTALITY AT INDEPTH SITES

Chapter 6

COMPARING MORTALITY PATTERNS

AT INDEPTH SITES

AbstractEmpirical mortality life tables are chronically lacking for Africa. This chapter pres-ents such tables for 19 INDEPTH sites for the 1995–99 period, with 17 of these inAfrica. The data compiled for the calculations represent 4 194 627 person–yearsof exposure and 56 977 deaths. To compare the overall levels of mortality at thevarious sites, an INDEPTH population standard was developed and used to stan-dardize observed crude death rates for Africa. Finally, the age- and sex-specificpatterns and rates of infant, child, and adult mortality are provided for each DSSsite, and mortality clusters are identified.

Introduction

Mortality data from Africa

Accurate data on mortality in Africa are still scarce. Until recently, the main tools forovercoming this shortcoming have been indirect demographic-estimation techniquesand model age-specific mortality schedules produced by Brass et al. (1973) (the Brassrelational system); Coale and Demeny (1966) (the CD model life-table system); andthe United Nations (1982) (the UN model life-table system). The Brass relational sys-tem is based on empirical data collected in West Africa during the middle of the 20thcentury. In contrast, neither the CD nor the UN model life-table system is built usingsignificant amounts of data collected from Africa. Moreover, all three of the systemsare based on data that are 30–50 years old. Given the dramatic demographic changesthat have affected Africa in the past 20–30 years and the fact that two of the systemsare based largely on data collected from other regions and the third is based on datafrom only one region of Africa, it may be problematic to use them in the currentAfrican context. No doubt, the World Fertility Survey (WFS) and the Demographicand Health Surveys (DHS) have remedied in part the above situation by increasingour knowledge of the level trends and differentials in infant and child mortality in thedeveloping world (Cleland and Scott 1987). However, complete mortality life tables

51

cannot be constructed from WFS and DHS data without relying on indirect methods.Finally, several African countries have since independence undertaken national cen-suses, but mortality data from these sources are often plagued with underreportingand need to be adjusted using hypotheses that are not always realistic.

Mortality data from INDEPTH sites

Data collected at DSS sites are often dismissed because they are collected from smallareas, a fact presumed to make the resulting mortality measures neither accurate norrepresentative. The modest population size of a DSS site does not really constitute amajor flaw, however, as even sites monitoring small populations can produce robustmeasures of age-specific mortality when data are aggregated over several years.Moreover, data collected over long periods from the same population living in thesame area can reveal important age-specific trends in the risk of death. Furthermore,when data from a number of widely dispersed sites are brought together, they providea measure both geographically and temporally representative of mortality conditions.Currently, only DSS sites provide data of use in depicting the temporal and geo-graphic contours of mortality patterns in Africa.

Each DSS site monitors a well-defined, prospectively linked population over aperiod of years. The longitudinal nature of the DSS ensures that demographic events(such as births, deaths, and migrations) and person–years of exposure are accuratelyrecorded. Keeping the data-collection rounds short, usually 3–4 months, minimizesthe likelihood of “losing” a respondent or failing to observe an event. Consequently,the data presented here are of unusually high quality with respect to coverage, com-pleteness, and accuracy of age.

This chapter presents data for age-specific counts of deaths and person–years ofexposure at 19 INDEPTH sites in the period 1995–99. The data are used to constructlife tables describing the mortality conditions at each of the sites in this period. Thelevels of child, adult, and overall mortality are compared across the sites, and standard-ized CDRs are presented for wider comparison. The next chapter presents a detailedexamination of the age patterns of mortality revealed in these data.

Age-specific mortality rates and life tables

Data

The data used in this chapter come from sites for which information on mortality wasavailable for at least a full year during the 1995–99 period (Table 6.1). The overallaverage length of the observation period for the contributing sites is 3.7 years. In total,the data yield 4 194 627 person–years of exposure, during which 56 977 deathsoccurred. An average of 16% of the person–years exposed were lived at ages youngerthan 5 years old, and an average of 37% of the deaths also occurred between birth and5 years of age. The CDR for both sexes combined ranges from a low of 7 per 1000 inAgincourt, South Africa, to 39 per 1000 in Bandim, Guinea-Bissau.

52 ✦ Mortality at INDEPTH Sites

Table 6.1. Summary of mortality data from 19 INDEPTH sites, 1995–99.

Reporting Period Observed Observed % deaths % PYs

DSS site period (years) deaths PYs CDR < age 5 < age 5

Agincourt, South Africa 1995–99 5 1 738 304 530 7.11 15.54 13.79Bandafassi, Senegal 1995–99 5 901 41 286 33.57 53.16 19.86Bandim, Guinea-Bissau 1995–97 3 1 830 64 434 38.65 56.01 27.69Butajira, Ethiopia 1995–96 2 834 72 873 19.20 41.49 16.94Dar es Salaam, Tanzania 1994/95–1998/99a 5 4515 354 041 21.75 27.44 13.87Farafenni, The Gambia 1995–99 5 1 201 81 872 21.23 45.05 17.12Gwembe, Zambia 1991–95 5 576 37 089 26.89 59.72 19.37Hai, Tanzania 1994/95–1998/99a 5 8 106 746 864 16.09 23.14 14.30Ifakara, Tanzania 1997–99 3 1 812 159 639 20.28 41.17 16.23Manhiça, Mozambique 1998–99 2 973 67 344 20.97 35.66 17.06Matlab comp.,b Bangladesh 1998 1 857 105 900 16.16 31.39 12.27Matlab treat.,c Bangladesh 1998 1 764 109 573 12.45 24.74 11.37Mlomp, Senegal 1995–99 5 374 37 051 13.75 20.59 10.80Morogoro, Tanzania 1994/95–1998/99a 5 9 548 538 286 30.01 29.03 13.01Navrongo, Ghana 1995–99 5 11 278 691 679 27.72 34.46 14.10Niakhar, Senegal 1995–98 4 1 993 116 133 24.30 51.03 18.05Nouna, Burkina Faso 1995–98 4 1 650 117 156 17.00 40.48 18.24Oubritenga, Burkina Faso 1995–98 4 6 967 478 315 24.83 49.63 17.40Rufiji, Tanzania 1999 1 1 060 70 563 33.96 35.47 16.32Average 3.68 13.58 37.64 16.20

Note: CDR, crude death rate (actual number of deaths per 1000 population); PY, person–years.a Reporting in midyear to midyear annual periods resulted in a 5-year reporting period running from 15 July 1994 to

15 July 1998.b Comparison area.c Treatment area.

Method of analysis

Although many sites reported data for longer periods, the following analysis isrestricted to the 1995–99 period. The aim here is to present the mortality profile ofthe INDEPTH sites for a recent period for which there was a maximum number ofcontributing sites.

Life tables were constructed in the standard fashion (Preston et al. 2001). Foreach site, nMx , the age-specific mortality rates for the age group x,x +n were calculatedas the ratio of deaths, nDx , to person–years exposed, nPYx , in the same age group.When calculating nqx , the probability of dying in age group x,x+n, one assumes thatthe average age at death, nax , equals half of the age interval, except for ages <5 years.In the age intervals 0–<1 and 1–4 years, the values of nax are calculated using the rela-tionships developed by Coale and Demeny, based on the their West model life-tablesystem (Preston et al. 2001). The open age interval encompassing ages ≥85 years isclosed in the usual way, by letting nL85 equal the ratio of l 85 to ∞M85. Standard errorsare calculated using formulae developed by Chiang (1984).

Crude death rate

To examine the overall level of mortality reported at each site and to compare thoseacross sites, we calculated the age-standardized crude death rate (ASCDR) and lifeexpectancy at birth. The CDR is the overall death rate obtained by taking the ratio ofthe total deaths in the population to the total person–years of exposure over a givenperiod. Life expectancy at birth is the number of years a newborn is expected to live if

Comparing Mortality Patterns at INDEPTH Sites ✦ 53

at each age he or she is subjected to the age-specific mortality rates under considera-tion. Both measures reflect the total risk of death faced by the population as a whole.

The CDR can also be expressed as the age-weighted average of age-specificmortality rates. As a result, the CDR is a function of both the age structure of the pop-ulation and its age-specific mortality rates, and variations in either schedule, from onesite to another, may yield spurious differences in CDRs. Because diverse populationsmay have significantly different age distributions, the CDR cannot be directly com-pared across different populations. To remove the influence of the age structure andmake such a comparison possible, it is necessary to substitute a standard age distribu-tion in place of the population’s true age distribution when calculating the CDR. Theresult is an ASCDR. There are several widely used standard age distributions, includ-ing the Segi and WHO standard age distributions (see Segi 1960; Estève et al. 1994).Both of these standards reflect populations with fairly low fertility and mortality.Consequently, they give significant weight to the middle years of life. All of theINDEPTH sites record information from fairly young populations with high fertilityand mortality. Under those conditions, the population has proportionally more youngpeople, giving it a “younger” age distribution. When the Segi or WHO standard agedistributions are applied to the INDEPTH data, they give too much weight to the highmortality rates prevailing at middle and older ages and too little to mortality atyounger ages. Consequently, the absolute level of the ASCDRs produced using thosestandards significantly overestimates the true level of mortality at the INDEPTH sites.

To address this problem and create ASCDRs that more accurately reflect thetrue level of mortality at the INDEPTH sites, we calculated the INDEPTH standardage distribution. We constructed an average age distribution for each site over theperiod 1995–99 by taking the weighted average of the person–years of exposure in


Table 6.2. Standard age distributions.

Age group (years) INDEPTHa Segib WHOc

0–4 0.149 418 0.120 0 0.088 65–9 0.142 497 0.100 0 0.086 910–14 0.131 040 0.090 0 0.086 015–19 0.104 564 0.090 0 0.084 720–24 0.078 289 0.080 0 0.082 225–29 0.063 646 0.080 0 0.079 330–34 0.057 554 0.060 0 0.076 135–39 0.054 802 0.060 0 0.071 540–44 0.043 456 0.060 0 0.065 945–49 0.036 307 0.060 0 0.060 450–54 0.033 110 0.050 0 0.053 755–59 0.030 741 0.040 0 0.045 560–64 0.025 024 0.040 0 0.037 265–69 0.019 660 0.030 0 0.029 670–74 0.013 432 0.020 0 0.022 175–79 0.008 473 0.010 0 0.015 280–84 0.004 740 0.005 0 0.009 1≥85 0.003 246 0.005 0 0.006 4

a Standard age distribution proposed by INDEPTH for sub-SaharanAfrica.

b Standard age distribution proposed by Segi (1960).c Standard global age distribution proposed by WHO (see Estève et

al. 1994).

each age group across all of the years for which data had been reported. The weightfor each year is the total number of person–years reported for all ages during thatyear. We calculated the INDEPTH standard age distribution by taking the weightedaverage of the individual site average age distributions in each age group. In this case,the weights are the total number of person–years in each of the individual site averageage distributions. The result is displayed in Table 6.2, along with Segi and WHO stan-dards.

In Figure 6.1, the younger age distribution of the INDEPTH standard, which istypical of developing countries, is contrasted with the much older population struc-tures of the Segi and WHO standards.

Figure 6.1. CDR and life expectancy at birth. Source: Segi and WHO standards (see Segi 1960; Estève et al. 1994). Note: WHO, World Health Organization.

Table 6.3 displays the CDR for each site and the ASCDRs calculated using boththe INDEPTH and Segi standard age distributions along with the values for lifeexpectancy at birth taken from Tables 6A.1–6A.19 (see Annex). Differences in theASCDRs are the result of differences in the underlying age-specific mortality schedulesmeasured at each site. Because they control for the age distribution of the population,both of the ASCDRs may be directly compared across the sites.

The INDEPTH standardized CDRs range from about 7 to about 33 per 1000 formales and from about 5 to about 27 per 1000 for females, revealing a very wide rangeof mortality at the INDEPTH sites. The figures for life expectancy at birth vary in a

0-4 5-910

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30-34

25-29

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50-54

55-59

60-64

80-84

70-74

65-69

75-79

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0.16

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0.08

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relationship that is loosely inverse to the values of the CDR (Figure 6.2), and theycover a similarly wide range: from 66 to 39 years for males and from 74 to 40 years forfemales. The data from Bandim are anomalous and reflect some unresolved questionsabout the way in which they were collected and reported.

Some geographic clustering occurs. Agincourt, in South Africa, is grouped withthe two sites in Bangladesh: the Matlab comparison and treatment areas. Also togetherat the low end of the spectrum are three rural sites in Tanzania: Hai, Rufiji, andIfakara; and one site in Senegal: Mlomp. In the middle of the pack are three sites inWest Africa: Nouna, Oubritenga, and Farafenni. At the high end is a mixture of sitesfrom West, East, and southern Africa. The absolute level of mortality varies consider-ably over space, with sites located close to each other having similar levels of mortality,but with a wide range of mortality levels measured in all major regions of Africa.

Table 6.3. Crude death rates and life expectancies at birth for 19 INDEPTH sites, 1995–99.

Male Female

DSS site CDR ASCDRa ASCDRb e0 (years) CDR ASCDRa ASCDRb e0 (years)

Agincourt, South Africa 5.93 7.42 9.43 66.12 4.65 4.90 5.90 74.38Matlab treat.,c Bangladesh 7.30 7.60 9.20 66.93 6.66 7.70 8.93 67.02Matlab comp.,d Bangladesh 8.70 9.58 11.24 63.40 7.50 9.14 10.37 64.87Mlomp, Senegal 10.35 10.80 12.51 60.46 9.83 8.59 9.68 64.78Hai, Tanzania 12.33 11.56 13.49 56.26 9.49 8.65 9.74 62.80Rufiji, Tanzania 14.67 12.19 13.57 53.40 15.35 12.61 13.28 52.18Ifakara, Tanzania 11.70 12.45 13.98 55.73 11.01 11.37 12.28 58.22Butajira, Ethiopia 11.65 12.50 13.79 55.81 11.25 12.44 13.50 56.68Nouna, Burkina Faso 13.74 13.62 14.46 54.20 14.42 14.41 15.71 53.06Oubritenga, Burkina Faso 15.68 14.93 15.95 51.63 13.58 13.05 13.53 55.08Farafenni, The Gambia 16.24 15.84 17.47 50.83 13.17 13.56 14.08 55.05Dar es Salaam, Tanzania 12.84 17.15 20.52 50.32 12.66 16.45 19.42 49.76Niakhar, Senegal 18.45 17.45 18.26 48.80 15.89 14.40 14.81 53.59Manhiça, Mozambique 17.00 17.50 20.11 47.47 12.41 11.36 12.60 58.12Navrongo, Ghana 17.66 18.07 20.42 47.22 15.10 15.82 17.66 51.39Gwembe, Zambia 18.69 19.27 21.89 47.32 16.82 17.95 19.67 53.66Morogoro, Tanzania 18.70 19.27 21.90 44.44 16.82 17.95 19.67 46.11Bandafassi, Senegal 23.49 20.62 21.62 44.74 20.36 18.30 18.71 47.54Bandim, Guinea-Bissau 31.35 32.86 38.63 35.86 25.65 27.48 31.42 38.91

Note: ASCDR, age-standardized crude death rate; CDR, crude death rate (actual number of deaths per 1000 population); e0, life expectancy at birth.a Standardized with INDEPTH standard age structure.b Standardized with Segi standard age structure (see Segi 1960).c Treatment area.d Comparison area.

For the most part the sex differentials are small, but they generally favourfemales, as expected. Two of the sites in southern Africa with significant male migra-tion — Agincourt, South Africa, and Manhiça, Mozambique — register substantial sexdifferentials, standing out in contrast to the rest of the sites. Bandim, in West Africa,also records a very substantial sex differential, but as noted above there may be amethodological explanation for this.


Figure 6.2. ASCDR and life expectancy at birth. Note: ASCDR, age-standardized crude death rate; comp., comparison area; e0, life expectancy at birth; treat., treatmentarea.

Child mortality

The measures of child mortality displayed in Table 6.4 are the life-table probabilitiesof dying in a specified age group: 1q0 for ages 0–<1 year, 4q1 for ages 1–4 years, and 5q0

for ages 0–<5 years — all taken from the life tables in Tables 6A.1–6A.19 (see Annex).The conventional infant mortality rate is also included. The life-table measures repre-sent the probability that a child who survives to the beginning of the specified ageinterval will die before reaching the end of that interval. A value of 0.1 for 1q0 indicatesthat 10% of newborns will die before their first birthday, and correspondingly a valueof 0.25 for 4q 1 indicates that 25% of the children reaching their first birthday will diebefore reaching their fifth birthday. We chose to present these measures because theyare intuitive and powerful and represent the fundamental probability of death, ratherthan a potentially ambiguously defined and difficult-to-interpret rate or ratio to livebirths, which would be affected by differentials in fertility between sites.

As shown in Figure 6.3, a wide range occurs in the level of child mortality. Theprobability that a newborn dies before reaching its fifth birthday ranges from 32 to255 per 1000 for males and from 34 to 217 per 1000 for females. The Agincourt site inSouth Africa has recorded a comparatively very low level of child mortality. In anothercluster, composed of the Matlab sites in Bangladesh, Mlomp in Senegal, and Hai inTanzania, all have reported low levels of child mortality, but not nearly as low as thelevel reported from the South Africa site. The next higher cluster is composed of sites

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807570656055

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from various regions of Africa, including Dar es Salaam, Tanzania; Butajira, Ethiopia;Ifakara, Tanzania; Nouna, Burkina Faso; and Manhiça, Mozambique. Following after,with 5q 0 very close to 175 per 1000 for males and females, are Farafenni, The Gambia;Rufiji, Tanzania; Navrongo, Ghana; Gwembe, Zambia; Morogoro, Tanzania; andOubritenga, Burkina Faso. The three remaining sites — Niakhar, Senegal; Bandim,Guinea-Bissau; and Bandafassi, Senegal — all have substantially higher values of 5q 0,closer to 225 per 1000. A wide range occurs in the level of child mortality, but exceptat the very lowest and very highest levels, no geographical clustering is apparent. Thelowest levels are definitely found in South Africa and Asia, and the highest levels arereported from West Africa.

It is also worth noting the very high levels of 1q0 reported from Rufiji,1

Tanzania, and Bandafassi, Senegal. Both of those values are extraordinarily high andindicate that the conditions for infants in those areas are among the mostunfavourable anywhere on the globe. Table 6.4 also displays the ratio of 1q0 to 4q1, toelucidate the changing risk of death children face before and after their first birthday.This ratio reveals that children in Rufiji who survive to age 1 year face a probability ofdeath improved by nearly a factor of four, whereas children in Bandafassi face a nearlyconstant probability of dying throughout the first 5 years of life.

Sex differentials in child mortality are fairly small and do not appear to consis-tently favour one sex over the other. Interestingly, this pattern is broken by four sites:Manhiça, Mozambique; Rufiji, Tanzania; Niakhar, Senegal; and Bandafassi, Senegal.In the last two cases, there is a clear differential favouring females, as there is inManhiça. In contrast, Rufiji records a substantial differential favouring males.


Table 6.4. Infant and child mortality at 19 INDEPTH sites, 1995–99.

Male (per 1000) Female (per 1000)

DSS Site IMR per 10001q

0 4q

1 5q

0 1q

0/

4q

1 1q

0 4q

1 5q

0 1q

0/

4q

1

Agincourt, South Africa 16.93 15.06 17.52 32.32 0.86 16.63 17.35 33.69 0.96Matlab treat.,a Bangladesh 50.58 47.38 15.92 62.54 2.98 59.88 20.88 79.51 2.87Matlab comp.,b Bangladesh 70.05 65.96 23.67 88.08 2.79 80.24 21.64 100.15 3.71Mlomp, Senegal 45.18 48.24 42.61 88.80 1.13 49.42 51.74 98.60 0.96Hai, Tanzania 67.13 66.78 26.73 91.73 2.50 56.54 26.68 81.71 2.12Dar es Salaam, Tanzania 71.13 66.38 50.86 113.86 1.30 67.20 52.49 116.16 1.28Butajira, Ethiopia 67.82 65.62 57.73 119.56 1.14 71.09 62.20 128.87 1.14Ifakara, Tanzania 93.22 76.12 52.23 124.37 1.46 86.09 50.27 132.03 1.71Nouna, Burkina Faso 40.85 34.31 107.53 138.15 0.32 42.71 106.82 144.97 0.40Manhiça, Mozambique 72.65 85.75 68.91 148.75 1.24 59.37 60.41 116.19 0.98Farafenni, The Gambia 74.65 68.04 110.47 171.00 0.62 66.46 109.12 168.32 0.61Rufiji, Tanzania 143.00 147.54 37.54 179.55 3.93 175.60 33.10 202.88 5.31Navrongo, Ghana 109.59 106.58 83.54 181.21 1.28 102.96 73.23 168.65 1.41Gwembe, Zambia NA 105.24 87.26 183.32 1.21 111.94 78.78 181.90 1.42Morogoro, Tanzania 116.73 105.24 87.26 183.32 1.21 111.94 78.78 181.90 1.42Oubritenga, Burkina Faso 96.49 102.25 95.97 188.41 1.07 91.88 104.84 187.09 0.88Niakhar, Senegal NA 89.80 146.84 223.45 0.61 72.16 129.14 191.98 0.56Bandim, Guinea-Bissau NA 112.37 129.78 227.57 0.87 101.52 128.31 216.80 0.79Bandafassi, Senegal 124.88 138.60 134.59 254.54 1.03 116.43 114.29 217.42 1.02

Note: IMR, infant mortality rate (number of deaths of infants <1 year old per 1000 live births in a given year); NA, not available; 1q0 , probability that a newborn will die before reaching its 1st birthday; 4q1, probability that a childthat has reached its 1st birthday will die before its reaching its 5th birthday; 5q0, probability that a newborn will diebefore reaching its 5th birthday; 1q0/4q1, ratio of probability of death faced by children before and after their 1stbirthday.a Treatment area.b Comparison area.

Adult mortality

In keeping with the life-table treatment of child mortality, the index chosen for adultmortality, 30 q 20 , is the probability that a person who has survived to age 20 will diebefore his or her 50th birthday. Values for 30 q 20 taken from Tables 6A.1–6A.19 (seeAnnex) are displayed in Table 6.5 along with values of 5q 0 and the ratio of 5q 0 to 30 q 20 .The information on child mortality is included to allow the calculation and display ofthe relationship between child and adult mortality for each site, embodied in the ratioof 5q 0 to 30 q 20 .

Very substantial ranges occur in the level of adult mortality: 63–501 per 1000for males and 59–421 per 1000 for females. A value of 500 per 1000 for 30q 20 indicatesthat fully half of the people who survive to age 20 do not live to reach their 50th birth-day. Additionally, a number of sites record substantial sex differentials in adult mortal-ity — Mlomp, Senegal; Agincourt, South Africa; Navrongo, Ghana; Hai, Tanzania; andManhiça, Mozambique, in particular. Also apparent is the opposite differential, inwhich female rates exceed those of males in two sites: Rufiji, Tanzania, and Dar esSalaam, Tanzania. HIV–AIDS and maternal mortality may play roles. Without moreinformation from the sites, we are unable to explain these differentials.

For the first time, Agincourt, South Africa, does not define the low end of therange. Where adult mortality is concerned, the Matlab sites in Bangladesh clearlystand out, with substantially lower risks of death than anywhere else, and in both thesesites a very small sex differential favours females. In both cases, nearly 95% of adults


1 Rufiji is the newest INDEPTH site and is reporting data for its first year of operation (see Table 7.2). The apparent high risk of deathfor infants revealed by the data from Rufiji may be in part an artifact, resulting from an age-reporting bias for an infant’s date of birthin first-year DSSs. This is due to the fact that in the first year of any DSS, unlike subsequent years, a large portion of the infants wouldbe born before the DSS started and their birth dates would be subject to maternal recall error. These errors decrease for infants bornduring the DSS, as such infants become registered soon after birth. This start-up bias would have less of an effect on under-five mortal-ity rates.

Figure 6.3. Child mortality. Note: Cont., control area; 5q0, probablity of dying between birth and <5 years of age; treat., treatment area.

Aginco

urtM

atla

b trea

t.

Mlo

mp

Hai

Rufiji

Oubriten

ga

Butajir

aIfa

kara

Nouna

Fara

fenni

Dar es

Sal

aam

Niakh

ar

Man

hiça

Bandaf

assi

Gwembe

Navro

ngo

Moro

goro

Bandim

275

250

225

200

175

150

125

100

75

50

25

0

Mat

lab co

mp.

Male Female

5q

0 p

er

100

0


reaching age 20 years survive to their 50th birthday. The next cluster appears atbetween 150 and 200 per 1000 and includes sites ranging from Mlomp in Senegal toRufiji in Tanzania (Figure 6.4). In all of these cases, the sex differential is small,except for Agincourt, South Africa, and favours females in all cases except for Rufiji,Tanzania. The last cluster covers a wide range: about 250–475 per 1000. This groupincludes the remainder of the sites and is marked by the very high risk of adult mortal-ity in Bandim and the substantial sex differentials in Navrongo, Ghana; Hai, Tanzania;and Manhiça, Mozambique.

As was the case with child mortality, the geographic clustering clearly separatesthe Asian sites from the African sites, but beyond that, there does not appear to be anysubstantial geographical clustering of similar risk of adult mortality within Africa. Thecluster with moderate risk includes sites from all major regions of Africa, as does thehigh-risk cluster.

The relationship between child and adult mortality reveals three distinctgroups: sites in Asia, sites in West Africa, and sites in the rest of Africa. The Asian andsome of the West African sites clearly record levels of child mortality that are higherthan the corresponding levels of adult mortality. Mortality at all ages is relatively low inAsia, so this finding is primarily the result of exceptionally low adult mortality. In fourWest African sites — Niakhar and Bandafassi, Senegal; Farafenni, The Gambia; andOubritenga, Burkina Faso — this is the result of unusually high child mortality, cou-pled with substantial adult mortality. It is our guess that in these cases malaria is theprimary reason why child mortality is so high, but this must be confirmed with moreinformation from those sites.

Table 6.5. Adult mortality and child–adult mortality ratio at 19 INDEPTH sites, 1995–99.

Male (per 1000) Female (per 1000)

DSS site5q

0 30q

20 5q

0/

30q

20 5q

0 30q

20 5q

0/

30q

20

Matlab treat,a Bangladesh 62.54 63.45 0.9856 79.51 59.43 1.3378Matlab comp.,b Bangladesh 88.08 72.35 1.2173 100.15 60.28 1.6614Mlomp, Senegal 88.80 159.03 0.5584 98.60 111.51 0.8842Niakhar, Senegal 223.45 165.25 1.3522 191.98 141.86 1.3533Agincourt, South Africa 32.32 196.35 0.1646 33.69 100.77 0.3344Nouna, Burkina Faso 138.15 199.93 0.6910 144.97 184.51 0.7857Farafenni, The Gambia 171.00 205.13 0.8336 168.32 149.88 1.1231Oubritenga, Burkina Faso 188.41 210.62 0.8945 187.09 157.60 1.1871Bandafassi, Senegal 254.54 226.27 1.1249 217.42 200.42 1.0848Butajira, Ethiopia 119.56 227.19 0.5263 128.87 193.86 0.6648Rufiji, Tanzania 179.55 236.29 0.7599 202.88 259.63 0.7814Ifakara, Tanzania 124.37 240.09 0.5180 132.03 185.07 0.7135Navrongo, Ghana 181.21 298.01 0.6081 168.65 188.86 0.8930Hai, Tanzania 91.73 304.77 0.3010 81.71 229.38 0.3562Dar es Salaam, Tanzania 113.86 331.46 0.3435 116.16 369.74 0.3142Manhiça, Mozambique 148.75 382.13 0.3893 116.19 197.39 0.5887Gwembe, Zambia 183.32 408.82 0.4484 181.90 372.81 0.4879Morogoro, Tanzania 183.32 409.03 0.4482 181.90 372.81 0.4879Bandim, Guinea-Bissau 227.57 500.75 0.4545 216.80 421.42 0.5145

Note: 5q0, probability that a newborn will die before reaching its 5th birthday; 30q20, probability that an adult whohas survived to age 20 will die before reaching his or her 50th birthday; 5q0/30q20, ratio of the probability that anewborn will die before reaching its 5th birthday to the probability that an adult who has survived to age 20 will diebefore reaching his or her 50th birthday.a Treatment area.b Comparison area.

Discussion

The data presented here are the first large compilation of high-quality data collectedover a large area of Africa at intensively operated longitudinal field sites. In light ofthe general lack of high-quality information describing contemporary mortality inAfrica, this is a unique and useful collection of data. The level of mortality varies con-siderably across the sites that have produced these data, and all but one or two appearto have produced very reasonable age-specific mortality schedules. A great deal ofadditional analysis will be applied to these data in the near future. The first extensionof the basic description of the levels and age patterns of mortality presented here isthe identification and thorough examination of the common underlying age patternsof mortality embodied in these data, presented in the following chapter.


Figure 6.4. Adult mortality. Note: Cont., control area; 30q20, probability of dying between ages 20 and 50 years; treat., treatment area.

Aginco

urt

Mat

lab tr

eat.

Mlo

mp

Hai

Rufiji

Oubriten

ga

Butajir

a

Ifaka

ra

NounaFa

rafe

nni

Dar es

Sal

aam

Niakh

ar

Man

hiça

Bandaf

assi

Gwembe

Navro

ngo

Moro

goro

Bandim

550

500

450

400

350

300

250

200

150

100

50

0

Mat

lab co

mp.

Male Female

30

q2

0 p

er

100

0

ANNEX: LIFE TABLES


Ta

ble

6A

.1.

Lif

e t

ab

le f

or

the

Ag

inco

urt

DS

S s

ite

, S

ou

th A

fric

a,

199

5–

99

.

Ag

e (

ye

ars

)n

Dx

nP

Yx

nM

xS

EnM

xn

qx

SE

nqx

l xS

El x

nd

xn

Lx

Tx

ex

(ye

ars

)S

Ee

x(y

ea

rs)

Ma

le

<159

3 87

70.

015

218

0.00

1 96

60.

015

064

0.00

1 94

610

0 00

00.

000

01

506

98 9

916

611

576

66.1

20.

639

91–

476

17 1

470.

004

432

0.00

0 50

40.

017

522

0.00

1 99

298

494

0.37

8 8

1 72

638

9 37

06

512

585

66.1

20.

626

65–

914

23 1

750.

000

604

0.00

0 16

10.

003

016

0.00

0 80

596

768

0.75

0 7

292

483

110

6 12

3 21

563

.28

0.61

3 1

10–1

414

20 1

190.

000

696

0.00

0 18

60.

003

473

0.00

0 92

796

476

0.80

6 8

335

481

542

5 64

0 10

558

.46

0.61

1 2

15–1

916

17 7

410.

000

902

0.00

0 22

50.

004

499

0.00

1 12

296

141

0.88

1 1

433

479

623

5 15

8 56

353

.66

0.60

9 2

20–2

432

14 0

140.

002

283

0.00

0 40

10.

011

352

0.00

1 99

595

708

0.98

9 6

1 08

647

5 82

64

678

940

48.8

90.

606

625

–29

4611

122

0.00

4 13

60.

000

604

0.02

0 46

90.

002

987

94 6

221.

332

01

937

468

267

4 20

3 11

544

.42

0.60

0 0

30–3

453

9 02

70.

005

871

0.00

0 79

50.

028

933

0.00

3 91

692

685

2.07

6 8

2 68

245

6 72

13

734

847

40.3

00.

587

735

–39

617

198

0.00

8 47

40.

001

062

0.04

1 49

30.

005

201

90 0

033.

275

93

735

440

681

3 27

8 12

636

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0.57

0 4

40–4

444

5 63

40.

007

810

0.00

1 15

50.

038

303

0.00

5 66

386

269

5.20

1 2

3 30

442

3 08

42

837

445

32.8

90.

545

945

–49

694

559

0.01

5 13

30.

001

754

0.07

2 90

90.

008

451

82 9

657.

196

96

049

399

701

2 41

4 36

129

.10

0.52

3 6

50–5

448

3 32

20.

014

448

0.00

2 01

10.

069

724

0.00

9 70

776

916

11.1

01 8

5 36

337

1 17

12

014

661

26.1

90.

483

455

–59

562

697

0.02

0 76

50.

002

634

0.09

8 70

00.

012

522

71 5

5315

.181

67

062

340

109

1 64

3 48

922

.97

0.44

4 4

60–6

441

1 98

00.

020

706

0.00

3 07

00.

098

435

0.01

4 59

764

491

20.3

59 9

6 34

830

6 58

31

303

381

20.2

10.

395

165

–69

561

733

0.03

2 31

10.

003

982

0.14

9 48

10.

018

422

58 1

4225

.410

48

691

268

984

996

798

17.1

40.

349

170

–74

581

352

0.04

2 88

90.

005

057

0.19

3 68

00.

022

836

49 4

5129

.853

99

578

223

312

727

814

14.7

20.

296

575

–79

701

021

0.06

8 56

60.

006

892

0.29

2 66

20.

029

419

39 8

7432

.162

311

669

170

194

504

502

12.6

50.

242

180

–84

2941

50.

069

925

0.01

0 88

20.

297

600

0.04

6 31

528

204

29.8

52 1

8 39

412

0 03

733

4 30

811

.85

0.17

3 9

≥85

2729

20.

092

455

NA

1.00

0 00

0N

A19

811

31.7

91 8

19 8

1121

4 27

121

4 27

110

.82

NA

Fe

ma

le

<165

3 86

60.

016

813

0.00

2 06

80.

016

631

0.00

2 04

610

0 00

00.

000

01

663

98 9

197

438

159

74.3

80.

645

51–

475

17 0

930.

004

388

0.00

0 50

20.

017

350

0.00

1 98

698

337

0.41

8 5

1 70

638

8 84

57

339

240

74.6

30.

626

95–

919

23 0

020.

000

826

0.00

0 18

90.

004

122

0.00

0 94

496

631

0.78

5 5

398

482

158

6 95

0 39

571

.93

0.60

9 7

10–1

412

19 9

430.

000

602

0.00

0 17

30.

003

004

0.00

0 86

696

232

0.86

2 1

289

480

440

6 46

8 23

767

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0.60

6 4

15–1

918

17 4

940.

001

029

0.00

0 24

20.

005

131

0.00

1 20

695

943

0.92

6 4

492

478

486

5 98

7 79

862

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0.60

4 0

20–2

430

15 0

980.

001

987

0.00

0 36

10.

009

886

0.00

1 79

695

451

1.05

0 9

944

474

896

5 50

9 31

257

.72

0.60

0 0

25–2

930

12 3

560.

002

428

0.00

0 44

10.

012

067

0.00

2 19

094

507

1.32

4 1

1 14

046

9 68

65

034

416

53.2

70.

592

330

–34

3610

365

0.00

3 47

30.

000

574

0.01

7 21

70.

002

845

93 3

671.

720

61

608

462

816

4 56

4 72

948

.89

0.58

2 7

35–3

937

8 57

20.

004

316

0.00

0 70

20.

021

351

0.00

3 47

291

759

2.36

7 3

1 95

945

3 90

04

101

913

44.7

00.

569

040

–44

367

025

0.00

5 12

40.

000

843

0.02

5 29

70.

004

163

89 8

003.

282

52

272

443

322

3 64

8 01

340

.62

0.55

2 2

45–4

920

5 11

10.

003

913

0.00

0 86

60.

019

375

0.00

4 29

087

529

4.51

5 8

1 69

643

3 40

43

204

691

36.6

10.

532

550

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223

572

0.00

6 15

90.

001

293

0.03

0 32

60.

006

367

85 8

335.

752

62

603

422

657

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005

784

0.00

1 30

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507

0.00

6 44

683

230

8.39

5 3

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341

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82

348

631

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488

260

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483

132

0.01

5 32

60.

002

129

0.07

3 80

30.

010

252

80 8

5710

.801

75

968

389

367

1 93

8 41

323

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0.46

7 0

65–6

963

3 35

10.

018

800

0.00

2 26

00.

089

779

0.01

0 79

174

890

16.1

37 6

6 72

435

7 64

01

549

046

20.6

80.

425

770

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662

086

0.03

1 64

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003

598

0.14

6 60

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016

671

68 1

6619

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49

994

315

847

1 19

1 40

617

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0.39

3 9

75–7

965

1 58

30.

041

055

0.00

4 59

40.

186

167

0.02

0 83

158

172

27.4

07 7

10 8

3026

3 78

887

5 55

915

.05

0.33

9 6

80–8

433

507

0.06

5 14

90.

009

622

0.28

0 11

90.

041

373

47 3

4332

.837

413

262

203

559

611

772

12.9

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283

6≥8

540

479

0.08

3 48

9N

A1.

000

000

NA

34 0

8155

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534

081

408

212

408

212

11.9

8N

A

Not

e: n

D x, o

bser

ved

deat

hs

betw

een

age

s x

and

x+n

; nd x

, num

ber

dyin

g be

twee

n a

ges

xan

d x+

n; e

x, e

xpec

tati

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f lif

e at

age

xfo

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e lif

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popu

lati

on; l

x, n

umbe

r of

sur

vivo

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t age

x

in th

e lif

e-ta

ble

popu

lati

on; n

Lx,p

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n–y

ears

live

d by

the

life-

tabl

e po

pula

tion

bet

wee

n a

ges

xan

d x

+n; n

Mx, o

bser

ved

mor

talit

y ra

te fo

r ag

es x

to x

+n; N

A, n

ot a

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able

; nPY

x, o

bser

ved

pers

on–y

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bet

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n a

ges

xan

d x

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bilit

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ng

betw

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s x

and

x+n

; SE

l x, s

tan

dard

err

or in

l x; S

E nMx, s

tan

dard

err

or in

nM

x; S

Enq

x, sta

nda

rd e

rror

in n

q x; S

Ee x, s

tan

dard

erro

r in

ex; T

x, p

erso

n–y

ears

live

d by

the

life-

tabl

e po

pula

tion

at a

ges

olde

r th

an x

.


Ta

ble

6A

.2.

Lif

e t

ab

le f

or

the

Ba

nd

afa

ss

i D

SS

sit

e,

Se

ne

ga

l, 1

99

5–

99

.

Ag

e (

ye

ars

)n

Dx

nP

Yx

nM

xS

EnM

xn

qx

SE

nqx

l xS

El x

nd

xn

Lx

Tx

ex

(ye

ars

)S

Ee

x(y

ea

rs)

Ma

le

<114

594

90.

152

785

0.01

1 77

60.

138

597

0.01

0 68

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an x

.


Chapter 7

INDEPTH MORTALITY PATTERNS

FOR AFRICA

AbstractMortality data from Africa compiled by the INDEPTH Network and includingover 6.4 million person–years of exposure are used to identify new mortalitypatterns. Seven age patterns of mortality emerge from these data, two of whichclearly show excess mortality due to HIV–AIDS. The emergent patterns arecompared with the existing model mortality patterns produced by Coale andDemeny (CD) and the United Nations (UN) and are demonstrated to be sub-stantially different. The principal-components technique is used to calculate15 principal components that account for all of the variation in the data. It isdemonstrated that the components are sufficiently general to accurately repro-duce the existing CD and UN model mortality patterns. The resulting compo-nent model of mortality is demonstrated through the construction of a hypo-thetical set of life tables combining the HIV–AIDS pattern of mortality with anunderlying pattern of mortality that is not affected by HIV–AIDS. This generaltechnique yields mortality patterns that might prevail if the populationdescribed by the underlying mortality pattern were infected with HIV–AIDS.

Mortality models and Africa

An individual’s probability of dying depends primarily on sex, age, health, geneticendowment, and the environment, all of which determine the risk of falling victim toillness or accident. The primary determinants of mortality interact in complex waysand depend in turn on a large and variable set of complex social determinants. As aresult, it has not been possible to formulate a general, theory-driven model of individ-ual risk of death. In lieu of a good general model, two widely used sets of model lifetables are the CD model, created by Coale and Demeny (1966), and the later UNmodel (United Nations 1982). In both cases, a large set of empirical mortality rates aresummarized to yield a small number of characteristic age patterns of mortality. Coaleand Demeny identified four patterns, which they called North, South, East, and Westto reflect the fact that each pattern is representative of the mortality pattern in a

83

particular region of Europe. For a similar reason, the UN’s patterns also bear regionalnames: Latin America, Chile, Far East, South Asia, and General. The UN General pat-tern is, as its name suggests, a general pattern that is not specific to a single location.

Each of the eight existing model mortality patterns (excluding the UN Generalpattern) results from the characteristic epidemiological profile of the region it repre-sents. For example, the UN South Asia pattern describes an age pattern of mortalitywith “very high rates under age fifteen and very high rates again at the oldest ages,with correspondingly lower mortality for the prime age groups.” This pattern isascribed to “high incidences of infectious, parasitic and diarrheal diseases at theyoungest ages and high mortality from diarrheal and respiratory diseases at the oldestages” (United Nations 1982, p. 13).

For large areas of the developing world, accurate information describing themortality of the population is not available because vital registration systems areincomplete and inaccurate. Where that is true, model mortality patterns are used tosubstitute for real information. Two important examples are population projectionsand estimates of child mortality. All population projections must include both existingmortality conditions and educated estimates of future mortality regimes. The Brassestimators of child mortality (United Nations 1983), widely used in areas where accu-rate data on child mortality are unavailable, rely on estimates of the age pattern ofchild mortality, and in most cases a model mortality pattern is used for that purpose.Moreover, model mortality patterns are used to evaluate data, to produce smoothed orcorrected versions of faulty data, and to extend or fill in the age range of incompletedata. Demographers working in regions where mortality data are inaccurate or incom-plete depend heavily on model mortality patterns to allow them to evaluate the datathey have and to make reasonable estimates and predictions.

None of the data used to create either of the widely used collections of modelmortality patterns came from sub-Saharan Africa. Consequently, it is not evident thatthe existing model mortality patterns adequately describe the age patterns of mortalityin Africa, and it is only because there is nothing else that they are applied to Africanpopulations at all. Furthermore, the emergence of the HIV–AIDS pandemic in Africahas radically altered the age pattern of mortality in large areas of the continent.Because the existing model mortality patterns do not contain an AIDS pattern of mor-tality, they are no longer appropriate under any circumstance where AIDS is a signifi-cant cause of death or where AIDS is anticipated as a significant cause of death in thenear future. This is an even more serious problem than it might first appear becauseof the crucial role that model mortality patterns play in routine demographic work inAfrica — precisely because of the substantial lack of comprehensive, accurate mortal-ity data.

This chapter presents seven age patterns of mortality derived almost exclusivelyfrom data collected in Africa, including two patterns resulting from excess mortalitycaused by AIDS. A 15-factor model is constructed to summarize the data, and that


model is used to isolate the AIDS-related component of mortality in the AIDS pattern.Last, the AIDS component is superimposed in various amounts on one of the patternsto generate a coarse set of model life tables that illustrates the effects of AIDS mortality.

Mortality data

To allow maximum flexibility in analysis, individual sites provided counts of deathsand person–years observed in standard 0 to 85+ age groups by sex for single years ofobservation for as many years of observation as possible. The majority of sites wereable to provide data in this format, although one or two provided time-aggregateddata. Table 7.1 summarizes the data for this work.

The overall aim of this work is to identify age patterns of mortality for Africaand Asia using longitudinal data from INDEPTH field sites. To adequately capture thevariation in mortality over time, the data from each site are grouped into 3-year inter-vals, or as close to 3-year intervals as possible and practical, to yield 70 site–periods.The annual data in each of those periods is aggregated to yield 70 site–period data setsfor each sex: 140 site–period data sets in all. Table 7.2 shows the periods chosen foreach site.

Table 7.1. Temporal aspects of INDEPTH mortality data.

Period of data Total years Aggregated Total person–years

DSS site collection of data years observed

Agincourt, South Africa 1992–99 8 — 405 311.46Bandafassi, Senegal 1980–99 14 — 144 475.61Bandim, Guinea-Bissau 1990–97 8 — 193 832.91Butajira, Ethiopia 1987–96 10 — 336 075.71Dar es Salaam, Tanzania 1992–99 8 — 485 446.30Farafenni, The Gambia 1990–99 10 — 98 073.70Gwembe, Zambia 1956–95 39 — 187 034.00Hai, Tanzania 1992–99 8 — 1 045 152.69Ifakara, Tanzania 1997–99 3 — 159 639.00Manhiça, Mozambique 1998–99 2 — 67 344.00Matlab comp.,a Bangladesh NA 2 1988, 1998 203 744.00Matlab treat.,b Bangladesh NA 2 1988, 1998 211 770.00Mlomp, Senegal 1985–99 14 — 106 593.48Morogoro, Tanzania 1992–99 8 — 741 412.41Navrongo, Ghana 1993–99 7 — 930 187.50Niakhar, Senegal 1985–98 14 1985–89, 1990–94, 1995–98 372880.00Nouna, Burkina Faso 1993–98 6 — 174 689.62Oubritenga, Burkina Faso 1994–98 5 — 482 100.40Rufiji, Tanzania 1999–99 1 — 67 842.50

Note: NA, not applicable.a Comparison area.b Treatment area.

INDEPTH Mortality Patterns for Africa ✦ 85


Ta

ble

7.2

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Principal-components analysis

Data summary

The goal is to identify a compact representation of the information contained in alarge set of observations of similar items. Principal-components analysis transforms theobservations to produce an equal number of components. These can reproduce all ofthe original observations when combined in the appropriate proportions. The compo-nents differ from the original observations in that they capture as much variation aspossible in as few components as possible. The first component accounts for the maxi-mum variation that one component can account for. After the analyst removes thevariation associated with the first component, the second component accounts for asmuch of the remaining variation as can be accounted for with one component. Theanalyst continues this process until all the variation in the original data set has beenaccounted for and the number of components equals the number of original observa-tions. The important consequence is that the majority of the variation in the data set isaccounted for in the first few components.

In this way a large set of observations may be summarized using a small numberof components. After deciding how much of the original variation must be retained,the analyst may choose to discard the higher order components accounting for theresidual variation.

Component model of mortality

The component model of mortality constructed here makes no substantive assump-tions regarding the underlying form of the age-specific mortality schedule. The modelmakes the general assumption that an arbitrary age-specific mortality schedule can bedecomposed into a small number of individual components and a negligible residualterm. Additionally, it is assumed that a small number of components together form auniversal set of age-specific mortality components and that, when combined in theappropriate proportions, they can reproduce any age-specific mortality schedule. Forthe purposes of this work, these assumptions encompass only the complete set of mor-tality data examined here; however, it has been demonstrated that the “universal” mor-tality components generated from the INDEPTH data are capable of reproducing allof the CD and UN model life-table mortality schedules to within a very small tolerance.

Assume there are n separate components of the age-specific mortality scheduleand g age groups. Let m represent the g � 1 vector of age-specific logit (nqx) values,and let C represent the g � n matrix whose i th column is the g � 1 vector containingthe i th component of mortality. Let a be an n � 1 vector of coefficients that deter-mines how much of each component is used to generate the mortality schedule, andlet r be a g � 1 vector of residuals, one for each age. Then equation [7.1] is a compactrepresentation of the full-component model of mortality:

m = Ca + r [7.1]

where m , C, a, and r are as defined above. Expanding this around the row for the20–24 age group reveals


. . . .. . . .. . . .

logit(5q20) = 5C20. ai + ... + 5C20

. an + r20. . . .. . . .. . . .

where 5C20 is the value of the i th component for the 20–24 age group; ai is the value ofthe coefficient on the i th component; and r20 is the value of the residual for the 20–24age group. Each of the column vectors contains g elements, one for each age group.

Once the matrix C has been identified through principal-components analysis(described below), the model may be used in many ways. First, it is informative toexamine the shape of the components themselves. The primary component (account-ing for the bulk of the variation in the data) represents the common underlying shapeof the schedule as a function of age. The second and higher order components defineage-specific variations on the basic shape. Moreover, it may be possible to associatecertain substantive interpretations with the components; for example, one may appearto affect the balance between child and adult mortality, and one may appear to con-tribute to or remove from a particular age group affected by a specific condition, suchas maternal or AIDS-related mortality.

Estimates of the coefficients a that transform the components into a given mor-tality schedule may be obtained through an ordinary linear least-squares regression ofthe mortality schedule against the components C . The residual identified in theregression is equivalent to r, and the regression coefficients are the elements of thevector a with the addition of an extra element to store the constant estimated in theregression. Let a� be the (n + 1) � 1 coefficient vector with the additional element tostore the constant generated in the regression model, and let C � be the g � (n + 1)matrix of components with one additional column containing all ones to accommo-date the constant in a�. The constant is interpreted as a measure of the overall level ofthe mortality schedule, whereas the coefficients indicate how much of each age pat-tern (component) is necessary to reproduce the overall age pattern in the originaldata. Interpreted in this way, the regression controls for level and provides an estimateof how much of each component is contained within the data, or how important eachindividual age pattern is in generating the age pattern observed in the data. Equation[7.2] represents the regression component model of mortality:

m = C�a� [7.2]

where m , C �, and a� are defined as above. Expanding this around the row for the20–24-year age group reveals

. . . .. . . .. . . .

logit(5q20) = 5C 20. ai + ... + 5C 20

. an + 1 . ac. . . .. . . .. . . .


ni

n

i

i

where 5C20 is the value of the i th component for the 20–24 age group; ai is the value ofthe coefficient estimated on the i th component; and ac is the constant term estimatedin the regression, taking the same value for all age groups. Each of the column vectorscontains g elements, one for each age group.

Ignoring the residual and postmultiplying C � by a� (equation [7.2]) yields theoriginal mortality schedule purged of the residual r. Together withC �, the (n + 1) � 1vector a� contains all the information needed to reproduce the original mortalityschedule to within r. In most cases the number of components (n + 1) necessary toadequately encode the mortality schedule is much less than g, the number of agegroups. As a result, a� is a compact representation of the mortality schedule thatencodes the fundamental shape of the schedule without the “noise” associated withthe high-order components and the residual term. Additionally, by adjusting the con-stant term contained in the last element of a�, it is possible to arbitrarily set the levelof the mortality schedule without affecting its age pattern.

The individual coefficient vectors associated with each mortality schedule rep-resent the most important dimensions of the mortality schedules and can be com-pared and grouped without worrying about the high-order noise associated with theindividual schedules. Moreover, by comparing only the coefficients corresponding tothe components and ignoring the constant, it is possible to compare individual mortal-ity schedules based only on their individual age patterns and not on differences intheir level. Correspondingly, by comparing only the constants associated with two mor-tality schedules, the influence of the age pattern is effectively removed (controlledfor), and it is possible to compare the mortality schedules based only on their level.

Principal components of INDEPTH mortality data

For each sex, logit (nqx) values are calculated for the standard 0–85 age groups (18 inall)1 in each of the site–periods according to equations [7.3] and [7.4]. This yields a70 � 18 data set consisting of one column for each site–period and one row for eachage group, with each cell containing a value of logit (nqx) corresponding to the speci-fied site–period and age group.

Equation [7.3] gives nqx as a function of nMx:

nq x = nMx [5.1]1 + n(1 – nax)nMx

where nqx is the life-table probability of death between ages x and x + n for those whosurvive to age x; n M x is the observed mortality rate (the ratio of deaths toperson–years lived) for those between ages x and x + n; and nax is the average propor-tion of years between ages x and x + n lived by those who die in that age interval.2

1 0, 1–4, 5–9, 10–14, 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70–74, 75–79, 80–84.

2 Without substantially more data tabulated by single year of age it is impossible to directly calculate or estimate the values of nax.Moreover, except for the youngest ages, the value of nax is always near 0.5. At the youngest ages, the values are much closer to 0.25.Additionally, the life table is not highly sensitive to the exact values chosen as long as they are close to 0.25 for ages <5 years and closeto 0.5 for ages >5 years. In this work, the value of nax used for ages >5 years is 0.5. For ages <5 years, the values for nax are for males0.33 for ages 0–1 years and 0.25 for ages 1–4 years; and for females, 0.35 for ages 0–1 years and 0.25 for ages 1–4 years. These areloosely adapted from the CD West model life-table system (Coale and Demeny 1966).


i


Ta

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7.3

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Equation [7.4] shows the calculation for logit (nqx):

logit (nqn) = [7.4]

The factor3 and score routines provided with the STATA statistical softwarepackage release 5.0 (StataCorp 1997)4 are used to calculate the principal componentsof the 70 � 18 covariance matrix5 associated with the data set described above. Eachage group (row) in the data set is given a weight equal to the total number ofperson–years of observation for the age group summed across all site–periods. Fifteenof the resulting 70 principal components are retained, and for both males and femalesthose 15 components account for greater than 99.99% of the variation in the data.

Male

The first 15 principal components of INDEPTH male mortality are contained inTable 7.3, and the first 5 components are shown in Figure 7.1. The primary (first)component, PC1, obviously represents the underlying age pattern of mortality, andtogether PC2–PC4 modify the age pattern in a way that is consistent with mortalitycaused by AIDS. PC2 in particular has the shape necessary to account for increasedmortality between the ages of 20 and 64 years. PC3 and PC4 allow modificationsbetween the ages of 20 and 49 years and during childhood.

Figure 7.1. First five principal components of INDEPTH male mortality. The first five principal components explain 98.94% of total variance.

3 The factor routine is used with the options [pc] to request principal-components analysis; [covariance], to specify that the covariancematrix is analyzed; and [weight], to specify the weighting.

4 Mention of a proprietary name does not constitute endorsement of the product and is given only for information.

5 The covariance matrix is used so that the observations are not standardized before the calculation. The resulting principal compo-nents refer to the unstandardized observations and can be directly recombined to produce age-specific mortality schedules that needno further transformation, except for the inverse logit, to produce values of nqx.

0-4 5-910

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40-44

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35-39

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50-54

55-59

60-64

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Ta

ble

7.4

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PC

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03.

5313

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0.98

370.

6458

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50.

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051

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870

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543

0.08

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Figure 7.2. First five principal components of INDEPTH female mortality. The first five principal components explain 98.40% of total variance.

The primary component crosses the x -axis between ages 5 and 9 years andagain between ages 30 and 34 years, with the result that as the coefficient of the pri-mary component increases, child and adult mortality increases while the mortality ofteenagers and young adults decreases. Consequently, the first coefficient determinesthe ratio of child and adult mortality to teenage and young-adult mortality. This islikely due to the fact that mortality of the very young and elderly is more sensitive toadverse (or advantageous) conditions than the mortality of the generally healthy androbust teenagers and young adults.6 Naturally then, this balance accounts for a greatdeal of the variation in the data and is therefore encoded in the first component.Remember that the overall level of mortality is governed by the value of the constantterm in equation [7.2], so the coefficient of the first component is really only respon-sible for the age balance, not for the absolute level of mortality at any age.

Female

The first 15 principal components of INDEPTH female mortality are contained inTable 7.4, and the first 5 components are shown in Figure 7.2. In broad terms they arevery similar to the male components. However, the primary component contains asignificant positive bulge between ages 20 and 44 years, which is absent on the maleprimary component (see Figure 7.3). The most likely explanation for this is that itaccounts for the maternal mortality experienced in the female population.Additionally, the second component describes a somewhat narrower, younger patternof deviation that at its peak is of slightly greater magnitude than that for the males (seeFigure 7.4). This likely results from the general fact that the effect of AIDS on femalemortality occurs at a younger and more focused age than its effect on male mortality.

1 2 3 4xx 5

xxxx

xxxxxxxx xxxx xxx xxxxxx

xxx xx xx xx xx xxx xxx xxxxxx

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6 It is also worth noting that the impact of the first component is not constant with age: when the value of the first component is close tozero, the absolute impact is much smaller than when the value of the first component is more distant from zero. An examination ofthe curve reveals that the absolute effect of the first component increases significantly with age past 39 years.

The third and fourth components are virtually identical for males and females, exceptat older ages. The data for older ages will not be interpreted, because they are morelikely to be inaccurate and the differences are large only for the oldest ages.

Male and female principal components contrasted

Figures 7.3–7.6 plot the first four principal components of INDEPTH mortality for themales and females together, to clearly demonstrate the differences between the maleand female components. These differences are discussed briefly above.

To examine the generality of the INDEPTH components of mortality, the exist-ing CD and UN model mortality patterns (at levels corresponding to a life expectancyat birth of 55 years) were regressed against the INDEPTH components of mortality ina simple linear ordinary least-squares regression. The regressions were run against all15 of the INDEPTH components, the first 10, and finally the first 5. In each case, thefit statistics were examined and the predicted mortality patterns were calculated andvisually compared with the fit patterns. Table 7.5 displays the R 2 fit statistic for thoseregressions. Using all 15 components produces near-perfect fits that are able to faith-fully reproduce the existing patterns in all respects. Reducing the number of compo-nents used has the expected effect of reducing the quality of the overall fit and failingto correctly model the high-frequency variation in the model patterns. Using 10 com-ponents still produces a very reasonable fit, and using 5 or 6 components is acceptablein most circumstances; however, with a small number of components, a substantial“smoothing” occurs, as a result of the lack of high-frequency components. This is actu-ally useful if the aim is to capture the fundamental shape of the mortality curve or ifthe data are “dirty” and the analyst needs the data to fit the basic shape but can ignorethe smaller bumps and dips, which may be meaningless.

Figure 7.3. First principal component of INDEPTH male mortality and female mortality contrasted. Thefirst principal component explains 87.12% (male) and 82.49% (female) of the total variance.

1-4 5-910

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-3-4-5

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Male

Age group (years)

Female


Figure 7.4. Second principal component of INDEPTH male mortality and female mortality contrasted. The second principal component explains 8.89% (male) and 11.76% (female) of the total variance.

Figure 7.5. Third principal component of INDEPTH male mortality and female mortality contrasted. The third principal component explains 1.53% (male) and 1.91% (female) of the total variance.

1-4 5-910

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Age group (years)

Male Female

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Age group (years)

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Figure 7.6. Fourth principal component of INDEPTH male mortality and female mortality contrasted. The fourth principal component explains 0.77% (male) and 1.50% (female) of the total variance.

INDEPTH mortality patterns

The overall aim of this work is to identify common age patterns of mortality in theINDEPTH data. The resulting patterns provide a distilled representation of the impor-tant mortality conditions experienced by the populations from which the data werecollected. Moreover, some understanding of the age patterns of mortality in Africa,based on empirical data from Africa, is invaluable to demographers and planners ofall kinds, who must account for present and future mortality in much of their work.

Component-clustering method

The most critical task in identifying the common underlying mortality patterns is toidentify clusters of similar patterns — in this case, clusters of site–periods with similarage patterns of mortality. This is a particularly difficult exercise that necessarilyinvolves some subjective input from the analyst.

A given age pattern of mortality can be observed at various levels resulting fromthe fact that there may be causes of mortality that affect all ages in roughly the sameway and consequently do not produce an age pattern. Given that, mortality schedulesmay cluster along two dimensions: age pattern and level. The age pattern of a mortal-ity schedule contains a lot of information regarding the epidemiological profile of thepopulation and is consequently of primary interest here.

One of the substantial advantages of the component model of mortality is thedistilled, parsimonious representation of a mortality pattern that results from regress-ing it on the components. The vector of regression coefficients contains independentinformation on the age pattern and level of the mortality schedule. That fact allowsthe creation of clusters of age patterns without respect to level.

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Age group (years)

Male Female


To create the age-pattern clusters, all 70 of the INDEPTH mortality schedulesfor both males and females are regressed against the appropriate sex-specific compo-nents of INDEPTH mortality. The coefficients corresponding to the first 4 principalcomponents are retained, and the other 11 plus the constant are discarded. The firstfour principal components account for 98.32% of the variation in the male data and97.66% of the variation in the female data, making them sufficient to capture all butthe finest nuances in the age pattern of mortality. These form a collection of 70 4 � 1coefficient vectors for each sex.

The agglomerative hierarchical clustering algorithm provided with the S-PLUS2000 Professional statistical software package (release 3) is used to identify clusters ofsimilar coefficient vectors for each sex.7 The Ward method used here is described bythe provider of the software as follows (MathSoft Inc. 1999, p. 102):

The basic hierarchical agglomerative algorithm starts with each object ina separate group. At each iteration it merges two groups to form a newgroup; the merger chosen is the one that leads to the smallest increasein the sum of within-group sums of squares. The number of iterations isequal to the number of objects minus one, and at the end all the objectsare together in a single group.


Table 7.5. The R2 values from linear regressions of existing model mortality patterns on the INDEPTH components.

Model Male Female

Fit with first 15 componentsNorth 0.999 999 77 0.999 956 79South 0.999 429 47 0.999 041 30East 0.999 991 92 0.999 997 87West 0.999 935 68 0.999 872 75Latin America 0.999 711 66 0.999 081 25Chile 0.999 993 61 0.999 869 67South Asia 0.999 841 72 0.999 336 33Far East 0.999 977 82 0.999 998 68General 0.999 950 98 0.999 775 79

Fit with first 10 components

North 0.999 855 85 0.998 970 05South 0.996 437 55 0.993 827 83East 0.999 569 20 0.999 577 40West 0.999 556 50 0.997 920 47Latin America 0.998 883 54 0.995 959 77Chile 0.999 496 56 0.999 092 39South Asia 0.998 156 76 0.996 751 65Far East 0.999 651 67 0.999 109 22General 0.999 659 60 0.998 246 04

Fit with first 5 components

North 0.998 866 69 0.996 337 04South 0.993 827 70 0.988 258 46East 0.996 563 74 0.994 030 00West 0.996 784 75 0.994 526 55Latin America 0.994 807 25 0.988 185 31Chile 0.994 486 95 0.980 204 60South Asia 0.992 722 03 0.983 421 85Far East 0.996 987 49 0.995 609 08General 0.996 377 00 0.994 121 56

Source: CD model (North, South, East, West) is from Coale and Demeny (1966); UN model (Latin America, Chile, South Asia, Far East, General)is from United Nations (1982).

7 S-Plus’s “agnes” routine was used with options: metric = euclidean, standardize = true, and linkage type = word.


Table 7.6. INDEPTH mortality age-pattern clusters

Male Female

ID Site–period Pattern ID Site–period Pattern

26 Bandafassi, Senegal: 1980–84 1 26 Bandafassi, Senegal: 1980–84 127 Bandafassi, Senegal: 1985–87 1 27 Bandafassi, Senegal: 1985–87 128 Bandafassi, Senegal: 1988–90 1 28 Bandafassi, Senegal: 1988–90 129 Bandafassi, Senegal: 1991–93 1 29 Bandafassi, Senegal: 1991–93 130 Bandafassi, Senegal: 1994–96 1 30 Bandafassi, Senegal: 1994–96 131 Bandafassi, Senegal: 1997–99 1 31 Bandafassi, Senegal: 1997–99 136 Butajira, Ethiopia: 1987–89 1 32 Bandim, Guinea-Bissau: 1990–91 137 Butajira, Ethiopia: 1990–91 1 40 Oubritenga, Burkina Faso: 1994–95 138 Butajira, Ethiopia: 1992–93 1 41 Oubritenga, Burkina Faso: 1996–98 139 Butajira, Ethiopia: 1994–96 1 43 Farafenni, The Gambia: 1996–97 140 Oubritenga, Burkina Faso: 1994–95 1 44 Farafenni, The Gambia: 1998–99 147 Gwembe, Zambia: 1984–86 1 45 Gwembe, Zambia: 1950–80 165 Niakhar, Senegal: 1985–89 1 49 Gwembe, Zambia: 1990–92 166 Niakhar, Senegal: 1990–94 1 50 Gwembe, Zambia: 1993–95 167 Niakhar, Senegal: 1995–98 1 51 Ifakara, Tanzania: 1997–99 169 Nouna, Burkina Faso: 1996–98 1 52 Manhiça, Mozambique: 1998–99 154 Matlab comp.,a Bangladesh: 1998 2 65 Niakhar, Senegal: 1985–89 155 Matlab treat.,b Bangladesh: 1988 2 66 Niakhar, Senegal: 1990–94 156 Matlab treat.,b Bangladesh: 1998 2 67 Niakhar, Senegal: 1995–98 159 Mlomp, Senegal: 1991–93 2 70 Rufiji, Tanzania: 1999 160 Mlomp, Senegal: 1994–96 2 53 Matlab comp.,a Bangladesh: 1988 21 Agincourt, South Africa: 1992–93 3 54 Matlab comp.,a Bangladesh: 1998 22 Agincourt, South Africa: 1994–95 3 55 Matlab treat.,b Bangladesh: 1988 23 Agincourt, South Africa: 1996–97 3 56 Matlab treat.,b Bangladesh: 1998 211 Dar es Salaam, Tanzania: 1998–99 3 57 Mlomp, Senegal: 1985–87 235 Bandim, Guinea-Bissau: 1996–97 3 61 Mlomp, Senegal: 1997–99 25 Dar es Salaam, Tanzania: 1992–93 4 2 Agincourt, South Africa: 1994–95 332 Bandim, Guinea-Bissau: 1990–91 4 3 Agincourt, South Africa: 1996–97 333 Bandim, Guinea-Bissau: 1992–93 4 4 Agincourt, South Africa: 1998–99 334 Bandim, Guinea-Bissau: 1994–95 4 7 Dar es Salaam, Tanzania: 1994–95 342 Farafenni, The Gambia: 1994–95 4 8 Dar es Salaam, Tanzania: 1995–96 343 Farafenni, The Gambia: 1996–97 4 9 Dar es Salaam, Tanzania: 1996–97 344 Farafenni, The Gambia: 1998–99 4 10 Dar es Salaam, Tanzania: 1997–98 345 Gwembe, Zambia: 1950–80 4 11 Dar es Salaam, Tanzania: 1998–99 346 Gwembe, Zambia: 1981–83 4 35 Bandim, Guinea-Bissau: 1996–97 349 Gwembe, Zambia: 1990–92 4 1 Agincourt, South Africa: 1992–93 453 Matlab comp.,a Bangladesh: 1988 4 33 Bandim, Guinea-Bissau: 1992–93 457 Mlomp, Senegal: 1985–87 4 34 Bandim, Guinea-Bissau: 1994–95 458 Mlomp, Senegal: 1988–90 4 42 Farafenni, The Gambia: 1994–95 461 Mlomp, Senegal: 1997–99 4 62 Navrongo, Ghana: 1993–95 462 Navrongo, Ghana: 1993–95 4 63 Navrongo, Ghana: 1996–97 463 Navrongo, Ghana: 1996–97 4 64 Navrongo, Ghana: 1998–99 464 Navrongo, Ghana: 1998–99 4 68 Nouna, Burkina Faso: 1993–95 468 Nouna, Burkina Faso: 1993–95 4 5 Dar es Salaam, Tanzania: 1992–93 570 Rufiji, Tanzania: 1999 4 6 Dar es Salaam, Tanzania: 1993–94 54 Agincourt, South Africa: 1998–99 5 12 Hai, Tanzania: 1992–93 56 Dar es Salaam, Tanzania: 1993–94 5 13 Hai, Tanzania: 1993–94 57 Dar es Salaam, Tanzania: 1994–95 5 14 Hai, Tanzania: 1994–95 58 Dar es Salaam, Tanzania: 1995–96 5 15 Hai, Tanzania: 1995–96 59 Dar es Salaam, Tanzania: 1996–97 5 16 Hai, Tanzania: 1996–97 510 Dar es Salaam, Tanzania: 1997–98 5 17 Hai, Tanzania: 1997–98 512 Hai, Tanzania: 1992–93 5 18 Hai, Tanzania: 1998–99 513 Hai, Tanzania: 1993–94 5 19 Morogoro, Tanzania: 1992–93 514 Hai, Tanzania: 1994–95 5 20 Morogoro, Tanzania: 1993–94 515 Hai, Tanzania: 1995–96 5 21 Morogoro, Tanzania: 1994–95 516 Hai, Tanzania: 1996–97 5 22 Morogoro, Tanzania: 1995–96 517 Hai, Tanzania: 1997–98 5 23 Morogoro, Tanzania: 1996–97 518 Hai, Tanzania: 1998–99 5 24 Morogoro, Tanzania: 1997–98 519 Morogoro, Tanzania: 1992–93 5 25 Morogoro, Tanzania: 1998–99 5

(continued)

Detailed discussions of clustering techniques and this particular algorithm arefound in Kaufman and Rousseeuw (1990), Struyf and Hubert (1997), and MathSoftInc. (1999).8 This routine was applied separately to the male and female data sets,each consisting of four columns (one for each coefficient described above) and70 rows (one for each site–period).

Clusters

The method described above identified five robust clusters in the male data and sevenrobust clusters in the female data, presented in Table 7.6. Because females are subjectto the additional risk of maternal mortality, their age patterns are always more com-plex, and so it is not surprising that two more clusters were identified in the femaledata. Categorizing the male data into the seven female clusters produces seven maleclusters that can be directly compared with the female clusters.

In many cases, periods from the same site are grouped in the same cluster,reassuring us that the clustering algorithm is identifying and grouping fundamentallysimilar mortality schedules. Where periods from the same site are assigned to variousclusters, mortality has been changing significantly over time, and the mortality sched-ules from two periods are substantially different.

Mortality patterns

After the clusters are identified, a characteristic age pattern of mortality is identifiedfor each cluster. In keeping with the use of the component model of mortality, wethen calculate, for each of the 15 coefficients derived from the regression of the indi-vidual site–period mortality schedules on the 15 components of INDEPTH mortality,the weighted average across the site–periods in each cluster. The weights used are theperson–years of observation in each site–period. This yields the average amount of


Table 7.6. (concluded)

Male Female

ID Site–period Pattern ID Site–period Pattern

20 Morogoro, Tanzania: 1993–945 5 36 Butajira, Ethiopia: 1987–89 621 Morogoro, Tanzania: 1994–95 5 37 Butajira, Ethiopia: 1990–91 622 Morogoro, Tanzania: 1995–96 5 38 Butajira, Ethiopia: 1992–93 623 Morogoro, Tanzania: 1996–97 5 39 Butajira, Ethiopia: 1994–96 624 Morogoro, Tanzania: 1997–98 5 58 Mlomp, Senegal: 1988–90 625 Morogoro, Tanzania: 1998–99 5 69 Nouna, Burkina Faso: 1996–98 641 Oubritenga, Burkina Faso: 1996–98 5 46 Gwembe, Zambia: 1981–83 748 Gwembe, Zambia: 1987–89 5 47 Gwembe, Zambia: 1984–86 750 Gwembe, Zambia: 1993–95 5 48 Gwembe, Zambia: 1987–89 751 Ifakara, Tanzania: 1997–995 5 59 Mlomp, Senegal: 1991–93 752 Manhiça, Mozambique: 1998–99 5 60 Mlomp, Senegal: 1994–96 7

a Comparison area.b Treatment area.

8 A number of clustering techniques were applied to both the raw and the transformed data and to the coefficient vectors, includingagglomerative hierarchical clustering, partitioning around K-means, partitioning around K-medoids, fuzzy partitioning, and divisivehierarchical clustering. Three different statistical software packages — STATA (StataCorp 1997), S-PLUS (MathSoft Inc. 1999), andMVSP (Multi-Variate Statistical Package [KCS 1998]) — were used, and in each case all of their clustering routines were tried. All ofthe methods produced essentially the same clusters but differed in the clarity of their output and in how they managed ambiguouscases. The agglomerative hierarchical algorithm provided with S-PLUS was eventually chosen, based on its clear and robust theoreticalunderpinnings and the fact that its output is easily understood and interpreted. Moreover, it appeared to provide the most robust clus-ters and the most efficient means of categorizing ambiguous cases.

each of the 15 components and the constant needed for each of the mortality sched-ules in a given cluster. When these average values are combined with the componentsthrough equation [7.2], the result is the weighted average mortality schedule for eachcluster. By varying the constant, the analyst can adjust mortality schedules to an arbi-trary level, and for convenience’s sake, the seven INDEPTH mortality patternspresented in Table 7.7 are adjusted to a level that yields a life expectancy at birth of55 years. Table 7.7 organizes the male and female patterns into the seven female-derived clusters. This arrangement facilitates comparison of the male and female pat-terns. The five male-derived patterns are retained when the male data are organizedinto the female-derived patterns; this simply creates two sets of two slightly redundantmale patterns. The author verified this by producing the male patterns based on boththe male- and female-derived clusters.


Table 7.7. INDEPTH mortality patterns.

Pattern

Age (years) 1 2 3 4 5 6 7

Male0 –1.1821 –1.0939 –1.6252 –1.3192 –1.3260 –1.3778 –1.21701–4 –1.3230 –1.4728 –1.7509 –1.4661 –1.5931 –1.3428 –1.39115–9 –1.6722 –1.9849 –2.0255 –1.7771 –1.9413 –1.5184 –1.700310–14 –2.1807 –2.3702 –2.3544 –2.1811 –2.2056 –1.8187 –2.082115–19 –2.2586 –2.5108 –2.2378 –2.2402 –2.1341 –1.8875 –2.186520–24 –2.1049 –2.4333 –1.9393 –2.1120 –1.8661 –1.8463 –2.134525–29 –1.9047 –2.2779 –1.6891 –1.9157 –1.6286 –1.8062 –2.070530–34 –1.7481 –2.1099 –1.5053 –1.7563 –1.4667 –1.7737 –2.026135–39 –1.6588 –1.9003 –1.3908 –1.5743 –1.3647 –1.7097 –1.811540–44 –1.5905 –1.7467 –1.2490 –1.4380 –1.2778 –1.5853 –1.683245–49 –1.4908 –1.5228 –1.1515 –1.3033 –1.2277 –1.4725 –1.479250–54 –1.3599 –1.2380 –1.0762 –1.1844 –1.2131 –1.3443 –1.330755–59 –1.2138 –0.9758 –0.9546 –1.0316 –1.1841 –1.2052 –1.167860–64 –1.0475 –0.7508 –0.7807 –0.8254 –1.0605 –1.0625 –0.898565–69 –0.8344 –0.5340 –0.5862 –0.6689 –0.8813 –0.8767 –0.624270–74 –0.6132 –0.3143 –0.3531 –0.5276 –0.6934 –0.6775 –0.327975–79 –0.3790 –0.0674 –0.1027 –0.3782 –0.4948 –0.4865 –0.115880–84 –0.1107 0.2082 0.1747 –0.2005 –0.2477 –0.3257 –0.0226

Female0 –1.1678 –1.0304 –1.4926 –1.2429 –1.2667 –1.4005 –1.19351–4 –1.2698 –1.3893 –1.6489 –1.4084 –1.5306 –1.3479 –1.26745–9 –1.6070 –1.9119 –1.9691 –1.7526 –1.8930 –1.5252 –1.565810–14 –2.1126 –2.3759 –2.3076 –2.1760 –2.1958 –1.8319 –2.167815–19 –2.0958 –2.3195 –2.1232 –2.2106 –2.0281 –1.8767 –2.501420–24 –1.9525 –2.1988 –1.8469 –2.0725 –1.6854 –1.8322 –2.350225–29 –1.8484 –2.1152 –1.6241 –1.9094 –1.4610 –1.7935 –2.106530–34 –1.8019 –2.1711 –1.4641 –1.8040 –1.3720 –1.7781 –1.791935–39 –1.7623 –2.1811 –1.3715 –1.7224 –1.3793 –1.7215 –1.549540–44 –1.7020 –1.9609 –1.3386 –1.6330 –1.4161 –1.6174 –1.531145–49 –1.6005 –1.6935 –1.2734 –1.4865 –1.4478 –1.4856 –1.674350–54 –1.4831 –1.4249 –1.2305 –1.3010 –1.4333 –1.2875 –1.592755–59 –1.3321 –1.1522 –1.0773 –1.0693 –1.3500 –1.1067 –1.408260–64 –1.1252 –0.8883 –0.9092 –0.7946 –1.1827 –0.9697 –1.098265–69 –0.8707 –0.6080 –0.6508 –0.6352 –0.9797 –0.8405 –0.782270–74 –0.6243 –0.3002 –0.4577 –0.4904 –0.7919 –0.7177 –0.503775–79 –0.3983 –0.0193 –0.2001 –0.3331 –0.5537 –0.6067 –0.186580–84 –0.2084 0.2012 0.1935 –0.1574 –0.2269 –0.4946 0.1573

Note: Units of logit (nqx).

Figures 7.7 and 7.8 plot the seven INDEPTH age patterns of mortality for malesand females, respectively. Figures 7.9–7.15 compare each of the seven INDEPTH agepatterns of mortality for males and females. The patterns are arbitrarily named 1–7,9

and a discussion of the patterns accompanies the plots.

Figure 7.7. Seven INDEPTH age patterns of mortality for males, adjusted to yield a life expectancy at birth of 55 years.

Figure 7.8. Seven INDEPTH age patterns of mortality for females, adjusted to yield a life expectancy at birth of 55 years.

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9 This is done to avoid the potential stigmatization from use of more descriptive names.

Figure 7.9. INDEPTH mortality pattern 1, adjusted to yield a life expectancy at birth of 55 years.

Pattern 1

The first pattern (Figure 7.9) is similar to the CD North and UN Latin Americanmodel life-table age patterns of mortality (see “Comparisons with the Coale andDemeny and United Nations model life tables,” below). There is no indication thatHIV–AIDS affects pattern 1, and the male and female age patterns are similar, withthe exception of a bulge in the female pattern during the reproductive years, presum-ably caused by maternal mortality. Pattern 1 is primarily derived from sites in WestAfrica over the entire period covered by the INDEPTH data set. HIV–AIDS has not yetbecome as significant a problem in West Africa as it is in Central and southern Africa,so a large impact of AIDS is not expected to be seen in the data from West Africa. It isworth noting that child mortality between the ages of 1 and 9 is significant and sub-stantially elevated above that shown by the most similar existing models, below. This isin keeping with the fact that malaria is a significant cause of death in West Africa, andit has a large impact on those age groups.

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Pattern 2

Pattern 2 (Figure 7.10) is the only pattern to contain significant contributions fromAsia, and it is in fact dominated by data from the Matlab project, in Bangladesh. Theonly other site to contribute data to this pattern is the Mlomp site, in Senegal. Again,the male and female patterns are similar, with the exception of maternal mortality.However, pattern 2 is strikingly different from all of the others in that the mortality ofchildren, teenagers, and young adults is comparatively very low, and correspondinglythe mortality of older adults is comparatively high. In keeping with the fact that thedata contributing to this pattern come from Bangladesh and Senegal, it is not surpris-ing that there is no evidence at all of an HIV–AIDS impact. Pattern 2 is very similar tothe UN South Asia pattern, as it should be, coming largely from South Asia (seebelow).

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Pattern 3

The sites contributing to pattern 3 (Figure 7.11) are almost exclusively located insouthern and East Africa: South Africa and Tanzania in particular. This pattern obvi-ously contains some influence of HIV–AIDS, but not nearly to the degree observed inpattern 5. The South African data come from the Agincourt site, where mortality isextraordinarily low compared with the other INDEPTH sites in Africa and whereHIV–AIDS is recognized but not yet impacting the population in the catastrophic waythat it is in other parts of southern and East Africa. The remainder of the data comefrom the Dar es Salaam site, where there appears to be a greater impact of HIV–AIDS.This pattern is most similar to the UN Far East pattern of mortality, corresponding tothe fact that infant and child mortality are very low compared with mortality at olderages. A noteworthy feature of this pattern is the fact that infant and child mortalitydoes not appear to be substantially elevated, as might be expected when HIV–AIDS isan important contributor to mortality.

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Pattern 4

Pattern 4 (Figure 7.12) is a variation on pattern 1, with the important difference mani-fested in the 35-69 years age range. At all other ages, patterns 1 and 4 are negligiblydifferent, except that infant and child mortality in pattern 4 is consistently slightlylower than in pattern 1. But between ages 35 and roughly 69 years, pattern 4 revealssignificantly higher mortality than pattern 1. This pattern is most similar to the UNGeneral pattern for females and UN Latin America for males. As was the case with pat-tern 1, most of the data contributing to pattern 4 come from West Africa.

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Pattern 5

The HIV–AIDS pattern of mortality is most clearly visible in pattern 5 (Figure 7.13).The data contributing to pattern 5 are derived from the three Tanzanian sites run bythe Adult Morbidity and Mortality Project in Dar es Salaam, Hai, and Morogoro. Avery striking bulge appears in the mortality of males between the ages of 20 and 54years and for females between the ages of 15 and 49 years. Additionally, the femalebulge is significantly narrower and more pronounced, corresponding to the fact thatthe female population is infected earlier and within a tighter age range. This pattern isnot particularly similar to any of the existing model patterns, but it is most closelymatched with the UN General (female) and UN Latin American (male) model pat-terns. Pattern 5 differs from pattern 3 mainly in the shape of the HIV–AIDS impact.The effect is more diffuse with age in pattern 3, meaning that mortality is elevatedthrough a broader age range, the magnitude of the elevation is more consistent, andthe differences between the sexes are less apparent. Pattern 3 is derived largely fromthe Dar es Salaam data, and this may reflect the fact that the epidemic is more maturein Dar es Salaam and has consequently had enough time to infect a wider age range ofpeople of both sexes. As with pattern 3, it is worth noting that infant and child mortal-ity do not appear to be substantially affected in a manner comparable to adult mortal-ity, and this is in contradiction to what is known about HIV prevalence and verticaltransmission. Further investigation is needed to determine why this effect is notprominently measured in these data.

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Pattern 6

Pattern 6 (Figure 7.14) is one of the two additional patterns identified in the femaledata. It is an interesting pattern that reveals very high mortality of children andteenagers, together with comparatively low mortality of infants and adults of all ages.This pattern is exhibited at sites in northeast and West Africa, with most of the datacoming from Ethiopia. Without additional information, it is impossible to speculateon what may be producing this unique pattern. The male pattern is most similar to theCD North model pattern, and the female pattern is closest to the CD West model,both of which embody high mortality in the same age ranges. They deviate from thosepatterns in that infant mortality is substantially lower than would be found in eithermodel pattern, and child and adolescent mortality is significantly higher: this might becalled the “Super North” pattern.

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Pattern 7

Pattern 7 (Figure 7.15), the other additional pattern identified in the female data, isalso of interest. It is derived from two sites in Central and West Africa. The reason whyit was identified in the female data is obvious: a very substantial bulge appears in thefemale age pattern between ages 25 and 44 years. This most likely results from veryserious maternal mortality, the risk of which increases with age. The site in Zambia is arural site without easy access to modern medical facilities, and this may contribute toan unusual risk of maternal mortality. The corresponding male pattern is similar topattern 6, and both are similar to the CD North model pattern. The CD North modelpattern contains higher child and teenage mortality, coupled with comparatively lowmortality at older ages. This is consistent with the fact that malaria is an importantcontributor to mortality at both sites.

Comparisons with the Coale and Demeny and

United Nations model life tables

The INDEPTH mortality patterns are explicitly compared with the existing CD and UNmodels to ensure that they are indeed new patterns and to demonstrate exactly howthey differ from the existing model mortality patterns. The method used is a simpleminimum sum of squared differences. Each INDEPTH mortality pattern is comparedwith all of the existing CD and UN model mortality patterns: CD patterns North,South, East, and West; and UN patterns Latin America, Chile, South Asia, Far East,and General. For each pair of patterns, the difference between the two is calculated

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for each age group, and those differences are squared and summed to yield the sum(over all ages) of the squared differences (SSD) between the two patterns. For eachINDEPTH pattern, the SDDs derived from the seven comparisons are ranked, and themembers of the pair with the smallest SDD are considered to be most similar. All of themortality patterns used in the comparisons are adjusted to a level corresponding to anlife expectancy at birth of 55 years.10 The SDDs are presented in Table 7.8, where boththe minimum and the next greater SDDs for each comparison are identified.

For each INDEPTH pattern, the age-specific deviations from the closest fitexisting model pattern are calculated and presented in Figures 7.16 and 7.17. Thosefigures clearly reveal that all of the INDEPTH patterns are systematically differentfrom the existing model mortality patterns. Both figures reveal clear peaks in the devi-ations for children (1-14 years) and young to middle-aged adults (25-49 years).Interestingly, infant and child mortality between the ages of 1 and 4 years is generallylower than the corresponding pattern. The peak in the deviations during childhoodmay be due to malaria and other diseases that have a large impact on children inAfrica but not elsewhere in the world, and it is clear that continued investigation isneeded to identify all of the factors contributing to childhood deviations. The peakduring the adult years is most pronounced for patterns 3 and 5, which are the two pat-terns affected by HIV–AIDS, and it is reasonable to assume that this peak is primarilydue to the impact of HIV–AIDS. It is curious to note that infant and child mortality inpatterns 3 and 5 does not appear to be elevated in a manner corresponding to theincrease in adult mortality. This suggests that the HIV–AIDS epidemic does not havean enormous impact on infant and child mortality or that all of the data used to gen-erate patterns 3 and 5 are defective with regard to infants and children. It seemsunlikely that all the data would be defective, along with being defective to the samedegree, and this points to the need for considerable investigation of the impact ofHIV–AIDS on infant and child mortality.


Table 7.8. Sum of squared differences comparing INDEPTH and existing mortality patterns.

Model

Pattern North South East West LA CH SA FE GL

Male1 0.2670 0.5550 0.7599 0.6035 0.3111 1.1682 0.8744 1.6724 0.64582 1.3819 0.6787 0.6538 0.9190 0.8760 1.0439 0.2265 1.1394 0.69183 1.1060 1.6448 1.2875 0.8313 1.0938 0.9075 2.3094 0.5066 0.7774

4 0.4041 0.7742 0.7219 0.4561 0.3640 0.8273 1.0159 0.9664 0.43445 0.6760 1.4443 1.3789 0.8996 0.7961 1.1767 2.3346 1.6279 1.02656 0.5118 1.4451 1.6459 1.2315 0.9998 2.2118 2.1365 2.5333 1.44867 0.4017 0.4548 0.5344 0.4985 0.4233 1.1231 0.5451 1.1824 0.4866

Female1 0.1763 0.4823 0.4573 0.3666 0.1727 0.6523 0.6724 1.0096 0.34282 1.4695 1.0966 0.8080 1.2731 1.0356 1.0373 0.4703 1.4209 0.97443 1.4447 2.2012 1.5312 1.0859 1.3253 1.1886 2.4018 0.4426 0.9283

4 0.4823 1.0188 0.7003 0.4749 0.3982 0.6471 1.0570 0.6098 0.3752

5 0.7861 1.5676 1.2496 0.7118 0.8045 0.9274 2.1636 0.7916 0.7728

6 0.3860 1.1897 1.2256 0.7723 0.7730 1.4854 1.7386 1.6242 0.93207 0.3837 0.5397 0.4040 0.4859 0.3905 0.7709 0.4704 1.0079 0.4050

Source: CD model (North, South, East, West) is from Coale and Demeny (1966); UN model (Latin America, Chile, South Asia, Far East, General) is from United Nations (1982).Note: CH, Chile; FE, Far East; GL, General; LA, Latin America; SA, South Asia. Bold, minimum; italic, next best.

10 The level of the INDEPTH patterns is set by adjusting the constant term in the component model of morality, and the CD- and UN-model mortality patterns used in the comparisons are generated by the United Nation’s computer program for the analysis of mor-tality data, MortPak-Lite (United Nations 1988), at a level corresponding to a life expectancy at birth of 55 years.

Figure 7.16. Age-specific deviations of INDEPTH male mortality patterns from those of best-fit existingmodels [logit (nqx)], adjusted to yield a life expectancy at birth of 55 years.

Figure 7.17. Age-specific deviations of INDEPTH female mortality patterns from those of best-fit existing models [logit ( nqx)], adjusted to yield a life expectancy at birth of 55 years.

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Demonstration of the HIV–AIDS model life-table system

Model life-table construction

The component model of mortality is capable of generating (and fitting) a very widerange of arbitrary mortality patterns. This makes it particularly well-suited for the cre-ation of model life tables. To demonstrate how the component model can be used tocreate a set of model life tables, we use the INDEPTH mortality components to isolate(in a set of coefficient deviations) the general age pattern of the impact of HIV–AIDS,and then add that impact in increasing quantities to the INDEPTH pattern-1 mortalityschedule, thus creating a set of life tables with decreasing life expectancies at birth cor-responding to an increasing impact of HIV. The result is a set of life tables with theunderlying age pattern defined by INDEPTH pattern 1 but with various levels ofHIV–AIDS mortality added to that.

Figures 7.18 and 7.19 display the male and female INDEPTH pattern-5 mortal-ity schedules with and without what is presumed to be the increase in mortality due toHIV–AIDS. Figure 7.20 presents the male INDEPTH pattern-1 mortality with and with-out an increase in mortality over the infant and childhood ages.11 In each case, the dif-ference between the two curves is fitted against the first 15 components of mortality(for the appropriate sex) to yield the coefficients presented in Table 7.9.

Figure 7.18. INDEPTH male mortality pattern 5, without and with the presumed increase in mortality due to HIV–AIDS (the HIV–AIDS bulge).

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11 There is no empirical pattern used to create the increase in infant and child mortality. It is simply created so that it could be includedin the model life tables.

Figure 7.19. INDEPTH female mortality pattern 5, without and with the presumed increase in mortalitydue to HIV–AIDS (the HIV–AIDS bulge).

Figure 7.20. INDEPTH male mortality pattern 1, with and without HIV–AIDS mortality for infants and children.

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-2.00

-2.25

-2.50

-2.75

-3.00

0

Age group (years)


1-4 5-910

-1415

-1920-2

4

40-44

30-34

25-29

35-39

45-49

50-54

55-59

60-64

80-84

70-74

65-69

75-79

Lo

git

of

the

pro

ba

bil

ity

of

dy

ing

: lo

git

(n

qx)

0.50

0.25

0.00

-0.25

-0.50

-0.75

-1.00

-1.25

-1.50

-1.75

-2.00

-2.25

-2.50

-2.75

-3.00

0

Age group (years)



The model life tables are constructed to produce a family of life tables with theunderlying mortality of INDEPTH pattern-1 mortality. The HIV–AIDS pattern of mor-tality is added to each of the members of the family in amounts sufficient to reducethe life expectancy at birth in 5-year increments. Equation [7.5] is a simple extensionof the component model of mortality that describes the relationship used to accom-plish this. In this case, the (n + 1) � 1 vector d� of HIV–AIDS coefficient deviations ismultiplied by � and added to the (n + 1) � 1 vector of coefficients, a�.12 The scalingfactor � determines how much of the HIV–AIDS pattern to add to the basic pattern ofmortality represented by the vector of coefficients, a�. Once that addition has beenaccomplished, the resulting vector is premultiplied by the matrix of components C �

to yield the logit-transformed probabilities of dying, logit (nq x). The relationship gov-erning the HIV-augmented model life table is given by the following equation:

m = C�(a� + �d�) [7.5]

where m , C�, a� , �, and d� are as defined above. Expanding this around the row forthe 20–24 age group reveals

. . . .. . . .. . . .

logit(5q20) = 5C20. (ai + � . di) + ... + 5C20

. (an + � . dn ) + 1.(ac + � . dc ). . . .. . . .. . . .


Table 7.9. Coefficient values estimated in fit of HIV-derived

deviations in logit (nqx) on the mortality

components.

Fit of adult deviations Fit of child deviations

Component Male Female Male Female

1 0.001 794 –0.004 217 –0.002 822 –0.003 9262 0.069 515 0.086 812 0.030 939 0.024 0633 –0.087 825 –0.093 468 0.048 722 0.046 5464 –0.046 538 0.007 340 –0.030 034 –0.033 0625 0.014 998 –0.053 602 –0.002 600 0.017 2916 0.007 024 0.071 480 –0.042 015 0.044 1767 0.057 843 –0.026 918 –0.001 601 0.029 7698 0.067 342 0.011 817 0.015 266 –0.036 0319 –0.035 387 0.055 790 –0.012 263 0.029 19910 –0.030 752 0.070 519 0.028 062 0.006 90311 –0.048 241 0.037 762 –0.013 452 0.043 73612 0.040 329 –0.028 917 –0.001 339 –0.004 03113 0.003 209 0.082 885 0.032 003 0.025 62114 0.091 293 0.089 362 0.050 373 –0.013 28715 0.126 678 –0.048 030 –0.008 452 0.030 330

Constant 0.062 364 0.079 854 –0.030 420 –0.028 344

12 Remember that the prime (�) indicates that the matrices and vectors include the column and row needed to store the constant and itscoefficient. Also, n is the number of components used, and g is the number of age groups.

ni

where 5C20 is the value of the i th component for the 20–24 age group; a i is the valueof the coefficient on the i th component; � is a single scalar applied to the vector ofcoefficient deviations; d i is the coefficient deviation for the i th component; a c is theconstant term, which takes the same value for all age groups; and d c is the deviationfor the constant term. Each of the column vectors contains g elements, one for eachage group.

Once the logit (nq x) values have been calculated, the inverse logit produces val-ues for nq x to be substituted into a life table and used to calculate its other columns,including life expectancy. The model life tables are calculated through an iterative,target-seeking process that varies � until the desired value for the life expectancy isattained (see Figures 7A.1–7A6 and Tables 7A.1–7A.4 in the Annex).

Conclusion

Data describing mortality at 19 sites in Africa and Asia are used to identify seven newage patterns of mortality, six of which originate solely from Africa. A componentmodel of mortality is constructed from the raw data and used to identify clusters ofsimilar age patterns of mortality, and these patterns are compared with the existingCD and UN model life-table age patterns of mortality and demonstrated to be system-atically and individually different from the existing models. This finding supports thenotion that unique age patterns of mortality occur in Africa and that routine demo-graphic and epidemiological estimations calculated from African data must take thisinto account. To make these data useful to practicing demographers and epidemiolo-gists, a set of model life tables based on these patterns must be constructed. INDEPTHis pursuing the construction of a set of INDEPTH model life tables for Africa, usingthe component model of mortality and based on the age patterns of mortality pre-sented here.


i

ANNEX: AIDS-DECREMENTED MODEL LIFE TABLES

Figure 7A.1. Male life-table probability of dying (nqx), decreased by AIDS mortality in 5-year increments(initial life expectancy at birth, 45 years).

Figure 7A.2. Female life-table probability of dying (nqx), decreased by AIDS mortality in 5-year increments (initial life expectancy at birth, 45 years).

1-4 5-9

20-24

40-44

30-34

25-29

35-39

45-49

50-54

55-59

60-64

80-84

70-74

65-69

75-79

Lif

e-t

ab

le p

rob

ab

ilit

y o

f d

yin

g,

nq

x

Age group (years)

0.700.650.600.550.500.450.400.350.300.250.200.150.100.050.00

0

e0 = 45 xxe0 = 40 e0 = 35 e0 = 30 e0 = 25

xxxxxxxxxxxxxx

xxxxxxxxxxxxxx xxxxxx

xx

xxx

xx

xxx

xxx

xxxxxxxxxxxxxxxxx xxxxxxxxxxxx

xxxxxxxxxxx

xxx

xxxxxx

xxxx

xx

xxxxxx

1-4 5-9

40-44

30-34

25-29

35-39

45-49

50-54

55-59

60-64

80-84

70-74

65-69

75-79

Lif

e-t

ab

le p

rob

ab

ilit

y o

f d

yin

g,

nq

x

Age group (years)

0.700.650.600.550.500.450.400.350.300.250.200.150.100.050.00

0

e0 = 45 xxe0 = 40 e0 = 35 e0 = 30 e0 = 25

xxxxxxxx

xxxxxxxx

xxxxxxxxxxx

xxxxxx

xxxxxxxx

x

x

xxx

x

xx

xxxxxxxxxxxxxxxxxxx

xxxxxxxxxx

xx

xx

xxxxx

xxxxxxxx

xxxxxxxxx

xx

xx

xxxxx


Figure 7A.3. Male life-table probability of surviving (Px), decreased by AIDS mortality in 5-yearincrements (initial life expectancy at birth, 45 years).

Figure 7A.4. Female life-table probability of surviving (Px), decreased by AIDS mortality in 5-yearincrements (initial life expectancy at birth, 45 years).

1-4 5-910

-1415

-1920-2

4

40-44

30-34

25-29

35-39

45-49

50-54

55-59

60-64

80-84

70-74

65-69

75-79

Lif

e-t

ab

le p

rob

ab

ilit

y o

f s

urv

ivin

g, P

x

Age group (years)

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

0

e0 = 45 xxe0 = 40 e0 = 35 e0 = 30 e0 = 25

xxxxxxxxxxxxxxxxxx

xxx

xx

xx

xxxxxxxxxxxxx

xx

xxxxxxxxx

1-4 5-910

-1415

-1920-2

4

40-44

30-34

25-29

35-39

45-49

50-54

55-59

60-64

80-84

Lif

e-t

ab

le p

rob

ab

ilit

y o

f s

urv

ivin

g, P

x

Age group (years)

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

0

e0 = 45 xxe0 = 40 e0 = 35 e0 = 30 e0 = 25

xxxxx

xxx

xxxx xx xx

xxxx

xxx

xxxxxxxxxxxx

xx

xx xx xx xx xxxx

xxx

xx xxx

xxxxx


Figure 7A.5. Male life expectancy (ex, or average remaining lifetime for a person who survives to the beginning of the indicated age interval), decreased by AIDS mortality in 5-year increments (initial life expectancy at birth, 45 years).

Figure 7A.6. Female life expectancy (ex, or average remaining lifetime for a person who survives to the beginning of the indicated age interval), decreased by AIDS mortality in 5-yearincrements (initial life expectancy at birth, 45 years).

1-4 5-910

-1415

-1920-2

4

40-44

30-34

25-29

35-39

45-49

50-54

55-59

60-64

80-84

70-74

65-69

75-79

Lif

e e

xp

ect

an

cy,

ex (

ye

ars

)

Age group (years)

65

60

55

50

45

40

35

30

25

20

15

10

5

0

0

e0 = 45 xxe0 = 40 e0 = 35 e0 = 30 e0 = 25

xxxxxxxxxxxxxxxx

xxxxxxxxxxxxxxxxxxxxxxxxxxxx

xxxxxxxxxxxxxxxxxxxxxxx

xxxxxxxxxxxxxxxxx

xxxxxxxx

xxxxx

xxxxxxx

xxxxxxxx

1-4 5-910

-1415

-1920-2

4

40-44

30-34

25-29

35-39

45-49

50-54

55-59

60-64

80-84

70-74

65-69

75-79

Lif

e e

xp

ect

an

cy,

ex (

ye

ars

)

Age group (years)

65

60

55

50

45

40

35

30

25

20

15

10

5

0

0

e0 = 45 xxe0 = 40 e0 = 35 e0 = 30 e0 = 25

xxxxxxxxxxxxx

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

xxxxxxxxxxx

xxxx

xxxx

xxxxxxxxx

xxxxxxx



Ta

ble

7A

.1.

Mo

de

l li

fe t

ab

les

fo

r IN

DE

PT

H p

att

ern

1:

life

ex

pe

cta

ncy

of

60

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ea

rs d

ecr

em

en

ted

by

HIV

–A

IDS

mo

rta

lity

.

Re

du

ctio

n i

n e

0(y

ea

rs)

Ma

leF

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e (

ye

ars

)0

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0.0

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5.0

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nq

x

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354

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413

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0.05

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626

0.05

8 83

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634

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057

153

0.06

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0.06

6 20

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0.02

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50.

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408

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240

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10.

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118

0.03

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0.03

3 18

70.

033

854

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40.

010

159

0.01

0 16

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164

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167

0.01

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011

849

0.01

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8

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90.

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511

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0.00

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169

0.01

1 45

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362

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839

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0.83

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80.

823

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839

297

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826

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0.82

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2

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0.84

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959

0.82

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60.

815

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55–5

921

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418

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118

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318

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18.6

818

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2

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14.6

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314

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15.0

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Documents

POPULATION AND HEALTH IN DEVELOPING COUNTRIES