Enhancing FP/RH Decision Making through GIS Data Linking

GIS Data Linking to

Enhance Multi-sectoral Decision Making for

Family Planning and Reproductive Health:

A Case Study in Rwanda

James Stewart

MEASURE Evaluation PRH

May 16, 2013

Organization of the Webinar

Speaker Information

Acknowledgements

Introduction

GIS data linking considerations for multi-sectoral and/or

multi-program data

Examples of GIS linking, visualization, and analysis

based on data for Rwanda

Lessons learned

Question and answer session

2

Speaker Information

James Stewart

Geographer / Senior

Spatial Analyst with

MEASURE Evaluation

15 years of experience

as a GIS professional

[email protected]

3

Acknowledgements

Based on their significant contributions to the

development of the case study, special thanks are

extended to the following individuals:

Dr. Fidel Ngabo, Director of Maternal and Child Health

(MCH), Rwanda Ministry of Health (MOH).

Dr. Charles Ntare, Head of Integrated Health

Management Information Systems/HMIS, Rwanda

MOH.

4

Acknowledgements (continued)

Mr. Randy Wilson, Senior Advisor, Health Information

Systems and Data Use, Management Sciences for

Health.

Mr. Norbert-Aimé Péhé, Country Director, USAID |

DELIVER PROJECT, John Snow, Inc.

Mr. Max Kabalisa, Mr. Jovith Ndahinyuka, and Mr.

Charles Nzumatuma, also of the USAID | DELIVER

PROJECT in Rwanda.

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Acknowledgements (continued)

MEASURE Evaluation PRH also extends sincere

appreciation to everyone in Rwanda who participated

in or facilitated stakeholder interviews conducted in

September 2011.

Organizations represented:

MCH and HMIS units at the MOH

USAID Monitoring & Evaluation Management Services

(MEMS) Project

MEASURE Evaluation

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INTRODUCTION

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Value of Linking Multi-sectoral

Data using a GIS

Family planning and

reproductive health

(FP/RH) services help

provide the foundation

for a healthy, stable,

and economically

viable society.

Kigali, Rwanda, Sep. 2011

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Data using a GIS

Past global strategies have often led to the

implementation of FP/RH programs that operate in

isolation, despite the value of integrated approaches.

The effectiveness of FP/RH decision making can be

undermined by a lack of information from other

sectors (e.g., education or food security), or from

other health areas (e.g., MCH or HIV/AIDS).

OVC FP/RH HIV EDU AGRICULTURE

PTMCT TB FOOD

SECURITY TRANSPORT POVERTY

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Data using a GIS

OVC FP/RH HIV EDU AGRICULTURE

PTMCT TB FOOD


Global Health Initiative (GHI) principle number five

emphasizes the need for strategic coordination and

integration to increase the impact of health programs.

“The integration of health sector activities and the

integration of health sector activities with activities in

other sectors – such as water and sanitation, education,

food security, agriculture, economic growth, microfinance,

and democracy and governance – can potentially achieve

high-yield gains for health.”

Source: www.ghi.gov, May 2013.

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http://www.ghi.gov/


Data using a GIS

Multi-sector or multi-program integration can be

facilitated by linking data sources.

Linking multi-sectoral data sources is often deterred

by information systems that are developed and

maintained independently of one another, leading to

datasets that are unconnected or ‘stovepiped.’

OVC FP HIV EDU AGRICULTURE

PTMCT TB FOOD


11


Data using a GIS

Through its ability to

link data using

common geographic

identifiers, a

geographic

information system

(GIS) can help

overcome this

‘stovepiping’ of data.

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Data using a GIS

After multi-sectoral links

have been established, a

GIS can

Enhance visualization

and analysis of FP/RH

program data.

Make program data

much easier to

understand and to use

for evidence-based

decision making.

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Data using a GIS

Many benefits:

Provides maps, which are highly visual tools.

Establishes a more comprehensive foundation for

decision making.

Increases data demand and use.

Helps identify data quality issues.

Supplies shared knowledge base for stakeholder

cooperation.

Facilitates better targeting of interventions.

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Data using a GIS

Facilitates answers to geography-based questions:

Do areas with a higher modern contraceptive prevalence

rate (MCPR) exhibit lower HIV prevalence among women

of reproductive age in union?

Is unmet need for FP different in urban and rural areas?

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To explore these benefits, MEASURE Evaluation

PRH sponsored a case study in Rwanda (fall 2011).

Rwanda was selected as a case study for two

primary reasons:

1. Designated by the USAID Office of PRH as a priority

country for the support of FP/RH programming.

2. Possesses a national spatial data infrastructure

(NSDI) that is mature enough to facilitate GIS data

linking and analysis.

Case Study in Rwanda

16

Case Study

in Rwanda

Goal was to explore data

linking opportunities using

free and open source GIS

solutions.

Available in the

publications section of the

MEASURE Evaluation

site.

www.measureevaluation.org/

publications/sr-12-74

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Goals of the Webinar

Based on the Rwanda experience:

Highlight the value of common geographic identifiers in

key data sources.

Identify free and open source software (FOSS) solutions

for GIS data linking, visualization, and analysis.

Show how these GIS solutions can be used with multi-

sectoral and/or multi-program data to enhance evidence-

based decision making.

Provide lessons learned to help accelerate the effective

use of multi-sectoral GIS data linking.

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GIS DATA LINKING

CONSIDERATIONS

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Key Data Sources

Field visit in 2011 focused on exploring data linking

opportunities to provide useful examples.

Some key data sources could not be accessed for

GIS linking during the field visit because of their

confidential or sensitive nature (e.g., PBF, TRACnet).

Others could not be accessed because of timing of

visit (e.g., HMIS, SISCom).

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In this context, focused on data sources that had a

higher likelihood of being available in other countries.

Primary data sources and sectors represented:

Rwanda Demographic and Health Survey 2010:

FP/RH, HIV, education, and nutrition.

USAID | DELIVER PROJECT: FP (commodities).

National Agricultural Survey, 2008: food security.

Poverty Household Surveys for 2000 to 2011: poverty.

Key Data Sources

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Common Geographic Identifiers

for Rwanda

Primary consideration

for data linking.

Linked key data

sources using

crosswalk.

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GIS Options Explored

Focused on free and open source software (FOSS)

solutions to complement existing tools:

Excel to Google Earth (E2G)

Single indicator maps using Excel.

Quantum GIS (QGIS)

Multi-indicator and publication-quality maps as well as

advanced GIS analysis to extend functionality of DHIS 2.

OpenGeoDa

Simple but effective exploratory spatial data analysis (ESDA)

using data in shapefile format.

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Excel to Google Earth (E2G)

Quick and simple program

from MEASURE Evaluation.

Color-shaded (choropleth)

map of a single variable

without a GIS.

Displayed on Google Earth’s

rich base map.

Useful for data quality

checks and illustrating

reports.

Good option for non-GIS

specialists working in Excel.

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www.measureevaluation.org/e2g

Quantum GIS (QGIS)

Fully functional GIS.

Excellent for multi-

sectoral GIS data linking,

visualization, and

analysis.

Publication-quality maps.

Perform advanced GIS

analysis.

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www.qgis.org

OpenGeoDa

Percent Married Women Age 15–49

using Any Method of Contraception

Data Source: Rwanda DHS 2010, Table D.32.

26

geodacenter.asu.edu

EXAMPLES OF GIS LINKING

AND ANALYSIS FOR RWANDA

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28

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Comparing the Two

No discernible correlation between general use of

contraception, which includes both traditional and modern

methods, and HIV prevalence.

No spatial overlap between districts with highest % of women using

contraception and districts in Kigali with highest HIV prevalence.

Districts with lowest contraception use do not appear to coincide

with either a lower or higher HIV prevalence.

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QGIS: Contraception Use vs. HIV

Prevalence

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Prevalence

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Prevalence

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QGIS: Linking FP, Education, and

Poverty Data

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QGIS: Linking FP, Nutrition, and

Food Security Data

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Linking FP/RH Program Data with

FP Commodities Data Example: Women using Any Modern Method of

Contraception (MCPR) versus Couple Years of

Protection (CYP)

Integrating FP commodities data from USAID | DELIVER

PROJECT represents significant data linking opportunity

for many FP/RH programs.

Relies on same data linking principles used in previous

sections.

This example can be used as a model for integrating

USAID | DELIVER PROJECT data into an existing HMIS.

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FP Commodities Data Used district-level geographic identifiers for linking.

Summarized CYP by district using Supply Chain Manager

(SCM) data.

CYP calculated relatively easily using routinely collected

data and CYP conversion factors from USAID.

CYP data need to be adjusted for unreported data and inventory

balance errors.

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FP Commodities Data CYP is simple indicator of volume of FP commodities

distribution for a given geographic area.

As simple sum of estimated contraceptive method

durations:

Does not take into account differences in sizes of reported

areas or underlying populations.

Unsuitable for choropleth mapping.

First necessary to normalize calculations based on

proportion of district populations corresponding to women

of reproductive age.

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QGIS: Linking FP/RH Data with the

USAID | DELIVER PROJECT

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Map of MCPR vs. CYP

Highlights ability of multi-program data linking to

uncover unexpected patterns and relationships.

Shows how linking indicators in a single map can

help target geographic areas for potential

interventions.

Illustrates the usefulness of GIS data linking for

conducting cross-database comparisons.

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LESSONS LEARNED

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Lessons Learned

Three categories:

Key data sources

Common geographic identifiers

Software

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Lessons Learned:

Key Data Sources

Some datasets are easily accessed and are at an

appropriate geographic scale for analysis (e.g.,

DHS).

Others may be difficult to access and use for a

variety of potential reasons, such as

Data confidentiality concerns;

Organizational barriers to data sharing;

Geographic scale issues; and

Timing of data access request.

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Lessons Learned:

Key Data Sources

To overcome data access limitations, recommend

setting up local stakeholder meetings to

Discuss data linking benefits.

Identify opportunities to collaborate.

Develop an action plan.

Establish long-term working relationships.

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Lessons Learned:

Key Data Sources

Supply Chain Manager (SCM) database from the

USAID | DELIVER PROJECT:

Need to work closely with USAID | DELIVER PROJECT

staff to obtain data adjusted to 100% reporting rate.

Highly important to have accurate population data for

normalizing CYP.

USAID | DELIVER PROJECT a key partner for GIS data

linking.

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Lessons Learned:

Key Data Sources

HIV/AIDS Data Management System:

FP/RH programs could benefit from linking HMIS to non-

identifiable, aggregated data from HIV/AIDS system.

Example based on stakeholder interviews:

Could facilitate a more rapid response to such question as,

“Is there an uptake of FP and HIV testing referrals

associated with integration of FP/RH and HIV services?”

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Lessons Learned:

Key Data Sources

Performance-Based Financing (PBF) System:

Linkage of HMIS and PBF data could provide a cross-

check of common indicators.

Such a national-level data display and feedback

mechanism could provide strong incentive for health

centers to perform.

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Lessons Learned:

Common Geographic Identifiers

Excellent availability of geographic data and identifiers

for Rwanda:

Could be downloaded from the NISR or MOH sites.

Provides a good model for other countries to follow.

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Lessons Learned: Software

Free and open source GIS software options have

advanced in recent years:

E2G and Google Earth are accessible to non-GIS

specialists.

QGIS offers high degree of functionality.

GeoDa provides point-and-click geographic data

visualization and analysis.

These solutions can complement existing systems.

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Summary and Conclusions

Multi-sectoral/multi-program integration offers many

benefits and is a GHI priority.

Multi-sectoral/multi-program integration can be facilitated

by GIS data linking, which requires common geographic

identifiers.

Free and open source GIS solutions can meet many data

linking needs of FP/RH programs.

The Rwanda case study can serve as a reference for how

to apply multi-sectoral GIS data linking to enhance FP/RH

decision making.

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Thank you.

Questions?

www.measureevaluation.org/prh

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MEASURE Evaluation Population

and Reproductive Health (PRH) is

funded by the U.S. Agency for

International Development (USAID)

through cooperative agreement

associate award number GPO-A-00-

09-00003-00 and is implemented by

the Carolina Population Center at

the University of North Carolina at

Chapel Hill, in partnership with

Futures Group, Management

Sciences for Health, and Tulane

University. The opinions expressed

are those of the authors and do not

necessarily reflect the views of

USAID or the U.S. government.

Technology

Enhancing FP/RH Decision Making through GIS Data Linking