MEDIATING CAUSES FOR HIGH LEVEL OF INFANT MORTALITY … · Dr.Muthulakshmi Reddy Maternity Benefit Scheme - 11 2.5. The Demographic Scenario of Tamil Nadu compared with India - 13

MEDIATING CAUSES FOR HIGH LEVEL OF INFANT MORTALITY RATE AT THONDAMUTHUR BLOCK OF

COIMBATORE DISTRICT

Submitted to

State Planning Commission Government of Tamilnadu

Dr. A. ARULRAJ Principal Investigator

P.G. & Research Department of Economics, Rajah Serfoji Government College (Autonomous),

Thanjavur – 613 005.

March 2017

Executive Summary

The social and behavioural studies are playing a vital role in determining infant

mortality rate in India. The State Planning Commission encourages for

investigating the mediating cause for high level cause for the infant mortality at

Thondamuthur Block in Coimbatore District. However, the project findings base

on econometric models. Public health services (Practitioners services, hospital

Services in Outpatients and Hospital Services in Inpatients) resulted insignificant

impact on determining Infant mortality and health of the children at

Thondamuthur Block. Hence it is not a factor for the high level cause for the

Infant Mortality. Social Problems, Infrastructure facilities and economic problems

dimensions resulted marginal impact on infant mortality. Since the state

government provides various schemes for pregnant women during maternity

period and also for improving child health. But the Social Problems, Infrastructure

facilities and economic problems dimensions created psychological problems

during maternity period. SEM empirically proved that psychological discomfort

dimension is the mediated factor for the cause of high level cause for the infant

mortality at Thondamuthur block in Coimbatore district. The present study

scientifically proves that psychological discomfort factor is a medicating cause for

the high level cause for the infant mortality at Thondamuthur Block in Coimbatore

district. It means if there is any change in psychological discomfort factor then it

will reflects immediately in Infant Mortality. Psychological Discomfort

management alone the one and only solution for eradicating the infant mortality

completely and the same has been scientifically proved with the help of structural

equation model. Psychological Discomfort factor includes emotions of pregnant

mothers, behaviour of husband, mother-in-law, friends and other relatives, lack of

parents and friends support, sexual harassment in working place, varsity health

hazards in informal sector, continuous sex inducement during the pregnancy time

due to continuous drinking habit of the spouse and not proper interval between

two babies. The policy makers, social science scientist, consultants and the

Government should carefully make use of the said factor to eradicate the infant

mortality at Thondamuthur Block in Coimbatore District. There is a need to create

a committee to make ‘Effective Management of Psychological Discomfort’ factor

to eradicate infant mortality completely.

TABLE OF CONTANTS

Abbreviations

List of Tables

List of Figures

Page No.

Section - 1 Introduction 1

Section - 2 Reproductive and Child Health in Tamilnadu:

An Over View

7

Section - 3 Infant Death in Coimbatore District: A Prelude 16

Section - 4 Mediating Causes for High Level of Infant Mortality

Rate: Findings from Field Survey

26

Section - 5 Findings, Conclusion and Policy Recommendations 69

References

Appendix - I

a) Questionnaire - English

b) Questionnaire – Tamil

Appendix - II

Village/Urban/Block level Data of Coimbatore District

Appendix - III

Bayesian Convergence Distribution for “MEDIATING CAUSES

FOR HIGH LEVEL OF INFANT MORTALITY RATE”

Regress Model

Appendix - IV

Bayesian Convergence Distribution for “MEDIATING CAUSES

FOR HIGH LEVEL OF INFANT MORTALITY RATE”

Structural Equation Latent Model

Appendix - V

Case Studies

ABBREVIATIONS

AD : Anno Domini

AMOS : Analysis of Moment Structures

FGD : Focus Group Discussion

GDP : Gross Domestic Product

HDI : Human Development Index

HSCs : Health Sub-Centres

ICDS : Integrated Child Development Services

IGMSY : Indra Gandhi Mathirthva Sahyog Yojana

IMR : Infant Mortality Rate

NGO : Non-government Organisation

PHCs : Primary Health Centres

SEM : Structural Equation Modeling

SIMA : Southern India Mills Association

SPSS : Statistical Package for Social Sciences

UPHCs : Urban Primary Health Centres

List of Tables

S. No. Particulars P. No.

2.1. Caesarean services are provided in 128PHCs - 8

2.2. Beneficiaries for the year 2013 – 2014 - 10

2.3. Immunization Performance during 2011-12, 2012-13 &

2013-14

- 10

2.4. Dr.Muthulakshmi Reddy Maternity Benefit Scheme - 11

2.5. The Demographic Scenario of Tamil Nadu compared with

India

- 13

2.6. The Infrastructure Facilities under Family Welfare

Programme

- 13

2.7. Family Welfare Performance during 2013-14 - 13

2.8. Impact of Family Welfare Programme - 14

3.1. Public Health and Medical Services Rendered by

Organisations and Individual year from 2012 to 2013

- 17

3.2. Details of the Beds Strength, Average Out patients and In

patients in Coimbatore District year from 2013 to 2014

- 17

3.3. Primary Health Centres with Sub-Centres year from 2013 to

2014

- 18

3.4. Births and Deaths Registered in Coimbatore District year

from 2013 to 2014

- 18

3.5. Birth, Death and Infant Mortality Rates in Coimbatore

District year from 2013 to 2014

- 18

3.6. Infant Mortality Rate for India - 19

3.7. Infant Mortality Rate for Tamilnadu - 20

3.8. Infant Deaths for Coimbatore District - 21

3.9. Infant Death for Coimbatore Corporation - 22

3.10. Infant Death for Coimbatore Urban - 23

3.11. Infant Death for Coimbatore Rural - 24

3.12. Infant Death for Thondamuthur Block - 25

4.1 Age wise classification of Pregnant Women Households - 26

4.2. Educational Qualification of Pregnant Women Households - 28

4.3. Occupation of Pregnant Women Households - 30

4.4. Religious Classification of the Pregnant Women Households - 31

4.5. Income wise Classification of Pregnant Women Households - 32

4.6. Community wise Classification of Pregnant Women

Households

- 33

4.7. Types of Family of Pregnant Women Households - 34

4.8. Residential Status of Pregnant Women Households - 35

4.9. Living conditions of Pregnant Women Households - 36

4.10. Size of Landholding of Pregnant Women Households - 37

4.11. Toilet Facilities of Pregnant Women Households - 38

4.12. Selection Preference of Hospitals by the Respondents - 39

4.13. Hospital Selection by the Respondents - 40

4.14. Medical Insurance of Pregnant Women Households - 41

4.15. Any Infant Death of Pregnant Women Households - 42

4.16. Summary of the Various Goodness of Fit Statistics and Other

Values Corresponding to the “HIGH LEVEL OF INFANT

MORTALITY RATE” Structural Equation Latent Model

- 61

List of Figures

S. No.

Particulars Page No.

1.1. Proposed Conceptual Mediated Model for High Level cause for

the Infant Mortality Rate at Thondamuthur Block in

Coimbatore district

- 5

2.1. Trend Plot of Caesarean section delivery - 9

2.2. Trend plot for Dr.Muthulakshmi Maternity Scheme of Amount

Disbursed

- 11

2.3. Trend plot for Dr.Muthulakshmi Maternity Scheme of No. of

Beneficiaries

- 12

3.1. Trend Analysis Plot of Infant Mortality Rate for India from 2000

to 2012

- 19

3.2. Trend Analysis Plot of Infant Mortality Rate for Tamilnadu from

2000 to 2012

- 20

3.3. Trend Analysis Plot of Infant Deaths for Coimbatore District

from (2011-2012) to (2014-2015)

- 21

3.4. Trend Analysis Plot of Infant Death for Coimbatore Corporation

from (2011-2012) to (2014-2015)

- 22

3.5. Trend Analysis Plot of Infant Death for Urban Total from (2011-

2012) to (2014-2015)

- 23

3.6. Trend Analysis Plot of Infant Death for Rural Total from (2011-

2012) to (2014-2015)

- 24

3.7. Trend Analysis Plot of Infant Death for Thondamuthur Block

from (2011-2012) to (2014-2015)

- 25

4.1. Shows the AMOS Output with Regression Weights of

“MEDIATING CAUSES FOR HIGH LEVEL OF INFANT MORTALITY

RATE” Mediated Model

- 50

4.2. Posterior frequency Polygon distribution of the Mediating

Factor (IMR) and (PD) Regression Weight (W13)

- 52

4.3. Posterior frequency Histogram distribution of the Mediating


- 52

4.4. Posterior frequency Trace plot of the Mediating (IMR) and (PD)

Regression Weight (W13)

- 53

4.5. Posterior frequency Autocorrelation plot of the Mediating


- 53

4.6. Two-dimensional Surface plot of the marginal posterior

frequency distribution of the mediating factor (PD with (IMR)

and (HSI) Regression Weight (W9) and (W13)

- 54

4.7. Two-dimensional Histogram plot of the marginal posterior

frequency distribution of the mediating factor (PD) with (IMR)


- 54

4.8. Two-dimensional Contour plot of the marginal posterior

frequency distribution of the mediating factor (PD) with (IMR)


- 55

4.9. Shows AMOS path diagram output for the Overall Mediated for

“HIGH LEVEL OF INFANT MORTALITY RATE” Structural

Equation Latent Model

- 59

4.10

.

Posterior frequency Polygon distribution of the Mediating

Factor (PD) and (IMR) Regression Weight (W70)

- 62

4.11

.

Posterior frequency Histogram distribution of the Mediating


- 62

4.12

.

Posterior frequency Trace plot of the Mediating Factor (PD)

and (IMR) Regression Weight (W70)

- 63

4.13 Posterior frequency Autocorrelation plot of the Mediating


- 63

4.14 Two-dimensional Surface plot of the marginal posterior

frequency distribution of the mediating factor (PD) and (IMR)


- 64

4.15 Two-dimensional Histogram plot of the marginal posterior



- 65

4.16 Two-dimensional Contour plot of the marginal posterior



- 65

5.1 Empirically Proved Conceptual Model for High Level of Infant

Mortality Rate

- 71

1

SECTION - 1

INTRODUCTION

1.1. Introduction The Infant Mortality Rate (IMR) is one of the important indicators for the health

status of a country. In developing countries, background characteristics such as

mother’s literacy, urban/rural residence, and household economic status are

likely to affect a child’s condition at birth as well as its environment, thus affecting

infant and child mortality (Hobcraft, McDonald, and Rutstein 1984; Mosley and

Chen 1984; United Nations 1985; 1991; 1998). Infant and child mortality are

determined by biological endowment of the children at birth and after birth. The

present study empirically examines the Mediating Cause for high level Infant

Mortality Rate at Thondamuthur block in Coimbatore district, Tamilnadu. The main

purpose of the study is to identify the mediating cause of High level infant

mortality with the aid of socioeconomic characteristics of mothers and

households, demographic characteristics of children and health care behaviours

of mothers. There are many dimensions associated with variations in infant and

child mortality rate. In the present study we made an attempt to isolate the

effects of individual variables.

Maternal and child health services in India are designed to provide basic health

services to vulnerable groups of pregnant women through programmes such as

the Minimum Needs Programme, the Child Survival and Safe Motherhood

Programme, and the Reproductive and Child Health Programme (IIPS 1995;

Ministry of Health and Family Welfare 1998). These report discuses the estimated

effects of women’s healthcare behaviour such as antenatal visits, tetanus

immunization, and place of delivery on neonatal mortality and the same will be

useful for evaluating current maternal and child health programmes. There are

many dimensions influencing the high level cause for the infant mortality in the

world. But, in India, the child sex dimension is most important factor for high level

infant mortality rate. In most populations, male mortality is higher than female

mortality at almost all ages (Heligman 1983; United Nations Secretariat 1988). In

South Asia, however, female mortality is higher than male mortality at many ages

(Ghosh 1987; Office of the Registrar General, India 1994; Pebley and Amin 1991;

2

Preston 1990), especially during the post neonatal and childhood periods. Excess

female mortality at post neonatal and childhood ages in India and other South

Asian countries is believed to result from son preference, which leads to

differential treatment of sons and daughters in terms of food allocation,

prevention of diseases and accidents, and treatment of illness (United Nations

1998).

In India, many researchers have documented evidence of son preference and

discrimination in caring for sons and daughters (Basu 1989; Das Gupta 1987;

Muhuri and Preston 1991). Studies on infant and child mortality in India also

document large variations among states in the degree of son preference and

associated excess female child mortality (Arnold, Choe, and Roy 1998; IIPS 1995;

Mutharayappa et al. 1997). Their results show that excess female mortality tends

to be higher in northern states, where the traditional family system is strongly

patriarchal, than in southern states with less of a patriarchal tradition. The strong

patriarchal tradition in northern India includes customs related to marriage, living

arrangements and support for elderly parents, and funeral rituals that assign

many privileges and duties exclusively to sons (Arnold, Choe, and Roy 1998;

Caldwell, Reddy, and Caldwell 1989; Dyson and Moore 1983; Kapadia 1966;

Karve 1965; Kishor 1995; Koenig and Foo 1992). At marriage, dowry payments

impose a heavy financial burden on the parents of girls, while after marriage

wives typically move in with their husbands’ families, weakening ties with their

own parents. Such customs may cause parents to desire more sons than

daughters and to discriminate against daughters, and this in turn may result in

excess female post neonatal and child mortality. It will be difficult to eliminate son

preference and associated excess female child mortality quickly in India because

long standing traditions are slow to change. Some observers have noted,

however, that the degree of son preference may be declining somewhat (Visaria

1994). Maternal and child health programmes that provide supple maternal

nutrition and basic health care to all children, regardless of sex, may also help to

reduce excess female child mortality (Pebley and Amin 1991). In Tamilnadu, few

blocks were affected the said dimensions but the government has taken

necessary steps to reduce the Infant mortality rate. The rest of the dimensions

have not discussed and the same have been influenced on infant mortality which

3

are socio-economic factors, mother’s literacy, households heads religion and

caste, exposure to mass media, access to a flush or pit toilet, birth order,

mother’s age at childbirth, previous birth interval, mortality of an older sibling and

psychological discomfort.

According to the report of Save the Children, an international NGO, one-fifths of

the world’s new born deaths occur in India. India also has the highest under-five

mortality with over 2 million children dying before their fifth birthday. About 90%

of these deaths are preventable. One-third of all malnourished children live in

India and 46% of children under-3 years are underweight. A child’s chances of

survival vary in different states. The infant mortality rate in Orissa is 96 per 1000

live births, in Kerala it is only 14 per 1000 live births.

The 11th plan (2007-12) has drawn attention to IMR in differences districts. Out of

32 districts, Dharmapuri, Krishnagiri, Villupuram, Thiruvannamalai and

Perambalur were rated as the most backward districts based on Human

Development Index (HDI). There are many indicators which form part of Human

Development Index among them Infant Mortality Rate is one of the important

indicator. There are many factors are associated with economic backwardness

such as income, employment, productive assets especially land holding,

percentage of people depending upon agriculture, land less labour, access to

institutional credit, indebtedness etc. In addition, social factors are also

determine the welfare of a society which consists of caste, gender, literacy,

education, health, access to basic facilities like water and sanitation etc. It is not

only known from many studies but also these social and economic factors

influence each other and play an important role in determining the outcome of

welfare programme. The present study investigates how economic and social

factors influence the Infant Mortality Rate.

1.2. Economic Backwardness and Its Relationship influence High Level of IMR

at Thondamuthur Block in Coimbatore District Good health is necessary for empowerment. Rural healthcare services suffer from

a shortage in public sector infrastructure. Majority of women from rural areas

were working in an unorganised sector and they were paid less. They were

suffering from many hazardous diseases and their health status is degrading year

4

by year. Healthcare services and programmes conducted by PHCs either directly

or indirectly related to the health status of women. The present study empirically

examines the mediating cause for high level infant mortality rate at

Thondamuthur block in Coimbatore district.

1.3. Objectives of the study

1. To investigates the factors influencing high level Infant Mortality Rate at

Thondamuthur Block in Coimbatore District;

2. To gain a better understanding about the mediating causes for high level

Infant Mortality Rate and;

3. To explain the various dimensions influencing high level Infant Mortality

Rate in the study area.

Based on the objectives of the study the following proposed conceptual model

has been framed.

1.4. Proposed Conceptual Mediated Model for High Level of Infant Mortality

Rate There are eight dimensions were framed for the present study which are; i)

Practitioners Services, ii) Hospital Services in Outpatients, iii) Hospital Services in

Inpatients, iv) Social Problems, v) Infrastructure Facilities, vi) Psychological

Discomfort, vii) Economics Problems and viii) High Level of Infant Mortality Rate.

The present study segregates dependent and independent variables. Dependent

variables include Psychological Discomfort and High Level of Infant Mortality Rate

and the independent variables consist of Practitioners Services, Hospital Services

in Outpatients, Hospital Services in Inpatients, Social Problems, Infrastructure

Facilities and Economics Problems. There is a need to study how and what extent

the independent variables make changes in the dependent variable. The

proposed conceptual research model shows the mediating cause for High level of

Infant mortality rate for intensive understanding of the present study.

5

Figure 1.1: Proposed Conceptual Mediated Model for High Level cause for the Infant Mortality Rate at Thondamuthur Block in Coimbatore district

Practitioners Services

Hospital Services in Outpatients

Hospital Services in Inpatients

Social Problems

Infrastructure Facilities

Economic Problems

Demographic Variable

High Level of Infant Mortality Rate

Psychological Discomfort

1.5. Methodology The study has undertaken in two stages which are District/block level analysis

based on secondary data and Field visit analysis based on primary data.

1.5.1. District/block level analysis based on secondary data: We have taken district/block level data for analysing the mediating cause for high

level of Infant Mortality Rate for that Data have been collected from the PHCs

Thondamuthur Block, Anganwadis and District Planning Cell in Coimbatore

District.

1.5.2. Field visit analysis based on primary data 1.5.2.1. Selection of Block The detailed study has been made in Thondamuthur block since the block

belongs to most backwardness in economic conditions and Higher Infant Mortality

Rate levels.

1.5.2.2. Selection of villages The field visit has been carried out in four villages namely Theethippalayam,

Mathavarayapuram, Devarayapuram and Ikkarai Booluvampatti for collecting the

6

primary data. Focus group discussions (FGD) have been conducted with

knowledgeable people in the villages and government officials with the help of

detailed check list.

1.5.2.3. Primary survey The primary survey of households with different economic background has been

undertaken in the said four villages be part of 25 pregnant women households in

each village. A detailed questionnaire (See Appendix – I) has been used to collect

data to find out the mediating cause for the high level infant mortality rate in the

study area.

1.5.2.4. Case studies under special schemes In addition, case studies under special schemes have been undertaken in the

said Blocks. For this purpose, the list of pregnant women Households has been

procured from the concerned officers of the Departments of Health at the district

level. There are five case studies (Appendix V) have been disclosed in this report.

Data analysis were performed with Statistical Package for Social Sciences (SPSS)

using techniques that included descriptive statistics, correlation analysis and

AMOS package for Structural Equation Modeling (SEM) and Bayesian estimation

and testing.

7

SECTION – 2

REPRODUCTIVE AND CHILD HEALTH IN TAMILNADU: AN OVER VIEW

Tamilnadu has emerged as a model state in India for providing health services.

The Government hospitals are providing good services for urban poor. Arulraj. A &

Rethinasivkumar. G (2012) examined the services quality of government

hospitals in Tamilnadu. The results reveal that the government of Tamilnadu

provided the best service quality for the preceding five years. The Government of

Tamilnadu is also at the forefront of communicable and non-communicable

diseases. Tamilnadu is one of the best performing state in the country for

implementing the reproductive and child health schemes.

Public Health and Preventive Medicine Department was formed during 1923 in

Tamil Nadu which is engaged in protecting and promoting the health of the people

by immunization, health education, application of hygiene and sanitary measures,

monitoring of drinking water quality and environmental hazards thereby reducing

the burden of morbidity, infant mortality and disability in the State.

Primary Health Centres and Health Sub-centres are playing an important role in providing Health services. 2.1. Primary Health Centres (PHCs) Primary Health Centres have been brought under the control of the Director of

Public Health and Preventive Medicine from 1st March 1996. A Primary Health

Services in rural areas are established for a population of about 20,000 in hill

areas and 30,000 in other areas. There are 1751 Primary Health Centres (PHCs)

including 402 upgraded PHCs and 134 Urban Primary Health Centres (UPHCs)

which are providing health services for the rural population. These centres are

providing health services for the rural population as there are universal

immunizations, reproductive and child health, vector Borne Diseases control and

school health programme. All PHCs are functioning on 24X7 bases. During the

Year 2013-2014, 91936470 out-patients at an average rate of 142 per day per

PHC and 1532915 in-patients at an average rate of 72 in patients per month per

primary health centre were getting treatments in the PHCs.

8

2.2. Health Sub Centres (HSCs) A Health Sub Centres (HSCs) are established for population up to 3,000 in hill

areas and 5000 in other areas. Each centre is named as Village Health Nurse

(VNH). There are 8706 Health sub centres which are functioning very effectively

for quality of health of the People in Tamilnadu. The health sub centres are

playing a vital role in improving the quality of health services to pregnant women

in rural areas.

2.3. Deliveries conducted at PHCs HSCs and PHCs ensure that every woman is tracked and provided intuitional care

and services during ante natal, natal and Post natal period. The Village Health

Nurse at the HSC level and the sector health nurse at higher level followed 24x7

nursing provisions in the PHCs and also ensured that 2.5 lakh deliveries occurred

in the PHCs. Improvements in PHCs facilities, upgrading all PHCs as 24x7 PHCs,

introduction of birth companion scheme, provision of ultrasonogram machines,

upgradation of PHCs with 30 beds with operation theatre, enhancement of

Dr.Muthulakshmi Reddy Maternity Benefit Scheme assistance from Rs 6,000 to

Rs. 12,000, provision of diet for antenatal mothers, organizing maternity picnics

to PHCs, drop back for delivered mothers and establishment of birth waiting

homes are responsible for the quantum increase in PHCs deliveries.

2.4. Caesarean services provided by PHCs

Table 2.1: Caesarean services are provided in 128 PHCs S.No Year Deliveries

1 2007-2008 366 2 2008-2009 1285 3 2009-2010 2694 4 2010-2011 4962 5 2011-2012 8320 6 2012-2013 8649 7 2013-2014 11435

Source: (Annual Report Of Health And Family Welfare, 2014 -2015 Govt. Of Tamilnadu)

9

Figure 2.1: Trend Plot of Caesarean services provided by 128 PHCs

2014201320122011201020092008

12000

10000

8000

6000

4000

2000

0

Year

Caes

area

n se

ctio

n de

liver

y

MAPE 40MAD 585MSD 404957

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot for Caesarean section delivery Linear Trend ModelYt = -2264 + 1913*t

Trend analysis figure 2.1 reveals the Caesarean services provided by Primary

healthcare centres. The trend plot that shows the original data, and the fitted

trend line, the output also displays the fitted trend equation Yt = -2264+1913*t.

The Caesarean services provided by 128 PHCs data shows a general upward

trend, though with an evident cyclic factor. The trend model appears to fit well to

the overall trend. Trend analysis clearly stated that caesarean services provided

by 128 PHCs from 2007- 2008 to 2013 - 2014. It indicates the upward trend

which means the caesarean deliveries have been increased year by year.

2.5. Establishment of Urban Primary Health Centres During 2012–2013, Government has issued orders for the establishment of 135

Urban Primary Health Centres in small urban areas of Tamil Nadu under the

administrative control of the Director of Public Health and Preventive Medicine.

Subsequently, two Urban PHCs were sanctioned. Among 137 Urban PHCs, three

Urban PHCs were handed over to Chennai Corporation.

2.6. Menstrual Hygiene Programme Under this scheme 18 packs of sanitary napkins (six pads per pack) in a year at

the rate three packs for two months for each adolescent girl (10–19 years) in

rural areas both school going and non school going girls are provided. Sanitary

Napkins are delivered by the government institutions to post natal mothers at the

10

rate of seven packs each (six pads per pack). Sanitary Napkins are being given to

each women prison inmate and to 525 female inpatients in the Institute of

Maternal Health, Chennai at the rate of 18 packs (six pads per pack) in a year.

While implementing the programme it was assessed that 80% of (10-19 yrs) girls

have attained puberty and hence the target was revised as 3279028 adolescent

girls.

Table 2.2: Beneficiaries for the year 2013 – 2014

Sl. No.

Category Number of Sanitary Napkins

Packs required per year

Total Quantity required (Pack)

Type of Sanitary Napkin

1 Adolescent girls Rural

(10-19 yrs) attained puberty

3279028 x 18 59022504 Belt less Type

2 Post-natal Mothers 725770 x 7 5080390 Belt Type 3 Women Prison inmates 729 x 18 13122 Belt Type 4 Female inpatients in

IMH Chennai 525 x 18 9450 Belt Type

Source: Department of Public Health and Preventive Medicine (2014-2015) 2.7. Immunization Programme Annually, around 11.52 lakhs pregnant women and 10.48 lakhs infants are being

targeted under immunization programme. The State has reported more than 95 %

coverage over the years.

Table 2.3: Immunization Performance during 2011-12, 2012-13 & 2013-14

Vaccine 2011 - 2012 2012 - 2013 2013 - 2014 Target Achievement % Target Achievement % Target Achievement %

(Fig. In Lakhs) (Fig. In Lakhs) (Fig. In Lakhs) Tetanus Toxiod

(AN Mothers)

11.97 11.72 98 11.88 11.36 96 11.52 11.10 96

DPT* 10.83 10.65 98 10.83 10.46 97 10.48 10.26 98 POLIO 10.83 10.65 98 10.83 10.49 97 10.48 10.30 98 BCG 10.83 10.56 98 10.83 10.42 96 10.48 10.19 97

MEASLES 10.83 10.62 98 10.83 10.54 97 10.48 10.30 98 JE 3.29 2.81 85 3.34 3.12 93 3.21 3.25 101

* From 2011-12 onwards, DPT was replaced with Pentavalent vaccination. Source: Department of Public Health and Preventive Medicine (2014-2015)

2.8. Dr.Muthulakshmi Reddy Maternity Benefit Scheme The assistance under this scheme has been enhanced from Rs 6000 to Rs

12000 by our Hon’ble Chief Minister with effect from 01.06.2011. Assistance

11

under this scheme is disbursed in three instalments to poor pregnant women. The

details of beneficiaries under this scheme are as follows:

Table 2.4: Dr.Muthulakshmi Reddy Maternity Benefit Scheme

Sl.No Year Amount Allotted (Rs. In Crore)

Amount Disbursed (Rs. In Crore)

No. of Beneficiaries

1. 2006 - 2007 100.00 100.00 241095 2. 2007 - 2008 300.00 296.64 679831 3. 2008 - 2009 350.00 349.26 579821 4. 2009 - 2010 360.00 358.60 599126 5. 2010 - 2011 360.00 347.51 581790 6. 2011 - 2012* 660.00 515.11 673093 7. 2012 - 2013 716.77 639.54 670313 8. 2013 - 2014 716.77 652.16 6636623

*including beneficiaries who have availed enhanced assistance of Rs.12,000 each. Source: Department of Public Health and Preventive Medicine (2014-2015)

Figure 2.2: Trend plot for Dr.Muthulakshmi Maternity Scheme of Amount Disbursed

20142013201220112010200920082007

700

600

500

400

300

200

100

Year

Am

ount

Dis

burs

ed MAPE 16.63MAD 42.93MSD 2809.65

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot for Dr.Muthulakshmi Reddy Maternity Benefit Scheme of Amount DisbursedLinear Trend ModelYt = 82.4 + 72.2*t

Trend Analysis 2.2 stated that the increasing trend of amount disbursed under

Dr.Muthulakshmi Reddi Maternity Benefit Scheme from 2006 – 2007 to 2013 –

2014. The trend plot that shows the original data, and the fitted trend line, the

output also displays the fitted trend equation Yt = 82.4+72.2*t. The trend model

appears to fit well to the overall trend. The trend plot clearly stated that

Dr.Muthulakshmi Reddy maternity benefit scheme is a successful scheme hence

the No. of beneficiaries have been increased year by year and the same disclosed

in trend analysis 2.3. The Government of Tamilnadu has launched a revised

Dr.Muthulakshmi Reddy maternity benefit scheme. This scheme ensures

adequate ante natal, natal and post natal care and encourage institutional

delivery, nutritional support and immunisation to the mother and child. The

12

maternity benefit is very useful during maternity period for rural poor pregnant

women as an economic independent. The cash assistance is given in three

instalments on conditional basis and restricted to two deliveries.

Figure 2.3: Trend plot for Dr.Muthulakshmi Maternity Scheme of No. of Beneficiaries

20142013201220112010200920082007

700000

600000

500000

400000

300000

200000

Year

No. o

f Be

nefic

iari

es MAPE 1.85265E+01MAD 7.52079E+04MSD 1.10998E+10

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot for Dr.Muthulakshmi Reddy Maternity Benefit Scheme of No. of BeneficiariesLinear Trend Model

Yt = 416127 + 37769*t

Trend Analysis 2.3 shows that the increasing trend of Dr.Muthulakshmi Reddi

Maternity Benefit Scheme from 2006 – 2007 to 2013 – 2014. The trend plot that

shows the original data, and the fitted trend line, the output also displays the

fitted trend equation Yt = 416127+37769*. The trend model appears to fit well

to the overall trend. For the financial year 2013-14, Rs.716.77 crore allocated for

the programme and Rs.652.16 crore disbursed to 6,63,623 mothers were

benefited through this programme. From 1st October 2012, benefits under the

scheme are distributed directly to the bank account of the beneficiaries through

Electronic Clearing Systems (ECS). On an average, 6 lakh women benefit from the

scheme every year.

2.9. Family Welfare Programme The Department of Family Welfare was functioning only as a wing of the Medical

and Public health Directorates. A separate Directorate of Family Welfare was

formed during 1983. Subsequently, the Family Welfare Programme was

implemented as a people's programme by involving all the other departments.

The main objective of the Directorate is to stabilize the population growth as well

as to improve the maternal and child health status thereby reducing the vital

indicators such as the IMR and MMR. Today, Tamil Nadu is considered as a model

13

State for the other States in the country in the implementation of the Family

Welfare Programme.

2.9.1. Demographic Scenario The Demographic Scenario of Tamil Nadu and India are as follows:

Table 2.5: The Demographic Scenario of Tamil Nadu compared with India Sl.No. Indicator India Tamilnadu

1. Population 1210726932 72147030 2. Decennial Growth Rate (%) 17.7 15.6 3. Sex Ratio (female per 1000 males) 943 996 4. Density of Population (per sq. K.M.) 382 555 5. Literacy Rate (%) Male 86.8 80.9

Female 73.4 64.6 Source: 2011 Census

6. Crude Birth Rate (per 1000 population) 21.6 15.7 7. Crude Death Rate (per 1000 population) 7.0 7.4 8. Infant Mortality Rate (per 1000 live births) 42 21 9. Natural Growth Rate (%) 1.45 0.83

Source: SRS 2012 2.9.2. The Infrastructure Facilities under Family Welfare Programme

Table 2.6: The Infrastructure Facilities under Family Welfare Programme Sl.No. Medical Institutions Total

1. Primary Health Centres 1751 2. Health Sub-Centres 8706 3. Rural Family welfare Centres 382 4. Post Partum Centres 110 5. Urban Family 108 6. Urban Health Posts 193 7. Voluntary Organisations 27 8. Approved Private Nursing Homes 2040

Source: Department of Public Health and Preventive Medicine (2014-2015) 2.9.3. Family Welfare Performance during 2013-14 can be seen from the table

below

Table 2.7: Family Welfare Performance during 2013-14 Sl.No. Programme 2013 - 2014 2013 - 2014 % of achievement

Assigned ELD 1. Sterilisation 400000 323310 80.8 2. I.U.C.D. Insertion 433000 379152 87.6 3. C.C. Users 180000 91605 50.9

Source: Department of Public Health and Preventive Medicine (2014-2015)

14

2.9.4. Impact of Family Welfare Programme is given in the table below

Table 2.8: Impact of Family Welfare Programme 1. Contraceptive Prevalence Rate (NFHS-3) 2005 - 2006 60% 2. Sterilization Acceptors with two and less than two living children

2013 - 2014 (up to February 2014) 77.2%

3. Percentage of Higher Order Births (2012) 9.0 4. Mean age at acceptance of Sterilization (2012-2013) 27.2 5. Mean number of living children to the sterilization acceptors (2012 -

2013) 2.2

Source: Department of Public Health and Preventive Medicine (2014-2015) 2.10. Integrated Child Development Services The overall goal of the ICDS is to improve the nutritional and health status of

preschool children, pregnant and nursing women. Hence, the State Government

has initiated various proactive measures to create the present environment

for strengthening and restructuring of Integrated Child Development Services

Scheme towards addressing nutrition challenges among children under six,

adolescent girls and mothers. ICDS has been the centre-piece of the

comprehensive strategies for providing a continuum of care in a life-cycle

approach aimed towards impacting mother and child development. Integrated

Child Development Services Scheme covers 35.37 lakh direct beneficiaries

(nutrition) and 43.36 lakh indirect beneficiaries i.e. children availing health

services, weight monitoring, counselling etc., through 54,439 Child Centres

(comprising 49,499 Main Anganwadi Centres and 4,940 Mini Anganwadi Centres)

functioning in 434 Child Development Blocks. As per National Family Health

Survey NFHS-3 Data 2006, in Tamilnadu33.2% of children in the age group of 0

to 3 years were under the underweight category and it has been reduced to 17.23

% by March 2014. ICDS Data as on March 2014 indicates that 82.78 % (19.69

lakh) are normal children, 17.13 % (4.07 lakh) are moderately underweight

children and 0.10% (2,489) only are severely underweight (SUW) children. During

the 12th Five Year Plan (2012-2017), ICDS schemes has been restructured to

carry out programmatic management and instructional reforms in a phased

manner where Anganwadi Centres are repositioned as a “ Vibrant early childhood

development centre” to become the first outpost for learning, health and nutrition

by providing additional human resources and infrastructure. Nevertheless, in the

study area ICDSs are playing a vital role to improve the health services for

pregnant women.

15

2.11. Indira Gandhi Mathirthva Sahyog Yojana (IGMSY) IGMSY Scheme (Conditional maternity benefit scheme) is being implemented in

synergy with Dr. Muthulakshmi Reddy Maternity Benefit Scheme for pregnant

women and lactating mothers by providing cash incentive to maintain them

healthy and to manage loss of income during the period of conception and

convalescence. The state is providing more schemes for child health care. The

schemes are effectively implementing for ensuring the child health care in rural

and urban areas. Hence, the state got the second lowest one among the major

states in the country for the indicators.

16

SECTION - 3

INFANT DEATH IN COIMBATORE DISTRICT: A PRELUDE

The section indicates the Infant Mortality Rate in Country level, State level, District

Level and Block level.

3.1. Coimbatore District – General Profile The third largest city of the state, Coimbatore, is one of the most industrialized

cities in Tamil Nadu, known as the textile capital of South India or the Manchester

of the South, the city is situated on the banks of the river Noyyal, Coimbatore

existed even prior to the 2nd or 3rd century AD by Karikalan, the first of the early

Cholas. Among its other great rulers were Rashtrakutas, Chalukyas, Pandyas,

Hoysalas and the Vijayanagara kings. When Kongunadu fell to the British along

with the rest of the state, its name was changed to Coimbatore and it is by this

name that it is known today, except in Tamil, in which it is called Kovai.

In the rain shadow region of the Western ghats, Coimbatore enjoys a very

pleasant climate all the year round, aided by the fresh breeze that flows through

the 25 kms long Palakkad gap. The rich black soil of the region has contributed to

Coimbatore's flourishing agriculture industry and it is in fact the successful growth

of cotton that served as a foundation for the establishment of its famous textile

industry. The first textile mills came as far back as 1888 but there are now over a

hundred mills. The result has been a strong economy and a reputation as one of

the greatest industrial cities in South India.

There are more than 25,000 small, medium, large sale industries and textile mill.

Coimbatore is also famous for the manufacture of motor pump sets and varied

engineering goods. The development of Hydro electricity from the Pykara Falls in

the 1930 led to a cotton boom in Coimbatore. Coimbatore serves as an entry and

exit point to neighbouring Kerala and the very popular hill station of

Udhagamandalam (Ooty) is 70 kms from Coimbatore. It is the disembarking point

for those who want to take the Mountain train that runs from Mettupalayam just

35 kms away from Coimbatore, regular bus services also available daily from

Coimbatore to Ooty and other districts, towns and major cities.

17

The growth of textiles naturally led to the inception of textile machinery

manufacturing. Today, some of the best known global brands in textile machinery

and component manufacturing are home grown enterprises. Even in the late

1800s, Coimbatore district had cotton cleaning and pressing factories and was

exported to Mumbai (then Bombay) and England. A spinning mill was established

around this time and even back then, the textile industry employed over 300

people. The Southern India Mills' Association (SIMA) was established in 1933, is

very active in the Coimbatore region and governs most of the textile industry in

South India. SIMA has a membership spread across the southern states and

protects the interests of the textile mills and its workers.

3.1.1. Public Health Profile in Coimbatore District Table 3.1: Public Health and Medical Services Rendered by Organisations and Individual

year from 2012 to 2013 Sl.No. Items Numbers

1. Hospitals 1161 2. Dispensaries 253 3. Sanitary Centres 24 4. Nursing Home 414 5. Maternity & Child Welfare Clinic 210 6. Allopathic 1460 7. Ayurvedic 62 8. Unani 18 9. Homeopathy 85

Source: Directorate of District HEALTH, Coimbatore

Table 3.2: Details of the Beds Strength, Average Out patients and In patients in Coimbatore District year from 2013 to 2014

Place Type Beds Average OP/Day

Average IP/Day

Annur NTK* 30 691 29 Kottur NTK* 56 364 23 Mettupalayam TK** 116 1039 113 Periyanayakkanpalayam NTK 10 335 35 Pollachi HQRS*** 170 1896 179 Sulur NTK* 30 513 27 Thondamuthur NTK* 36 434 27 Valparai TK** 38 232 14 Vettaikaranputhur NTK* 26 323 13

Total 512 5827 460 Source: Directorate of Medical and Rural Health Services

(*Non-Taluk Hospital, **Taluk Hospital & ***District Head Quarters Hospital)

18

Table 3.3: Primary Health Centres with Sub-Centres year from 2013 to 2014 Sl.No. Name of the

Block/Municipality No. of Primary Health

Centres No. of Sub-Centres

1. P.N.Palayam 7 30 2. S.S.Kulam 2 13 3. Perur 4 20 4. Madukkarai 5 21 5. Thondamuthur 4 19 6. Karamadai 7 36 7. Pollachi North 3 21 8. Pollacchi South 4 18 9. Analmalai 7 32

10. Valparai 3 38 11. Kinathukadavu 3 19 12. Sultanpet 2 15 13. Sulur 4 26 14. Annur 3 20

Total 58 328 Source: Directorate of District HEALTH, Coimbatore

Table 3.4: Births and Deaths Registered in Coimbatore District year from 2013 to 2014

Live Births Registered Deaths Registered Infant Deaths 77371 29909 17

Source: Directorate of District HEALTH, Coimbatore Table 3.5: Birth, Death and Infant Mortality Rates in Coimbatore District year from 2013

to 2014 Birth Rate Death Rate Infant Mortality Rate

15.9 4.02 6.24 Source: Directorate of District HEALTH, Coimbatore

19

3.2. Infant Mortality Rate

3.2.1. Infant Mortality Rate in India

Table 3.6: Infant Mortality Rate for India Year Number of Infant Deaths

(Per year for every 1000 live births) 2000 68 2001 66 2002 64 2003 60 2004 58 2005 58 2006 57 2007 55 2008 53 2009 50 2010 47 2011 44 2012 42

Source: Sample Registration System (SRS) Bulletins

Figure 3.1: Trend Analysis Plot of Infant Mortality Rate for India from 2000 to 2012

2012201120102009200820072006200520042003200220012000

70

65

60

55

50

45

40

YearNum

ber

of I

nfan

t D

eath

s (P

er y

ear

for

ever

y 1

00

0 li

ve b

irth

s)

MAPE 1.73610MAD 0.91970MSD 1.25402

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot forInfant Mortality Rate for IndiaLinear Trend Model

Yt = 70.038 - 2.07143*t

Trend Analysis figure 3.1 reveals that the trend of Infant mortality rate in India

from 2000 to 2012. The trend plot that provides the original data, and the fitted

trend line, the output also displays the fitted trend equation Yt = 70.038-

2.07143*t. The IMR/1000 Live Births for India data shows a general downward

trend. The trend plot clearly stated that IMR in India reducing step by step in

manner.

20

3.2.2. Infant Mortality Rate in Tamilnadu

Table 3.7: Infant Mortality Rate for Tamilnadu Year Number of Infant Deaths

(Per year for every 1000 live births) 2000 51 2001 49 2002 44 2003 43 2004 41 2005 37 2006 37 2007 35 2008 31 2009 28 2010 24 2011 22 2012 21

Source: Sample Registration System (SRS) Bulletins

Figure 3.2: Trend Analysis Plot of Infant Mortality Rate for Tamilnadu from 2000 to 2012

2012201120102009200820072006200520042003200220012000

55

50

45

40

35

30

25

20

YearNum

ber

of I

nfan

t D

eath

s (P

er y

ear

for

ever

y 1

00

0 li

ve b

irth

s)

MAPE 2.62174MAD 0.85207MSD 1.10059

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot for Infant Mortality Rate for Tamilnadu Linear Trend Model

Yt = 53.385 - 2.53846*t

Trend Analysis figure 3.2 reveals the downward trend of Infant mortality rate in

Tamilnadu from 2000 to 2012.The trend plot that provides the original data, and

the fitted trend line, the output also displays the fitted trend equation Yt =

53.385-2.53846*t. The IMR/1000 Live Births for Tamilnadu data indicates a

general downward trend. The trend plot clearly stated that IMR of Tamilnadu

diminishing year by year.

21

3.2.3. Infant Mortality Rate in Coimbatore District

Table 3.8: Infant Deaths for Coimbatore District Year Infant Death

2011-2012 397 2012-2013 336 2013-2014 293 2014-2015 297


Figure 3.3: Trend Analysis Plot of Infant Deaths for Coimbatore District from (2011-2012) to (2014-2015)

2015201420132012

400

380

360

340

320

300

280

Year

Infa

nt D

eath

MAPE 5.065MAD 16.250MSD 274.575

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot of INFANT DEATHS FOR COIMBATORE DISTRICTLinear Trend Model

Yt = 416.5 - 34.3000*t

Trend Analysis figure 3.3 reveals the Infant mortality rate of Coimbatore districts

from 2012 to 2015. The trend plot that provides the original data, and the fitted

trend line, the output also displays the fitted trend equation Yt = 416.5-

34.3000*t. The Infant mortality rate in Coimbatore District data pointed out

general downward trend. Hence the data clearly reveals that the government has

taken necessary steps to reduce IMR by an implementation of several schemes.

22

Table 3.9: Infant Mortality Rate in Coimbatore Corporation Year Infant Death

2011-2012 60 2012-2013 41 2013-2014 47 2014-2015 47


Figure 3.4: Trend Analysis Plot of Infant mortality rate in Coimbatore Corporation from (2011-2012) to (2014-2015)

2015201420132012

60

55

50

45

40

Year

Infa

nt D

eath

MAPE 10.1120MAD 4.7500MSD 34.5750

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot of Infant Death for COIMBATORE CORPORATIONLinear Trend Model

Yt = 57.0 - 3.30000*t

Trend analysis figure 3.4 reveals the trends in the Infant mortality rate in

Coimbatore Corporation from 2012 to 2015. The trend plot that provides the

original data, and the fitted trend line, the output also displays the fitted trend

equation Yt = 57.0-3.30000*t. Trend Analysis figure shows that the downward

trend of Infant death in Coimbatore Corporation. Even though it is the positive

sign the year 2015 the infant mortality is above the trend line.

23

Table 3.10: Infant Mortality Rate in Coimbatore Urban Year Infant Death

2011-2012 68 2012-2013 45 2013-2014 51 2014-2015 54

Source: Directorate of District HEALTH, Coimbatore Figure 3.5: Trend Analysis Plot of Infant mortality rate in Urban from (2011-2012) to (2014-2015)

2015201420132012

70

65

60

55

50

45

Year

Infa

nt D

eath

MAPE 12.3576MAD 6.5000MSD 55.0500

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot of Infant Death for URBAN TOTALLinear Trend Model

Yt = 63.5 - 3.60000*t

Trend Analysis figure 3.5 reveals the downward trend of Infant mortality rate in

urban from 2012 to 2015. The trend plot that provides the original data, and the

fitted trend line, the output also displays the fitted trend equation Yt = 63.5-

3.60000*t. Trend Analysis figure shows that the downward trend of Infant death

in Coimbatore Urban. Even though it is the positive sign the year 2015 the infant

mortality is above the trend line.

24

Table 3.11 Infant Mortality Rate in Coimbatore Rural Year Infant Death

2011-2012 329 2012-2013 291 2013-2014 242 2014-2015 243


Figure 3.6: Trend Analysis Plot of Infant Mortality Rate in Coimbatore Rural from (2011-2012) to (2014-2015)

2015201420132012

340

320

300

280

260

240

220

Year

Infa

nt D

eath

MAPE 3.830MAD 9.750MSD 141.575

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot of Infant Death for COIMBATORE RURAL TOTALLinear Trend Model

Yt = 353.0 - 30.7000*t

Trend Analysis figure 3.6 stated that the downward trend of Infant Mortality Rate

in rural area at Coimbatore District from (2011 – 2012) to (2014 – 2015). The

trend plot that provides the original data, and the fitted trend line, the output also

displays the fitted trend equation Yt = 353.0-30.7000*t. Trend Analysis figure

shows that the downward trend of Infant death in Coimbatore Rural. Even though

it is the positive sign the year 2015 the infant mortality is above the trend line.

25

3.2.4. Infant Mortality Rate at Thondamuthur Block in Coimbatore District

Table 3.12: Infant Mortality Rate in Thondamuthur Block Year Infant Death

2011-2012 34 2012-2013 31 2013-2014 21 2014-2015 24

Source: Directorate of District HEALTH, Coimbatore Figure 3.7: Trend Analysis Plot of Infant Mortality Rate in Thondamuthur Block from (2011-2012)

to (2014-2015)

2015201420132012

34

32

30

28

26

24

22

20

Year

Infa

nt D

eath

MAPE 9.53863MAD 2.25000MSD 7.25000

Accuracy Measures

ActualFits

Variable

Trend Analysis Plot of Infant Death for THONDAMUTHUR BlockLinear Trend Model

Yt = 37.50 - 4.00000*t

Trend Analysis figure 3.7 reveals that the downward trend of Mortality Rate in

Thondamuthur Block from 2012 to 2015. The trend plot that provides the original

data, and the fitted trend line, the output also displays the fitted trend equation Yt

= 37.50-4.00000*t. Trend Analysis figure shows that the downward trend of

Infant death in Thondamuthur Block. Even though it is the positive sign the year

2015 the infant mortality is above the trend line.

It is clearly stated that District Infant Mortality Rate is sharply a decline over the

period. It ensures that the district health department is playing a decent role in

improving the health status of the people in Coimbatore district.

26

SECTION- 4

MEDIATING CAUSE FOR HIGH LEVEL CAUSE OF INFANT MORTALITY: FINDINGS FROM FIELD SURVEY

The field survey has undertaken to analyse the role of different dimensions for

high level causes of infant mortality in the sample block. The section is divided

into two parts which are socio-economic dimensions of the respondents and

mediating high level causes for infant mortality rate in the study area.

4.1. Part 1: Socio-Economic Dimensions 4.1.1. Age wise Classification of the Respondents

Table 4.1: Age wise classification of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Below 21 years 9 36.00 22 years to 31 years 16 64.00 32 years to 41 years 0 0 42 years & above 0 0

Total 25 100.00 Mathavarayapuram Below 21 years 12 48.00 22 years to 31 years 13 52.00 32 years to 41 years 0 0 42 years & above 0 0

Total 25 100.00 Devarayapuram Below 21 years 12 48.00 22 years to 31 years 13 52.00 32 years to 41 years 0 0 42 years & above 0 0

Total 25 100.00 Ikkarai Booluvampatti Below 21 years 10 40.00 22 years to 31 years 15 60.00 32 years to 41 years 0 0 42 years & above 0 0

Total 25 100.00 Overall Total Below 21 years 43 43.00 22 years to 31 years 57 57.00 32 years to 41 years 0 0 42 years & above 0 0

Total 100 100.00 Source: Primary Data

27

The table 4.1 reveals the age wise classification of pregnant women households

of selected panchayats. It is inferred that 43 percent of the respondents were in

the age group of below 21 years, 57 percent of the respondents were in the age

group between 22 to 31 years, none of the respondents in the age group between

32 to 41 years and 42 years & above. Young mothers faced difficulties in

pregnancies and deliveries because of their physical immaturity. They have

limited knowledge and confidence in caring for infants and young children.

28

4.1.2. Educational Qualification of the Respondents

Table 4.2: Educational Qualification of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Schooling Dropout 11 44.00 Secondary Education 8 32.00 Higher Secondary Education 3 12.00 Under Graduate 2 8.00 Post Graduate 1 4.00

Total 25 100.00 Mathavarayapuram Schooling Dropout 5 20.00 Secondary Education 8 32.00 Higher Secondary Education 7 28.00 Under Graduate 4 16.00 Post Graduate 1 4.00

Total 25 100.00 Devarayapuram Schooling Dropout 14 56.00 Secondary Education 3 12.00 Higher Secondary Education 6 24.00 Under Graduate 2 8.00 Post Graduate 0 0

Total 25 100.00 Ikkarai Booluvampatti Schooling Dropout 8 32.00 Secondary Education 9 32600 Higher Secondary Education 3 12.00 Under Graduate 4 16.00 Post Graduate 1 4.00

Total 25 100.00 Overall Total Schooling Dropout 38 38.00 Secondary Education 28 28.00 Higher Secondary Education 19 19.00 Under Graduate 12 12.00 Post Graduate 3 3.00


The table 4.2 reveals the Educational Qualification of pregnant women

households of the selected panchayats. In developing countries, mother’s

educational level, as indicated here by literacy status, tends to have a strong effect

on the mortality of young children (Govindasamy and Ramesh 1997; Hobcraft,

McDonald, and Rutstein 1984; Mosley and Chen 1984; United Nations 1985; 1991;

1998). The above table inferred that 38 percent of the total pregnant women

household were schooling dropout, 28 percent were completed their secondary

education; 19 percent were completed their higher secondary education, 12

29

percent were completed their under graduation and only 3 percent were

completed their post graduation. It is observed that the majority of the

respondents are in category of school dropouts. Literate mothers usually give

birth to healthier babies because they themselves tend to be healthier than

mothers who are illiterate. In addition, literate mothers are more likely to provide

their children with a healthy environment and nutritious food than are illiterate

mothers, even when other conditions are similar.

30

4.1.3. Occupation of the Respondents

Table 4.3: Occupation of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Un Employed 19 76.00 Agriculture 3 12.00 Private 3 12.00 Government 0 0 Self Employee 0 0

Total 25 100.00 Mathavarayapuram Un Employed 22 88.00 Agriculture 1 4.00 Private 0 0 Government 1 4.00 Self Employee 1 4.00

Total 25 100.00 Devarayapuram Un Employed 21 84.00 Agriculture 3 12.00 Private 1 4.00 Government 0 0 Self Employee 0 0

Total 25 100.00 Ikkarai Booluvampatti Un Employed 16 64.00 Agriculture 2 8.00 Private 5 20.00 Government 1 4.00 Self Employee 1 4.00

Total 25 100.00 Overall Total Un Employed 78 78.00 Agriculture 9 9.00 Private 9 9.00 Government 2 2.00 Self Employee 2 2.00


The table 4.3 provides the occupation of pregnant women households of selected

panchayats. It is inferred from the table that 78 percent of the respondents were

unemployed, 9 percent of the respondents were belongs to agricultural and allied

activities, 9 percent of the respondents were got their employment in private

companies, 2 percent of the respondents were got their employment in

Government and 2 percent were self employed. Most of the pregnant women are

unemployed which may be stimulated for psychological problems and other family

problems.

31

4.1.4. Religious Classification of the Respondents

Table 4.4: Religious Classification of the Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam 25 100.00 Hindu 25 100.00 Islam 0 0 Christian 0 0

Total 25 100.00 Mathavarayapuram Hindu 25 100.00 Islam 0 0 Christian 0 0

Total 25 100.00 Deverayapuram Hindu 25 100.00 Islam 0 0 Christian 0 0

Total 25 100.00 Ikkarai Booluvampatti Hindu 24 96.00 Islam 0 0 Christian 1 4.00

Total 25 100.00 Overall Total Hindu 99 99.00 Islam 0 0 Christian 1 1.00


The Table 4.4 reveals the classification of the religion of pregnant women

households of selected panchayats. It is inferred that 99 percent of the

respondents were belongs to Hindu religion, 1 percent of the respondents were

belongs to Christianity. It is concluded that the majority of the respondents were

belongs to Hindu Religion. Religion and membership in a scheduled caste or

scheduled tribe is known to affect many aspects of life in India and is likely to

affect levels of infant and child mortality as well. Some of the effect of religion and

caste/tribe membership on mortality may be due to differences in life-style based

on traditions and beliefs. Such differences may include customary practices

related to childbirth, infant feeding, and healthcare. These practices inferences

the infant and child mortality rate.

32

4.1.5. Income wise Classification of the Respondents

Table 4.5: Income wise Classification of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Below Rs.50000 19 76.00 Rs.50001 to Rs.150000 5 20.00 Rs.150001 to Rs.250000 1 4.00 Rs.250001 & above 0 0

Total 25 100.00 Mathavarayapuram Below Rs.50000 11 44.00 Rs.50001 to Rs.150000 12 48.00 Rs.150001 to Rs.250000 2 8.00 Rs.25001 & above 0 0

Total 25 100.00 Devarayapuram Below Rs.50000 15 60.00 Rs.50001 to Rs.150000 9 36.00 Rs.150001 to Rs.250000 1 4.00 Rs.250001 & above 0 0

Total 25 100.00 Ikkarai Boluvampatti Below Rs.50000 16 64.00 Rs.50001 to Rs.150000 8 32.00 Rs.150001 to Rs.250000 1 4.00 Rs.250001 & above 0 0

Total 25 100.00 Overall Total Below Rs.50000 61 61.00 Rs.50001 to Rs.150000 34 34.00 Rs.150001 to Rs.250000 5 5.00 Rs.250001 & above 0 0


Table 4.5 provides the annual income of pregnant women households of selected

panchayats. It is inferred that 61 percent of the respondents were earned below

50000 per annum, 34 percent of the respondents were earned between 51000

to 150000, 5 percent of the respondents were earned between 150001 to

250000 and nobody in between 250001 and above. It is observed that majority

of the respondents were earned below 50000 per annum. Poverty leads to IMR

fluctuation.

33

4.1.6. Community wise Classification of the Respondents

Table 4.6: Community wise Classification of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Backward Class 4 16.00 Most Backward Class/Denoted Class 7 28.00 Schedule Caste 11 44.00 Schedule Tribe 3 12.00

Total 25 100.00 Mathavarayapuram Backward Class 13 52.00 Most Backward Class/Denoted Class 4 16.00 Schedule Caste 2 8.00 Schedule Tribe 6 24.00

Total 25 100.00 Deverayapuram Backward Class 8 32.00 Most Backward Class/Denoted Class 8 32.00 Schedule Caste 5 20.00 Schedule Tribe 4 16.00

Total 25 100.00 Ikkarai Booluvampatti Backward Class 12 48.00 Most Backward Class/Denoted Class 2 8.00 Schedule Caste 4 16.00 Schedule Tribe 7 28.00

Total 25 100.00 Overall Total Backward Class 37 37.00 Most Backward Class/Denoted Class 21 21.00 Schedule Caste 22 22.00 Schedule Tribe 20 20.00


The table 4.6 provides the community of pregnant women households of selected

panchayats. It is inferred that 37 percent of the respondents were belongs to BC

community, 21 percent of the respondents were belongs to MBC/DC community,

22 percent of the respondents were belongs to SC community and 20 percent of

the respondents were belongs to ST community. It is observed that the majority of

the respondents belong to backward class category.

34

4.1.7. Family types of the Respondents

Table 4.7: Types of Family of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Joint Family 17 68.00 Nuclear Family 8 32.00 Total 25 100.00 Mathavarayapuram Joint Family 15 60.00 Nuclear Family 10 40.00 Total 25 100.00 Devarayapuram Joint Family 21 84.00 Nuclear Family 4 16.00 Total 25 100.00 Ikkarai Booluvampatti Joint Family 19 76.00 Nuclear Family 6 24.00 Total 25 100.00 Overall Total Joint Family 72 72.00 Nuclear Family 28 28.00 Total 100 100.00

Source: Primary Data The table 4.7 reveals the family types of pregnant women households of selected

panchayats. Types of family have been classified in to nuclear and joint family the

table clearly stated that 72 percent of the respondents were preferred joint family

and 28 percent of the respondents were preferred nuclear family. It is observed

majority of the respondents preferred joint family.

35

4.1.8. Residential Status of the Respondents

Table 4.8: Residential Status of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Own House 23 92.00 Rent House 2 8.00 Total 25 100.00 Mathavarayapuram Own House 25 100.00 Rent House 0 0 Total 25 100.00 Devarayapuram Own House 20 80.00 Rent House 10 20.00 Total 25 100.00 Ikkarai Booluvampatti Own House 21 84.00 Rent House 4 16.00 Total 25 100.00 Overall Total Own House 89 89.00 Rent House 11 11.00 Total 100 100.00

Source: Primary Data The table 4.8 provides the residential status of pregnant women households of

selected panchayats. It is inferred that 89 percent of the respondents were

resided in their own house and 11 percent of the respondents were resided in

rental house. It is observed that majority of the respondents resided in their own

house.

36

4.1.9. Living conditions of the Respondents

Table 4.9: Living conditions of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Concrete 16 64.00 Tiled 5 20.00 Terrace 4 20.00 Thatched 0 0 Total 25 100.00 Mathavarayapuram Concrete 15 60.00 Tiled 8 32.00 Terrace 2 8.00 Thatched 0 0 Total 25 100.00 Devarayapuram Concrete 7 28.00 Tiled 11 44.00 Terrace 5 20.00 Thatched 2 8.00 Total 25 100.00 Ikkarai Booluvampatti Concrete 8 32.00 Tiled 9 36.00 Terrace 5 20.00 Thatched 3 12.00 Total 25 100.00 Overall Total Concrete 46 46.00 Tiled 33 33.00 Terrace 16 16.00 Thatched 5 5.00 Total 100 100.00

Source: Primary Data The table 4.9 reveals the living conditions of pregnant women households. It is

inferred that 46 percent of the respondents were lived in concrete house, 33

percent of the respondents were lived in tiled house, 16 percent of the

respondents were lived in Terrace house and 5 percent of the respondents were

lived in thatched house. It is observed that the majority of the respondents were

lived in concrete house.

37

4.1.10. Size of Landholding of the Respondents

Table 4.10: Size of Landholding of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Landless 2 8.00 Marginal 20 80.00 Large 3 12.00 Total 25 100.00 Mathavarayapuram Landless 0 0 Marginal 24 96.00 Large 1 4.00 Total 25 100.00 Deverayapuram Landless 4 16.00 Marginal 20 80.00 Large 1 4.00 Total 25 100.00 Ikkarai Booluvampatti Landless 2 8.00 Marginal 21 84.00 Large 2 8.00 Total 25 100.00 Overall Total Landless 8 8.00 Marginal 85 85.00 Large 7 7.00 Total 25 100.00

Source: Primary Data The table 4.10 provides the size of landholding of pregnant women households. It

is inferred that 8 percent of the respondents were not having lands; 85 percent of

the respondents were having marginal lands and 7 percent of the respondents

were having large size of lands. It is observed that the majority of the respondents

were having marginal size of lands.

38

4.1.11. Toilet Facilities of the Respondents

Table 4.11: Toilet Facilities of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Common Toilet 1 4.00 Separate Toilet 13 52.00 Open Toilet 11 44.00 Total 25 100.00 Mathavarayapuram Common Toilet 2 8.00 Separate Toilet 18 72.00 Open Toilet 5 20.00 Total 25 100.00 Deverayapuram Common Toilet 0 0 Separate Toilet 8 32.00 Open Toilet 17 68.00 Total 25 100.00 Ikkarai Booluvampatti Common Toilet 0 0 Separate Toilet 7 28.00 Open Toilet 28 72.00 Total 25 100.00 Overall Total Common Toilet 3 3.00 Separate Toilet 46 46.00 Open Toilet 51 51.00 Total 25 100.00

Source: Primary Data The table 4.11 provides the toilet facilities of pregnant women households of

selected panchayats. It is inferred 3 percent of the respondents make use of

common toilets; 46 percent of the respondents make use of individual (separate)

toilet and 51 percent of the respondents make use of open toilets. It is observed

that the majority of the respondents make use of open toilet.

39

4.1.12. Selection Preference of Hospitals by the Respondents

Table 4.12: Selection Preference of Hospitals by the Respondents Panchayats No. of Respondents Percentage (%)

Theethippalayam Self Decision 8 32.00 Family Doctor Recommendation 8 32.00 Friends and Relatives Recommendation 6 24.00 Beneficiaries Recommendation 3 12.00

Total 25 100.00 Mathavarayapuram Self Decision 8 32.00 Family Doctor Recommendation 11 44.00 Friends and Relatives Recommendation 6 24.00 Beneficiaries Recommendation 0 0

Total 25 100.00 Devarayapuram Self Decision 13 52.00 Family Doctor Recommendation 9 36.00 Friends and Relatives Recommendation 1 4.00 Beneficiaries Recommendation 2 8.00

Total 25 100.00 Ikkarai Boluvampatti Self Decision 8 32.00 Family Doctor Recommendation 7 28.00 Friends and Relatives Recommendation 10 40.00 Beneficiaries Recommendation 0 0 Total 25 100.00 Overall Total Self Decision 37 37.00 Family Doctor Recommendation 35 35.00 Friends and Relatives Recommendation 23 23.00 Beneficiaries Recommendation 5 5.00


The table 4.12 reveals the selection of hospital of pregnant women households of

selected panchayats. It is inferred that 37 percent of the respondents have taken

Self Decision, 35 percent of the respondents were followed family doctor

recommendation, 23 percent of the respondents were followed their friends and

relatives recommendation and 5 percent of the respondents were followed the

Beneficiaries Recommendations for selection of hospital. It observed that the

majority of the respondents made their Self Decision.

40

4.1.13. Hospital Selection by the Respondents

Table 4.13: Hospital Selection by the Respondents Panchayats No. of Respondents Percentage (%)

Theethippalayam Government Hospital 23 92.00 Private Hopital 2 8.00 Total 25 100.00 Mathavarayapuram Government Hospital 23 92.00 Private Hopital 2 8.00 Total 25 100.00 Devarayapuram Government Hospital 23 92.00 Private Hopital 2 8.00 Total 25 100.00 Ikkarai Booluvampatti Government Hospital 22 88.00 Private Hopital 3 12.00 Total 25 100.00 Overall Total Government Hospital 91 91.00 Private Hopital 9 9.00 Total 100 100.00

Source: Primary Data The table 4.13 provides the types of hospital of pregnant women households of

selected panchayats. It is inferred 91 percent of the respondents were chosen

Government Hospital and 9 percent of the respondents were chosen Private

Hospitals. It is observed that the majority of the respondents were chosen

Government Hospitals.

41

4.1.14. Medical Insurance of the Respondents

Table 4.14: Medical Insurance of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Nil Insurance 7 28.00 Government Insurance 1 4.00 Private Insurance 0 0 Chief Ministers’ Medical Insurance Scheme 17 68.00 Total 25 100.00 Mathavarayapuram Nil Insurance 5 20.00 Government Insurance 0 0 Private Insurance 0 0 Chief Ministers’ Medical Insurance Scheme 20 80.00 Total 25 100.00 Devarayapuram Nil Insurance 1 4.00 Government Insurance 8 32.00 Private Insurance 0 0 Chief Ministers’ Medical Insurance Scheme 16 64.00 Total 25 100.00 Ikkarai Booluvampatti Nil Insurance 3 12.00 Government Insurance 0 0 Private Insurance 0 0 Chief Ministers’ Medical Insurance Scheme 22 88.00 Total 25 100.00 Overall Total Nil Insurance 16 16.00 Government Insurance 9 9.00 Private Insurance 0 0 Chief Ministers’ Medical Insurance Scheme 75 75.00 Total 100 100.00

Source: Primary Data The table 4.14 reveals the medical insurance benefit obtained by the pregnant

women households of selected panchayats. It is inferred 16 percent of the

respondents were not aware of medical insurance and they were not obtained the

same, 9 percent of the respondents were obtained Government Insurance, 75

percent of the respondents were obtained Chief Minister Medical Insurance

Schemes and none of the respondents obtained private insurance. It is observed

the most of the respondents were obtained Chief Minister Medical Insurance

Schemes.

42

4.1.15. Infant Death of Pregnant Women Households

Table 4.15: Infant Death of Pregnant Women Households Panchayats No. of Respondents Percentage (%)

Theethippalayam Yes 3 12.00 No 22 88.00 Total 25 100.00 Mathavarayapuram Yes 4 16.00 No 21 84.00 Total 25 100.00 Devarayapuram Yes 4 16.00 No 21 84.00 Total 25 100.00 Ikkarai Booluvampatti Yes 2 8.00 No 23 92.00 Total 25 100.00 Overall Total Yes 13 13.00 No 87 87.00 Total 100 100.00

Source: Primary Data The table 4.15 provides the infant deaths in past of pregnant women households

of selected panchayats. It is inferred that 13 percent of pregnant women

household said previous infant death happened and 87 percent of pregnant

women household said infant death has not happened in the preceding years.

The majority of the respondents said the infant death has not happened in the

preceding years.

4.1.16 HEALTH SERVICES AVAILABILTIY Thondamuthur block is a revenue block in Coimbatore district, Tamilnadu. It is

immediate vicinity to Narasipuram road around 15Kms from Coimbatore city.

Thennamanalur, Vedapatti, Sundapalayam, Narasipuram, Poochiyur,

Devarayipuram, Viraliyur, Mathampatty, Pooluvapatty and Alandurai are vicinity to

Thondamuthur Block. The census of the year 2011 stated that Thondamuthur

population is around 1,24,390, out of that 52 percent were male and rest of the

48 percent were females. The Literacy rate in Thondamuthur block was 68

percent of the total population, out of which male literacy consists of 79 percent

and the female literacy, include 56 percent, which is more than that of National

Average 59.5 Percent. In the study area 15 percent of the total population were

43

belongs to the age group of less than 6 years. It was found that 10668 male

agriculture labours and 646 female agriculture labours were in Thondamuthur

Block. There is a well organised network of Primary Health Centres is situated at

Thondamuthur Block for having medical care. Hence the people need not travel

beyond 15 Kms to procure medical care for their health. The population of the

study initially conducted in all Primary Health Centres at Thondamuthur Block in

Coimbatore District. In addition to that there are three primary health centres

located at Kaliveerapalayam, Pooluvapatty and Karamadai. Each PHC operates

through different sub centres at different villages in the Thondamuthur Block. The

Thondamuthur block has created a distinct name in the health map of

Coimbatore district by organising medical camp, creating awareness about health

care, educating people in undertaking pre-natal and Post-natal care and having a

well equipped hospital with adequate staff to handle emergencies.

Kaliveerapalayam primary health centre include five sub centres with a total

population of 46,672. Pooluvapatty primary health centre consists of seven sub

centres with a total population of 43,044. Karamadai Primary health centre

consists of five sub centres with a total population of 46,325. Each PHC includes

two sub centres, one is immediate vicinity to Primary Health Centres and another

one is far away from Primary Health Centres have been selected for the study.

Poochiyur, Alandurai and Mathampatty are immediate vivinity to Primary Health

Centres and Narasipuram, Sadivayal and Chettipalayam were far away form the

said Primary Health Centres. The health care system in Thondamuthur block is

owned by the state government of Tamilnadu. The Thondamuthur Block consists

of few private hospitals and clinics for providing medical service. District Health

Centres are responsible for curative activities, preventive programme,

surveillance and health services. District hospitals are supposed to serve as

referral institutions for all polyclinics in the districts. They are also providing

training facilities for health staff working in polyclinics and Community health

centres in the district. Community Health Centres (CHC) are responsible for

providing health care including preventive, ambulatory, imapatient services and

for referring complicatory causes to high levels for medical care.

44

4.1.17 ACCESSIBILTY In Thondamuthur block, the primary health centres have been classified as Sub-

Centre, Primary health centre and Community health centre; and the services of

these three centres are also assisted by the presence of Rural Welfare Centres.

The Sub-centres provide first level contact between the primary health centres

and community health centres. Tasks assigned to these health institutions vary

from state to state. In some state the Auxiliary Nursery Midwifes (ANMs) stationed

in sub-centres to perform the deliveries and refer the complicated cases to

Primary Health Centres. In some state emphasis inter-personal communication

leads behavioural change in maternal and child health, family welfare, nutrition,

immunisation, diarrhoeal control and control of communicable disease. The

Primary Health Centres consists of referral units to five to six sub-centres. The

activities of PHC include curative, preventive and promotive health care as well as

family welfare services. Community Health Centres serve as fireferral units (Furs)

for four to five PHCs and also provide facilities for obstetric care and specialist

consultations. According to norms each CHC should have at least 30 beds, one

operation theatre, X-ray machine, labour room, laboratory facilities, in addition to

that it has to be staffed with four medical specialists like Surgeon, Physician,

Gynaecologist and Paediatrician. The Primary Health Centres provide curative,

preventive and promotive health and family welfare services in rural area for a

population of about 30,000. PHC should have essential infrastructure, staff,

equipments and supplies. PHC must also have critical infrastructure like

continuous water supply, electricity, labour room, laboratory, telephone, functional

vehicle, etc. PHC ought to have at least one medial officer, one laboratory

technician and health assistants both males and females. PHC must hold critical

equipments like deep freezer, vaccine carrier, BP instrument, autoclave, etc.

Supply of contraceptives, normal delivery kit/labour room kit, essential obstetric

kit, all vaccines, IFA tablets and ORS packets. Primary Health Centres have the

major responsibility of providing both preventive and curative health care services

in the area. Primary Health Centres have limited facilities and expertises hence

they cannot provide complete obstetric care to woman. Some of the upgraded

PHCs and Community Health Centres have been categorised as first referral units

and these facilities have been provided with specialized equipments and kits to

provide maternal health care, particularly obstetric care (EmOC). Emergency

45

cases can be referred form the sub-centres and primary health centres to these

FRUs. PHC functioning on 24x7 basis in Thondamuthur Block.

4.1.18 VILLAGE HEALTH NURSES In the study area, Village Health Nurses (VHNs) were moderately doing well. The

respondents not only make use of VHNs for acquiring the benefit of government

free service programme, but also procuring the monetary benefits during the

pregnancy period. They gave importance for sending data to their higher officials.

During the field survey, VHNs reported that there are two factors influence the

cause of high level of Infant Mortality Rate (IMR) in Thondamuthur village which

are 1) Social positioning inclusive of households, education and Occupation and

2) Mother’s Education. Village Health Nurses were performing well in the study

area for trying to reduce the IMR. But the psychological factors were influencing

more than that of other factors during the pregnancy period and the same leads

to high level of IMR in the study area.

4.1.19 THE GROWTH CHART In Thondamuthur block, the general aim of the growth chart is to describe the

child’s birth weight, physical growth and breast feeding as well as associated

factors, from birth to 24 months of age. At the individual level we found birth is

rapid economic and technological development. At the individual level we found

that child birth weight is associated with household economy and literacy of

mother. Also child birth and growth were statistically significant and associated

with economic conditions and mother’s education. In the study area, we found

that Low Birth Weight (LBW) of a child has been defined by WHO, the normal

weight of a child at birth is to be less than 2,500 gm (5.5 pounds).

AS per field survey, the live babies with median weight are 3000 gm and LBW is

1500 gm. The study reveals that 60 percent of the pregnancy women were

affected by the psychological discomfort which led to poor intake of food during

the pregnancy period. The mother’s education is very important factor for the

breastfeeding for the child for the first 6 months. In addition to that 38 percent of

the respondents were a school dropout which leads to Low Birth Weight of the

child in the study area.

46

4.1.20 AFFORDABILITY As far as health expenditure in rural area is concerned affordability is playing an

important role. The results reveal that there is an inadequate spending capacity of

the respondents for their health in the study area. Since 61 percent of the

respondents were earned below 50000 p.a., 34 percent of the respondents were

earned between 51000 to 150000 p.a., 5 percent were earned between 150001

to 250000 p.a. and none of the respondents lies in between 250001 & above.

4.1.21 HEALTH UTILITY Utilities are cardinal values that represent the strength of an individual’s

performance for specific related outcomes. The results of the study reveal that 90

percent of respondents were having treatment in Primary Health Centres and 10

percent of the respondents were having their treatment in Private hospitals. In the

study area, there is no Religious and NGOs Hospital. In Tamilnadu we find 24

hours delivery services are reported to be available. As per field survey reveals

that 37 percent of the respondents have taken self decision, 35 percent of the

respondents were followed their family doctor recommendation, 23 percent of the

respondents were followed their friends and relatives recommendations and 5

percent of the respondents were followed their Beneficiaries Recommendations

for hospital decision. It is observed that the majority of the respondents made

their self decision according to their economic conditions in the study area. In

addition to the said result, it is informed that 91 percent o f the respondents were

chosen government hospitals and 9 percent of the respondents were chosen

private hospitals for obtaining their heath care services. It is observed that the

majority of the respondents were chosen Primary Health Centres.

4.1.22 CARE OF THE HOSPITALS The Primary Health Care Centres run by the government of Tamilnadu. The

primary health centres are upgrading their infrastructure and other facilities for

the benefit of the public health.

4.1.23 PREGNANCY PERIOD (10/10 RULES) As per field survey, 90 percent were aware about the post pregnancy (10/10

rules), Village Health Nurses were trying to create awareness about 10/10 rules

47

to the respondents during the pregnancy period. The respondents were not able

to follow the rules. Hence it creates many problems for the child and mother. The

study reveals that 80 percent of the respondents were affected through the

psychological discomfort from mother-in-laws and family members, 60 percent of

the respondents were affected due to husband habits like drinking alcohol and

smoking, 80 percent of the respondents were not aware of exercise practices, 95

percent of the respondents were not wearing comfortable dresses and other

things, 90 percent of the respondents were not aware about the cleanliness and

80 percent of the respondents were procuring counter medicines and herbal

medicines without consulting medical practitioners. Hence it is difficult to follow

the 10/10 rule for the pregnant women in the rural area.

4.1.24 SOCIAL ASPECTS The Literacy is one of the important factors in social aspects and health aspects.

Hence, literate mother usually gives birth to healthier babies because they

themselves tend to be more healthier than that of illiterate mother. In addition to

that literate mother are more likely to provide their children with a healthy

environment and nutritious food than an illiterate mother. As per field survey, 38

percent of the respondents were school dropouts among the total pregnant

women households, 28 percent of the respondents were completed their

secondary education, 19 percent of the respondents have completed their higher

secondary education, 12 percent of the respondents have completed their under

graduate and only 3 percent of the respondents have completed their post

graduation. It is observed that majority of the respondents are in the category of

school dropouts. Also it is inferred that 99 percent of the respondents were

belongs to Hindu religion, 1 percent of the respondents were belongs to Christian

religion and it is concluded that the majority of the respondents were belongs to

Hindu religion. In addition to that 37 percent of the respondents were belongs to

the Backward class community, 21 percent of the respondents were belongs to

the Most Backward class community, 22 percent of the respondents were

belongs to the Scheduled class community, 20 percent of the respondents were

belongs to the Scheduled Tribe community and it is observed that majority of the

respondents were belong to Backward class community.

48

In addition, the study stated that 72 percent of the respondents prefer joint

family, 28 percent of the respondents prefer nuclear family and it is observed that

majority of the respondents prefer Joint family. 89 percent of the respondents

were residing in their own house, 11 percent of the respondents were residing in

rental house and it is observed that majority of the respondents were residing in

their own house.

The study reveals that 46 percent of the respondents were living in concrete

house, 33 percent of the respondents were living in tiled house, 16 percent of the

respondents were living in terrace house, 5 percent of the respondents were living

in thatched house and it is observed that majority of the respondents were living

in concrete house. In addition to that it is observed that 13 percent of the

respondents among pregnant women household said that infant death happened

earlier and 87 percent of the respondents among women household said that

infant death not happened in the preceding years.

4.1.25 FINANCIAL ASPECTS The financial crisis is an important factor for determining the health expenditure

of a family. As per filed survey, the respondents were very week in financial

aspects. Hence, many respondents were expecting the government free schemes

for their medical expenses. The result stated that 8 percent of the respondents

not having any land, 85 percent of the respondents were having marginal land

and 7 percent of the respondents were having large size of lands. It is observed

that the majority of the respondents were having marginal size of landholding. As

far as medical insurance is concerned 16 percent of the respondents were not

aware of medical insurance, 9 percent of the respondents were obtained

Government insurance, 75 percent of the respondents were obtained Chief

Minister Medical Insurance Schemes and none of the respondents have obtained

private insurance. It is observed that majority of the respondents were obtained

Chief Minister Medical Insurance Schemes.

49

4.2. Part - 2: Mediating Cause for High Level cause for the Infant Mortality in the Study Area

This section consists of the statistical analysis and testing the hypotheses of the

present study. Primary and secondary data were properly analysed for the entire

sample. Data analysis were performed with Statistical Package for Social

Sciences (SPSS) using techniques that included descriptive statistics, correlation

analysis and AMOS package for Structural Equation Modeling (SEM) and Bayesian

estimation and testing (Senthilkumar. N and Arulraj. A, 2011), AMOS 20.0

(Arbuckle and Wothke 2006), a computer programme for formulating, fitting and

testing SEM to observed/primary data, was used for SEM and the data

preparation was conducted with SPSS 20.0. This section provides an empirical

study on Mediating Cause for High Level of Infant Mortality Rate in the study area.

4.2.1. “MEDIATING CAUSE FOR HIGH LEVEL CAUSE FOR THE INFANT MORTALITY”

Model The analysis presents the constructions and validation of Structural Equation

Modeling (SEM) of ‘Mediating Cause for High Level Cause for the Infant Mortality

Mediated model’ with the dimensions of Practitioners Services (PS), Hospital

Services in Outpatients (HSO), Hospital Services in Inpatients (HSI), Social

Problems (SP), Infrastructure Facilities (IF) and Economic Problems (EP).

Psychological Discomfort (PD) is the Mediating factor and outcome of High Level

cause for the Infant Mortality at Thondamuthur Block in Coimbatore District with

the help of AMOS graphics environment.

4.2.2. Regression Mediated Structural Equation Model for “MEDIATING CAUSE

FOR HIGH LEVEL CAUSE FOR THE INFANT MORTALITY” In hierarchical regression, the predictor variables are entered in sets of variables

according to a pre-determined order that may infer some causal or potentially

mediating relationships between the predictors and the dependent variable

(Francis, 2003). Such situations are frequently of interest in the social sciences.

The logic involved in hypothesizing mediating relationships is that “The

Independent Variable Influences the Mediator Which, In Turn, Influences the

Outcome” (Holmbeck, 1997). However, an important pre-condition for examining

mediated relationships is that the independent variable is significantly associated

with the dependent variable prior to testing any model for mediating variables

50

(Holmbeck, 1997). Of interest is the extent to which the introduction of the

hypothesized mediating variable reduces the magnitude of any direct influence of

the independent variable on the dependent variable. Hence the researcher

empirically tested the hierarchical regression for the model conceptualized in

figure 4.1 with the aid of AMOS 20.0 graphics environment.

Figure 4.1: Shows the AMOS Output with Regression Weights of “MEDIATING CAUSE FOR HIGH

LEVEL CAUSE FOR THE INFANT MORTALITY” Mediated Model

The Regression analysis reveals that the various dimensions are influencing the

high level cause for the infant mortality at Thondamuthur Block in Coimbatore

District. The visual representations of results suggest that the relationships

between the various dimensions and Mediated factor Psychological Discomfort at

Thondamuthur Block in Coimbatore District. Practitioners services is .14 followed

by Hospital services in Outpatients is .57, Hospital services in Inpatients is .60,

Social Problems is .21, Infrastructure Facilities is .33 and Economic problems is

1.13. The r2 value .07 confirms that the Psychological Discomfort is the mediating

51

factor. Practitioners’ services, Hospital services in Outpatients, Hospital services

in Inpatients, Social problems and Infrastructure Facilities resulted significant

impact on mediated factor, Psychological Discomfort. Economic Problem resulted

insignificant impact on the mediated factor. The analysis clearly stated that

Psychological Discomfort is the Mediated factor which determines the high level

cause for the Infant Mortality. The results of the SEM suggested that the

government should concentrate more on Psychological Discomfort than other

dimensions such as Practitioners’ services, Hospital services in Outpatients,

Hospital services in Inpatients, Social problems, Infrastructure Facilities and

Economic Problem. Psychological Discomfort is the Mediated factor for High level

Cause for the Infant Mortality and the same has been scientifically proved with

the help of above SEM.

4.2.3. Bayesian Estimation and Testing for Regression Mediated Structural

Equation Model of “MEDIATING CAUSE FOR HIGH LEVEL CAUSE FOR THE INFANT MORTALITY”

Many management scientist are most familiar with the estimation of these

models using software that analyses covariance matrix of the observed data (e.g.

LISREL, AMOS, EQS), the researcher adopt a Bayesian approach for estimation

and inference in AMOS 20.0 environment (Senthilkumar. N and Arulraj. A, 2011;

Arbuckle and Wothke, 2006). Since, it offers numerous methodological and

substantive advantages over alternative approaches (See Appendix - III).

4.2.4. Posterior Diagnostic Plots of “MEDIATING CAUSE FOR HIGH LEVEL CAUSE

FOR THE INFANT MORTALITY” To check the convergence of the Bayesian MCMC method the posterior diagnostic

plots are analysed. The following figures (figure 4.2 and 4.3) show the posterior

frequency polygon distribution and posterior frequency histogram distribution of

the parameters across the 94000 samples. The Bayesian MCMC diagnostic plots

reveals that for all the figures the normality is achieved, so the structural equation

model fit is accurately estimated.

52

Figure 4.2: Posterior frequency Polygon distribution of the Mediating Factor (IMR) and (PD)


Figure 4.3: Posterior frequency Histogram distribution of the Mediating Factor (IMR) and (PD) Regression Weight (W13)

The trace plot also called as time-series plot shows the sampled values of a

parameter over time. This plot helps to judge how quickly the MCMC procedure

converges in distribution. The following figure (figure 4.4) shows that the trace

plot of the mediated factor Psychological Discomfort (PD) with other dimension

across 94000 samples. If we mentally break up this plot into a few horizontal

sections, the trace within any section would not look much different from the

trace in any other section. This indicates that the convergence in distribution

takes place rapidly and MCMC procedure very quickly forgets its starting values.

53

Figure 4.4: Posterior frequency Trace plot of the Mediating (IMR) and (PD) Regression Weight (W13)

To determine how long it takes for the correlations among the samples to die

down, autocorrelation plot which is the estimated correlation between the

sampled value at any iteration and the sampled value k iterations later for k = 1,

2, 3,…. is analysed for the “MEDIATING CAUSE FOR HIGH LEVEL CAUSE FOR THE

INFANT MORTALITY”. The figure 4.5 shows the correlation plot of mediated factor

Psychological Discomfort (PD) with other dimension across 94000 samples. The

figure exhibits that at lag 100 and beyond, the correlation is effectively 0. This

indicates that by 90 iterations, the MCMC procedure has essentially forgotten its

starting position. Forgetting the starting position is equivalent to convergence in

distribution. Hence it is ensured that convergence in distribution was attained and

that the analysed samples are indeed samples from the true posterior

distribution.

Figure 4.5: Posterior frequency Autocorrelation plot of the Mediating Factor (IMR) and (PD)


54

Even though marginal posterior distributions are very important, they do not

reveal relationships that may exist among the two parameters. The summary

table provided in Appendix - III and the posterior frequency plots given in the

figure 4.6 and figure 4.7 describe only the marginal posterior distributions of the

parameters. Hence to visualize the relationships among pairs of Parameters in

two-dimensional. The posterior two - dimensional surface plots and posterior two -

dimensional histogram plots following figures (figure 4.6 and figure 4.7) provides

bivariate marginal posterior plots of the “MEDIATING CAUSE FOR HIGH LEVEL

CAUSE FOR THE INFANT MORTALITY” for the overall mediated factor Psychological

Discomfort (PD) with other dimensions across 94000 samples. From the two

figures it is revealed that the two dimensional surface plot and histogram plot also

signifies the interrelationship between the mediating variable Psychological

Discomfort (PD) with the other dimensions of High Level Cause for the Infant

Mortality.

Figure 4.6: Two-dimensional Surface plot of the marginal posterior frequency distribution of the

mediating factor (PD with (IMR) and (HSI) Regression Weight (W9) and (W13)

Figure 4.7: Two-dimensional Histogram plot of the marginal posterior frequency distribution of the

mediating factor (PD) with (IMR) and (HSI) Regression Weight (W9) and (W13)

55

The following figure 4.8 shows that the two-dimensional contour plots of the

mediated factor Psychological Discomfort (PD) with other dimension across

94000 samples. Ranging from dark to light, the three shades of gray represent

50%, 90% and 95% credible regions, respectively. From the figure, it is revealed

that the sample citizen’s responses are normally distributed.

Figure 4.8: Two-dimensional Contour plot of the marginal posterior frequency distribution of the

mediating factor (PD) with (IMR) and (HSI) Regression Weight (W9) and (W13)

The various diagnostic plots featured from figure 4.2 to figure 4.8 of the Bayesian

estimation of convergence of MCMC algorithm confirms the fact that the

convergence takes place and the normality is attained. Hence absolute fit of the

“MEDIATING CAUSE FOR HIGH LEVEL CAUSE FOR THE INFANT MORTALITY” which

is empirically tested with mediating factor with the dimensions Psychological

Discomfort (PD) with the other dimensions of High Level cause for the Infant

Mortality. Psychological Discomfort is the Mediated factor for High level Cause for

the Infant Mortality and the same has been scientifically proved with the help of

above SEM. Hence Psychological Discomfort should be mandatory because

Psychological Discomfort is Mediating cause for High level cause for the Infant

Mortality at Thondamuthur block in Coimbatore district.

56

4.3. MEDIATING CAUSES FOR HIGH LEVEL CAUSE FOR THE INFANT MORTALITY LATENT MODEL

Mediating Cause for High Level cause for the Infant Mortality with the help of SEM

Model, the researcher could find out that, the Mediating effects on various

dimensions. This analysis has been done with the help of primary data from the

respondents in the study area. Let us see, the results of various dimensions

influence on High Level of Infant Mortality Rate in the study area.

4.3.1. Mediating Cause for High Level Cause for the Infant Mortality at

Thondamuthur Block in Coimbatore District 4.3.1.1. Structural Equation Modeling of Overall Mediated ‘HIGH LEVEL CAUSE

FOR THE INFANT MORTALITY’ Latent Model We have defined its dimension based on the setting used to explore the

construct. If Overall Mediated for “HIGH LEVEL CAUSE FOR THE INFANT

MORTALITY” Latent Model is to be applicable in the Indian context, the

dimensions and the sub dimensions on High Level cause for the Infant Mortality

have to be reliable and valid in measuring High Level cause for the Infant

Mortality at Thondamuthur Block in Coimbatore District. The Latent Model

examines the relative importance of dimensions of High Level cause for the Infant

Mortality and Psychological Discomfort (PD) is the mediating factor for the cause

of High Level cause for the Infant Mortality at Thondamuthur Block in Coimbatore

District.

The Overall Mediated for “HIGH LEVEL CAUSE FOR THE INFANT MORTALITY”

Latent Model examines the relative importance of Psychological Discomfort (PD)

as a mediating cause for the High Level Cause for the Infant Mortality at

Thondamuthur Block in Coimbatore District. Overall Mediated for “HIGH LEVEL

CAUSE FOR THE INFANT MORTALITY” Mediated Latent Model includes the

measurement of sub dimensions of High Level cause for the Infant Mortality are

as follows:

There are eight dimensions have been used in the present study which are I. Practitioners Services (PS) dimension includes several sub dimensions which

are Medical Records (PS1), Pharmacy Services (PS2), Medical Equipments (PS3),

Doctors Services (PS4), Nursing Services (PS5) and Insurance Coverage Facilities

57

(PS6); II. Hospital Services in Outpatients (HSO): dimension consists of several

sub dimensions which are Seating (HSO1), Waiting (HSO2), Emergency Services

(HSO3), Diagnostic Services Locations (HSO4), Cleanliness of Outpatients

departments (HSO5), Public Toilets (HSO6), Wheel Chair/Stretcher (HSO7) and

Restaurant (HSO8); III. Hospital Services in Inpatients (HSI) dimension includes

several sub dimensions which are Room Space, Amenities and Furniture (HSI1),

Lighting (HSI2), Wall Colour (HSI3), Bath/Toilet Facility (HSI4), Hot Water Facilities

(HSI5), Attenders (HSI6), Diet Services (HSI7) and Housekeeping (HSI8; IV. Social

Problems (SP) consists of several sub dimensions which are Child Marriage (SP1),

Gender (SP2), Discrimination (SP3), Less interest on Female Baby (After

Scanning) (SP4), Caste (SP5), Religion (SP6), Social Structure (SP7), Cultural

Norms (SP8), Value System (SP9) Inter-Caste Marriage (SP10) and Cultural Shift

(SP11); V. Infrastructure Facilities (IF) dimension includes several sub dimensions

which are Ambulance Services (IF1), Hospital Infrastructure (IF2), Poor Sanitation

(IF3), Hygiene (IF4), Safe Drinking Water (IF5), Distance of Hospital Services (IF6),

Distance of Hospital Location (IF7), Distance of Ambulance Services 15 km (IF8)

and Lack of Transport Facilities (IF9); VI. Psychological Discomfort (PD) consists of

several sub dimensions which are Emotion (PD1), Social Problems (PD2),

Financial Problems (PD3), Husband (PD4), Mother - in – law (PD5), Relatives of

Husband’s Family (PD6), Lack of Parents Support (PD7), Verbal use by others

(PD8), Problems Faced At Work Place Daily (PD9), Sexual Harassment from

colleagues and Higher Officials (PD10), Over Crowded Public Transport System

(PD11), Lack of Time Management (PD12), Vulnerable to Working Conditions

Across Diverse Occupation Informal Sectors (PD13), Varsity of Health Hazards

Informal Sectors (PD14), Continually Sexual Inducement by During the Pregnancy

(PD15), Continually Drank and Sexual Inducement by During the Pregnancy

(PD16) and No Long Gap for Another Baby Birth (PD17); VII. Economic Problems

(EP) dimension consists of several sub dimensions which are Limited access to

Cash and Credit (EP1), Nutrition (EP2), Shift into agrarian Changes (EP3),

Migration (EP4), Low Income (EP5) and Industrialization (EP6); VIII. High Level of

Infant Mortality Rate (IMR) dimension includes several sub dimensions which are

Lack of Knowledge and Skills to Care for New Born Child (IMR1), Breast Feeding

Problems (IMR2), Duration of hospital stay normal birth (IMR3), Infant Care,

Genital cleaning and ear cleaning (IMR4) and finally asking to the respondents

58

that they are agreeing for the said factor for cause for High level cause of Infant

Mortality Rate (IMR5).

After identifying a potential model that best explains the data in terms of theory

and model fit, a Confirmatory Factor Analysis (CFA) using Structural Equation

Modeling (SEM) was used to test the invariance of the factorial model. All tests of

model invariance begin with a global test of the equality of covariance structures

across groups (Joreskog, 1971). The data for all groups were analysed

simultaneously to obtain efficient estimates (Bentler, 1995). The constraints

include, from weaker to stronger. The study has been done in step by step

manner which are (1) Model Structure, (2) Model Structure and Factor Loadings,

and (3) Model Structure, Factor Loadings, and Unique Variance.

4.3.1.2. Evaluation of Model Fit According to the usual procedures, the goodness of fit is assessed by checking

the statistical and substantive validity of estimates, the convergence of the

estimation procedure, the empirical identification of the model, the statistical

significance of the parameters, and the goodness of fit to the covariance matrix

(Senthilkumar.N and Arulraj.A, 2011). The root mean squared error of

approximation (RMSEA) is selected as such a measure. Values equal to 0.05 or

lower are generally considered to be acceptable (Browne and Cudeck, 1993). The

sampling distribution for the RMSEA can be derived, which makes it possible to

compute confidence intervals. These intervals allow researchers to test for close

fit and not only for exact fit, as the χ2 does. If both extremes of the confidence

interval are below 0.05, then the hypothesis of close fit is rejected in favor of the

hypothesis of better than close fit. If both extremes of the confidence interval are

above 0.05, then the hypothesis of close fit is rejected in favor of the hypothesis

of bad fit (Senthilkumar. N and Arulraj. A, 2011). Several well-known goodness-of-

fit indices (GFI) were used to evaluate model fit: the chi-square2 (χ2), the

comparative fit index, the unadjusted GFI, the normal fit index (NFI), the Tucker-

Lewis index (TLI), the RMSEA and the standardized root mean square error

residual.

59

Figure 4.9: Shows AMOS path diagram output for the Overall Mediated for “HIGH LEVEL CAUSE FOR THE INFANT MORTALITY” Structural Equation Latent Model

60

Figure 4.9 provides AMOS’s path diagram output for the Overall Mediated for

“HIGH LEVEL CAUSE FOR THE INFANT MORTALITY” Structural Equation Latent

Model, From SEM model the decision maker can understand that Practitioners

Services (PS) dimension consists of 6 sub dimensions, Hospital Services in

Outpatients (HSO) dimension consists of 9 sub dimensions, Hospital Services in

Inpatients (HSI) dimension consists of 9 sub dimensions, Social Problems (SP)

dimension consists of 10 sub dimensions, Infrastructure Facilities (IF) dimension

consists of 9 sub dimensions, Psychological Discomfort (PD) dimension consists

of 17 sub dimensions, Economic Problems (EP) dimension consists of 6 sub

dimensions and High Level cause for the Infant Mortality dimension consists of 5

sub dimensions. The RMSEA fit statistics for the model was 0.08, which was

considered as a best fit model (Brown and Cudeck, 1993; Diamantopoulos and

Siguaw, 2000). The path diagram shows the Psychological Discomfort (PD) is the

mediating cause for the High Level cause of Infant Mortality. The regression co-

efficient 0.15 signifies the impact of mediating factor Practitioners Services (PD)

on the other Dimensions towards the cause for the High Level cause for the Infant

Mortality at Thondamuthur Block in Coimbatore District, Tamilnadu.

4.3.1.3. Evaluation of “HIGH LEVEL CAUSE FOR THE INFANT MORTALITY”

Mediated Latent Model The following table 4.16 provides the summary of the various goodness-of-fit

statistics and other values corresponding to the Overall Mediated for “HIGH LEVEL

CAUSE FOR THE INFANT MORTALITY” Structural Equation Latent Model. Also the

last column in the table provides the acceptable level for the various goodness-of-

fit statistics and other values. The below table it is revealed that all the criterions

of goodness-of-fit statistics and other measures of statistics are acceptable for

the Overall Mediated for “HIGH LEVEL CAUSE FOR THE INFANT MORTALITY”

Structural Equation Latent Model.

61

Table 4.16: Summary of the Various Goodness of Fit Statistics and Other Values Corresponding to the “HIGH LEVEL CAUSE FOR THE INFANT MORTALITY” Structural

Equation Latent Model

S.No. Measures of fit

“HIGH LEVEL CAUSE FOR THE INFANT MORTALITY”


Acceptable level for good fit

1. Chi-square2 (χ2) at p 0.01 4467.671 Significant 2. Degree of freedom (d.f) 2392 Accepted 3. Comparative Fit Index (CFI) 0.425 >0.90 4. Bentler – Bonett Indes or Normed Fit

Index (NFI) 0.312 >0.90

5. Root Mean Squared error of Approximation (RMSEA) 0.078 <0.08

Accepted 6. Non Centrality Parameter (NCP) 3247.265 Accepted 7. Non Centrality Parameter, Lower

Boundary (NCPLO 90) 3031.610 Accepted

8. Non Centrality Parameter, Upper Boundary (NCPHI 90) 3470.158 Accepted

9. Parsimony adjusted NFI (PNFI) 0.292 Accepted 10. Parsimony adjusted CFI (PCFI) 0.397 Accepted 11. Minimum value of Discrepancy (FMIN) 56.962 Accepted 12. Lower Limit of FMIN (LO90) 30.622 Accepted 13. Upper Limit of FMIN (HI90) 35.052 Accepted 14. Browne-Cudeck Criterion (BCC) 7362.598 Accepted 15. ECVI 61.710 Accepted 16. LO90 of ECVI 59.531 Accepted 17. HI90 of ECVI 63.961 Accepted 18. MECVI of ECVI 74.370 Accepted 19. HOELTER .05 45 <= 20. HOELTER .01 45 Atleast 200 21. Regression Co-Efficient 0.15 <1.92

Source: Amos 20.0 output 4.3.1.4. Bayesian Estimation and Testing of “HIGH LEVEL CAUSE FOR THE

INFANT MORTALITY” Structural Equation Latent Model In this section has been adopted for the procedure of assessing convergence of

MCMC algorithm of maximum likelihood. To estimate the MCMC convergence the

researcher has adopted two methods namely, convergence in distribution,

convergence of posterior summaries. The values of posterior mean accurately

estimate the “HIGH LEVEL CAUSE FOR THE INFANT MORTALITY” Structural

Equation Latent Model. From the above table the highest value of Convergence

Statistics (C.S) is 1.001 which is less than the 1.002 conservative measures

(Gelman et. al. 2004) (See Appendix - IV).

62

4.3.1.5. Posterior Diagnostic Plots of “HIGH LEVEL CAUSE FOR THE INFANT MORTALITY” Structural Equation Latent Model

To check the convergence of the Bayesian MCMC method the posterior diagnostic

plots are analysed. The following figures (figure 4.10 and figure 4.11) show the

posterior frequency polygon distribution and posterior frequency histogram

distribution of the parameters across the 98000 samples. The Bayesian MCMC

diagnostic plots reveals that for all the figures the normality is achieved, so the

structural equation latent model fit is accurately estimated. The trace plot also

called as time-series plot shows the sampled values of a parameter over time.

Figure 4.10: Posterior frequency Polygon distribution of the Mediating Factor (PD) and (IMR)


Figure 4.11: Posterior frequency Histogram distribution of the Mediating Factor (PD) and (IMR) Regression Weight (W70)

This plot helps to judge how quickly the MCMC procedure converges in

distribution. The following figure (figure 4.12) shows the trace plot of the “HIGH

63

LEVEL CAUSE FOR THE INFANT MORTALITY” Structural Equation Latent Model for

the mediated factor Psychological Discomfort (PD) to High Level cause for the

Infant Mortality dimension across 98000 samples. If we mentally break up this

plot into a few horizontal sections, the trace within any section would not look

much different from the trace in any other section. This indicates that the

convergence in distribution takes place rapidly. Hence the Overall Mediated for


Model MCMC procedure very quickly forgets its starting values.

Figure 4.12: Posterior frequency Trace plot of the Mediating Factor (PD) and (IMR) Regression

Weight (W70)

To determine how long it takes for the correlations among the samples to die

down, autocorrelation plot which is the estimated correlation between the

sampled value at any iteration and the sampled value k iterations later for k = 1,

2, 3,…. is analysed for the Overall Mediated Inclusive Growth for “HIGH LEVEL

CAUSE FOR THE INFANT MORTALITY” Structural Equation Latent Model.

Figure 4.13: Posterior frequency Autocorrelation plot of the Mediating Factor (PD) and (IMR)


64

The figure 4.13 provides the correlation plot of the Overall Mediated Inclusive

Growth for “HIGH LEVEL CAUSE FOR THE INFANT MORTALITY” Structural Equation

Latent Model for the mediated factor Psychological Discomfort (PD) to High Level

cause for the Infant Mortality dimension across 98000 samples. The figure

exhibits that at lag 100 and beyond, the correlation is effectively 0. This indicates

that by 90 iterations, the MCMC procedure has essentially forgotten its starting

position. Forgetting the starting position is equivalent to convergence in

distribution. Hence it is ensured that convergence in distribution was attained and

that the analysed samples are indeed samples from the true posterior

distribution. Even though marginal posterior distributions are very important, they

do not reveal relationships that may exist among the two parameters. The

summary table provided in Appendix - IV and the posterior frequency plots given in

the figure 4.14 and figure 4.15 describe only the marginal posterior distributions

of the parameters. Hence to visualize the relationships among pairs of

Parameters in two-dimensional. The posterior frequency surface plot and

posterior frequency histogram plot following figures (figure 4.14 and figure 4.15)

provides bivariate marginal posterior plots of the Overall Mediated for “HIGH

LEVEL CAUSE FOR THE INFANT MORTALITY” Structural Equation Latent Model for

the mediated factor Psychological Discomfort (PD) with other dimensions across

98000 samples.

Figure 4.14: Two-dimensional Surface plot of the marginal posterior frequency distribution of the

mediating factor (PD) and (IMR) and (HSI) Regression Weight (W68) and (W70)

65

Figure 4.15: Two-dimensional Histogram plot of the marginal posterior frequency distribution of the mediating factor (PD) and (IMR) and (HSI) Regression Weight (W68) and (W70)

From the two figures it is revealed that the two dimensional surface plot and

histogram plot also signifies the interrelationship between the mediating variable

Psychological Discomfort (PD) with the other dimensions of High Level cause for

the Infant Mortality and Hospital Services in Inpatient. The following figure 4.16

displays the two-dimensional plot of the bivariate posterior frequency distribution

density across 98000 samples. Ranging from dark to light, the three shades of

gray represent 50%, 90% and 95% credible regions, respectively. From the figure,

it is revealed that the sample citizen’s responses are normally distributed. The

various diagnostic plots featured from figure 4.10 to figure 4.16 of the Bayesian

estimation of convergence of MCMC algorithm confirms the fact that the

convergence takes place and the normality is attained. Hence absolute fit of


Model.

Figure 4.16: Two-dimensional Contour plot of the marginal posterior frequency distribution of the

mediating factor (PD) and (IMR) and (HSI) Regression Weight (W68) and (W70)

66

From the “HIGH LEVEL CAUSE FOR THE INFANT MORTALITY” Structural Equation

Latent Model which is empirically tested with mediating factor with the

dimensions Practitioners Services (PD) Hospital Services in Outpatients (HSO),

Hospital Services in Inpatients (HSI), Social Problems (SP), Infrastructure Facilities

(IF), Economic Problems (EP) and High Level cause of Infant Mortality Rate (IMR) it

is evident that the High Level cause for the Infant Mortality should concentrate on

the Psychological Discomfort (PD) is the most important aspect of High Level

cause for the Infant Mortality at Thondamuthur Block in Coimbatore District, Tamil

Nadu.

It is probable that a significant proportion of the remaining obstetric morbidity is

related to the social and psychological background and lifestyle of pregnant

women. These factors include socio-demographic characteristics of the woman

(Dott & Fort 1975; Brennan & Lancashire 1978; Eisner et al. 1979; Hendershot

1979; Osbourne et al. 1981; Robinson et al. 1982; Naeye & Peters 1982; Carn-

Hill et al. 1983; Murphy et al. 1984; Ericson et al. 1984), her attitude to the

pregnancy (Heinstein 1967; Pohlman 1969; Laukaran & van den Berg 1980), her

mental health state (Gunter 1963; Nuckolls et al. 1972; Jones 1978; Standley et

al. 1979; Newton et al. 1979), and such behavioural factors as cigarette smoking

(Meyer et al. 1976; Rantakallio 1979; Harlap & Shiono 1980), alcohol

consumption (Jones et al. 1974; Hanson et al. 1976; Ouellette et al. 1977;

Olegsard et al. 1979; Harlap & Shiono 1980; Kline et al. 1980; English & Bower

1983), analgesic use (Turner & Collins 1975) or the decision to work during

pregnancy (Waldron et al. 1982; Chamberlain & Garcia 1983; Mamelle et al.

1984).

The generic "stress" or "anxiety" construct was formulated from several

instruments or variables: a measure of pregnancy symptoms; a stress measure

which is a modification of the Life Events Inventory (Holmes and Rahe 1967),

which includes items from the Hassles Scale Anxiety Scale Questionnaire (Calteli

1957); four separate subscales (Maternal Attitude to Pregnancy Instrument) (Blau

et al. 1964). Self esteem was assessed by the Rosenberg instrument (Rosenberg

1965); loci of control, by the sex role attitudes were evaluated by the Cole

instrument (Cole 1979).

67

In recent years a handful of key epidemiological studies have generated

discussion on the application of econometric methods to overcome its associated

problems (Briscoe, Akin et al. 1990).

4.4. Results of SEM Model The SEM models provide a degree of covariance and invariance’s of sub-

dimensions in determining the infant mortality in the study area. The social and

behavioural studies are playing a vital role in determining infant mortality rate in

India. The State Planning Commission encourages for investigating the mediating

cause for high level cause for the infant mortality at Thondamuthur Block in

Coimbatore District. However, the project findings base on econometric models.

The SEM models provide a degree of covariance and invariance’s of sub-

dimensions in determining the cause of high level cause for the infant mortality in

the study area. Public health services (Practitioners services, hospital Services in

Outpatients and Hospital Services in Inpatients) resulted insignificant impact on

determining Infant mortality and health of the children in Thondamuthur Block.

Hence it is not a factor for the high level cause for the Infant Mortality. However, it

is notable that psychological discomfort dimension is a significant determinant

dimensions for the cause of high level cause for the infant mortality in

Thondamuthur Block. Social Problems, Infrastructure facilities and economic

problems dimensions resulted marginal impact on infant mortality. Since the state

government provides various schemes for pregnant women during maternity

period and also for improving child health. But the Social Problems, Infrastructure

facilities and economic problems dimensions created psychological problems

during maternity period. Within the last decade, considerable interest has

developed in the possible roles of stress and other psychosocial factors as

mediators of pregnancy outcomes. Interest in this area also has been stimulated

by evidence that psychological factors influence immune function (Ader 1981,

Cohen 1991, Jemmott and Locke 1984) and by the recognition that psychosocial

factors may play a role, yet undefined, in premature birth (Centers for Disease

Control 1991). Recent reports indicate that neuroendocrine, bio chemical, and

psychosocial factors interact to influence the immune system (Geiser 1989) and

many stressful life events have been reported in association with decreases in

measures of human immune status. Social support has been reported by Geiser

68

(1989) to mediate the impact of stress on the immune response. Others suggest

a relationship between stress and low birth weight (Newton and Hunt 1984). The

role of the catecholamine, adrenaline and nor adrenaline, and cortical as

indicators of stress induced by human external and internal environmental

demands, and their increased excretion during pregnancy, has been firmly

established. SEM empirically proved that psychological discomfort dimension is

the mediated factor for the cause of high level cause for the infant mortality at

Thondamuthur block in Coimbatore district.

69

SECTION - 5

FINDINGS, CONCLUSION AND POLICY RECOMMENDATIONS

The study is meant to evaluate the dimensions associated with infant mortality at

Thondamuthur block in Coimbatore District. We summarise the findings based on

the analysis which are as under.

5.1. Findings from Socio-Economic Dimensions of the Respondents Age: We observed that majority of the respondents were in the age groups

between 22 years to 31 years. Very young mothers faced difficulties in their

pregnancies and deliveries, because of their physical immaturity. They have

limited knowledge and confidence in caring for their infants and young children.

Women are in the category over 30 years suffered age-related problems during

their pregnancy and delivery.

Educational Qualification: We observed that the majority of the respondents were

School Drops out. Literate mothers create healthier environment and provide

nutritious food to their infants than compared with illiterate mother.

Occupation: We observed that most of the pregnant women were unemployed

which stimulate psychological and family problems.

Religion: We observed that the majority of the respondents were belongs to Hindu

religion. Religion and membership in a scheduled caste or scheduled tribe is

known to affect many aspects of life in India and is likely to affect levels of infant

and child mortality as well. Some of the effect of religion and caste/tribe

membership on mortality may be due to differences in life-style based on traditions

and beliefs. Such differences may include customary practices related to

childbirth, infant feeding, and healthcare. These practices inferences the infant

and child mortality rate.

Income: We observed that the majority of the respondents’ family annual income

is below Rs.50000/ lived in joint families with own concrete houses. The majority

respondents belong to marginalised group (Dalits & Tribes). The majority of

respondents were made use of PHCs and Chief Minister insurance Schemes.

70

5.2. Findings for Mediating Cause for High Level Cause for the Infant Mortality in the Study Area

The social and behavioural studies are playing a vital role in determining infant

mortality rate in India. The State Planning Commission encourages for

investigating the mediating cause for high level cause of infant mortality at

Thondamuthur Block in Coimbatore District. However, the project findings base on

econometric models. The SEM models provide a degree of covariance and

invariance’s of sub-dimensions in determining the cause of high level cause of

infant mortality in the study area. Public health services (Practitioners services,

hospital Services in Outpatients and Hospital Services in Inpatients) resulted

insignificant impact on determining Infant mortality and health of the children in

Thondamuthur Block. Hence it is not a factor for the high level cause for the Infant

Mortality. However, it is notable that psychological discomfort dimension is a

mediating dimensions for the high level cause for the infant mortality at

Thondamuthur Block in Coimbatore district. Social Problems, Infrastructure

facilities and economic problems dimensions resulted marginal impact on infant

mortality. Since the state government provides various schemes for pregnant

women during maternity period and also for improving child health. But the Social

Problems, Infrastructure facilities and economic problems dimensions created

psychological problems during maternity period. Within the last decade,

considerable interest has developed in the possible roles of stress and other

psychosocial factors as mediators of pregnancy outcomes. Interest in this area

also has been stimulated by evidence that psychological factors influence

immune function (Ader 1981, Cohen 1991, Jemmott and Locke 1984) and by the

recognition that psychosocial factors may play a role, yet undefined, in premature

birth. Recent reports indicate that neuroendocrine, bio chemical and psychosocial

factors interact to influence the immune system (Geiser 1989) and many stressful

life events have been reported in association with decreases in measures of

human immune status. Social support has been reported by Geiser (1989) to

mediate the impact of stress on the immune response. Others suggest a

relationship between stress and low birth weight (Newton and Hunt 1984). The

role of the catecholamine, adrenaline and nor adrenaline and cortical as

indicators of stress induced by human external and internal environmental

demands and their increased excretion during pregnancy, has been firmly

71

established. SEM empirically proved that psychological discomfort dimension is

the mediated cause for the high level cause for the infant mortality at

Thondamuthur Block in Coimbatore District.

5.3. Empirically Proved from Conceptual Model

Figure 5.1: Empirically Proved Conceptual Model for High Level cause for the Infant Mortality

Practitioners Services

Hospital Services in Outpatients

Hospital Services in Inpatients

Social Problems

Infrastructure Facilities

Economic Problems

Demographic Variable

High Level cause of Infant Mortality Rate

Psychological Discomfort

The conceptual research model empirically proved (figure 5.1). Thus, the present

study has focused on High Level cause for the Infant Mortality. This report

identifies that the Psychological Discomfort is the mediating cause for the High

Level cause for the Infant Mortality at Thondamuthur Block in Coimbatore District.

5.4. Conclusion and Policy Recommendations The present study has been made to eradicate the infant mortality completely.

There are many studies discussed about the Infant Mortality Rate exclusively. This

is the only study has been made to find out the mediating cause for the high level

cause of infant mortality. It is found from the previous studies that the Infant

Mortality Rate has increasing due to various factors such as child sex, mothers’

literacy, caste, birth order and mothers’ age etc...

72

In this study, we have scientifically proved that psychological discomfort is the

mediating cause for the high level cause for infant mortality. Hence social

scientist, policy makers and government should concentrate on psychological

discomfort to eradicate Infant Mortality permanently.

Medical practitioners’ services such as doctor services, nursing services,

pharmacy services and medical equipment services development will not make

any changes in Infant Mortality.

Improvement in hospital services, promoting emergency services, diagnostic

services, cleanness services, lab services and housekeeping services have not

helped to reduce Infant Mortality.

Social factors such as child marriage, gender discrimination, caste, religion, inter-

caste marriage and cultural shift are important factors but the same have not

been directly influenced on the high level cause for the Infant Mortality.

Infrastructure facilities include ambulance services, sanitation services and

transportation services will not support to reduce Infant Mortality.

Finance is the backbone of everything. Without finance nothing can be done.

Economic factor includes financial crises of the poor, lack of procurement of

nutrition, migration due to low income and industrialization. The results from our

study stated that economic problem is not a mediating cause for the high level

cause for the Infant mortality. Hence the policy maker need not give much

importance to the economic problem factor.

The present study scientifically proves that psychological discomfort factor is a

medicating cause for the high level cause for the infant mortality at

Thondamuthur Block in Coimbatore district. It means if there is any change in

psychological discomfort factor then it will reflects immediately in Infant Mortality.

Psychological Discomfort management alone the one and only solution for

eradicating the infant mortality completely and the same has been scientifically

proved with the help of structural equation model.

73

Psychological Discomfort factor includes emotions of pregnant mothers,

behaviour of husband, mother-in-law, friends and other relatives, lack of parents

and friends support, sexual harassment in working place, varsity health hazards

in informal sector, continuous sex inducement during the pregnancy time due to

continuous drinking habit of the spouse and not proper interval between two

babies.

Psychological Discomfort is the mediating cause for the high level cause for the

Infant mortality is not only applicable at Thondamuthur Block in Coimbatore

district but also applicable in Tamilnadu, India. Hence, it is clear that there is a

tiny change the above said factor which will cause immediate impact in Infant

Mortality.

The policy makers, social science scientist, consultants and the Government

should carefully make use of the said factor to eradicate the infant mortality at

Thondamuthur Block in Coimbatore District. We should take necessary steps to

concentrate on Psychological Discomfort factor instead of concentrating on other

factor such as medical practitioner’s services, transportation services, economic

issues of the poor and hospital services.

Hence we conclude form the study that Psychological Discomfort is a mediating

cause for the high level cause for the Infant mortality is not only applicable at

Thondamuthur Block in Coimbatore district, but also applicable in Tamilnadu and

India.

There is a need to create a committee to make ‘Effective Management of

Psychological Discomfort’ factor to eradicate infant mortality completely.

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Appendix - I (a)

MEDIATING CAUSES FOR HIGH LEVEL OF INFANT MORTALITY RATE AT THONDAMUTHUR BLOCK OF COIMBATORE DISTRICT

Questionnaire

Please indicate your responses with the appropriate level of Agree for the factors mentioned below.

1. Highly disagree 2.Disagree 3.Some what ok 4.Undesired 5.Some what disagree 6. Agree 7. Highly agree

S. No

Questions Highly disagree - Highly agree

Practitioners Services 1. Medical Records 1 2 3 4 5 6 7 2. Pharmacy Services 1 2 3 4 5 6 7 3. Medical Equipments 1 2 3 4 5 6 7 4. Doctors Services 1 2 3 4 5 6 7 5. Nursing Services 1 2 3 4 5 6 7 6. Insurance Coverage Facilities 1 2 3 4 5 6 7 Hospital Services in Outpatients 7. Seating 1 2 3 4 5 6 7 8. Waiting 1 2 3 4 5 6 7 9. Emergency Services 1 2 3 4 5 6 7 10. Diagnostic Services Locations 1 2 3 4 5 6 7 11. Cleanliness of Outpatients departments 1 2 3 4 5 6 7 12. Public Toilets 1 2 3 4 5 6 7 13. Wheel Chair/Stretcher 1 2 3 4 5 6 7 14. Restaurant 1 2 3 4 5 6 7 15. Lab Facilities 1 2 3 4 5 6 7 Hospital Services in Inpatients 16. Room Space, Amenities and Furniture 1 2 3 4 5 6 7 17. Lighting 1 2 3 4 5 6 7 18. Wall Colour 1 2 3 4 5 6 7 19. Bath/Toilet Facility 1 2 3 4 5 6 7 20. Hot Water Facilities 1 2 3 4 5 6 7 21. Attenders 1 2 3 4 5 6 7 22. Diet Services 1 2 3 4 5 6 7 23. Housekeeping 1 2 3 4 5 6 7 24. Lab Facilities 1 2 3 4 5 6 7 Social Problems 25. Child Marriage 1 2 3 4 5 6 7 26. Gender Discrimination 1 2 3 4 5 6 7 27. Less interest on Female Baby (After Scanning) 1 2 3 4 5 6 7 28. Caste 1 2 3 4 5 6 7 29. Religion 1 2 3 4 5 6 7 30. Social Structure 1 2 3 4 5 6 7 31. Cultural Norms 1 2 3 4 5 6 7 32. Value System 1 2 3 4 5 6 7

33. Inter-Caste Marriage 1 2 3 4 5 6 7 34. Cultural Shift 1 2 3 4 5 6 7 Infrastructure Facilities 35. Ambulance Services 1 2 3 4 5 6 7 36. Hospital Infrastructure 1 2 3 4 5 6 7 37. Poor Sanitation 1 2 3 4 5 6 7 38. Hygiene 1 2 3 4 5 6 7 39. Safe Drinking Water 1 2 3 4 5 6 7 40. Distance of Hospital Services 1 2 3 4 5 6 7 41. Distance of Hospital Location 1 2 3 4 5 6 7 42. Distance of Ambulance Services 15 km 1 2 3 4 5 6 7 43. Lack of Transport Facilities 1 2 3 4 5 6 7 Psychological Discomfort 44. Emotion 1 2 3 4 5 6 7 45. Social Problems 1 2 3 4 5 6 7 46. Financial Problems 1 2 3 4 5 6 7 47. Husband 1 2 3 4 5 6 7 48. Mother - in - law 1 2 3 4 5 6 7 49. Relatives of Husband’s Family 1 2 3 4 5 6 7 50. Lack of Parents Support 1 2 3 4 5 6 7 51. Verbal use by others 1 2 3 4 5 6 7 52. Problems Faced At Work Place Daily 1 2 3 4 5 6 7 53. Sexual Harassment from colleagues and

Higher Officials 1 2 3 4 5 6 7

54. Over Crowded Public Transport System 1 2 3 4 5 6 7 55. Lack of Time Management 1 2 3 4 5 6 7 56. Vulnerable to Working Conditions Across

Diverse Occupation Informal Sectors 1 2 3 4 5 6 7

57. Varsity of Health Hazards Informal Sectors 1 2 3 4 5 6 7 58. Continually Sexual Inducement by During the

Pregnancy 1 2 3 4 5 6 7

59. Continually Drank and Sexual Inducement by During the Pregnancy

1 2 3 4 5 6 7

60. No Long Gap for Another Baby Birth 1 2 3 4 5 6 7 Economic Problems 61. Limited access to Cash and Credit 1 2 3 4 5 6 7 62. Nutrition 1 2 3 4 5 6 7 63. Shift into agrarian Changes 1 2 3 4 5 6 7 64. Migration 1 2 3 4 5 6 7 65. Low Income 1 2 3 4 5 6 7 66. Industrialization 1 2 3 4 5 6 7 High Level of Infant Mortality Rate 67. Lack of Knowledge and Skills to Care for New

Born Child 1 2 3 4 5 6 7

68. Breast Feeding Problems 1 2 3 4 5 6 7 69. Duration of hospital stay normal birth 1 2 3 4 5 6 7 70. Infant Care, Genital cleaning and ear cleaning 1 2 3 4 5 6 7 71. Do you Agree that above are face cause for

High level of Infant Mortality Rate 1 2 3 4 5 6 7

Personal Information 1. Name : 2. Age : 1) Below 21 years 2) 22 years to 31

years 2) 32 years to 41 years 2) 42 years &

above 3. Educational

Qualification : 1) Schooling

Dropout 2) Secondary Education

3) Higher Secondary Education

4) Under Graduate

5) Post Graduate

4. Occupation : 1) Un Employed 2) Agriculture 3) Private 4) Government 5) Self Employee

5. Religion : 1) Hindu 2) Islam 3) Christian 6. Annual Income : 1) Below Rs.50000 2) Rs.50001 to

Rs.150000 3) Rs.150001 to Rs.250000

4) Rs.250001 & Above

7. Community : 1) Backward Class 2) Most Backward Class/Denoted Class

3) Schedule Cast 4) Schedule Tribe 8. Types of Family : 1) Joint Family 2) Nuclear Family 9. Residential House : 1) Own House 2) Rent House

10. Types of House : 1) Concrete 2) Tiled

3) Terrace 4) Thatched

11. Size of Landholding

: 1) Landless 2) Marginal 3) Large

12. Toilet Facilities : 1) Common Toilet 2) Separate Toilet

3) Open Toilet

13. Recommendation of Hospital

: 1) Self Decision 2) Family Doctor Recommendation

3) Friends and Relatives Recommendation

4) Beneficiaries Recommendation

14. Types of Hospital : 1) Government Hospital 2) Private Hospital 15. Medical Insurance : 1) Nil Insurance 2) Government Insurance

3) Private Insurance 4) Chief Minister Insurance Scheme

16. Any Infant Death : 1) Yes 2) No

Appendix - I (b)

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28. º¡¾¢ (1) (2) (3) (4) (5) (6) (7) 29. Á¾õ (1) (2) (3) (4) (5) (6) (7) 30. ºã¸ ¸ð¼¨ÁôÒ (1) (2) (3) (4) (5) (6) (7) 31. ¸Ä¡îº¡Ã ÅÆ¢Ó¨È (1) (2) (3) (4) (5) (6) (7) 32. Á¾¢ôÒ Ó¨È¸û (1) (2) (3) (4) (5) (6) (7) 33. ¸ÄôÒ ¾¢ÕÁ½õ (1) (2) (3) (4) (5) (6) (7) 34. ÀñÀ¡ðÎ Á¡üÈõ (1) (2) (3) (4) (5) (6) (7)

¯û ¸ð¼¨ÁôÒ Åº¾¢¸û 35. ¬õÒÄýŠ §º¨Å (1) (2) (3) (4) (5) (6) (7) 36. ÁÕòÐÅÁ¨É ¯û ¸ð¼¨ÁôÒ Åº¾¢ (1) (2) (3) (4) (5) (6) (7) 37. Ì¨ÈÅ¡É ¯¼ø¿Äõ ¸¡ìÌõ ²üÀ¡Î (1) (2) (3) (4) (5) (6) (7)

38. Í¸¡¾¡Ãõ (1) (2) (3) (4) (5) (6) (7) 39. À¡Ð¸¡ì¸ôÀð¼ ÌÊ¿£÷ (1) (2) (3) (4) (5) (6) (7) 40. ÁÕòÐÅÁ¨É ¦¾¡¨Ä× (1) (2) (3) (4) (5) (6) (7) 41. ÁÕòÐÅÁ¨É «¨ÁóÐûÇ

þÕôÀ¢¼ò¾¢ü¸¡É àÃõ (1) (2) (3) (4) (5) (6) (7)

42. 15 ¸¢. Á¢ ¦¾¡¨ÄÅ¢ø ¬õÒÄýŠ §º¨Å

(1) (2) (3) (4) (5) (6) (7)

43. §À¡ìÌÅÃòÐ Åº¾¢Â¢ý¨Á (1) (2) (3) (4) (5) (6) (7) ¯ÇÅ¢Âø ¡£¾¢Â¡É ÁÉ ¯¨Çîºø

44. ¯½÷îº¢ÅºôÀÎ¾ø (1) (2) (3) (4) (5) (6) (7) 45. ºã¸ô À¢Ãîº¨É¸û (1) (2) (3) (4) (5) (6) (7) 46. À½ô À¢Ãîº¨É¸û (1) (2) (3) (4) (5) (6) (7) 47. ¸½Å÷ (1) (2) (3) (4) (5) (6) (7) 48. Á¡Á¢Â¡÷ (1) (2) (3) (4) (5) (6) (7) 49. ¸½Å¡¢ý ÌÎõÀ ¯ÈÅ¢É÷¸û (1) (2) (3) (4) (5) (6) (7) 50. ¦Àü§È¡¡¢ý ¬¾ÃÅ¢ý¨Á (1) (2) (3) (4) (5) (6) (7) 51. À¢ÈÃ¡ø ÀÂýÀÎò¾ôÀÎõ ¦º¡ü¸û (1) (2) (3) (4) (5) (6) (7) 52. À½¢Ò¡¢Ôõ ¿¢ÚÅÉò¾¢ø ¾¢Éó§¾¡Úõ

²üÀÎõ À¢Ãîº¨É¸û (1) (2) (3) (4) (5) (6) (7)

53. ¯¼ý À½¢Ò¡¢§Å¡÷ ÁüÚõ ¯Â÷ «¾¢¸¡¡¢¸Ç¡ø ²üÀÎõ À¡Ä¢Éò ¦¾¡ó¾Ã×¸û

(1) (2) (3) (4) (5) (6) (7)

54. §À¡ìÌÅÃòÐ ¦¿È¢ºø (1) (2) (3) (4) (5) (6) (7) 55. ¸¡Ä §ÁÄ¡ñ¨Á þý¨Á (1) (2) (3) (4) (5) (6) (7) 56. Å¨ÃÂÚì¸ôÀ¼¡¾ Ð¨ÈÂ¢ø Á¡ÚÀð¼

À½¢Â¢ø À¡¾¢ôÒ ¾ÃìÜÊÂ Å¨¸Â¢ø À½¢Ò¡¢Ôõ ¿¢¨Ä

(1) (2) (3) (4) (5) (6) (7)

57. Å¨ÃÂÚì¸ôÀ¼¡¾ Ð¨ÈÂ¢ø ²üÀÎõ ¯¼ø ¿Äì §¸Î

(1) (2) (3) (4) (5) (6) (7)

58. ¸÷ôÀ¸¡Äò¾¢ø ¦¾¡¼÷ À¡Ä¢Âø àñÎ¾ø

(1) (2) (3) (4) (5) (6) (7)

59. ¸÷ôÀ¸¡Äò¾¢ø ¦¾¡¼÷ ÌÊôÀÆì¸õ ÁüÚõ À¡Ä¢Âø àñÎ¾ø

(1) (2) (3) (4) (5) (6) (7)

60. «Îò¾ ÌÆó¨¾ À¢ÈôÀ¢ü¸¡É þ¨¼¦ÅÇ¢Â¢ý¨Á

(1) (2) (3) (4) (5) (6) (7)

¦À¡ÕÇ¡¾¡Ã À¢Ãîº¨É¸û 61. À½õ ÁüÚõ ¸¼ý ¦ÀÚÅ¾¢ø º¢ì¸ø (1) (2) (3) (4) (5) (6) (7) 62. ºòÐ½× (1) (2) (3) (4) (5) (6) (7) 63. Å¢Åº¡Â Á¡üÈò¾¢üÌ ¿¸÷¾ø (1) (2) (3) (4) (5) (6) (7) 64. þ¼ô¦ÀÂ÷× (1) (2) (3) (4) (5) (6) (7) 65. Ì¨ÈÅ¡É ÅÕÅ¡ö (1) (2) (3) (4) (5) (6) (7) 66. ¦¾¡Æ¢øÁÂÁ¡¾ø (1) (2) (3) (4) (5) (6) (7)

º¢Í þÈôÒ Å£¾õ «¾¢¸¡¢ò¾ø; 67. «È¢Å¢ý¨Á ÁüÚõ º¢Í ÀÃ¡Á¡¢ò¾Ä¢ø

¾¢È¨ÁÂ¢ý¨Á (1) (2) (3) (4) (5) (6) (7)

68. ¾¡öôÀ¡ø °ðÎÅ¾¢ø º¢ì¸ø (1) (2) (3) (4) (5) (6) (7) 69. Í¸ôÀ¢ÃÅºò¾¢ý §À¡Ð

ÁÕòÐÅÁ¨ÉÂ¢ø ¾íÌõ ¸¡Ä «Ç× (1) (2) (3) (4) (5) (6) (7)

70. º¢Í ÀÃ¡Á¡¢ôÒ, À¡ÖÚôÒ ÁüÚõ ¸¡Ð Íò¾õ ¦ºö¾ø

(1) (2) (3) (4) (5) (6) (7)

71. §ÁüÜÈ¢Â¨Å¸û º¢Í þÈôÒ Å£¾õ «¾¢¸¡¢ì¸ì ¸¡Ã½õ ±ýÀ¨¾ ²üÚì ¦¸¡ûÅ£Ã¡.

(1) (2) (3) (4) (5) (6) (7)

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3) 32 ÅÂÐ Ó¾ø 41 ÅÂÐ Å¨Ã

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16. ²¾Ûõ º¢Í þÈôÒ

: 1) ¬õ 2) þø¨Ä

APPENDIX – II

VILLAGE/URBAN/BLOCK LEVEL DATA OF COIMBATORE DISTRICT

Village Wise Data of Thondamuthur Block

Table 1: Population of Thondamuthur Block Villages No.

Household Total Male Female

Thennammanallur 1443 (7.84%) 5098 (7.68%) 2491 (7.52%) 2607 (7.84%) Devarayapuram 1802 (9.78%) 6417 (9.68%) 3144 (9.49%) 3273 (9.87%) Jagirnaickenpalyam 334 (1.82%) 1486 (2.24%) 813 (2.45%) 673 (2.04%) Vellaimalaipattinam 916 (4.97%) 4066 (6.13%) 2032 (6.13%) 2034 (6.13%) Narasipuram 831 (4.51%) 3078 (4.64%) 1574 (4.75%) 1504 (4.53%) Madavarayapuram 1797 (9.75%) 6365 (9.59%) 3226 (9.74%) 3139 (9.46%) Ikkaraibooluvampatti 1834 (9.95%) 6361 (9.59%) 3183 (9.61%) 3178 (9.58%) Madampatti 1999 (10.85%) 6771 (10.22%) 3359 (10.14%) 3412 (10.28%)

Theethipalayam 2386 (12.95%) 8629 (13.02%) 4296 (12.97%) 4333 (13.06%)

Boluvampatti(R.F) 78 (0.42%) 224 (0.35%) 119 (0.36%) 105 (0.33%) Perur Chettipalayam (CT) 5004 (27.16%) 17809 (26.86%) 8891 (26.84%) 8918 (26.88%)

Total 18424 (100%) 66304 (100%) 33128 (100%) 33176 (100%) Source: District Planning Commission, Coimbatore.

Table 2: 0-6 age group population of Thondamuthur Block

Villages Total Percentage Male Percentage Female Percentage Thennammanallur 450 7.52 225 7.28 225 7.76 Devarayapuram 538 8.98 278 9.00 260 8.97 Jagirnaickenpalyam 91 1.52 54 1.75 37 1.28 Vellaimalaipattinam 275 4.59 149 4.82 126 4.35 Narasipuram 245 4.09 125 4.05 120 4.14 Madavarayapuram 619 10.34 342 11.07 277 9.56 Ikkaraibooluvampatti 536 8.95 270 8.74 266 9.18 Madampatti 595 9.94 312 10.09 283 9.77 Theethipalayam 847 14.14 425 13.75 422 14.56 Boluvampatti(R.F) 22 0.37 18 0.58 4 0.14 Perur Chettipalayam (CT) 1770 29.56 892 28.87 878 30.29

Total 5988 100.00 3090 100.00 2898 100.00 Source: District Planning Commission, Coimbatore.

Table 3: SC Population of Thondamuthur Block Villages Total Percentage Male Percentage Female Percentage

Thennammanallur 1838 14.47 901 14.25 937 14.69 Devarayapuram 1577 12.42 768 12.15 809 12.69 Jagirnaickenpalyam 403 3.17 207 3.27 196 3.07 Vellaimalaipattinam 790 6.22 400 6.34 390 6.12 Narasipuram 466 3.67 234 3.70 232 3.64 Madavarayapuram 510 4.02 274 4.34 236 3.70 Ikkaraibooluvampatti 1367 10.77 679 10.74 688 10.78 Madampatti 1384 10.91 700 11.07 684 10.73 Theethipalayam 1395 10.97 686 10.85 709 11.12 Boluvampatti(R.F) 0 0 0 0 0 0 Perur Chettipalayam (CT) 2968 23.38 1472 23.29 1496 23.46


Table 4: ST Population of Thondamuthur Block

Villages Total Percentage Male Percentage Female Percentage Thennammanallur 1 0.11 1 0.22 0 0 Devarayapuram 0 0 0 0 0 0 Jagirnaickenpalyam 0 0 0 0 0 0 Vellaimalaipattinam 15 1.63 6 1.30 9 1.97 Narasipuram 145 15.80 70 15.22 75 16.38 Madavarayapuram 53 5.77 32 6.96 21 4.59 Ikkaraibooluvampatti 495 53.92 240 52.17 255 55.68 Madampatti 9 0.98 4 0.87 5 1.09 Theethipalayam 25 2.72 16 3.48 9 1.97 Boluvampatti(R.F) 171 18.63 90 19.57 81 17.69 Perur Chettipalayam (CT) 4 0.44 1 0.22 3 0.66


Table 5: Literate of Thondamuthur Block

Villages Total Percentage Male Percentage Female Percentage Thennammanallur 3316 7.16 1830 7.29 1486 7.00 Devarayapuram 4078 8.80 2175 8.66 1903 8.97 Jagirnaickenpalyam 1115 2.41 672 2.68 443 2.09 Vellaimalaipattinam 2737 5.91 1447 5.76 1290 6.08 Narasipuram 2034 4.39 1145 4.56 889 4.19 Madavarayapuram 4587 9.90 2468 9.83 2119 9.99 Ikkaraibooluvampatti 4043 8.73 2273 9.05 1770 8.34 Madampatti 4748 10.25 2584 10.29 2164 10.20 Theethipalayam 6329 13.66 3424 13.64 2905 13.69 Boluvampatti(R.F) 112 0.24 64 0.25 48 0.23 Perur Chettipalayam (CT) 13228 28.55 7026 27.98 6202 29.23

Total 46327 100.00 25108 100.00 21219 100.00 Source: District Planning Commission, Coimbatore

Table 6: Illiterate of Thondamuthur Block Villages Total Male Female

Thennammanallur 1782 (8.92%) 661 (8.24%) 1121 (9.38%) Devarayapuram 2339 (11.71%) 969 (12.08%) 1370 (11.46%) Jagirnaickenpalyam 371 (1.86%) 141 (1.76%) 230 (1.92%) Vellaimalaipattinam 1329 (6.65%) 585 (7.29%) 744 (6.22%) Narasipuram 1044 (5.23%) 429 (5.35%) 615 (5.14%) Madavarayapuram 1778 (8.90%) 758 (9.45%) 1020 (8.53%) Ikkaraibooluvampatti 2318 (11.60%) 910 (11.35%) 1408 (11.78%) Madampatti 2023 (10.13%) 775 (9.66%) 1248 (10.44%) Theethipalayam 2300 (11.51%) 872 (10.87%) 1428 (11.94%) Boluvampatti(R.F) 112 (0.56%) 55 (0.69%) 57 (0.48%) Perur Chettipalayam (CT) 4581 (22.93%) 1865 (23.26%) 2716 (22.71%)

Total 19977 (100%) 8020 (100%) 11957 (100%) Source: District Planning Commission, Coimbatore

Table 7: Total Workers of Thondamuthur Block

Villages Total Male Female Thennammanallur 2591 (7.91%) 1626 (7.76%) 965 (8.17%) Devarayapuram 3453 (10.54%) 2082 (9.93%) 1371 (11.61%) Jagirnaickenpalyam 735 (2.24%) 461 (2.20%) 274 (2.32%) Vellaimalaipattinam 1891 (5.77%) 1051 (5.01%) 840 (7.11%) Narasipuram 1874 (5.72%) 1117 (5.33%) 757 (6.41%) Madavarayapuram 3097 (9.45%) 2003 (9.55%) 1094 (9.27%) Ikkaraibooluvampatti 3385 (10.33%) 2081 (9.93%) 1304 (11.04%) Madampatti 3594 (10.97%) 2159 (10.30%) 1435 (12.15%) Theethipalayam 3901 (11.90%) 2690 (12.83%) 1211 (10.26%) Boluvampatti(R.F) 145 (0.44%) 72 (0.34%) 73 (0.62%) Perur Chettipalayam (CT) 8106 (24.73%) 5623 (26.82%) 2483 (21.03%)


Table 8: Main Workers of Thondamuthur Block



Table 9: Cultivator of Thondamuthur Block Villages Total Male Female

Thennammanallur 303 (11.49%) 214 (11.68) 89 (11.08) Devarayapuram 521 (19.78%) 331 (18.07) 190 (23.67) Jagirnaickenpalyam 84 (3.19%) 76 (4.15) 8 (0.99) Vellaimalaipattinam 287 (10.89%) 196 (10.69) 91 (11.33) Narasipuram 103 (3.91%) 76 (4.15) 27 (3.36) Madavarayapuram 326 (12.38%) 213 (11.63) 113 (14.07) Ikkaraibooluvampatti 269 (10.21%) 162 (8.84) 107 (13.33) Madampatti 437 (16.58%) 297 (16.21) 140 (17.43) Theethipalayam 253 (9.60%) 222 (12.12) 31 (3.86) Boluvampatti(R.F) 0 (0%) 0 (0) 0 (0) Perur Chettipalayam (CT) 52 (1.97%) 45 (2.46) 7 (0.88)

Total 2635 (100%) 1832 (100) 803 (100) Source: District Planning Commission, Coimbatore

Table 10: Agri Labour of Thondamuthur Block

Villages Total Male Female Thennammanallur 894 (9.00%) 496 (9.58%) 398 (8.37%) Devarayapuram 1651 (16.63%) 875 (16.92%) 776 (16.34%) Jagirnaickenpalyam 181 (1.83%) 104 (2.01%) 77 (1.61%) Vellaimalaipattinam 1108 (11.16%) 545 (10.54%) 563 (11.84%) Narasipuram 347 (3.49%) 215 (4.15%) 132 (2.77%) Madavarayapuram 724 (7.29%) 422 (8.15%) 302 (6.35%) Ikkaraibooluvampatti 1396 (14.06%) 747 (14.43%) 649 (13.65%) Madampatti 1471 (14.83%) 720 (13.94%) 751 (15.79%) Theethipalayam 772 (7.77%) 362 (6.99%) 410 (8.64%) Boluvampatti(R.F) 0 (0%) 0 (0%) 0 (0%) Perur Chettipalayam (CT) 1384 (13.94%) 688 (13.29%) 696 (14.64%)


Table 11: Household of Thondamuthur Block

Villages Total Male Female Thennammanallur 12 (2.34%) 5 (1.68%) 7 (3.24%) Devarayapuram 53 (10.34%) 32 (10.84%) 21 (9.63%) Jagirnaickenpalyam 2 (0.38%) 2 (0.67%) 0 (0%) Vellaimalaipattinam 43 (8.36%) 22 (7.43%) 21 (9.63%) Narasipuram 22 (4.28%) 13 (4.39%) 9 (4.12%) Madavarayapuram 54 (10.50%) 32 (10.83%) 22 (10.09%) Ikkaraibooluvampatti 68 (13.24%) 42 (14.18%) 26 (11.92%) Madampatti 56 (10.89%) 26 (8.78%) 30 (13.76%) Theethipalayam 57 (11.08%) 34 (11.48%) 23 (10.55%) Boluvampatti(R.F) 0 (0%) 0 (0%) 0 (0%) Perur Chettipalayam (CT) 147 (28.59%) 88 (29.72%) 59 (27.06%)


Table 12: Other Workers of Thondamuthur Block Villages Total Male Female



Table 13: Marginal Workers Population of Thondamuthur Block


Total 5358 (100%) 2757 (100%) 2601 (100%) Source: District Planning Commission, Coimbatore.

Table 14: Cultivator for Marginal Workers Population of Thondamuthur Block

Villages Total Male Female Thennammanallur 18 (12.67%) 11 (15.94%) 7 (9.58%) Devarayapuram 16 (11.26%) 8 (11.59%) 8 (10.95%) Jagirnaickenpalyam 15 (10.56%) 12 (17.39%) 3 (4.10%) Vellaimalaipattinam 9 (6.33%) 0 (0%) 9 (12.32%) Narasipuram 3 (2.11%) 1 (1.44%) 2 (2.73%) Madavarayapuram 34 (23.94%) 13 (18.84%) 21 (28.76%) Ikkaraibooluvampatti 11 (7.74%) 5 (7.24%) 6 (8.21%) Madampatti 14 (9.85%) 6 (8.69%) 8 (10.95%) Theethipalayam 9 (6.33%) 5 (7.24%) 4 (5.47%) Boluvampatti(R.F) 0 (0%) 0 (0%) 0 (0%) Perur Chettipalayam (CT) 13 (9.15%) 8 (11.59%) 5 (6.84%)


Table 15: Agri Labour for Marginal Workers Population of Thondamuthur Block Villages Total Male Female

Thennammanallur 233 (7.10%) 145(8.92%) 88(5.31%) Devarayapuram 63(1.92%) 24(1.47%) 39(2.35%) Jagirnaickenpalyam 242(7.38%) 101(6.21%) 141(8.51%) Vellaimalaipattinam 52(1.58%) 18(1.10%) 34(2.05%) Narasipuram 416(12.68%) 159(9.79%) 257(15.52%) Madavarayapuram 591(18.02%) 393(24.19%) 198(11.96%) Ikkaraibooluvampatti 485(14.79%) 228(14.03%) 257(15.52%) Madampatti 209(6.37%) 64(3.94%) 145(8.76%) Theethipalayam 434(13.23%) 236(14.53%) 198(11.96%) Boluvampatti(R.F) 139(4.23%) 69(4.24%) 70(4.22%) Perur Chettipalayam (CT) 415(12.65%) 187(11.51%) 228(13.77%)

Total 3279(100%) 1624(100%) 1655(100%) Source: District Planning Commission, Coimbatore.

Table 16: Household for Marginal Workers Population of Thondamuthur Block

Villages Total Male Female Thennammanallur 16 (11.67%) 13 (19.69%) 3 (4.22%) Devarayapuram 8 (5.83%) 5 (7.57%) 3 (4.22%) Jagirnaickenpalyam 4 (2.91%) 3 (4.54%) 1 (1.40%) Vellaimalaipattinam 13 (9.48%) 2 (3.03%) 11 (15.49%) Narasipuram 6 (4.37%) 1 (1.51%) 5 (7.04%) Madavarayapuram 37 (27.00%) 23 (34.84%) 14 (19.71%) Ikkaraibooluvampatti 11 (8.02%) 4 (6.06%) 7 (9.85%) Madampatti 14 (10.21%) 3 (4.54%) 11 (15.49%) Theethipalayam 4 (2.91%) 2 (3.03%) 2 (2.81%) Boluvampatti(R.F) 0 (0%) 0 (0%) 0 (0%) Perur Chettipalayam (CT) 24 (17.51%) 10 (15.15%) 14 (19.71%)


Table 17: Other Workers for Marginal Workers Population of Thondamuthur Block



Table 18: Non Workers for Marginal Workers Population of Thondamuthur Block Villages Total Male Female



Urban wise data of Thondanmuthur Block

Table 19: Population of Thondamuthur Block

Urban No. Household Total Male Female Thondamuthur (TP) 3320 (16.92%) 11492 (16.39%) 5572 (16.06%) 5920 (16.73%) Dhaliyur (TP) 3208 (16.35%) 11500 (16.41%) 5758 (16.60%) 5742 (16.23%) Vedapatti (TP) 3209 (16.36%) 11658 (16.64%) 5758 (16.60%) 5900 (16.68%) Perur (TP) 2211 (11.27%) 8004 (11.42%) 4010 (11.56%) 3994 (11.29%) Thenkarai (TP) 2093 (10.67%) 7349 (10.49%) 3657 (10.54%) 3692 (10.43%) Booluvapatti (TP) 3575 (18.22%) 12853 (18.34%) 6386 (18.42%) 6467 (18.27%) Alanthurai (TP) 2004 (10.21%) 7221 (10.31%) 3547 (10.22%) 3674 (10.37%)

Total 19620 (100%) 70077 (100%) 34688 (100%) 35389 (100%) Source: District Planning Commission, Coimbatore.

Table 20: 0-6 age group population of Thondamuthur Block

Urban Total Percentage Male Percentage Female Percentage Thondamuthur (TP) 1051 16.96 525 16.67 526 17.26 Dhaliyur (TP) 1014 16.36 517 16.41 497 16.31 Vedapatti (TP) 1133 18.28 569 18.07 564 18.50 Perur (TP) 690 11.13 345 10.96 345 11.32 Thenkarai (TP) 567 9.16 299 9.50 268 8.79 Booluvapatti (TP) 1153 18.61 591 18.77 562 18.44 Alanthurai (TP) 589 9.50 303 9.62 286 9.38


Table 21: SC Population of Thondamuthur Block



Table 22: ST Population of Thondamuthur Block Urban Total Percentage Male Percentage Female Percentage

Thondamuthur (TP) 193 23.59 97 23.60 96 23.59 Dhaliyur (TP) 1 0.12 0 0 1 0.25 Vedapatti (TP) 0 0 0 0 0 0 Perur (TP) 0 0 0 0 0 0 Thenkarai (TP) 77 9.42 42 10.22 35 8.60 Booluvapatti (TP) 22 2.69 11 2.68 11 2.70 Alanthurai (TP) 525 64.18 261 63.50 264 64.86


Table 23: Literate of Thondamuthur Block



Table 24: Illiterate of Thondamuthur Block



Table 25: Total Workers of Thondamuthur Block



Table 26: Main Workers of Thondamuthur Block Urban Total Percentage Male Percentage Female Percentage

Thondamuthur (TP) 5168 16.23 3278 15.59 1890 17.48 Dhaliyur (TP) 5267 16.55 3477 16.54 1790 16.56

Vedapatti (TP) 4447 13.97 3321 15.80 1126 10.42 Perur (TP) 3531 11.09 2527 12.02 1004 9.29

Thenkarai (TP) 3912 12.29 2390 11.37 1522 14.07 Booluvapatti (TP) 5862 18.41 3815 18.14 2047 18.93 Alanthurai (TP) 3647 11.46 2215 10.54 1432 13.25


Table 27: Cultivator of Thondamuthur Block

Urban Total Percentage Male Percentage Female Percentage Thondamuthur (TP) 274 10.17 205 11.71 69 7.32

Dhaliyur (TP) 243 9.02 161 9.19 82 8.70 Vedapatti (TP) 58 2.15 45 2.57 13 1.38

Perur (TP) 25 0.93 23 1.32 2 0.21 Thenkarai (TP) 729 27.06 432 24.67 297 31.49

Booluvapatti (TP) 751 27.88 521 29.75 230 24.39 Alanthurai (TP) 614 22.79 364 20.79 250 26.51


Table 28: Agri Labour of Thondamuthur Block






Table 29: Household of Thondamuthur Block






Table 30: Other Workers of Thondamuthur Block Urban Total Percentage Male Percentage Female Percentage





Table 31: Marginal Workers Population of Thondamuthur Block






Table 32: Cultivator for Marginal Workers Population of Thondamuthur Block Urban Total Percentage Male Percentage Female Percentage





Table 33: Agri Labour for Marginal Workers Population of Thondamuthur Block






Table 34: Household for Marginal Workers Population of Thondamuthur Block Urban Total Percentage Male Percentage Female Percentage

Thondamuthur (TP) 24 11.21 4 5 20 14.93 Dhaliyur (TP) 14 6.54 2 2.5 12 8.96

Vedapatti (TP) 18 8.41 8 10 10 7.47 Perur (TP) 2 0.93 0 0 2 1.48

Thenkarai (TP) 13 6.08 2 2.5 11 8.21 Booluvapatti (TP) 120 56.08 60 75 60 44.77 Alanthurai (TP) 23 10.75 4 5 19 14.18


Table 35: Other Workers for Marginal Workers Population of Thondamuthur Block






Table 36: Non Workers for Marginal Workers Population of Thondamuthur Block






Block Wise data of Coimbatore District

Table 37: Population of Coimbatore District Urban No. Household

(%) Total

% Male

% Female

% Perianaickanpalam 57553 (6%) 205389 (5.93%) 103722 (5.99%) 101667 (5.88%) Sarcarsamakulam 13974 (1.45%) 48793 (1.44%) 24370 (1.4%) 24423 (1.41%)

Madukarai 40287 (4.2%) 146080 (4.24%) 73348 (4.24%) 72732 (4.20%) Thondamuthur 38044 (3.96%) 136381 (3.94%) 67816 (3.92%) 68565 (3.96%)

Annur 30457 (3.16%) 108673 (3.14%) 54323 (3.14%) 54350 (3.14%) Karamadai 73189 (7.63%) 260172 (7.53%) 129447 (7.48%) 130725 (7.56%)

Sulur 70178 (7.32%) 243687 (7.04%) 122530 (7.08%) 121157 (7.03%) Sulthanpet 22922 (2.36%) 77364 (2.23%) 38639 (2.23%) 38725 (2.24%)

Pollachi North 55504 (5.76%) 196521 (5.68%) 97641 (5.64%) 98880 (5.74%) Pollachi South 36026 (3.76%) 124755 (3.6%) 61979 (3.58%) 62776 (3.63%) Kinathukadavu 31606 (3.28%) 108269 (3.13%) 53896 (3.12%) 54373 (3.14%)

Anaimalai 61813 (6.61%) 217242 (6.28%) 107523 (6.24%) 109719 (6.34%) Corporation 426482 (44.51%) 1584719 (45.82%) 794063 (45.94%) 790656 (45.73%)

District Total 958035 (100%) 3458045 (100%) 1729297 (100%) 1728748 (100%) Source: District Planning Commission, Coimbatore.

Table 38: 0-6 age group population of Coimbatore District

Urban Total Percentage Male Percentage Female Percentage Perianaickanpalam 18509 5.79 9485 5.82 9024 5.78 Sarcarsamakulam 4397 1.37 2222 1.36 2175 1.39

Madukarai 13233 4.14 6792 4.16 6441 4.12 Thondamuthur 12185 3.84 6239 3.82 5946 3.82

Annur 9645 3.03 4975 3.04 4670 2.99 Karamadai 23023 7.20 11703 7.16 11320 7.25

Sulur 24168 7.56 12339 7.55 11829 7.57 Sulthanpet 5957 1.86 3022 1.85 2935 1.88

Pollachi North 16158 5.05 8204 5.04 7954 5.09 Pollachi South 10337 3.23 5287 3.23 5050 3.23 Kinathukadavu 8529 2.67 4375 2.68 4154 2.66

Anaimalai 16972 5.34 8674 5.34 8298 5.34 Corporation 156219 48.92 79913 48.95 76306 48.88 District Total 319332 100 163230 100 156102 100

Source: District Planning Commission, Coimbator

Table 39: SC Population of Coimbatore District Urban Total Percentage Male Percentage Female Percentage

Perianaickanpalam 25868 4.83 12990 4.86 12878 4.78 Sarcarsamakulam 9447 1.76 4719 1.76 4728 1.75


Annur 28616 5.33 14368 5.38 14248 5.29 Karamadai 44149 8.23 21985 8.23 22164 8.24




Source: District Planning Commission, Coimbatore.

Table 40: ST Population of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 45 0.15 23 0.16 22 0.15 Karamadai 8712 30.73 4317 30.30 4395 31.17



Anaimalai 7955 28.06 4032 28.30 3923 27.82 Corporation 1708 6.04 874 6.13 834 5.94

District Total 28342 100 14245 100 14097 100 Source: District Planning Commission, Coimbatore.

Table 41: Literate of Coimbatore District

Urban Total Percentage Male Percentage Female Percentage Perianaickanpalm 156097 5.92 83762 6.00 72335 5.84 Sarcarsamakulam 33640 1.27 18210 1.30 15430 1.24 Madukarai 106359 4.03 57336 4.14 49023 3.94 Thondamuthur 94519 3.58 51049 3.65 43470 3.50 Annur 71832 2.74 39598 2.83 32234 2.59 Karamadai 185053 7.02 99211 7.14 85842 6.94 Sulur 182064 6.90 97884 7.02 84180 6.78 Sulthanpet 51363 1.94 28491 2.04 22872 1.84 Pollachi North 146811 5.56 77948 5.58 68863 5.54 Pollachi South 88463 3.35 47641 3.41 40822 3.28 Kinathukadavu 71941 2.73 39755 2.85 32186 2.59 Anaimalai 150862 5.72 81716 5.85 69146 5.59 Corporation 1296903 49.24 672189 48.19 624714 50.33 District Total 2635907 100 1394790 100 1241117 100

Source: District Planning Commission, Coimbatore

Table 42: Illiterate of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 36841 4.48 14725 4.40 22116 4.53 Karamadai 75119 9.13 30236 9.03 44883 9.20





Table 43: Total Workers of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 56628 3.61 35896 3.34 20732 4.27 Karamadai 120923 7.73 82146 7.58 38777 7.99





Table 44: Main Workers of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 52155 3.64 33984 3.34 18171 4.34 Karamadai 109408 7.58 76450 7.47 32958 7.83





Table 45: Cultivator of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 7882 10.45 5035 10.18 2847 10.95 Karamadai 11694 15.50 7465 15.10 4229 16.26





Table 46: Agri Labour of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 14438 7.17 7731 7.34 6707 7.00 Karamadai 21908 10.88 12414 11.75 9494 9.94





Table 47: Household of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 2100 4.74 1162 4.49 938 5.00 Karamadai 6104 13.69 3334 12.89 2770 14.79





Table 48: Other Workers of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 27735 2.47 20056 2.38 7679 2.73 Karamadai 69702 6.24 53237 6.34 16465 5.87





Table 49: Marginal Workers Population of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 4473 3.58 1912 3.14 2561 4.00 Karamadai 11515 9.23 5696 9.36 5819 9.10





Table 50: Cultivator for Marginal Workers Population of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 319 6.63 126 5.55 193 7.64 Karamadai 740 15.39 342 15.08 398 15.67





Table 51: Agri Labour for Marginal Workers Population of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 1676 5.84 666 5.64 1010 5.98 Karamadai 3806 13.27 1704 14.43 2102 12.46





Table 52: Household for Marginal Workers Population of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 244 4.43 89 4.96 155 4.17 Karamadai 765 13.90 267 14.88 498 13.42





Table 53: Other Workers for Marginal Workers Population of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 2234 2.60 1031 2.29 1203 2.94 Karamadai 6204 7.23 3383 7.52 2821 6.94





Table 54: Non Workers for Marginal Workers Population of Coimbatore District Urban Total Percentage Male Percentage Female Percentage



Annur 52045 2.75 18427 2.85 33618 2.70 Karamadai 139249 7.39 47301 7.33 91948 7.39





APPENDIX - III

BAYESIAN CONVERGENCE DISTRIBUTION FOR “MEDIATING CAUSES FOR HIGH LEVEL OF INFANT MORTALITY RATE” REGRESSION MODEL

Table 1: Bayesian Convergence Distribution for “MEDIATING CAUSES FOR HIGH LEVEL OF INFANT

MORTALITY RATE” Regression Model

Regression weights

Mean S.E. S.D. C.S. Skewness Kurtosis Min Max Name

PD<--EP 1.1286 0.0017 0.1553 1.0001 0.0063 0.0527 0.3935 1.8507 W1

PD<--IF 0.3290 0.0027 0.1672 1.0001 -0.0394 0.1060 -0.3561 1.0000 W2

PD<--SP 0.2169 0.0016 0.1140 1.0001 0.0005 0.0893 -0.2671 0.7312 W3

PD<--HSI 0.6057 0.0035 0.2626 1.0001 0.0002 0.0404 -0.5001 1.7120 W4

PD<--HSO -0.5748 0.0030 0.2191 1.0001 -0.0108 0.0242 -1.5297 0.3549 W5

PD<--PS 0.1460 0.0042 0.3045 1.0001 -0.0076 0.0526 -1.2556 1.3504 W6

IMR<--PS 0.0535 0.0010 0.0989 1.0001 -0.0272 0.0718 -0.4067 0.4615 W7

IMR<--HSO -0.0194 0.0011 0.0733 1.0001 -0.0237 0.0533 -0.3672 0.3530 W8

IMR<--HSI 0.0853 0.0011 0.0872 1.0001 0.0198 0.0854 -0.3041 0.4763 W9

IMR<--SP 0.1237 0.0007 0.0366 1.0002 -0.0215 0.0689 -0.0222 0.2778 W10

IMR<--IF 0.0053 0.0008 0.0557 1.0001 0.0081 0.0661 -0.2241 0.2421 W11

IMR<--EP 0.1903 0.0010 0.0628 1.0001 0.0126 0.1071 -0.0982 0.5036 W12

IMR<--PD 0.0677 0.0005 0.0337 1.0001 -0.0146 0.1704 -0.0917 0.2307 W13

Means


PS 35.5823 0.0072 0.4328 1.0001 -0.0031 0.1223 33.4949 37.5018 M1

HSO 51.9128 0.0066 0.5486 1.0001 -0.0101 0.1336 49.4240 54.2708 M2

HSI 50.6571 0.0052 0.5025 1.0001 0.0178 0.0868 48.5695 52.7641 M3

SP 55.3209 0.0156 0.9347 1.0001 0.0333 0.1189 51.4667 60.6837 M4

IF 48.9765 0.0095 0.6679 1.0001 0.0047 0.0939 46.1269 52.2934 M5

EP 29.5971 0.0074 0.7201 1.0001 0.0203 0.0924 26.5260 33.5105 M6

Intercepts


PD 18.8278 0.1366 10.3126 1.0001 0.0399 0.1117 -21.0773 63.5741 I1

IMR 3.7258 0.0384 3.3533 1.0001 -0.0132 0.1078 -10.4059 18.5002 I2

Covariances


PS<->EP -1.7668 0.0543 3.4083 1.0001 -0.1196 0.1862 -17.7341 13.2941 C1

EP<->HSO 0.3576 0.0732 4.2635 1.0001 0.0250 0.2987 -19.6018 20.0613 C2

EP<->HSI -0.5606 0.0738 3.9539 1.0002 0.0196 0.3139 -18.7071 18.1469 C3

EP<->SP 30.9854 0.1634 8.0722 1.0002 0.5068 0.6856 4.8509 81.2720 C4

EP<->IF 23.7583 0.0862 5.8175 1.0001 0.4762 0.4815 2.3742 54.7915 C5

SP<->IF 22.4207 0.1652 7.1748 1.0003 0.4447 0.6002 -1.4480 68.7301 C6

HSI<->IF 7.8366 0.0628 3.7065 1.0001 0.2747 0.3780 -6.9318 30.2861 C7

HSO<->IF 9.0231 0.0495 4.0606 1.0001 0.2538 0.2837 -6.7203 31.8193 C8

PS<->IF 10.4412 0.0299 3.3104 1.0000 0.4141 0.4299 -3.2595 27.3530 C9

HSI<->SP 14.3197 0.0837 5.2757 1.0001 0.3217 0.3491 -6.1240 42.8339 C10

HSO<->SP 9.5049 0.0921 5.6307 1.0001 0.2652 0.3522 -11.1768 40.6987 C11

PS<->SP 4.5034 0.0536 4.3591 1.0001 0.1589 0.2740 -12.2499 27.2250 C12

HSO<->HSI 18.3062 0.0678 3.6993 1.0002 0.6194 0.6667 6.8002 37.1131 C13

PS<->HSI 14.4610 0.0568 2.9040 1.0002 0.6693 1.0548 5.0721 33.5268 C14

PS<->HSO 14.5448 0.0620 3.0737 1.0002 0.6723 0.9978 4.5370 32.7168 C15

Variances


PS 18.6017 0.0526 2.9041 1.0002 0.6937 1.0256 9.2647 35.7015 V1

EP 52.0141 0.1710 8.0414 1.0002 0.5951 0.5601 28.6473 96.7870 V2

HSO 29.8439 0.0909 4.6465 1.0002 0.6017 0.5933 16.1839 53.9898 V3

HSI 25.5717 0.0705 4.0066 1.0002 0.6677 0.8558 14.0393 49.4220 V4

SP 86.5290 0.2625 13.4160 1.0002 0.6164 0.8270 46.0772 174.5425 V5

IF 44.0898 0.1077 6.7579 1.0001 0.5995 0.7005 23.8240 85.1532 V6

e2 62.6980 0.1957 9.4879 1.0002 0.6120 0.5926 35.3138 114.0337 V7

e1 6.4161 0.0160 0.9781 1.0001 0.6684 1.0642 3.5906 14.1203 V8

Source: Amos 20.0 Output

APPENDIX - IV

BAYESIAN ESTIMATION AND TESTING OF “HIGH LEVEL OF INFANT MORTALITY RATE” STRUCTURAL EQUATION LATENT MODEL

Table 1: Bayesian Convergence Distribution for “HIGH LEVEL OF INFANT MORTALITY RATE”


Regression weights


PS2<--PS 1.2878 0.0251 0.1897 1.0001 0.5148 -0.0277 0.7956 2.0760 W1

PS3<--PS 1.2560 0.0242 0.1877 1.0000 0.3150 0.3821 0.6534 2.3481 W2

PS4<--PS 1.4360 0.0332 0.2261 1.0001 0.8037 0.4106 0.8771 2.3306 W3

PS5<--PS 0.6895 0.0252 0.1912 1.0000 0.1950 -0.1116 0.1160 1.3638 W4

PS6<--PS 1.2342 0.0584 0.3838 1.0001 0.3761 0.1437 -0.2752 2.6529 W5

HSO2<--HSO 0.8261 0.0766 0.4822 1.0001 1.6717 2.9830 -0.4061 3.0002 W6

HSO3<--HSO 0.7954 0.0613 0.3438 1.0000 1.3391 1.9350 0.1223 2.2170 W7

HSO4<--HSO 0.7601 0.0538 0.2941 1.0000 1.6603 2.6942 0.2336 2.0043 W8

HSO5<--HSO 1.6748 0.0979 0.5316 1.0000 1.5942 2.3396 0.7291 3.8872 W9

HSO6<--HSO 2.0060 0.1244 0.6500 1.0001 1.5703 2.3595 0.9353 4.7372 W10

HSO7<--HSO 0.8865 0.0537 0.3570 1.0001 1.4461 2.6952 -0.0004 2.6038 W11

HSO8<--HSO 1.5238 0.0897 0.4878 1.0001 1.6364 2.8668 0.7224 3.6032 W12

HSO9<--HSO 1.7285 0.0977 0.5290 1.0001 1.6155 2.2016 0.9430 3.9476 W13

HSI8<--HSI 0.4926 0.0295 0.1834 1.0001 0.2942 0.2399 -0.1578 1.0047 W14

HSI7<--HSI 1.1592 0.0751 0.3619 1.0001 0.8210 -0.4672 0.3599 2.0617 W15

HSI6<--HSI 0.6594 0.0342 0.2080 1.0001 0.4738 -0.4188 0.1056 1.4060 W16

HSI5<--HSI 1.1781 0.0631 0.3707 1.0002 0.5643 0.1100 0.1967 2.5858 W17

HSI4<--HSI 1.6031 0.0731 0.4248 1.0001 0.6085 0.7950 0.5678 3.2763 W18

HSI3<--HSI 0.8068 0.0435 0.2399 1.0001 0.5353 -0.0495 -0.0018 1.4980 W19

HSI2<--HSI 0.9141 0.0440 0.2285 1.0001 0.5774 -0.2778 0.3287 1.6286 W20

HSI1<--HSI 0.9032 0.0428 0.2151 1.0001 0.5792 -0.3165 0.3294 1.5876 W21

PD2<--PD 3.4897 0.2429 1.0864 1.0001 -0.3689 -1.1937 0.7382 5.2632 W22

PD3<--PD 2.7799 0.1529 0.7260 1.0001 -0.2413 -0.2446 0.5171 4.4113 W23

PD4<--PD 0.5263 0.0604 0.3401 1.0001 0.7000 0.3951 -0.3780 2.0647 W24

PD5<--PD 0.4991 0.0567 0.3384 1.0001 -0.3660 -0.1984 -0.9390 1.4316 W25

PD6<--PD 0.6122 0.0939 0.4606 1.0001 -0.0015 -1.0742 -0.3596 1.8121 W26

PD7<--PD -0.4238 0.0829 0.4160 1.0001 0.0306 -0.7306 -1.4692 0.7199 W27

PD8<--PD 1.1491 0.0673 0.3674 1.0001 0.2364 -0.7293 -0.0082 1.9927 W28

PD9<--PD 2.1822 0.1118 0.5605 1.0002 -0.1442 -0.5638 0.5334 3.7029 W29

PD10<--PD 3.4719 0.2526 1.1295 1.0001 -0.1540 -1.2372 0.7503 5.3186 W30

PD11<--PD 2.0583 0.1152 0.5803 1.0001 -0.0706 -0.1061 0.1987 3.4351 W31

PD12<--PD 2.5246 0.1990 0.9143 1.0001 0.0592 -1.2335 0.3442 4.1599 W32

PD13<--PD 3.0613 0.2204 0.9929 1.0001 -0.1670 -1.2088 0.6064 4.6756 W33

PD14<--PD 3.4887 0.2418 1.0846 1.0001 -0.1107 -1.2186 0.8352 5.2443 W34

PD15<--PD 3.8998 0.2655 1.1913 1.0001 -0.2189 -1.2966 0.8190 5.7606 W35

PD16<--PD 4.4809 0.3472 1.5413 1.0001 0.1199 -1.1892 0.9907 7.1780 W36

PD17<--PD 3.3367 0.2000 0.9207 1.0001 -0.5156 -0.8371 0.7366 4.7684 W37

SP2<--SP 2.8388 0.1597 0.7846 1.0002 -0.1116 0.0369 0.3811 4.5061 W38

SP3<--SP 2.8621 0.1225 0.6275 1.0001 -1.1160 1.2213 0.3820 4.4577 W39

SP4<--SP 6.0679 0.2506 1.1826 1.0001 -1.2786 2.2006 1.2665 7.9191 W40

SP5<--SP 6.2932 0.2435 1.1603 1.0001 -1.6703 3.1019 1.3048 8.2409 W41

SP6<--SP 4.9696 0.2160 1.0194 1.0001 -1.0457 1.5318 1.0863 6.7834 W42

SP7<--SP 3.5111 0.1483 0.7143 1.0001 -1.4191 1.8805 0.6605 4.6371 W43

SP8<--SP 3.4822 0.1422 0.6918 1.0002 -1.1455 1.2918 0.6430 4.7337 W44

SP9<--SP 4.0415 0.1985 0.9816 1.0001 -0.4986 -0.0342 0.6205 5.9500 W45

SP10<--SP 4.0498 0.1875 0.8886 1.0001 -0.9697 1.3150 0.7124 5.5563 W46

IF2<--IF 1.3954 0.0899 0.5040 1.0001 0.6401 -0.0841 0.1649 3.2273 W47

IF3<--IF 1.4537 0.1035 0.5945 1.0000 1.4993 3.0295 0.2859 4.2327 W48

IF4<--IF 1.4710 0.1009 0.5354 1.0001 0.1817 -0.8792 0.0965 3.0401 W49

IF5<--IF 1.4037 0.0576 0.4197 1.0001 0.4682 1.2880 0.1306 3.6974 W50

IF6<--IF 3.6282 0.2450 1.2218 1.0001 0.5005 -0.6241 0.7253 7.0554 W51

IF7<--IF 4.4379 0.2863 1.4492 1.0001 0.5097 -0.4892 1.3764 8.5224 W52

IF8<--IF 3.0370 0.1609 0.8764 1.0001 0.6584 -0.2026 0.7570 5.9211 W53

IF9<--IF 3.5308 0.2298 1.1846 1.0000 0.5958 0.0168 0.5742 7.3850 W54

EP2<--EP 0.7804 0.0160 0.1323 1.0001 0.3327 -0.0426 0.3417 1.3239 W55

EP3<--EP 1.4336 0.0281 0.1764 1.0001 0.6338 0.0091 0.8001 2.0611 W56

EP4<--EP 1.4393 0.0293 0.1799 1.0001 0.6667 0.1663 0.9467 2.1556 W57

EP5<--EP 1.2050 0.0244 0.1667 1.0001 0.5938 0.4325 0.6732 1.8568 W58

EP6<--EP 1.1618 0.0290 0.1737 1.0001 0.3473 -0.4960 0.6052 1.7485 W59

IMR4<--IMR 6.9962 0.4992 2.4662 1.0000 -0.3486 -0.8738 0.1610 11.6340 W60

IMR3<--IMR 9.8357 0.7101 3.4406 1.0001 -0.4644 -0.8371 1.5500 17.5790 W61

IMR2<--IMR 13.1786 0.9640 4.5935 1.0001 -0.3431 -0.8043 2.5199 22.1870 W62

IMR1<--IMR 14.8975 1.0313 4.9755 1.0001 -0.6599 -0.7632 3.0430 24.5328 W63

PD<--SP 0.1863 0.0318 0.2258 1.0001 0.3965 0.9866 -0.6374 1.1274 W64

PD<--PS -0.3319 0.0564 0.3581 1.0001 -1.8335 4.8451 -3.2922 0.4253 W65

PD<--IF 0.2036 0.0468 0.4261 1.0001 3.9935 22.4266 -0.8037 3.9549 W66

PD<--HSO -0.9994 0.1216 0.6795 1.0000 -1.5072 2.6507 -5.4351 -0.0428 W67

PD<--HSI 1.1319 0.1417 0.8103 1.0002 1.6112 2.4843 -0.0978 5.9010 W68

PD<--EP 0.2058 0.0244 0.1322 1.0001 -0.2013 1.4544 -0.3219 1.0320 W69

IMR<--PD 0.2047 0.0139 0.0793 1.0001 1.5320 2.9629 0.0544 0.5631 W70

Intercepts


PS1 5.9433 0.0062 0.0730 1.0001 0.2876 -0.0741 5.6947 6.2339 I1

PS2 5.8648 0.0127 0.0899 1.0001 0.0779 -0.6596 5.5817 6.1769 I2

PS3 6.0056 0.0126 0.0984 1.0001 -0.3148 0.1526 5.6846 6.3264 I3

PS4 5.9972 0.0098 0.0929 1.0001 0.0032 -0.3573 5.6625 6.3204 I4

PS5 6.0285 0.0102 0.0872 1.0001 -0.1951 0.0433 5.7287 6.3049 I5

PS6 5.6939 0.0128 0.1475 1.0001 0.0788 -0.0407 5.1499 6.2136 I6

HSO1 5.7644 0.0116 0.0971 1.0002 0.1199 -0.2231 5.4324 6.0993 I7

HSO2 5.5568 0.0109 0.1145 1.0001 0.0253 -0.0652 5.1382 6.0465 I8

HSO3 5.9338 0.0082 0.0851 1.0000 -0.1791 0.1111 5.6112 6.3266 I9

HSO4 5.8645 0.0058 0.0584 1.0001 -0.2195 0.2320 5.6424 6.0883 I10

HSO5 5.7518 0.0138 0.1004 1.0001 0.3299 -0.1995 5.4144 6.1288 I11

HSO6 5.5544 0.0116 0.1148 1.0000 0.1532 -0.1287 5.1891 6.0217 I12

HSO7 5.6394 0.0107 0.0954 1.0001 -0.3311 0.2984 5.2182 5.9948 I13

HSO8 5.8575 0.0081 0.0813 1.0001 0.0488 0.3963 5.4826 6.1741 I14

HSO9 5.9138 0.0117 0.0876 1.0001 -0.0563 0.1744 5.6285 6.2464 I15

HSI9 5.7587 0.0080 0.0745 1.0001 0.1327 -0.2836 5.5009 6.0141 I16

HSI8 5.7330 0.0080 0.0657 1.0000 0.0594 -0.2619 5.4990 6.0116 I17

HSI7 5.7161 0.0095 0.0887 1.0001 0.0209 0.0088 5.4180 6.0411 I18

HSI6 5.5950 0.0068 0.0715 1.0001 -0.0486 0.2848 5.2355 5.8644 I19

HSI5 5.2014 0.0157 0.1194 1.0001 -0.1583 -0.2720 4.7714 5.6501 I20

HSI4 5.2382 0.0155 0.1495 1.0001 0.3143 0.0716 4.6701 5.8332 I21

HSI3 5.7633 0.0103 0.0829 1.0001 0.2061 0.5996 5.4392 6.0757 I22

HSI2 5.8152 0.0089 0.0708 1.0001 0.1740 -0.4379 5.5923 6.0562 I23

HSI1 5.8255 0.0071 0.0625 1.0001 -0.0300 -0.0155 5.5916 6.0658 I24

PD1 4.5893 0.0164 0.1427 1.0000 0.0162 -0.2723 4.1289 5.1072 I25

PD2 4.8913 0.0111 0.1290 1.0001 0.0374 0.0832 4.4325 5.4300 I26

PD3 4.8345 0.0201 0.1499 1.0001 0.2159 -0.3918 4.3347 5.4892 I27

PD4 5.3689 0.0141 0.1137 1.0001 0.0992 -0.2620 5.0178 5.8128 I28

PD5 5.4490 0.0112 0.1132 1.0002 0.1814 -0.1099 5.1043 6.0014 I29

PD6 5.5037 0.0094 0.0987 1.0001 -0.1746 -0.0168 5.1093 5.9284 I30

PD7 5.8190 0.0133 0.1164 1.0001 -0.4449 0.7782 5.3621 6.2012 I31

PD8 5.4006 0.0118 0.1020 1.0001 0.1019 0.4437 4.9882 5.8092 I32

PD9 5.4129 0.0107 0.1156 1.0001 -0.0845 0.3276 4.8260 5.9269 I33

PD10 5.3647 0.0139 0.1225 1.0000 -0.2649 0.3272 4.8554 5.7824 I34

PD11 4.4648 0.0150 0.1358 1.0001 0.2392 0.0191 3.9675 5.1264 I35

PD12 4.7040 0.0160 0.1435 1.0001 0.1283 -0.0329 4.1001 5.2024 I36

PD13 4.9902 0.0146 0.1284 1.0001 0.0859 -0.0751 4.4689 5.5989 I37

PD14 4.8988 0.0140 0.1252 1.0001 0.2194 -0.0629 4.3989 5.3967 I38

PD15 4.7929 0.0123 0.1393 1.0001 0.2443 0.3632 4.2802 5.4398 I39

PD16 4.7395 0.0130 0.1411 1.0001 -0.0773 0.3527 4.0605 5.3255 I40

PD17 5.1277 0.0142 0.1479 1.0001 -0.0162 -0.0668 4.4993 5.7598 I41

SP1 4.8678 0.0170 0.1545 1.0001 0.0826 -0.0616 4.2374 5.4214 I42

SP2 5.1904 0.0129 0.1216 1.0002 0.3107 0.2328 4.7177 5.6681 I43

SP3 5.6778 0.0107 0.1091 1.0001 0.0664 -0.2589 5.1455 6.0812 I44

SP4 5.4813 0.0151 0.1346 1.0001 -0.2056 -0.0996 4.9619 6.0439 I45

SP5 5.5806 0.0170 0.1506 1.0001 -0.0097 -0.1465 5.0284 6.2818 I46

SP6 5.6969 0.0172 0.1310 1.0001 -0.1024 0.1008 5.2144 6.1572 I47

SP7 5.6863 0.0075 0.0928 1.0000 -0.1078 0.0742 5.2482 6.0621 I48

SP8 5.7880 0.0128 0.1124 1.0001 0.2151 -0.2064 5.3857 6.2089 I49

SP9 5.5059 0.0253 0.1802 1.0001 0.2228 -0.0712 5.0017 6.0792 I50

SP10 5.7646 0.0196 0.1323 1.0001 -0.2228 -0.0465 5.3811 6.2861 I51

IF1 5.8855 0.0135 0.1006 1.0001 0.2015 -0.0662 5.5862 6.2356 I52

IF2 5.8538 0.0076 0.0753 1.0001 0.1003 0.0592 5.5219 6.1720 I53

IF3 5.7125 0.0106 0.0913 1.0001 0.0377 -0.2394 5.3175 6.1472 I54

IF4 5.6663 0.0073 0.0861 1.0002 0.1379 0.1091 5.3336 6.0135 I55

IF5 5.6628 0.0097 0.0971 1.0001 0.0203 0.3398 5.2537 6.0649 I56

IF6 5.2563 0.0124 0.1251 1.0000 -0.0286 -0.0375 4.7956 5.6717 I57

IF7 5.1238 0.0138 0.1409 1.0001 0.2171 0.1709 4.5717 5.6377 I58

IF8 5.0504 0.0173 0.1316 1.0001 0.0875 -0.4653 4.5348 5.5768 I59

IF9 4.7996 0.0106 0.1392 1.0000 -0.2116 -0.1274 4.2174 5.2464 I60

EP1 4.6951 0.0138 0.1237 1.0000 0.0622 -0.1171 4.2498 5.2535 I61

EP2 5.1488 0.0146 0.1242 1.0001 0.0225 -0.4966 4.6590 5.6220 I62

EP3 4.8772 0.0154 0.1428 1.0001 0.1119 -0.1226 4.2778 5.4017 I63

EP4 5.0138 0.0183 0.1505 1.0002 0.3271 -0.0804 4.4769 5.6462 I64

EP5 4.8949 0.0155 0.1420 1.0001 0.0023 -0.1956 4.3190 5.4104 I65

EP6 5.0287 0.0147 0.1284 1.0001 -0.0712 0.0090 4.5271 5.5477 I66

IMR5 5.8305 0.0085 0.0861 1.0001 0.1841 -0.1682 5.5580 6.1456 I67

IMR4 5.4258 0.0131 0.1143 1.0001 -0.1055 0.0901 4.9292 5.8757 I68

IMR3 5.5443 0.0110 0.0958 1.0001 0.0923 0.1808 5.0959 5.8809 I69

IMR2 5.5281 0.0139 0.1119 1.0001 -0.1640 0.1905 5.0619 5.9177 I70

IMR1 5.3086 0.0119 0.1136 1.0000 -0.2338 0.3196 4.8219 5.8491 I71

Covariances


PS<->SP 0.0098 0.0014 0.0162 1.0001 0.9676 6.0889 -0.0948 0.1799 C1

EP<->SP 0.1000 0.0078 0.0482 1.0002 2.5838 9.7469 0.0063 0.4934 C2

PS<->HSO 0.1572 0.0076 0.0476 1.0001 0.1024 -0.5411 0.0350 0.3447 C3

HSO<->IF 0.0259 0.0025 0.0175 1.0001 1.0623 3.2751 -0.0504 0.1190 C4

HSI<->IF 0.0347 0.0028 0.0217 1.0001 1.2736 3.2649 -0.0255 0.1825 C5

EP<->IF 0.1684 0.0093 0.0641 1.0002 1.0744 1.6951 0.0394 0.5013 C6

EP<->PS -0.0321 0.0085 0.0667 1.0001 -0.1093 -0.2372 -0.3176 0.2049 C7

PS<->HSI 0.2198 0.0106 0.0651 1.0000 0.1081 -0.0869 0.0663 0.5860 C8

EP<->HSI -0.0064 0.0072 0.0639 1.0001 0.3350 0.8247 -0.2272 0.3294 C9

HSO<->HSI 0.2003 0.0118 0.0693 1.0002 0.5420 0.1681 0.0491 0.5513 C10

EP<->HSO -0.0271 0.0084 0.0543 1.0001 0.1000 -0.0672 -0.1876 0.1891 C11

PS<->IF 0.0439 0.0029 0.0232 1.0001 1.3558 3.2567 -0.0194 0.2109 C12

HSO<->SP 0.0218 0.0018 0.0156 1.0001 1.7741 7.4951 -0.0278 0.1631 C13

SP<->IF 0.0271 0.0026 0.0164 1.0001 2.9941 13.6794 -0.0111 0.1828 C14

HSI<->SP 0.0260 0.0022 0.0182 1.0000 1.8922 7.6643 -0.0422 0.1820 C15

Variances


PS 0.3027 0.0115 0.0824 1.0001 0.4540 0.3497 0.1111 0.8383 V1

HSO 0.2130 0.0158 0.0867 1.0002 -0.0978 -0.6471 0.0269 0.4836 V2

HSI 0.2602 0.0197 0.1056 1.0001 0.7468 1.0880 0.0662 0.8318 V3

SP 0.0559 0.0102 0.0577 1.0000 5.1880 33.8367 0.0158 0.8355 V4

IF 0.0919 0.0097 0.0560 1.0001 1.4641 3.1690 0.0154 0.4637 V5

EP 0.9599 0.0292 0.2390 1.0001 0.7749 1.3144 0.3909 2.1596 V6

e72 0.0002 0.0023 0.0187 1.0001 -1.0677 8.0380 -0.1855 0.1272 V7

e73 0.0032 0.0007 0.0039 1.0000 2.6486 7.4806 0.0002 0.0272 V8

e1 0.2851 0.0061 0.0474 1.0001 0.3881 -0.2130 0.1455 0.4581 V9

e2 0.2592 0.0057 0.0508 1.0001 0.4328 0.2765 0.0953 0.4785 V10

e3 0.3734 0.0055 0.0554 1.0000 0.4418 0.2421 0.2071 0.6472 V11

e4 0.2648 0.0075 0.0584 1.0001 0.3387 -0.4207 0.1088 0.4797 V12

e5 0.6212 0.0107 0.0975 1.0001 0.6632 0.0464 0.3579 0.9687 V13

e6 2.0024 0.0240 0.2627 1.0001 0.6364 0.6686 1.2169 3.3710 V14

e7 0.7039 0.0113 0.1036 1.0000 0.6106 0.5609 0.4378 1.2304 V15

e8 1.2746 0.0182 0.1737 1.0001 0.5480 0.6396 0.8071 2.5036 V16

e9 0.5752 0.0088 0.0788 1.0001 0.0982 -0.1321 0.2981 0.9102 V17

e10 0.2631 0.0042 0.0384 1.0002 0.2923 0.1449 0.1597 0.4406 V18

e11 0.4220 0.0084 0.0687 1.0001 0.5152 0.1439 0.2062 0.6881 V19

e12 0.6393 0.0073 0.1017 1.0001 0.6223 1.0859 0.3379 1.2667 V20

e13 0.7388 0.0119 0.1003 1.0001 0.6298 0.2308 0.4583 1.2024 V21

e14 0.3557 0.0066 0.0576 1.0000 0.7409 0.6885 0.2051 0.6367 V22

e15 0.2076 0.0040 0.0368 1.0000 0.2911 -0.0362 0.0851 0.3738 V23

e16 0.2942 0.0045 0.0443 1.0001 0.2490 -0.0004 0.1563 0.4693 V24

e17 0.4135 0.0086 0.0658 1.0001 0.4558 -0.4355 0.2367 0.6443 V25

e18 0.4199 0.0067 0.0645 1.0001 0.4478 1.1417 0.2199 0.8667 V26

e19 0.5049 0.0102 0.0817 1.0001 0.7842 0.6279 0.2802 0.8995 V27

e20 1.1727 0.0264 0.1798 1.0002 0.3582 -0.0630 0.6815 1.9091 V28

e21 1.7349 0.0255 0.2547 1.0001 0.6376 0.6624 0.9977 2.8913 V29

e22 0.5715 0.0080 0.0826 1.0001 0.4297 0.2002 0.3350 0.9694 V30

e23 0.3051 0.0055 0.0453 1.0000 0.3253 -0.1949 0.1747 0.4885 V31

e24 0.2575 0.0044 0.0409 1.0001 0.5923 0.2459 0.1445 0.4241 V32

e25 2.0688 0.0533 0.3356 1.0001 0.1040 -0.6533 1.0080 3.2097 V33

e26 1.1646 0.0202 0.1742 1.0001 0.5702 0.4086 0.6677 1.9208 V34

e27 1.5020 0.0216 0.2198 1.0001 0.6592 0.7612 0.8717 2.3580 V35

e28 1.4381 0.0372 0.2432 1.0001 0.6914 0.4049 0.8160 2.4739 V36

e29 1.2233 0.0197 0.1828 1.0001 0.4537 0.1309 0.7365 2.0057 V37

e30 1.0973 0.0246 0.1850 1.0001 0.5729 0.1330 0.5613 1.7225 V38

e31 1.4394 0.0342 0.2352 1.0001 0.6925 0.1708 0.8874 2.3291 V39

e32 1.0378 0.0148 0.1447 1.0001 0.3446 0.0650 0.5803 1.9230 V40

e33 1.1600 0.0425 0.2361 1.0001 0.9469 0.6168 0.6109 1.9091 V41

e34 0.7971 0.0127 0.1276 1.0001 0.3246 0.0377 0.4166 1.2741 V42

e35 1.7268 0.0255 0.2396 1.0001 0.2398 -0.2757 1.0005 2.7836 V43

e36 1.3687 0.0207 0.1919 1.0001 0.4971 0.4515 0.7515 2.1876 V44

e37 0.8888 0.0155 0.1350 1.0001 0.5584 0.3729 0.5281 1.5247 V45

e38 0.8297 0.0164 0.1327 1.0002 0.7803 1.3046 0.4949 1.5140 V46

e39 1.0005 0.0235 0.1811 1.0001 0.8705 1.0706 0.5543 1.9010 V47

e40 0.9926 0.0317 0.2039 1.0001 1.0988 2.1278 0.5230 1.8392 V48

e41 1.5213 0.0296 0.2326 1.0001 0.6619 0.9157 0.8521 2.5776 V49

e42 2.1541 0.0364 0.3026 1.0001 0.3738 0.1434 1.2825 3.4048 V50

e43 1.1520 0.0164 0.1637 1.0001 0.1908 0.1200 0.6554 2.0008 V51

e44 0.8972 0.0109 0.1184 1.0001 0.2291 -0.0356 0.5179 1.4497 V52

e45 0.3434 0.0073 0.0679 1.0001 0.7299 1.4993 0.1246 0.7029 V53

e46 0.4219 0.0085 0.0808 1.0001 0.1160 -0.4487 0.1766 0.7281 V54

e47 0.4469 0.0084 0.0817 1.0001 0.6211 0.9193 0.2386 0.8094 V55

e48 0.4936 0.0095 0.0846 1.0001 0.4144 0.0938 0.2273 0.9173 V56

e49 0.6508 0.0100 0.0986 1.0001 0.7335 1.7347 0.3590 1.2591 V57

e50 1.6466 0.0320 0.2571 1.0001 0.4746 0.1309 0.9370 2.8540 V58

e51 0.7151 0.0107 0.1114 1.0001 0.3793 -0.3323 0.4161 1.1973 V59

e52 0.7550 0.0165 0.1294 1.0002 0.9413 1.0061 0.3989 1.3110 V60

e53 0.4754 0.0047 0.0633 1.0001 0.3212 0.2406 0.2525 0.9025 V61

e54 0.6999 0.0168 0.1115 1.0001 0.5003 -0.2938 0.3863 1.1290 V62

e55 0.6887 0.0110 0.0974 1.0001 0.3295 0.0437 0.3697 1.1765 V63

e56 0.7616 0.0121 0.1187 1.0001 0.7114 0.8767 0.4316 1.3079 V64

e57 0.8750 0.0151 0.1487 1.0001 0.5288 0.8918 0.4518 1.8312 V65

e58 0.6163 0.0211 0.1653 1.0001 0.3725 0.2053 0.1638 1.3994 V66

e59 1.0233 0.0163 0.1581 1.0001 0.3467 0.3781 0.5364 1.7952 V67

e60 1.2586 0.0288 0.2145 1.0001 0.5627 0.2901 0.5789 2.0453 V68

e61 0.7850 0.0131 0.1176 1.0001 0.4655 0.1738 0.4137 1.4019 V69

e62 1.0729 0.0152 0.1480 1.0001 0.4302 0.5384 0.5542 1.8871 V70

e63 0.4885 0.0077 0.0871 1.0001 0.6878 1.1166 0.2247 0.9568 V71

e64 0.4698 0.0082 0.0890 1.0001 0.7577 0.8989 0.1518 0.9410 V72

e65 0.9198 0.0165 0.1460 1.0001 0.5277 0.6039 0.5155 1.5433 V73

e66 0.8184 0.0128 0.1269 1.0001 0.6728 0.8215 0.4175 1.5730 V74

e67 0.8875 0.0280 0.1673 1.0001 0.6417 -0.3004 0.5213 1.3911 V75

e68 1.0273 0.0177 0.1512 1.0001 0.6551 0.5825 0.6068 1.7175 V76

e69 0.5605 0.0133 0.1019 1.0001 0.7265 1.3900 0.2727 1.0620 V77

e70 0.4762 0.0087 0.0898 1.0001 0.3942 0.5036 0.2082 0.9670 V78

e71 0.4750 0.0146 0.1117 1.0002 0.3378 0.5048 0.1484 0.8860 V79

Source: SPSS 20.0 Output

APPENDIX V

Case Studies Case study one The case study reveals the bitter experience of a girl named Pushpa. Her husband Dinesh is

a sixteen years old coolie, a 8th drop out belonging to sc community. Pushpa confessed her

love affair with Dinesh and became pregnant at the age of 15. Further, she told her parents

were agri labours. Due to lack of transport facility, Pushpa was not often able to visit hospital

and her poor feeding womb had lost nutritious food to help the growth of the child. The

inadequacy of transportation led to more infant deaths in the block.

Case study two

Another case study in the village unfolds the story of Amaravathi who conveyed her

experience from Devarayipuram village. She conceived at the age of 17 and her baby’s

health was good. Her second pregnancy was diagnosed with in three month from the date of

the birth the first baby. But the second happened to die within a week from the date of birth

and the same happened again for her next child. She informed that her husband was

alcoholic that resulted in poor nutrition and her bad health. It was found that she had no

time to visit hospital owing to her profession of daily wage worker. There was no preferred

interval between two babies. Therefore awareness about government schemes had to be

created. Because of anxiety and practical difficulty in their life, they had lost their children.

Many psychological problems effected in more infant deaths in the block.

Case study three

%A girl named Bakiyalakshmi told her difficulty that she faced in the village, Devarayipuram.

Kathirvel, her husband was a coolie and she was also working on a daily basis earning her

wages from rupees 150 to 200 per day. The entire amount earned was used for their day to

day activities alone and saved nothing. She further said her first baby had been dead and

her second one was also in serious condition. Though she was making use of 108

ambulance services for her emergency purpose it had never reached her destination in time.

Due to this lack of transportation infant death rates was higher in the block.

Case study four

Kamatchi from Vellurukampalayam narrated her bitter experience. She said her husband

was a coolie and they were getting the meagre wages Rupees 200 to 300. Her husband was

an alcoholic had the habit of spending most of his wages in consuming alcohol. The rest of

the amount was used for their family maintenance. During the period Dr.Muthulakshmi

Reddy Maternity Scheme was not easy to avail. They spend much money and savings in

private hospital instead. Money problem was the prime cause the psychological discomfort

during the pregnancy period. it had also resulted in weight loss of the baby and sometimes

led to the infant death.

Case study five

A 17 year old Priya from Vellimalai got married to a tailor. She had a year old baby and was

pregnant undergoing treatment at PHC in Pooluvampatty. She informed that she had

received good treatment. She also added let her husband had taken good care of her

health. In spite of all these comforts they suffered financial crises. She said they were

depressed with their debt of their family and the same created more psychological problems

during her pregnancy time. Hence she had last her first child

Documents

MEDIATING CAUSES FOR HIGH LEVEL OF INFANT MORTALITY … · Dr.Muthulakshmi Reddy Maternity Benefit Scheme - 11 2.5. The Demographic Scenario of Tamil Nadu compared with India - 13