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DETERMINANTS OF INFANT MORTALITY IN KENYA

A HOUSEHOLD LEVEL ANALYSIS

By

SUSAN KABURA NGIGI

X50/64673/2010

Research Paper submitted to the School of Economics, University of Nairobi,

in Partial Fulfillment of the Requirements for the Award of the Degree of

Master of Arts in Economics

November, 2012

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DECLARATION

I declare this research project is my original work which has not been presented for a degree

award in this University or any other institution of higher learning. Where other peoples work

has been used acknowledgements have been duly made.

SignatureDate.

Susan K. Ngigi

We the undersigned have certified the students work and thus the project has been submitted

with our approval as University supervisors.

SignatureDate.

Prof. Jane Mariara

Signature..Date..

Dr. Mercy Mugo

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ACKNOWLEDGEMENT

I am grateful to God for the gift of life and for installing the seed of knowledge in me and more

so for good health and energy to handle my studies well up to the end. To my loving mother for

the sacrifice despite all the challenges, that ensured I attained basic education. To my sisters and

brothers thank you for the support so far. God bless you all.

This work received a lot of support from various people who in one way or the other guided and

motivated me to the end. Special thanks go to my supervisors Prof. Jane Mariara and Dr. Mercy

Mugo whose guidance and comments made this project a reality. I appreciate your patience andmotivation. I also thank Dr. Wawire and Dr. Obere whose lectures on research methods helped

me a lot in writing this paper.

I am greatly indebted to the University of Nairobi for sponsoring my postgraduate studies which

I could not have managed on my own. Special thanks also to the AERC also for facilitating the

Joint Facility for the elective units.

I also appreciate the School of Economics lecturers for their intellectual guidance in the field of

Economics. To my classmates thank you for making the learning process enjoyable and for the

valuable discussions. Special regards to Grace Njeri and Jane Kanina for academic and moral

support and for their valuable input in this project.

Finally my special recognition goes to my husband Geoffrey Muriuki for his valuable support

throughout my entire study period. Thank you for your understanding, your financial and moral

support; you made my learning easier and for this I salute you. God bless you abundantly and all

the best in your studies too.

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DEDICATION

I dedicate this research project to my loving husband (Jeff) for your undying love, support and

understanding throughout my Masters study.

To our Angel Shikoh; your kicks in my womb kept me awake in the library and reminded me of

the beauty of life. You greatly motivated my study. I wish you a long healthy life daughter.

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TABLE OF CONTENTS

DECLARATION ............................................................................................................................. ii

ACKNOWLEDGEMENT ..............................................................................................................iii

DEDICATION ................................................................................................................................iv

TABLE OF CONTENTS ................................................................................................................ v

LIST OF ACRONYMS .................................................................................................................vii

LIST OF TABLES ...................................................................................................................... viii

LIST OF FIGURES ........................................................................................................................ ix

ABSTRACT .................................................................................................................................... x

CHAPTER ONE: INTRODUCTION ..............................................................................................1

1.1 Project Background ............................................................................................................... 1

1.2 Statement of the problem ....................................................................................................... 5

1.3 Research Objectives ...............................................................................................................7

1.4 Relevance of the Study .......................................................................................................... 7

1.5 Organization of the Study ......................................................................................................7

CHAPTER TWO: LITERATURE REVIEW .................................................................................. 9

2.0 Introduction ...........................................................................................................................9

2.1 Theoretical Literature Review ............................................................................................... 92.1.1 Health care Production and child mortality framework by Mosley and Chen (1984) ....9

2.2 Empirical Literature Review ................................................................................................11

2.2.1 Studies Done in Kenya ..................................................................................................11

2.2.2 Studies outside Kenya ...................................................................................................13

2.3 Literature Summary ............................................................................................................. 16

CHAPTER THREE: METHODOLOGY ...................................................................................... 18

3.1 Introduction ..........................................................................................................................18

3.2 Analytical Framework ........................................................................................................ 18

3.3 Estimation Technique ..........................................................................................................20

3.4 Variable Definitions .............................................................................................................21

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3.5 Data Source and Analysis tool .............................................................................................24

CHAPTER FOUR: DATA ANALYSIS, RESULTS AND DISCUSSION .................................. 25

4.1 Introduction ..........................................................................................................................25

4.2 Descriptive results ...............................................................................................................25

4.3 Econometric analysis ...........................................................................................................30

4.4 Discussion of Results ...........................................................................................................33

5.1 Introduction ..........................................................................................................................35

5.2 Research Summary ..............................................................................................................35

5.3 Conclusion ...........................................................................................................................35

5.4 Policy Implications based on key Results ........................................................................... 36

5.5 Limitations of the study ....................................................................................................... 37

5.6 Suggestions for further study ...............................................................................................38

REFERENCES

....................................................................................................................................................... 39

APPENDIX ...................................................................................................................................42

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LIST OF ACRONYMS

WHO World Health Organization

MDGs Millennium Development Goals

KDHs Kenya Demographic Health Survey

IMCI Integrated Management of Childhood Illnesses

IMR Infant Mortality Rate

DHS Demographic and Health survey

LPM Linear Probability Model

LOGIT Logistic Regression Model

OLS Ordinary Least Squares

HIV Human Immunodeficiency Virus

NFHS National Family Health Survey

SRS Sample Registration System

KSPA Kenya Service Provision Assessment Survey

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LIST OF TABLES

Table 1.1: Some Health Indicators in Kenya (1990-2010)..............................................................2

Table 3.2: Variables Acronym Definition.....................................................................................20

Table3.3: Variable Definitions and Apriori expectation...............................................................22

Table 4.4: Infant Mortality Rate as per 2208 KDHs data..............................................................25

Table 4.5: Sample statistic on the Independent variables used in the study..................................26

Table 4.6: Mothers age in relation to Infant death.......................................................................27

Table 4.7: Mothers Education in relation to Infant death.............................................................28

Table 4.8: Other independent variables in relation to infant mortality..........................................29

Table 4.9: Results from Logit model for Infant mortality in Kenya.............................................31

Table 4.10: Marginal effects from logit models............................................................................32

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LIST OF FIGURES

Figure 1.1: Infant Mortality and Under-five Mortality trend in Kenya (per 1000 live births)......42

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ABSTRACT

The major objective of this study was to establish the determinants of health status in Kenya as

proxied by infant mortality. The trend for infant mortality in Kenya has been on the decline but

the levels are still high as compared to the MDGs target. Infant mortality rate is a good measure

of economic development of a country as it indicates the quality of health services at a basic

level and it is a sensitive indicator since infants depend on the socioeconomic conditions of their

environment for survival.

The study used individual household level data from the 2008 Kenya Demographic and Health

Survey to examine the primary predictors of a child dying before celebrating their first birthday.

The study employed a logit regression model due to the categorical nature of the dependent

variable and also it was mostly used in literature by other researchers. Both standard coefficients

and marginal effects are presented but the study discussion is based on marginal effects.

Analysis of the study shows that in Kenya mothers age, household wealth, infants birth size,

mothers education and tetanus Immunization were the major determinants of infant mortality.

Based on the findings a number of policies are recommended which can help efforts in the

reduction of infant mortality. To begin with the study recommends initiatives to encourage

women to give birth at the middle ages, educating women past primary level, educating women

on healthy lifestyle during pregnancy and the need to attend ante-natal clinic and to receive

recommended immunization and improving households wealth standards would help reduce the

probability of having high infant mortality rate in Kenya.

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CHAPTER ONE: INTRODUCTION

1.1 Project Background

Health is a state of complete physical, mental and social wellbeing not merely the absence of

disease or infirmity (WHO, 1948). According to the WHO declaration of Alma-Ata (1978),

health is a basic human right and it is fundamental for sustained economic and social well being

of a country. Attainment of a health level that will permit individuals to be socially and

economically productive depends on co-operation between individuals and the government.

The health status of a country is measured by the level of various indicators. The commonly used

indicators of a society are life expectancy at birth, mortality rate and morbidity rate. Life

expectancy at birth indicates the number of years a newborn infant would live if prevailing

patterns of mortality at the time of its birth were to stay the same throughout its life (World

Bank, 2008). Mortality rates refer to death rate. There are a number of different mortality rates

namely; neonatal mortality rate, infant mortality rate, under five mortality rate, maternal

mortality rate and crude death rate. Morbidity rates on the other hand refer to the rate of disease

prevalence in a population (World Bank, 2008).

Generally, health status indicators have been improving. However these indicators are still poor

in developing countries compared to the similar indicators in developed countries. For example,

life expectancy in Sub Saharan countries in 2007 was 49 years on average compared to 77 years

in developed nations. Similarly infant mortality rates for the more developed nations was on

average of 52 deaths per 1000 while for Sub Saharan countries was on average of 92 deaths per

1000 (Population Reference Bureau, 2007). An overview of Kenyas case is given in table 1.1

below

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Table 1.1: Some Health Indicators in Kenya (1990-2010)

Years Infant Mortality

Rate (per 1000 livebirths)

Under five Mortality

Rate (per 1000 livebirths)

Life Expectancy

at birth (total)

Fertility rate,

total (births perwoman)

1990 64.3 99.4 59.3 6.0

1991 66.1 102.7 59.0 5.8

1992 67.6 105.4 58.5 5.7

1993 69.1 108.3 57.8 5.5

1994 70.7 111.6 57.0 5.4

1995 71.8 114.2 56.1 5.3

1996 72.6 116.3 55.2 5.21997 72.7 117.2 54.3 5.1

1998 71.8 116.4 53.5 5.1

1999 70.2 114.0 52.8 5.0

2000 68.6 111.1 52.3 5.0

2001 66.9 108.1 52.0 5.0

2002 65.5 105.3 52.0 5.0

2003 64.2 102.7 52.1 5.0

2004 62.8 100.1 52.5 4.9

2005 61.4 97.5 53.0 4.9

2006 60.0 94.6 53.7 4.9

2007 58.8 92.1 54.4 4.8

2008 57.3 89.3 55.1 4.8

2009 56.3 87.0 55.8 4.8

2010 55.1 84.7 56.0 5.0

Source: data.worldbank.org/data-catalog/world-development-indicators

Table 1.1 shows trends in heath indicators in Kenya. There is remarkable improvement in child

mortality since 1997 to 2010. At first infant mortality rate increased from 64 deaths per 1000 in

1990 to 72 deaths per 1000 in 1997. However after 1997 it has been on the decline up to 55

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deaths per 1000 in 2010. In the same way, under-5 mortality rate increased from 99 deaths per

1000 in 1990 to 117 deaths in 1997 and has also been in a decline since then up to the level of 85

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deaths per 1000 in 2010. The country is making progress in lowering the rates although 55

infants out of 1000 still die before celebrating their first birthday while 85 children out of 1000

die between one and five years of age. This implies loss in human capital which has negative

impacts on a countrys future economic development.

One of the Millennium Development Goals (MDGs) is the reduction of child mortality with a

target of reducing child and infant mortality rate by two thirds between 1990 and 2015. Kenyas

infant mortality rate was 64.4 deaths per 1,000 live births in 1990 (KDHS, 1993). To achieve the

MDG goal 4 there is need to reduce infant mortality rate to 22 deaths per 1,000 live births by

2015. According to the World Bank data for Kenya, the infant mortality rate in Kenya for the

year 2004-2008 was on average at 60 per 1,000 live births. This shows that despite the declining

trends in infant mortality rates we are still very far from achieving the MDG goal.

Kenyas first health framework; 1994-2010, lays out elements of a sound health care delivery

system, capable of promoting health, preventing disease, promoting life and nurturing well-being

to the highest possible health standards in response to the population needs. To meet its

objectives, the government has implemented various interventions which target various

determinants of health including nutrition, maternal education, safe water provision, adequate

sanitation, proper housing, increased government expenditure on health, intervention on

reproductive health services to control high population growth, and creation of a favorable

environment for increased private sector, and community involvement in health service

provision. Provision of insurance services was also expanded, with increased numbers of

insurance firms and covered persons. In addition introduction of exemptions for user fees for

some specific health services was done, including treatment of children less than 5 years,

maternity services in dispensaries and health centers, TB treatment in public health facilities, and

immunization services.

Other interventions targeting infants included the malezi bora strategy which focused on child

immunization, vitamin A supplementation, deworming of children under five years and pregnant

women, treatment of childhood illnesses, management of HIV, ownership and use of treated

mosquito nets for children and expectant mothers, preventive treatment of malaria during

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pregnancy, and treatment of childhood fever. There was also the Integrated Management of

Childhood Illnesses (IMCI) approach that looks at improving management and services in health

care facilities as well as improving family and community health practices. Also there has been

exclusive breastfeeding programmes (GOK, 2010).

Despite implementing most of these policies, the health status indicators show a slow response in

performance as shown in table 1.1. For instance according to the KDHS (2008) data, infant

mortality dropped only by 32 percent in the 2008-09 survey as compared to the 2003 survey.

This change is from 77 deaths per 1,000 to 52 deaths per 1,000. Correspondingly, the under-five

mortality rate decreased to 74 deaths per 1,000 live births in 2008-09 from 115 in 2003 giving a

35.7% decline.However, the neonatal mortality rate only reduced by 6.1% from 33 to 31 per

1000 live births. Its effect on under five mortality increased to a contribution of 42% of the underfive mortality compared to its contribution of 29% in 2003 (KDHs).

1.2 Statement of the problem

Kenya is committed to achieving the MDGs targets by 2015. Based on the success made so far,

more progress could even be achieved despite the challenges. This study chose to examine the

determinants of infant mortality in Kenya. The study focus was motivated by the fact that infant

mortality rate indicates the quality of health services at a basic level and it is the most sensitive

of all indicators since infants depend on the socioeconomic conditions of their environment for

survival. Also, infant mortality has negative effects on a countrys future human resource thus

ensuring infants health safeguards their future contribution to the economic well being both at

home and nationally. Thus, the level of infant mortality would present a measure of how well a

society meets the needs of its people (Bicego and Ahmad, 1996). Finally infant mortality rate

illustrates the lasting impact of childhood health into adulthood. Childhood health has an impact

on adult health, education, economic performance and social status which in turn impacts

economic development of a country (Santere and Neun, 2010).

There has been declining trends in infant mortality rates in Kenya since 1997. This decline has

been attributed to the various interventions implemented in the health sector. However, despite

the efforts by the government, these figures are still high as compared to the MDG target of 22

deaths per 1,000 specific for Kenya. Given this, we can hypothesize that there could be many

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factors behind these low improvements in the health status that cannot be amended through

increasing government health sector spending. There is need therefore, to establish the

underlying factors and to point remedial measures which would work to alleviate the problem.

Similarly there is need to focus on one particular area of concern which could have greater

impacts on the infant mortality rates other than utilizing scarce resources on areas with very low

impacts. For instance neonatal care, need receive more attention since it contributed 42% of the

under five mortality as per KDHs data, (2008). A clear understanding of the determinants of

infant mortality is therefore important which calls for deeper investigation of the determining

factors of Infant mortality in Kenya.

In Kenya the study done by Elmahdi (2008) considered socioeconomic determinants to be more

important in determining infant mortality. His study was more concerned with thesocioeconomic determinants. On the other hand Mutunga (2004) examined infant and child

mortality relationship with households environmental and socioeconomic characteristics and

found both as having significant impact on child mortality. Both studies used KDHS data for

2003. Wamae et al. (2009) assessed the health practices in the management of child illnesses in

health centers and concluded health providers do not conduct full investigation and counseling of

sick children and thus are responsible for the rising trends on child mortality. The study used

KSPA data. This study has used recent demographic data KDHS (2008) which is more

appropriate to consider after the major interventions mentioned above were implemented in the

National Health strategic plan I and II (2000-2010). Also there was need to establish the

important factors in infant mortality since each study reviewed focused on specific determinants

of choice. This study therefore sought to fill this information gap in literature and make

appropriate policies to our policy makers by estimating a model that has demographic,

socioeconomic, biological, environmental, and maternal and child health care factors to establish

which of them greatly determine infant mortality. This was aimed at helping the existing

framework in order to stress the important of various factors in the mediation process for

reducing infant mortality.

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1.3 Research Objectives

The general objective of this study was to empirically examine the determinants of infant

mortality in Kenya.

The specific objectives were;

1. To investigate whether demographic, socio-economic, environmental and child/maternal

health care factors have an influence on infant mortality.

2. To analyze the impact of mothers health knowledge on infant mortality.

3. Based on the findings in 1 and 2 above, to make policy recommendations aimed at

reducing infant mortality rate in Kenya.

1.4 Relevance of the Study

This study was relevant as it will help add to the existing literature on child and infant mortality

in Kenya. It used recent demographic data which was more appropriate to consider in assessing

the impacts of various government interventions in child mortality. This is because earlier studies

on infant mortality have used past KDHs data and there was need to use recent KDHs data for

2008 which is more appropriate to consider after the major interventions mentioned above were

implemented in the National Health strategic plan I and II (2000-2010). This study has been done

at the initial years of the implementation of the Kenya health policy plan 2010-2030 and in such;

policy recommendation offered may help the government in its effort of reducing infant

mortality with an aim of achieving the specific MDG target of 22 deaths per 1000 for Kenya by

2015.

1.5 Organization of the Study

In chapter one, the study presented the project background, problem statement, justification of

the study and the study objectives. Chapter two reviewed both the theoretical and empirical

literature relevant to this study. Chapter three outlines the methodological approach that was

employed in this study and it includes the analytical framework, variable definition, estimation

technique together with a description and analysis of data and data sources. Chapter four presents

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results and interpretation of findings from the study. Finally, Chapter five gives a summary of

the study, conclusions and policy implications.

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CHAPTER TWO: LITERATURE REVIEW

2.0 Introduction

This chapter presents the theoretical and empirical literature on child health and mortality.

Theoretical literature presents the health production model and child mortality framework by

Mosley and Chen (1984), while the empirical literature discusses studies done on infant mortality

in Kenya, other sub-sahara African countries and outside world.

2.1 Theoretical Literature Review

2.1.1 Health care Production and child mortality framework by Mosley and Chen (1984)

Health care production according to Santere and Neun (2010) is influenced by a variety of inputs

among them nutrients, genetic endowments, exercise, lifestyle and the amount of medical care

consumed given the technological-biological production relationship. In household health

production according to Rosenzweig and Schultz (1983) input preferences made by households

are considered to be determined by the health technology. Health technology is the human

biological mechanisms through which behavior affects health. The household preferences are

also determined by prices and income of the household. They note that in order to conclude on

causal relations between health and inputs there is need to control for heterogeneity and thereforeestimate health technology from models in which inputs affecting health are choice variables by

themselves. E.g mothers education

This production framework was developed further by Mosley and Chen (1984) while trying to

provide an approach to the investigation of determinants of child survival in developing

countries. In their framework all economic, environmental and social determinants of child

mortality operate through a common set of biological mechanisms (proximate determinants) to

impact on mortality. For instance educated mothers may have lower infant mortality because the

skills learnt will help them in making choices regarding contraception, preventive care, treatment

and child nutrition. This model was first introduced by Davis and Blake (1956) in a study on

fertility. The proximate determinants framework is very similar to the health production function

for households.

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The model identifies a set of proximate determinants that determine mortality directly. They are

grouped into five categories. The first four categories affect the rate of a child moving from a

state of healthiness to that of sickness while the last category; personal illness control, influence

the rate of getting sick as well as the rate of recovery. This is through preventive measures and

treatment seeking behavior. The categories include: Maternal factors (age, parity and birth

interval); environmental factors (air, skin/soil, insect vectors, inanimate objects and food/water);

nutrients deficiency (calories, proteins and the micronutrients); injury (intentional or accident)

and Personal illness control (preventive measures and medical treatment). A change in any of the

determinants will influence a childs health either to the better or to the worse.

The Mosley and Chen (1984) framework was integrated by Shultz (1984) into an economic

choice model. This model explores the biasness of the direct association between health inputsand an individual's health outcome as employed in epidemiological research. The argument is

that individuals initial health endowment differs and their health inputs are determined by their

knowledge of their endowment. The framework is based on the microeconomic model of the

family. Individuals allocate their time and resources in response to the value of their time,

nonhuman capital endowments and the relative prices for their inputs and outputs. The market

determines the value of the time of persons working since the market sets the wage

rate.

Thus individuals allocate their resources to yield optimal quantities for all their choice variables

for instance contraceptive use, nutrients intake, medical care, whether to breastfeed or not, food

purchase and child care time for a given set of constraints; wages and prices and for a given

production technology. Mothers education is used as a close correlate of women wages. This

model gives two sets of equations and suggests methods of estimation for each. The first is the

demand equation which predicts the proximate determinants as a function of cultural, social and

economic factors. It can be estimated using OLS but for discrete choice variables, Logit or probit

functions can be used. The second equation is the health production equation and it predicts the

health outcome as a function of proximate determinants. It can be estimated using Logit or

structural equation methods.

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Millard (1994) also proposed an approach to the investigation of determinants of child survival

in developing countries. The model by Millard is similar to that by Mosley and Chen in terms of

levels of causes of child mortality. However it differs by the introduction of three tiers of causes

of child mortality in trying to emphasize parental behavior and child care traditions in the

economic, social, cultural, political and environmental context. The three layers are proximate

(immediate biomedical causes of death e.g. malnutrition), intermediate (behavioral patterns that

increase exposure to the proximate causes e.g. breastfeeding patterns) and ultimate causes

(economic, political and socio-cultural factors)

According to Hill (2003), other modeling strategies have been in use after the Mosley and Chen

model. For instance there are models that link background factors and proximate factors with

mortality; such as factors related to immunization. The models are implemented step by stepstarting with background factors and then the proximate factors. A reduced-form model of net

association of background variables and child mortality is then established. For example

Ajakaiye and Mwabu (2007) have employed such a modeling strategy for the determination of

birth weight while considering tetanus vaccination as a health input factor while behaviors like

prenatal care and behavioral changes during pregnancy to be the intermediaries.

2.2 Empirical Literature Review

There are various studies regarding the determinants of infant mortality which show significant

association between demographic, socioeconomic factors, environmental factors and infant

mortality.

2.2.1 Studies Done in Kenya

In Kenya there are several studies on infant and child mortality using KDHs and KSPA data.

Kamau (1998) for instance used KDHs data for 1993 in his unpublished research paper focusing

on child survival determinants in Machakos, Kilifi and Taita Taveta districts. The study showed

significant relationship between marital status, mothers level of education, place of delivery, age

at first birth, religion and availability of toilets with incidence of child mortality.

Mutunga (2004) in his unpublished Masters research paper sought to show that socio-economic

and environmental characteristics (mothers education, sources of drinking water, electricity,

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The study suggests that improved skills for health workers are needed for better services in child

health management.

2.2.2 Studies outside Kenya

Studies done in regard to determinants of infant and child mortality globally show evidence of

significant association between socioeconomic, demographic, environmental and infant-child

characteristics (Hosseinpoor, 2005; and Caldwell, 1979). Caldwell (1979) for instance gave input

in regard to effect of mothers education on infant mortality with reference to Nigeria. The study

concluded that higher education lowered the rate of infant mortality through factors like hospital

delivery, increased ante natal care for pregnant mothers and changing traditional family

relationships. Supporting Caldwells explanation, Hobcraft (1993) explained that education can

contribute to child survival by making women more likely to marry and enter motherhood later,

have fewer children, utilize prenatal care and immunize their children.

Maternal education is usually used as a proxy for other household characteristics. Medrano et al.

(2000) used mothers education as a measure of health knowledge and the socioeconomic status.

Their study used cross sectional data for South Africa Integrated Household Survey for 1993.

They used nave regression to assess the relationship between mothers education and child

health and the findings show positive relation with increased knowledge leading to childs good

health. Similarly Kovsted et al. (2003) using mothers religion as a measure of health knowledge

showed mothers knowledge was important in determining child health.

In another level, mothers education has been shown to have positive and inverse impact on child

health. Desai and Alva (1998) using DHS data from 22 countries found that infant mortality was

lower among educated women, and that although this effect intensify with the inclusion of other

socioeconomic factors in their models, maternal education remained significant. In contrast, Hill

et al. (2001) observed an inverse relationship between mothers educational level and economic

status and child mortality in the late 1980s and 1990s using the 1993 and 1998 Kenya DHS

data. This study was done using multivariate analysis. The variables examined were mothers

education, wealth status, residence, maternal age, preceding birth interval and birth order. The

authors made a conclusion that HIV epidemic was the most probable cause of increased child

mortality not the socioeconomic or demographic factors.

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Beenstock and Sturdy (1990) also found important role for female literacy in determining infant

mortality in their study among several Indian states. However the study concluded that maternal

literacy had a weaker effect on child survival in Sub-Saharan Africa. This could be due to

environmental factors, family wealth and other demographic factors unique to this part of the

continent. Similarly in India Claeson et al. (2000) analyzed the countrys SRS and NFHS data

and observed that non income factors (maternal and child health interventions) play a significant

role in reducing infant and child mortality in India. The study also observed that girls are 30

percent more likely than boys to die before their fifth birthday. This was attributed to son

preference in India, which is manifested in lower spending on health for girls and higher

prevalence of immunization among boys. Conversely the United Nations secretariat (1988)

carried out a study on sex differentials on life expectancy and mortality in less developed

countries and their results showed that male infant have higher probability of dying than female

infants.

According to Jones et al. (2006) the main causes of child mortality presented in India are disease

related (diarrhoea, pneumonia for under-five children and tetanus for new born), pre-term

delivery and sepsis. However the major cause of death is under nutrition. Due to inadequate data

the study employed an analytical overview through study groups.

While Uganda DHS for 3 surveys and employing a reduced form probit model, Ssewanyana and

Younger (2005) investigated the infant mortality determinants in Uganda in order to assess the

likelihood of Uganda meeting MDG goal 4. The results show that improvement in mothers

primary education and vaccination service greatly impacts infant mortality. However household

income and infant mortality were correlated negatively to a small extent. This implied that even

if Ugandas rapid growth continued the impact on infant mortality would be small.

A study by Hosseinpoor et al. (2005)on the socioeconomic inequality in infant mortality in Iran

using ordered probit model concluded that other factors beyond the health care delivery system

significantly contribute to high infant mortality rate in Iran. The study recommended additional

interventions to be done in the area of environmental and sanitation in order to reduce infant

mortality. This would work best if implemented in rural areas so as to curb the socioeconomic

inequality in mortality among urban and rural areas in Iran. The study used DHS data done in

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Iran in 2000. This data covered a long period of time (early 1980s to early

2000s).

Alves and Belluzzo (2005) investigated the determinants of infant mortality rates in Brazil using

panel data for the years 1970, 1980, 1990 and 2000. Pooled OLS was used to estimate the model

while considering variables like sewage services, illiteracy rates and dummy variable for

economic development. Their findings confirm that poor child health (in terms of mortality rates)

in Brazil could be explained by the levels of education, sanitation and poverty. Moreover, the

paper showed that mothers education was the most important variable given that for every

additional year of schooling, average mortality rates declined by more than 7%.

Pandey, et al. (1998) estimated the socioeconomic determinants of infant mortality and observed

that mothers literacy, household heads religion, place of delivery, cooking fuel, mothers

exposure to mass media and wealth were significant variables. However religion was found to

have a modest impact in some region of Iran. Hazard models were used to analyze the

relationship using NFHS data. Mothers tetanus immunization during pregnancy was also found

to reduce neonatal mortality. In another level, the study also found a U-shaped relationship

between birth order and mothers age at birth with infant mortality. Thus when age increases

from teenage to matured mother mortality falls and it rises as one move to elderly mother. Other

studies have also documented evidence of a reverse pattern in the association between maternal

age at birth and infant mortality, with teenage and older mothers having elevated risks of child

loss (Koenig, 1992; Pebley, 1991).

A study by Da vanzo et al. (2004) supports these findings. They used high-quality longitudinal

dataset in Bangladesh to establish the relationship between birth interval and child mortality. The

findings using Cox proportional hazard model show that preceding inter-birth intervals of less

than 24 months in duration are associated with significantly higher risks of early neonatal

mortality whereas, intervals of less than 36 months are associated with higher risks of late child

mortality. Effects of short intervals are stronger the younger the child.

Madise, (2003) did a study which aimed at comparing the socioeconomic and demographic

determinants of child mortality in Zambia and found that at neonatal stage demographic factors

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(e.g. birth orders, mothers age ) were more important determinants of infant mortality while at

the post neonatal stage both demographic and socioeconomic e.g. place of residence, were

important determinants.

Several studies have considered households socioeconomic status in terms of their source of

drinking water, sanitation, source of cooking oil and income level. Where data on income levels

is not collected it is proxied by wealth. According to Mosley and Chen (1984) the effect of

socioeconomic factors on mortality is through environmental hazards, maternal factors, injury

and nutritional status. Kabubo-Mariara et al. (2012) shows that water and sanitation variables

reduce the hazard ratio when other factors affecting mortality are not controlled but when

controlled the variables gave a positive relationship with mortality. Fayehun (2010),

hypothesized that variations in household environments among sub-Saharan countries couldaffect childrens survival chances. The study found that there are significant relationships

between the household environment and child survival. Some of the differences in childhood

mortality could be explained by levels of household environmental health hazards and by

maternal socioeconomic status.

Source of cooking fuel is another considered variable that affect child mortality. Mutunga (2004)

found clean sources of cooking fuel to be significant in reducing death in households. This could

be due to a reduction in indoor air pollution.

2.3 Literature Summary

In conclusion, major health determinants that have been put forward in literature are

socioeconomic, demographic and environmental factors. Also health services and behavior that

promote and increase health stock have been associated with improved health status. For instance

tetanus injection for pregnant mother, higher education/health knowledge for mothers, access to

clean water and sanitation were found to have high impacts on child survival. In another level,

high mortality rates were observed in girls than boys due to some households preference of

boys.

Therefore there are many factors that have been argued to be the determinants of infant

mortality. The choice of variables used in the present study was determined by data availability.

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Nevertheless most of the determinants of infant mortality presented in literature, like mothers

age at the time of birth, mothers education, place of residence, religion, household wealth, birth

interval, access to sanitation, source of drinking water and tetanus injection of mothers were

analyzed.

Majority of the studies reviewed in Kenya were mainly concerned with some determinants not

all. For instance, Mutunga (2004) was concerned with impacts of environmental and socio-

economic factors on child mortality, Kabubo-Mariara et al. (2012) focused on effect of physical

environment on child survival while Elmahdi (2008) was more concerned on the socio-economic

determinants of infant mortality. On another level Kamau (1998) focused on child survival

determinants in arid and semi arid lands of Kenya thus did not consider the whole country. This

study will add to the existing knowledge on child mortality in Kenya while employing a modelthat incorporates most of the variables available in the socio-economic, demographic,

environmental and biological categories as well as maternal and child health care factors to

establish which of them greatly determine infant mortality.

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CHAPTER THREE: METHODOLOGY

3.1 Introduction

This chapter presents the tools of analysis and describes the data as well as methods of analysis

that will be used. The methodology of the study was motivated by the reviewed literature both

theoretical and empirical in chapter 2 and the type of data that is available.

3.2 Analytical Framework

From reviewed literature child/infant survival was found to be determined by socioeconomic

factors which affect the intermediate/proximate determinants of health. These proximate

determinants are the intermediate variables between the socioeconomic determinants and themortality risk (Mosley and Chen, 1984). The variables include; (maternal and environmental

factors, nutrient deficiency, injuries and disease control). In this regard the household production

framework given by Schultz (1984) will be modified and used to analyze the impacts of various

covariates on child survival. As Hill (2003) puts it; this framework has stood the test of time and

still provides the conceptual basis for many studies on child survival. For instance this model

was modified and used by Mwabu (2008) in his study of child health production in Kenya and

Foloko (2009) Masters thesis on determinants of child mortality in Lesotho.

Child health production function is given as a linear function;

H = F (Y, I,k, )..(1)

Where,

Y is the proximate biological inputs to child health (immunization, cooking fuel, water andsanitation environment)

I is a child health input such as curative and preventive medical care

K is the health knowledge possessed by the household (e.g. fertility and child spacing

techniques) and

is the child health endowment due to genetic or environmental conditions not influenced by the

parents.

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The proximate inputs Y are chosen by the household in a manner to reduce the health outcome

(mortality). They depend on the child health endowment (), maternal/household preferences

(PR), prevailing prices in the market and specific constraints posed by the households physical

environment (P) and household wealth (W). Thus our reduced-form input demand function will

be;

Y=F (, PR, P, W).. (2)

The reduced form demand functions were given as the objects of study in the Shultz (1984)

model.

Equation (1) and (2) shows that child health can be explained by proximate biological inputs to

child health (Y), child health input (I), Household knowledge (K), household wealth (W), and achilds health endowment .

Based on equation (1) and (2) this study estimated a model which included variables from the

categories in the equations.The choice of variables was motivated by the Mosley

and Chen (1984) framework.

These factors consisted of;

Xi,j which is a vector of maternal characteristics (maternal age, education, religion, number of

children ever had, marital status and family/residential factors). This was to help capture the

households choice of proximate inputs and its impact on mortality.

Xk,j which is a vector of household characteristics (household wealth, access to water and

sanitation, source of fuel). This helped capture the variables impact on child health.

Xl,j which is a vector of biological and child endowment factors (birth order, birth size, gender

and birth spacing). This helped capture the childs health endowment and its impact on mortality.

Xm,j which is a vector of health services variables (place of delivery and tetanus injection). This

helped capture the impact of health intervention in reducing mortality risk

ej is the error term.

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The study estimated the following model;

Lj = 0 + iXi,j + kXk,j + Xl,jl + mXm,j + ej.(3)

Where Li is the dependent variable based on the probability of a child dying before the first

birthday. It took only two values; 1 if the child was reported alive and 0 if the child was reported

dead.

The final model while including all variables was as given in equation (4)

Lj = 0 + 1 MAGE + 2 MAGESQ + 3TNO + 4MRTS + 5MEDU1 + 6MAREL1 + 7 TORE1 +

8 HW1 + 9 ATW1 + 10 ATS1 + 11 BORD1 + 12 BSIZE11 + 13 G + 14 BSPA1 + 14

PLOD1 + 15 TTI1 + ei.. (4)

Table 3.2: Variables Acronym Definition

Acronym Definition

MAGE mothers age

MAGESQ Mothers age squared

TNO Total number of children the mother has

MRTS Marital status of the mother

MEDU mothers highest level of education

MAREL mothers religion

TORE households type of residence

HW households wealthATW households access to water

ATS households access to sanitation

SCF households source of cooking fuel

BORD birth order of the infant

BSIZE birth size of the infant

G Gender of the infant

BSPA birth spacing

PLOD place of delivery of the infant

TTI tetanus toxoid injection for the infants mother

3.3 Estimation Technique

One problem the estimation encountered was on prevailing prices for health input goods given

that DHS data did not collect information on prices. We followed, Kovsted et al. (2003) where

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we assumed identical prices for all the households and thus we estimated child health as a

function of proximate biological inputs to child health (Y), child health input (I), household

health knowledge (K), household wealth (W), and a childs health endowment .Similarly since

health knowledge is assumed to be endogenous a bias could arise when parents are aware of a

given health condition not known to the researcher. In this regard, mothers education was used

to assess health knowledge (Medrano et al.2000).

The estimation was done using the binary choice models since mortality rate is a discrete

variable. Thus the dependant variable took only two values; 1 if the child was reported dead and

0 if the child was reported alive. The objective was to find the probability of the child dying or

not dying. Three approaches could have been used; the linear probability model (LPM), the

logistic regression model (logit) and the probit model (Gujarati, 2007).

The LPM is the simplest to use but has some serious limitations. The error term violates the

assumption of normality, the model suffers from heteroskedasticity and more seriously, there is

always the possibility of the estimated probability lying outside the 0-1 bounds (Gujarati, 2007).

Other limitations are limited usefulness of R2 and marginal effects are constant. Furthermore, the

LPM is logically not a very attractive model even if the above limitations are solved in that it

assumes that the conditional probabilities increase linearly with the values of the explanatory

variables. A probability model that has an S-shaped feature of the cumulative distributionfunction is preferred. In practice the logistic and the normal cumulative distribution functions are

chosen, giving rise to the logit and the probit models respectively. The study presented estimates

using the logit model although the study estimated both logit and probit models with no

significant differences in coefficients. Logit model is often used in literature (see Sewanyana and

Younger, 2005; Da vanzo, 2004 and Elmahdi, 2008). This was noted to be as a result of the ease

in interpreting results for logit model in terms of marginal effects which is impossible with probit

model.

3.4 Variable Definitions

This section defines the variables which were analyzed in the study.

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Table3.3: Variable Definitions and Apriori expectation

Variables Definitions Apriori expectation

Maternal characteristics

Mothers age (MAGE) Very young and very old

women are expected to

increase child mortalityrisk. (Pandeyet al. 1998)

Mothers education (MEDU) Mothers highest level of

education;

None-(0)

Primary -(1)

Secondary -(2)

Higher -(3)

Higher education is

expected to improve child

health and survival,(Alves and Belluzzo, 2005)

Type of residence (TORE) Urban (1),

Rural (0)

Rural residents are

expected to have high

child mortality risks thanurban residents, (Kabubo-

Mariara et al., 2012)

Mothers Religion(MAREL) Has Religion (1),

No religion (0)

Religion impactsknowledge which is

expected to improve

health. (Pandey, et al.,

1998)

Total number of children (TNO)

Marital status of the mother

(MRTS)

Married (1)

Not married(0)

Children Characteristics

Gender (G) Male (1),

Female (0)

Boys are expected to havehigh mortality risks than

girls, (Claeson et al.,2000)

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Birth Order (BORD) The birth order of the child in

the family;

First order -(1)

2-3 birth order-(0)

Above 3 birth order -(2)

First order and above 3

birth order are expected tohave negative impacts on

mortality, (Da vanzo et al.

2004)

Birth spacing (BSPA) Less than 24 months (1)

More than 24 months (0)

Better spacing is expected

to have positive impacts

on mortality, (Da vanzo etal. 2004)

Birth size (BSIZE) small/ very small -(1)

average size-(0)

Large/ very large -(2)

Small size and very large

infants are expected to

have high mortality risks,(Elmahdi, 2008)

Household characteristics

Households wealth (HW) Poor -(1)

Middle -(0)

Rich -(2)

Household wealth is

expected to impact child

health positively, (Pandey,

et.al., 1998)

Source of cooking fuel (SCF) Electricity-(1)

LPG-(2)

Kerosene (3)

wood -(4)

coal/charcoal -(5)

dung -(6)

grass- (7)

Availability of clean

cooking fuel free from airpollution is expected to

improve child health,

(Mutunga, 2004)

Access to sanitation (ATS) No facility-(0)

Flush toilet-(1)

Ventilated improved pit (VIP) -

(2)

Clean human waste

disposal facilities are

expected to improvehealth, (Mutunga, 2004)

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Pit Toilet-(3)

Other-(4)

Access to water (ATW) Piped water -(1)

public tap -(2)

open well -(3)

Rainwater -(4)

Other -(5)

River/Natural reserves- (6)

Bottled water- (7)

Availability of clean

drinking water is expectedto improve child health

and survival, (Mutunga,

2004)

Health service variables

Place of delivery (PLOD) Hospital (1)(0) Otherwise

Availability of basichealth amenities improveshealth, (Mosley and Chen,

1984)

Tetanus Toxoid injection (TTI) Immunized (1)

(0) Otherwise

Injection with tetanustoxoid for pregnant

mothers improves child

health and survival,

(Ssewanyana andYounger, 2005)

3.5 Data Source and Analysis tool

The study used KDHS data for 2008/9 collected in Kenya. The data was downloaded from

Measure DHS website after obtaining permission to download. The sample size for all

interviewed women was 8,444 women aged between 15 to 49 years selected from 400 samples

throughout Kenya. The survey utilized a two-stage sample based on the 1999 Population and

Housing Census and was designed to produce separate estimates for key indicators for each

province in Kenya.The study population for this analysis includes a sample of women whoreported information on infants born one year before the study period. The observations at this

level were 2425 representing mothers who had infants born for the period.

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CHAPTER FOUR: DATA ANALYSIS, RESULTS AND DISCUSSION

4.1 Introduction

This chapter presents the research findings for this study. Descriptive statistics are presented first

then the findings on infant mortality rate in Kenya as well as the determining factors. The results

are presented in tables followed by a discussion on the same. Logit results are presented and

discussed.

4.2 Descriptive results

This study utilized data collected for women between the ages of 15-49years. Before the analysiswas done various variables were regrouped to make the analysis more meaningful. For instance

mothers age was grouped into seven categories to allow for analysis of very young and very old

womens effect on infant mortality. Birth order was recoded to 2-3 birth order, first order and

above 3 birth order following the study by Da Vanzo et al. (2004). Similarly as per the studys

finding this study adopted their categorization on birth spacing into two; less than 24 months and

more than 24 months. Other grouping was done on access to sanitation, place of delivery and

access to water depending on the number of observations recorded and the appropriateness to the

analysis.

Table 4.4: Infant Mortality Rate as per 2208 KDHs data

Number of infant dead 127

Number of infants alive 2,298

Total 2425

infant deaths per 1000 live births (IMR) 55.27

As shown in table 4.4 infant mortality rate as per the KDHs data was 55.27 deaths per 1000 live

births. This was as reported in the KDHs report. As earlier alluded in the introduction of thisresearch Kenya still suffers from high infant mortality rate as compared to the MDGs target of

22 deaths per 1000 live births specific for Kenya. Our findings indicate that 5% of infants born

are likely to die before celebrating their first birthday.

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Table 4.5: Sample statistic on the Independent variables used in the study

Variable Value Names Frequency Percent

MAGE 14-19 years 260 10.72

20-24 years 774 31.92

25-29 years 616 25.430-34 years 445 18.35

35-39 years 231 9.53

40-44 years 80 3.3

45-49 years 19 0.78

MEDU No education 478 20

Primary Education 1,395 58

Secondary Education 428 18

Higher Education 124 5

MAREL No religion 86 3.55

Has religion 2,339 96.45

TORE Rural 1,812 74.72

Urban 613 25.28

HW Middle 380 15.67

Poor 1,117 46.06

Rich 928 38.27

ATW Piped Water 404 16.66

Public Tap 304 12.54

Open well 947 39.05

Rainwater 35 1.44

Other 53 2.19

River/Natural reserves 682 28.12

ATS No facility 589 24.29

Flush toilet 246 10.14

VIP 348 14.35

Pit toilet 1,221 50.35

Other 21 0.87

G Female 1,145 47.22

Male 1,280 52.78

PLOD Home 1,313 54.14

Hospital 1,107 45.65

TTI Not Immunized 330 14.35Immunized 1,951 84.86

The study utilized various variables as motivated by the Mosley and Chen (1984)

framework. Table 4.5 gives a summary of the variables included in the model

for infant mortality.

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From the findings in regard to maternal characteristics, majority of the

women interviewed were between 20 and 24 years (32%) while only 0.78%

of the interviewed women were between 45-49 years. In total all women

interviewed were between 14 and 49 years. 58% had primary education, 18% had

secondary education, 20% had no education while only 5% had higher education. In regard to

religion, 96% of the respondents had some religion; Christianity or Muslim while 4% did not

have any religion. 75% resided in rural areas with only 25% residing in urban areas.

Households reported various characteristics which helped in analysis. Majority of the households

(46%) were reported to be poor, 38% were reported to be rich while 16% were in the middle

category. They reported their sources of water to be piped water (17%), public tap (13%), open

well (39%), rainwater (2%), other sources (3%) and river/natural reserves (28%). Consequently50% used pit toilets, 14% used ventilated improved toilets (vip), 10% used flush toilet, while

25% reported they did not have any facility.

In regard to child characteristics, 53% of reported infants were male while 43% were female.

In this study, place of delivery and tetanus injection were used as health facility related variables.

Out of the respondent women 54% were reported to have delivered their children at home while

46% had delivered in a hospital either private or public. 84% had received tetanus injection while

pregnant while only 16% did not receive the injection.

Other important statistics are reported as below.

Table 4.6: Mothers age in relation to Infant death

age groups in

years

Number of infants

alive

Number of infant

dead

infant deaths per 1000 live births

(IMR)

15-19yrs 246 14 57

20-24yrs 741 33 45

25-29yrs 585 31 53

30-34yrs 418 27 6535-39yrs 218 13 60

40-44yrs 73 7 100

45-49yrs 17 2 117.6

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The mean age of interviewed women was 26years with the minimum age in child bearing

reported as 15 years. According to Pandey et al. (1998) study very young and very old women

had high risks of child and infant deaths. This study supports these findings as shown in table 4.3

where women aged15-19 years, 40-44 years and 45-49 years had high infant deaths of 57, 100

and 117 per 1000 live births respectively as compared to 45 and 53 infant deaths per 1000 live

births among women aged 20-24years and 25-29 years respectively.

Table 4.7: Mothers Education in relation to Infant death

Mothers Education

(MEDU)

Number of infants

alive

Number of infant

dead

infant deaths per 1000 live

births (IMR)

No education 456 22 48.2

Primary Education 1,317 78 59.2

Secondary Education 407 21 51.6

Higher Education 118 6 50.8

This study used mothers education to assess health knowledge. In contrast to literature findings

women with no education had the lowest infant mortality rate (48%) followed by women with

higher education (50.8%), women with secondary education (51.6%) and the highest infant

mortality rate was reported among women with primary education.

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Table 4.8: Other independent variables in relation to infant mortality

Variable

Value

names

Number of

infants alive

Number of

infant dead

infant deaths per 1000 live

births (IMR)

MAREL No religion 82 4 48.8

Has religion 2216 123 55.5

TORE Rural 1723 89 51.7

Urban 575 38 66.1

HW Middle 355 25 70.4

Poor 1067 50 46.9

Rich 876 52 59.4

ATW Piped Water 377 27 71.6

Public Tap 287 17 59.2

Open well 907 40 44.1

Rainwater 34 1 29.4Other 48 5 104.2

River/Natura

l reserves 645 37 57.4

ATS No facility 566 23 40.6

Flush toilet 230 16 69.6

VIP 332 16 48.2

Pit toilet 1150 71 61.7

Other 20 1 50

G Female 1089 56 51.4

Male 1209 71 58.7PLOD Home 1255 58 46.2

Hospital 1042 65 62.4

BSIZE Average 1216 50 41.1

Small/Very

small 380 30 78.9

Large/Very

large 684 42 61.4

From table 4.8 infant mortality rate was reported to be high among women with religion (55%),

those women who resided in urban areas (66%), women from household reported to be in the

middle wealth quantile (70%), those who used piped water (71%), those who used flush toilets

(69%) and those women who delivered in hospital. In regard to child characteristics infant

mortality rate was reported to be high among male infants and infants born with small or very

small birth size.

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On the other hand infant mortality rate was reported to be low among women with no religion,

women residing in rural areas, those who delivered at home, those from poor households and

those from households that used rainwater. In regard to child characteristics infant mortality rate

was reported to be low among female infants and large infant birth size.

4.3 Econometric analysis

The study estimated the relationship between infant mortality and various determinants; maternal

characteristics, household characteristics, infants biological and health endowment as well as

health service variables.

The study examined how well the model fitted the data in order to obtain meaningful results. In

terms of likelihood ratio statistics, all the models reported passed the goodness of fit. This

implies that for every one of these models, there is at least one variable that is not equal to zero.

This means that the dependent variable (infant mortality) is better explained by at least some of

the independent variables than the constant alone.

The study estimated the model using LPM and Logit. However only logit results are presented

and discussed. In order to assess effect of health knowledge on infant mortality a second model

was estimated while omitting mothers education so as to check the change on the significance,

sign and effect of the remaining variables on infant mortality. This followed Medrano et al.,

(2000) study.

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Table 4.9: Results from Logit model for Infant mortality in Kenya.

Model One Model Two

Coef. Std Error Coef. Std Error

Mage 0.168 0.129 Mage 0.176 0.127

Magesq-0.004* 0.002 Magesq

-0.004* 0.002

Tno 0.149 0.102 Tno 0.147 0.101

Married 0.17 0.302 Married 0.175 0.302

Primary Education -0.22 0.358

Secondary Education -0.026 0.447

Higher Education -0.11 0.604

Has religion 0.134 0.639 Has religion 0.065 0.63

Urban 0.201 0.334 Urban 0.201 0.333

Poor 0.598* 0.311 Poor 0.617* 0.309

Rich 0.25 0.336 Rich 0.266 0.333

Public Tap 0.063 0.411 Public Tap 0.054 0.411

Open well 0.509 0.363 Open well 0.489 0.361

Rainwater 0.671 1.068 Rainwater 0.697 1.066

Other -0.832 0.546 Other -0.821 0.542

River/Natural reserves 0.015 0.384 River/Natural reserves -0.002 0.383

Flush toilet -0.003 0.545 Flush toilet -0.032 0.531

Vip 0.082 0.434 Vip 0.054 0.429

Pit toilet -0.219 0.341 Pit toilet -0.266 0.328

Small/Very small size

-

0.651* 0.287 Small/Very small size

-

0.641* 0.287

Large/Very large size

-

0.449* 0.242 Large/Very large size

-

0.456* 0.242

First order 0.536 0.386 First order 0.548 0.383

Above 3 birth order -0.334 0.377 Above 3 birth order -0.354 0.374

Male -0.081 0.214 Male -0.081 0.214

Less than 24 months -0.292 0.276 Less than 24 months -0.291 0.276

Hospital -0.237 0.253 Hospital -0.229 0.251

Immunized

-

0.135* 0.332 Immunized

-

0.155* 0.33

_cons 1.116 2.126 _cons 0.939 2.087

Number of observations 2265

Number of

observations 2265

Prob > chi2 0.0636 Prob > chi2 0.0337

* signifies parameter was statistically significant at the 10% level

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In model one, the independent variables used include, mothers age, mothers age squared, total

number of children, marital status, religion, education, households type of residence, wealth,

access to sanitation, sources of water, tetanus injection, place of delivery, birth size, birth order

and birth spacing of the infant. The second model omits education.

Table 4.10: Marginal effects from logit models

Marginal effects after

Logit

y = Pr(L) (predict)= 0.96416021

Marginal effects after

logit

y = Pr(L) (predict)= .96401291

Variable dy/dx Std Error dy/dx Std Error

MAGE 0.006 0.004 MAGE 0.006 0.004

MAGESQ 0.000* 0.000 MAGESQ 0.000* 0.000

TNO 0.005 0.004 TNO 0.005 0.003

Married 0.006 0.012 Married 0.006 0.012

Primary Education -0.007 0.012 Primary Education

Secondary Education -0.001 0.016 Secondary Education

Higher Education -0.004 0.023 Higher Education

Has religion 0.005 0.025 Has religion 0.002 0.023

Urban 0.007 0.011 Urban 0.007 0.011

Poor 0.020* 0.011 Poor 0.021* 0.011

Rich 0.008 0.011 Rich 0.009 0.011

Public Tap 0.002 0.014 Public Tap 0.002 0.014

Open well 0.017 0.011 Open well 0.016 0.012Rainwater 0.017 0.020 Rainwater 0.018 0.020

Other -0.042 0.038 Other -0.041 0.038

River/Natural reserves 0.001 0.013 River/Natural reserves 0.000 0.013

Flush toilet 0.000 0.019 Flush toilet -0.001 0.019

VIP 0.003 0.014 VIP 0.002 0.014

Pit toilet -0.008 0.012 Pit toilet -0.009 0.011

Small/Very small size

-

0.028* 0.015 Small/Very small size

-

0.028* 0.015

Large/Very large size

-

0.017* 0.010 Large/Very large size

-

0.017* 0.010

First order 0.016 0.010 First order 0.017 0.010Above 3 birth order -0.012 0.014 Above 3 birth order -0.013 0.014

Male -0.003 0.007 Male -0.003 0.007

Less than 24 months -0.011 0.011 Less than 24 months -0.011 0.011

Hospital -0.008 0.009 Hospital -0.008 0.009

Immunized

-

0.004*

0.011 Immunized

-

0.005* 0.010

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* signifies parameter was statistically significant at the 10% level

4.4 Discussion of Results

Mothers age squared

An increase in the age of the mother by one year increases the probability of infant mortality by

0.014 percent. The impact is the same when controlling for health knowledge using mothers

education. These findings concur with earlier expectations on the effect of mothers age on infant

mortality. It is expected that very young mothers may experience difficult pregnancies and

deliveries because of their physical immaturity. They are also likely to have limited knowledge

and confidence in caring for infants. Similarly, women who are very old may also experience age

related problems during pregnancy and delivery. Similar results were also found by Madise et al

(2003) in Zambia. Our findings support literature findings but contradicts Medrano et al. (2000)

findings where educated mothers were expected to be more knowledgeable and thus to have low

infant mortality.

Poverty

An increase in the proportion of women from households classified as poor relative to

households classified as middle class by 1 percent would increase the probability of infant

mortality by 2.05 percent. The impact of poverty increases to 2.12 percent when the model is

controlled for health knowledge. This could imply that even though households could be poor,with knowledge they may come up with ideas to help them improve their living condition and

thus reduce infant mortality. The findings support our apriori expectations where wealthy

households are expected to have low infant mortality.

Birth size

The size of the baby at birth, either small or large, as reported by the mother is used in the study

as a proxy for the childs birth weight since most of the children did not have birth weights.

Infants reported born of small/very small size and those reported born of large/very large size

had a 2.79 percent and 1.69 percent lower probability of infant deaths as compared to infants

reported born of medium size. These findings contradict the expected results as infants born with

small and large sizes were expected to have high mortality risk as discussed by Elhamadi (2003).

Nevertheless other reserchers like Pandey, (1998) had reported similar results in India.

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Immunization

Mothers immunized against tetanus while they were pregnant had a 0.45 percent reduced risk of

having their children die before celebrating their first birthday. The impact was higher (0.51

percent) where health knowledge was not considered.

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CHAPTER FIVE: SUMMARY, CONCLUSIONS AND POLICY

IMPLICATIONS

5.1 Introduction

This chapter outlines the summary on research findings, conclusion of the study as well as some

policy recommendations with limitations of the study and suggestions for future research.

5.2 Research Summary

The major objective of this study was to establish empirically the determinants of infant

mortality in Kenya. This was motivated by the fact that despite the reported decline in infant

mortality the level was still high as per the MDG standard. Similarly infant mortality rate

illustrates the lasting impacts of childhood health into adulthood and will thus impact economic

performance of a country.

The objectives of the study were to investigate whether demographic, socioeconomic,

environmental and child health characteristics were important in explaining infant mortality in

Kenya. This study was relevant as it would add to the existing knowledge in literature on infant

mortality in Kenya.

The study used individual household level data from the 2008 Kenya demographic and healthsurvey. The logistic regression model was used due to the categorical nature of the dependent

variable and he marginal effects were obtained using STATA statistical package. The results are

given in chapter four and marginal effects were discussed.

5.3 Conclusion

From the analysis the study found out that mothers age was a key determinant to infant health. It

did not matter whether the mother was knowledgeable or not as the impact was the same. As a

woman ages their chances of having their children not celebrating their first birthday were higher

by 0.014 percent. Therefore its important to encourage women to give birth at the middle ages

where infant mortality rate risk was lower.

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The study found that households classified as poor had high risks of infant deaths as compared to

the middle household category. Household wealth explains a lot in regard to households living

standard in terms of education attained, ability to pay for hospital visits and use of better

sanitation. Therefore there is need to improve households wealth so as to improve child survival.

We found that education is very important in explaining infant survival. In this study education

was used to assess health knowledge of mothers. Mothers with higher education were less likely

to have their infant die before celebrating their first birthday as compared to mothers with no

education. Similarly when education was included in the model the impact of the other variables

on infant mortality was lower as compared to their impacts when education was omitted. Thus

education is key to ensuring women are knowledgeable in terms of infants health during

pregnancy and afterwards. This is important as it determines infants survival.

Finally the study established lower infant mortality rate among women who had been immunized

against tetanus while they were pregnant. Similarly large and small sized infants were found to

have lower risk of infant death as compared to the middle infants. These findings contradict

literature findings and could be as a result of the sample used. Majority of the interviewed

women were from rural areas where belief on having a large sized infant signifies health could

be rampant. Also the categorization was based on a womans judgment and thus the results

would have been biased as size could be subjective.

5.4 Policy Implications based on key Results

The findings of the present study indicate that infant mortality risk is high as a woman ages.

Therefore this study recommends that women be encouraged to bear children between 20 and 34

years of age and to have fewer children so as they get enough time to take care of the children.

This can be done through making family planning services accessible to all especially in rural

areas.

Poverty has been found to be a great determinant of infant mortality in Kenya. The study found

that majority of the respondent women were poor and lived in rural areas. There is need therefore

to come up with programmes to assist women get financial empowerment. For instance the

government can encourage commercial farming by promoting infrastructure development,

opening up markets for various farm products by farmers, offering credit facilities at lower rate

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to farmers, selling farm inputs at subsidized prices and also educating farmers on new farming

techniques that will yield more output. Similarly women need be encouraged to join hands and

mobilize resources for development.

It was also noted from the results that the birth size of the infant is important in determininginfant mortality in Kenya. The focus here could be directed on promoting healthy lifestyle for

pregnant woman to take care of factors that could contribute to birth weight. This could be in the

area of nutrition, exercise and avoidance of cigarette smoking.

Similarly the results have shown that women with education are more knowledgeable and

experience lower infant mortality than those with no primary education. With the inception of

free primary education many children are acquiring primary education but are unable to progress

to secondary due to factors like lack of fees despite the government subsidizing the fees in

secondary school. In this regard the government could introduce a policy of providing free

secondary education so that more females are motivated to go further with their studies which

may in the long run result in reduction of infant mortality.

Finally the study observed that women who had been immunized when pregnant had lower risk

of encountering infant death. Thus its important to encourage women to attend ante natal clinic

regularly for checkup and for general education.

5.5 Limitations of the study

This study aimed to inform policy regarding the determinants of infant mortality in Kenya. It was

limited to the data already collected which was collected in the year 2008. This is the most recent

available data and thus may not give up to date information. This was as a result of limited time

to carry out the study and the amount of resources one may require to collect such data.

Similarly the data had missing values which affected the quality of output. This was as a result of

recall problem among interviewed women and biasness in reporting especially in the area ofbirth size. This could have been as a result of misplaced birth records, misreporting of deaths and

omissions of birth reporting for the children who die very young.

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5.6 Suggestions for further study

In the light of the above shortcomings, a more comprehensive study would be required that

would investigate the determinants of infant mortality by estimating two different models for

neonatal, infant and child mortality capturing all the determinants of infant mortality including

the ones outlined in this section . Recent collected data would also be appropriate. This would

give to date information and would help capture the impacts of various variables in the children

categories as what could affect infants would be different from what affects under five children.

This will help inform policy effectively.

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