DETERMINANTS OF ADVERSE PREGNANCY OUTCOMES Djoko Hartono

260 Pages·2017·20.55 MB·English

DETERMINANTS OF ADVERSE PREGNANCY OUTCOMES
A Case Study in Selected Rural Areas of West Java, Indonesia
Djoko Hartono
A thesis submitted for the degree of
Doctor of Philosophy
at the Australian National University
October 1995
DECLARATION
Unless it is indicated otherwise, this thesis is my own original work.
Djoko Hartono
October 1995.
iii
ACKNOWLEDGEMENTS
I would like to acknowledge several individuals and the funding agency for their
invaluable assistance towards the completion of my study at the Australian National
University. Thanks are due to the supervisory panel, Dr.Alan Gray, Dr.Terence H. Hull and
Dr.Lorraine Corner, especially to Dr. Gray who has been acting as the main supervisor in
Canberra, where my study was undertaken. Thanks are due also to Professor Gavin Jones,
the Head of the Demography Program, the Research School of Social Sciences, where I was
enrolled as a PhD student in Demography.
I wish to thank to the Australian Agency for International Development (AusAID)
for their continuous support in providing funding for my study in Australia. I am very
appreciative of the assistance given by all AusAID training officers during the course of my
study.
1 would like also to thank Dr.Yulfita Rahardjo, the Director of the Centre for
Population and Manpower Studies, the Indonesian Institute of Sciences, for the permission
given to me to study in Australia and also for her encouragement toward the completion of
my study. Thanks also go to Dr.Budi Utomo, the former Director of the Health Research
Unit, the University of Indonesia; Dr. Anhari Achadi and Dr.Endang L.Achadi at the Faculty
of Public Health, the University of Indonesia; and the MotherCare Project of John Snow,
Inc. for their permission to use the data from the Indramayu study. Thanks are extended to
the computer staff at the Centre for Child Survival Study, the University of Indonesia and to
all the field supervisors and interviewers employed in the Indramayu Study for their
cooperation during my data preparation and collection in the Indramayu Regency.
Sincere thanks go to all my teachers, including my Academic Skills lecturer, Marian
May, and my English editor, Malcolm Ashton, for their invaluable assistance during the
course of my study. My thanks are also extended to all my fellow students and friends who
shared ideas with me during my stay in Canberra.
To Tri Hartati, my wife, and Adhi Wicaksono, my son, I wish to express my sincere
gratitude for their invaluable support, understanding and patience during the course of my
study in Australia. Without such understanding it would have been impossible to complete
the study on time. A similar expression of gratitude also goes to my parents and relatives
who have undoubtingly given encouragement toward the completion of my study.
A B S T R A C T
The study addresses the determinants of adverse pregnancy outcomes, especially
neonatal deaths and stillbirths, in developing countries. The study is conducted in the
Indramayu Regency of West Java Province, Indonesia. A wide range of quantitative and
qualitative methods was employed to study this important public health issue.
Using community-based longitudinal data derived from the Indramayu Health and
Family Planning Prospective Study and the MotherCare Study (1990 to 1993), a series
of bivariate and multivariate analyses was conducted. It was found that the level of
neonatal deaths and stillbirths in the study area was higher than the national average.
Socio-demographic as well as bio-medical factors were significantly related to the high
level of neonatal deaths and stillbirths. While school attendance had the direct effect of
reducing the risk of stillbirth, teenage pregnancy increased the risk of neonatal death
through the indirect effects of short pregnancy intervals and low birth weight.
Neither the maternal nutritional depletion theory, nor the excess of preterm
deliveries in the short pregnancy interval group explained the mechanism through which
a short pregnancy interval increased the risk of neonatal death. It is likely that an intra-
familial mortality effect was the causal mechanism.
The findings suggest that efforts to alleviate the level of neonatal deaths and
stillbirths should not only focus on medical initiatives but also on specific social
interventions. Although extensive public health campaigns and the provision of adequate
maternal and child health care are necessary, they alone are not sufficient. Social
interventions designed to promote late marriage and delay of first birth through
improving women's education are required to enhance medical efforts to reduce the high
level of neonatal deaths and stillbirths in the study area or areas with similar conditions.
VI
TABLE OF CONTENTS
Page
DECLARATION ii
ACKNOWLEDGEMENTS iii
ABSTRACT v
TABLE OF CONTENTS vi
LIST OF TABLES ix
LIST OF FIGURES AND MAPS xiii
ABBREVIATIONS AND GLOSSARY OF TERMS xiv
CHAPTER 1: INTRODUCTION
1.1 Background 3
1.2 Aims and Research Questions 8
1.3 Conceptual Framework and Literature Review 9
CHAPTER 2: RESEARCH METHODS
2.1 Study Design 36
2.2 Data Source and Sampling Methods 36
2.3 Study Population and Respondents 39
2.4 Variables Under Study 39
2.5 Data Collection 54
2.6 Data Processing 55
2.7 Methods of Analysis 56
vii
2.8 Strengths and Limitations of the Study 61
CHAPTER 3: DESCRIPTION OF THE STUDY SITE
3.1 The General Overview of Geography and Socio-Demography of the
Indramayu Regency 63
3.2 A General Overview of Health Status and Health Services in the Indramayu
Regency 67
3.3 The General Characteristics of the Study Population 69
CHAPTER 4: THE ASSESSMENT OF THE QUALITY OF GESTATIONAL
AGE AND BIRTH WEIGHT DATA
4.1 The Need for Assessing Gestational Data Quality 79
4.1.1 Results of Univariate Analysis 80
4.1.2 Results of Bivariate Analysis 82
4.2 Gestational Age Data Validation 85
4.3 Results of the Data Validation Process 91
4.3.1 The Quality of Gestational Age Data 104
4.3.2 The Quality of Birth Weight Data 106
CHAPTER 5: DETERMINANTS OF NEONATAL DEATH
5.1 Results of Factor Analysis 118
5.2 Results of Bivariate Analysis 126
5.3 Results of Multivariate Analysis 128
5.3.1 Multivariate Analysis for Each Period of Pregnancy 129
5.3.2 Multivariate Analysis During the Full Course of Pregnancy 135
viii
CHAPTER 6: DETERMINANTS OF STILLBIRTH
6.1 Results of Factor Analysis 146
6.2 Results of Bivariate Analysis 149
6.3 Results of Multivariate Analysis 150
6.3.1 Results of Multivariate Analysis for Each Period of Pregnancy 151
6.3.2 Results of Multivariate Analysis During the Pre-pregnancy and
Antenatal Periods 152
CHAPTER 7: DETERMINANTS OF THE INTERVENING VARIABLES
7.1 Determinants of Inter-Pregnancy Interval 158
7.1.1 Results of Bivariate Analysis 161
7.1.2 Results of Multivariate Analysis 162
7.2 Determinants of Low Birth Weight 169
7.2.1 Results of Factor Analysis 171
7.2.2 Results of Bivariate Analysis 174
7.2.3 Results of Multivariate Analysis 175
CHAPTER 8: CONCLUSIONS, POLICY IMPLICATIONS, AND
AREAS FOR FURTHER STUDY
8.1 Conclusions 181
8.2 Policy Implications 188
8.3 Proposed Areas for Further Study 199
BIBLIOGRAPHY 201
APPENDICES 216
IX
LIST OF TABLES
Table Page
Table 3.1 Socio-Demographic Characteristics of the Respondents and their
Husbands in Gabus Wetan and Sliyeg, 1993 70
Table 3.2 Stillbirth, Neonatal and Perinatal Mortality Rates in the Indramayu
Study, 1993 74
Table 3.3 The Infant and Perinatal Mortality Rates in Sliyeg and Gabus Wetan
Sub-Districs, 1993 75
Table 3.4 Proportion of Low Birth Weight and Short Pregnancy Interval in
Gabus Wetan and Sliyeg, 1993 76
Table 4.1 Incidence of Pre-term Deliveries in Various Research Centres 80
Table 4.2 The Proportion of Pre-term Deliveries in the Indramayu Study, 1990-
1993 81
Table 4.3 Births by Gestation (percentage) 81
Table 4.4 The 10th and 90th Percentiles of Birthweight by Gestation Based on
the Indramayu (1990-1993) and Lubchenco Data 84
Table 4.5 Total and Selected Cases in the Data Validation Process 88
Table 4.6 Number of Cases Based on the Categorized Gestational Age in Each of
the Two Sub-Districts, 1990-1993 91
Table 4.7 Socio-Demographic Characteristics of Respondents Classified by the
Category of Gestational Age Data, Gabus Wetan, 1990-1993 92
Table 4.8 Socio-Demographic Characteristics of Respondents Classified by the
Category of Gestational Age Data, Sliyeg, 1990-1993 94
Table 4.9 Logistic Regression of the Likelihood of Measuring the Birthweight at
Less Than Seven Days, Gabus Wetan and Sliyeg, 1993 97
Table 4.10 Pregnancy Histories of Respondents Classified by the Category of
Gestational Data, Gabus Wetan, 1990-1993 99
X
Table 4.11 Pregnancy Histories of Respondents Classified by the Category of
Gestational Age Data, Sliyeg, 1990-1993 101
Table 4.12 Socio-Demographic Characteristics of Cases with Missing Data on
Birth weight, Both Study Sites, 1990-1993 103
Table 4.13 Number of Cases Beyond the Range of Biological Plausibility,
Classified According to the Category of Gestational Age Data and
Study Site 104
Table 4.14 Prevalence of Pre-Term Deliveries in Each of the Two Sub-Districts,
Classified According to the Category of the Validated Gestational Age
Data, 1990-1993 105
Table 4.15 Prevalence of Births by Gestation Classified by the Category of the
Validated Gestational Age Data, Sliyeg and Gabus Wetan, 1990-1993 106
Table 4.16 Birth Weight in the Indramayu Study, 1990-1993 107
Table 4.17 Median Birth Weights, Median Gestation Periods, and the Proportion
of Low Birth Weight Infants in the Indramayu Study (1990-1993) and
Other Areas (1961) 108
Table 4.18 The Percentage Distribution of Low Birth Weight infants in the
Indramayu Study (1990-1993) and Other Areas (1984) 109
Table 4.19 Neonatal Deaths in the Indramayu Study (1990-1993), Classified by
Whether or not the Baby was LBW and the Category of Data
Validation 110
Table 4.20 Mean Birth Weights by Gestational Age of the Indramayu Study (1990-
1993) and Standard Birth Weight 111
Table 4.21 Mean Birth Weights by Gestational Age of the Indramayu Study (1990-
1993) and Standard Birth Weight 112
Table 4.22 The 10th and 90th Percentile Distribution of Birth Weight by
Gestational Age Based on the Indramayu and Lubchenco Data 113
Table 4.23 The 10th and 90th Percentile Distribution of Birth Weight by
Gestational Age Based on the Indramayu and Lubchenco Data 114
Table 5.1 Varimax Rotated Component Analysis Factor Matrix (pre-pregnancy
period), Indramayu Study, 1993 120