Contextual Influences on the

Use of Antenatal Care in Nepal

Stephen A. Matthews

Bina Gubhaju

Department of Sociology and the

Population Research Institute,

The Pennsylvania State University

September 2004

ORC Macro

DHS Geographic Studies 2
Editors: Sidney Moore, Adrienne Kols

Designer: Katherine Senzee

Suggested citation:

Matthews, Stephen A., and Bina Gubhaju. 2004. Contextual influences on the use of 

antenatal care in Nepal. DHS Geographic Studies 2. Calverton, Maryland USA: ORC 

Macro. 
iii

Contents

Contents

Maps, Tables and Figures v

Acknowledgements vii

Abstract ix

1 Introduction: A Contextual Analysis Framework 1

2 Theoretical Overview 5

3 Study Site and Data Description 9

3.1 Nepal Demographic and Health Survey 9

3.2 Sample 10

3.3 Outcome Measures 10

3.4 Independent Variables 11

3.5 Summary 19

4 Analytical Methods 21

5 Modeling Results 23

5.1 Any Antenatal Care 23

5.2 Models Predicting Any Use of Antenatal Care 25

5.3 Four or More Antenatal Care Visits 29

5.4 Models Predicting Extensive Use of Antenatal Care 30

6 Discussion 35

6.1 Overall Analytical Findings 35

6.2 Implications for Research and Policy 35

6.3 DHS Geocodes and Geographical Information Systems 36

References 37

Appendix A Other Antenatal and Maternal Outcomes 43

Appendix B NDHS Variables in the Analysis 49

B.1 Outcome Measures 49

B.2 Predictors 49

Appendix C Logistic Model Results for Antenatal Care using

Development Region Dummy Variables 55

Appendix D HGLM Predictors of Four or More Antenatal

Visits (Model 4) 57

v

Maps, Tables and Figures

Maps, Tables and Figures

Map 1 Gender Development Index (GDI) by district 2

Map 2 Gender Empowerment Measure (GEM) by district 2

Map 3 DHS clusters by sub-region and district 4

Map 4 Ecological Zones, Development Regions, and DHS sub-regions

in Nepal 12

Map 5 Location of main hospitals in Nepal 13

Map 6 Usage rate for any antenatal care (women making at least one visit)

by DHS cluster, relative to the average for all clusters 24

Map 7 Proportion of women making at least one antenatal care visit

by sub-region 24

Map 8 Usage rate for extensive antenatal care (women making at least

four visits) by DHS cluster,relative to the average for all clusters 30

Map 9 Average number of antenatal care visits by district 34

Map A.1 Usage rate for antenatal care during the first trimester by DHS

cluster, relative to the average for all clusters 44

Map A.2 Tetanus toxoid immunization rate (at least two injections) by DHS

cluster, relative to the average for all clusters 45

Map A.3 Number of tetanus toxoid injections women received, by sub-region 45

Table 1 Use of antenatal care in Nepal 9

Table 2 Outcome measures 11

Table 3 Geographic variables 11

Table 4 Control and empowerment measures 15

Table 5 Individual and partner characteristics 17

Table 6 Household characteristics 18

Table 7 Logistic regression: Predictors of any use of antenatal care 26

Table 8 HGLM: Predictors of any use of antenatal care 27

Table 9 Logistic regression: Predictors of four or more antenatal care visits 31

Table 10 HGLM: Predictors of four or more antenatal care visits 32

Table A.1 Other antenatal and maternal outcomes 43

Table A.2 Logistic regression: Predictors of antenatal care during the first

trimester, using sub-region 46

Table A.3 Logistic regression: Predictors of at least two tetanus toxoid

injections, using sub-region 47

Table B.1 Ethnicity in Nepal 53

Table C.1 Logistic regression: Predictors of any use of antenatal care, using

Development Region 56
Table D.1 HGLM: Predictors of four or more antenatal visits, Model 4, using 

average number of antenatal care visits in district 58

Figure 1 Construction of multilevel dataset 3

Figure 2 Model of antenatal health care use among married women in Nepal 5
vii

We wish to thank Livia Montana of ORC Macro for assistance in providing access to 

geographic data for the 2001 Nepal DHS clusters. We acknowledge support from the 

Department of Sociology and the Population Research Institute at the Pennsylvania 

State University.

Acknowledgements

Acknowledgements

ix

Abstract

This report explores the degree to which contextual factors are determinants of individual behavior, specifically regarding the use of antenatal services. Geographic variation in 

gender development and empowerment across districts in Nepal suggest that research 

on women’s antenatal care behaviors may be enhanced by incorporating contextual data 

at the district level with individual and household level data from the 2001 Nepal Demographic and Health Survey (NDHS). This study of antenatal care uses DHS cluster 

geocodes to link with contextual data at the district level. The analysis focuses on two 

dichotomous outcome measures associated with antenatal care: (a) whether a woman 

received any antenatal care, and (b) for those women who made at least one antenatal 

care visit, whether she made four or more antenatal care visits during the pregnancy.

A renewed focus on women’s empowerment and place can help direct attention to 

the study of social contexts and processes, particularly those relating to maternal health 

behaviors. Many studies of the use of maternal health services have focused on individual-level or micro characteristics. In contrast, this study explores the use of hierarchical models, specifically hierarchical generalized linear models (HGLM), to investigate 

whether contextual or macro characteristics also matter. The macro-micro framework 

postulates that social forces at the macro level determine micro-level opportunities and 

constraints and thereby influence individual decisions. 

Policies and programs conceived without consideration for local context and place 

will have limited impact, unless they are informed by data that appreciates the vital 

connection between women’s health and women’s status across different spatial scales 

and analytical levels.

Abstract

Introduction: A Contextual Analysis Framework 1

1

Introduction: A Contextual 

Analysis Framework

Most studies of maternal health outcomes focus on individual-level data from largescale surveys. Over the last fi fteen years, however, demographers and public health 

researchers have become interested in contextual issues and the use of multilevel modeling techniques (Balk, 1994; Degraff et al., 1997; Diez-Roux, 1998; Diez-Roux, 2001; 

Duncan et al., 1998; Entwisle et al., 1984; Entwisle et al., 1986; Entwisle et al., 1989; 

Hermalin, 1985; Hirschman and Guest, 1990; Magadi et al., 2000; Pebley et al., 1996; 

Sastry, 1996; Smith, 1989). Th is methodological focus is relevant when examining the 

eff ects of macro (or contextual) factors on social behavior played out at a micro (or 

individual and household) level. It can assess the extent to which individual behavior is 

infl uenced by personal characteristics and the attributes of the larger community. 

Empirical investigations have examined gender context at the macro level and its 

impact on the use of antenatal care and reproductive behavior in Bangladesh (Balk, 

1994), Nepal (Morgan and Niraula, 1996), India (Chacko, 2001; Stephenson and Tsui, 

2002), and Nigeria (Kritz et al., 2000). Th ese studies have found that regional diff erences in aggregate measures of the position of women produce signifi cant diff erences in 

individual behavior. While the status of an individual woman is important, these studies show that the macro-level context of gender equality surrounding an individual also 

contributes substantially to diff erences in reproductive and maternal health outcomes. 

Such work is a motivation for this study.

Th e specifi c aim of this study is to investigate whether place (defi ned as district for 

the purposes of this research) matters for the use of antenatal health care in Nepal. Th e 

study focuses on two contextual measures of women’s status and empowerment: 

• Th e gender development index (GDI) assesses disparities in basic human capabilities between men and women, specifi cally regarding life expectancy, educational 

attainment, and income. 

• Th e gender empowerment measure (GEM) assesses gender deprivation based on 

participation and empowerment; it focuses on women’s participation in economic, 

political, and professional activities. 

Both of these measures have been derived for the district level in Nepal based on 

data from the 1998 Nepal Human Development Report (NSAC, 1998).1

Th e use of antenatal care is expected to be lower among women living in districts 

with low GDI and GEM scores as compared to women living in districts with high 

GDI and GEM scores. GDI and GEM scores vary widely within Nepal (see Maps 1 

and 2).2 Both measures also exhibit high degrees of spatial autocorrelation (the Moran’s 

1 Technical details on the construction of the GDI and GEM can be found in the Nepal 

Human Development Report (NSAC, 1998), Annex 3.1, pp. 257–260.

2 It is important to note that at the district level the correlation between GDI and GEM is 

.725 (signifi cant at .01). Th us this analysis does not include both GDI and GEM in the same 

model. 

The context of 

gender equality 

contributes 

substantially to 

differences in 

health outcomes.
2 Introduction: A Contextual Analysis Framework

Source: NSAC, 1998

< –1 standard deviation

Within 1 standard deviation of mean

> 1 standard deviation

Pokhara Kathmandu

50 0 50 100 Miles

3

8

13 7

12

6

11

10

5

2

1

4

9

1

2

3

4

5

6

7

= Eastern Mountain

= Central Mountain

= Western Mountain*

= Eastern Hills

= Central Hills

= Western Hills

= Mid-Western Hills

8

9

10

11

12

13

= Far-Western Hills

= Eastern Terai

= Central Terai

= Western Terai

= Mid-Western Terai

= Far-Western Terai

* Western, Mid-Western and Far-Western Mountain are combined to form the Western Mountain Sub-Region used by NDHS 2001.

Map 1

Gender Development Index (GDI) by district

Map 2

Gender Empowerment Measure (GEM) by district

Source: NSAC, 1998

< –1 standard deviation

Within 1 standard deviation of mean

> 1 standard deviation

Pokhara Kathmandu

50 0 50 100 Miles

1

2

3

4

5

6

7

= Eastern Mountain

= Central Mountain

= Western Mountain*

= Eastern Hills

= Central Hills

= Western Hills

= Mid-Western Hills

8

9

10

11

12

13

= Far-Western Hills

= Eastern Terai

= Central Terai

= Western Terai

= Mid-Western Terai

= Far-Western Terai

3

8

13 7

12

6

11

10

5

2

1

4

9

* Western, Mid-Western and Far-Western Mountain are combined to form the Western Mountain Sub-Region used by NDHS 2001.
Introduction: A Contextual Analysis Framework 3

I for GDI = .6321 and for GEM = .5889—both highly signifi cant at p = .001). Th at is, 

districts with high GDI scores tend to be located near other districts with high GDI 

scores (in the Kathmandu Valley, Pokhara, and the southeastern portion of the Eastern 

Development Region), while districts with low scores tend to be located near other 

districts with low scores (in the Far-Western and Mid-Western Mountains and Hills 

and in the area north of the Kathmandu Valley). GEM scores show a similar, but not 

identical, regional clustering (Map 2).

Geographic variation in gender development and empowerment variables across 

districts strongly suggests that considering contextual measures may enhance research 

on women’s use of maternal health care. 

Contextual variables can be generated by aggregating data collected at the individual 

level, and that is the approach commonly taken by demographers and sociologists. In 

contrast, this study takes advantage of Demographic and Health Survey (DHS) cluster 

geocodes (latitude and longitude coordinates) and uses geographic information systems 

(GIS) to link to, and create, district-level attributes.3 Researchers used GIS software to 

spatially join (1) 251 DHS clusters and the attribute data on individual women within 

each cluster with (2) district-level boundary fi les and their attributes. In other words, a 

multilevel dataset was generated (see Figure 1 and Map 3).

Th e choice of district as the second level of analysis is a pragmatic one. Comprehensive and reliable data are more widely available at the district level than for any smaller 

unit (e.g., village development committees or wards). In addition, the district off ers 

a reasonable balance between small communities and large, heterogeneous ecological zones or development regions within Nepal (Hirschman and Guest, 1990). Larger 

administrative units in Nepal, such as Development Regions or sub-regions, often encompass diverse physical environments and heterogeneous populations, and they frequently diff er in population size and distribution and in area.

Level

2 A B C

1 1 2 3 4 5 6 . . . 51 52 53 54 . . . 249 250 251

District

Cluster

Figure 1

Construction of multilevel dataset

3Th e existence of latitude and longitude coordinates for Nepal DHS clusters allows researchers to link DHS to geographically defi ned contextual databases (as does the availability of district codes within DHS data fi les).

Considering 

contextual 

measures may 

enhance research 

on women’s 

use of maternal 

health care.
4 Introduction: A Contextual Analysis Framework

Map 3

DHS clusters by sub-region and district

Source: NDHS 2001

Pokhara Kathmandu

50 0 50 100 Miles

1

2

3

4

5

6

7

= Eastern Mountain

= Central Mountain

= Western Mountain*

= Eastern Hills

= Central Hills

= Western Hills

= Mid-Western Hills

8

9

10

11

12

13

= Far-Western Hills

= Eastern Terai

= Central Terai

= Western Terai

= Mid-Western Terai

3 = Far-Western Terai

8

13 7

12

6

11

10

5

2

1

4

9

* Western, Mid-Western and Far-Western Mountain are combined to form the Western Mountain Sub-Region used by NDHS 2001.
Theoretical Overview 5

2

Theoretical Overview

Critics of the survey approach to demographic inquiry have urged that 

greater attention be paid to the context in which people make demographic decisions... large-scale surveys tend to pull the actors out of their 

dramatic context and place them on an empty stage.

—Ruth Dixon Mueller (2000, p. 97)

The main objective of this study is to explore the impact of ecological attributes on 

the use of antenatal care by married women, both before and after the inclusion of 

individual-level behavioral determinants. The theoretical model is adapted from a layout introduced by Hirschman and Guest (1990) as part of their work on reproductive 

behavior and is illustrated in Figure 2 (see also Kritz et al., 2000). 

Work linking individual and contextual data is firmly grounded in the literature on 

women’s health status and, more broadly, women’s empowerment. The demographic 

literature is paying increasing attention to the issue of women’s status, because empirical research worldwide consistently finds that variables related to women’s status are 

negatively correlated with fertility, maternal health, and mortality (Sen and Batliwala, 

Antenatal Care Utilization Individual and Household Factors Contextual Factors

Any use of antenatal care

Number of antenatal care 

visits

Other possible health 

outcomes:

Antenatal care during the 

first trimester 

At least two tetanus toxoid 

injections

Geographic area (sub-region)

Urban/rural location 

Distance to nearest hospital 

Age

Parity

Want child

Empowerment measures:

Refuse sex with husband

Opinions about wife beating 

Decisionmaking

Problems getting health services

Land ownership

Relationship to head of household

Woman’s education

Partner’s education

Listens to radio

Watches TV

Woman’s employment

Partner’s employment

Religion

Ethnicity

Household utilities

Women’s status:

GDI

GEM

Adapted from Hirschman and Guest (1990) and Kritz et al. (2000).

Figure 2

Model of antenatal health care use among married women in Nepal
6 Th eoretical Overview

2000). Th ere is a general consensus that women’s status is an important determinant 

of reproductive behavior and maternal health, especially in places where the status of 

women varies considerably, as is the case in Nepal. 

Demographers typically have relied on traditional variables such as women’s education and employment to measure women’s status (Watkins, 1993; Presser, 1997). Studies in developing countries show strong empirical evidence of a correlation between 

women’s education and a couple’s fertility, age at marriage, desired family size, contraception, and use of maternal health services (Sen et al., 1994). However, critics of such 

measures note that these variables are at best proxy measures and do not fully capture 

the dynamics involved in measuring women’s status. Indeed, the multidimensionality 

of women’s status and its complex relations with demographic behavior has only recently started to receive attention. Growing recognition that the relationship between 

women’s status and demographic outcomes is not as straightforward as indicated by 

previous research has led to a shift from the concept of women’s status to women’s 

empowerment (Riley, 1997). 

Sen and Batliwala (2000) point out the importance of power relations at multiple 

levels, or contexts, within which women’s lives are enmeshed: these include the household/family, community/village, market, and state levels. Women’s subordinate status at 

one level is reinforced by power relations at other levels. Th us “even if power relations 

are eased or overturned at one level, e.g. within the household, they may continue to 

hold women in their grip through community-level strictures or ideologies, or through 

gender biased laws or discriminations in markets” (Sen and Batliwala, 2000, p.21).

Both Dixon-Mueller (2000) and Sen and Batliwala (2000) emphasize the interaction between macro and micro processes. Th e macro-micro framework asserts that 

social changes at the macro level determine opportunities and constraints at the micro 

level, thereby infl uencing individual decisions (Axinn and Barber, 2001; Axinn and 

Yabiku, 2001). Although an individual woman’s ability to gain control over her life is 

imperative for demographic change, external forces operating at the macro level are just 

as important. Th ey can create a context conducive to empowering women, which may 

then result in improved maternal health outcomes. As previously noted, improvements 

in women’s education and employment may not bring about necessary demographic 

changes if the context is not empowering.4

While the empowerment literature recognizes the potential importance of contextual variables, the degree to which such variables are explicitly incorporated in the analy-

4 Several empirical studies have examined the eff ects of gender systems on reproductive and 

maternal health outcomes. Marked diff erences in gender systems in northern and southern 

India have prompted numerous studies in which researchers have repeatedly found diff erences 

in fertility and other reproductive outcomes between the two regions (Dyson and Moore, 1983; 

Jain, 1989). Much of the diff erence has been attributed to varying levels of development and, 

most notably, the varying position of women in the two regions. Th us some argue that gender 

systems in diff erent contexts play an important role in shaping reproductive behavior (Kritz et 

al., 2000). In Pakistan, the eff ect of education on fertility has only been observed in urban areas, 

where opportunities for education translate into employment and decision-making. In Bangladesh, Cleland et al. found a strong correlation at the individual level between exposure to formal 

schooling and fertility. When looking at South Asia as a whole, however, they found less support 

and concluded that the relationship is highly context specifi c. Similarly Jejeebhoy (1995) argues 

that importance should be given to the cultural context at diff erent times and at diff erent levels 

of development when examining education’s infl uence on fertility. Sometimes women with no 

formal education have lower fertility and higher contraceptive use simply because they live in a 

society where the overall educational level is high (Riley, 1997). Furthermore, in contexts of high 

gender equity even small increments in education reduce fertility, whereas in contexts of low 

gender equity relatively high levels of education are needed to bring about changes in women’s 

empowerment (Dixon-Mueller, 2000). Th us, these processes are linked and the links are context 

dependent.

The multidimensionality of women’s 

status and its 

complex relations 

with demographic 

behavior has only 

recently started to 

receive attention.
Theoretical Overview 7

sis varies. Not much research has linked individual-level data with contextual measures 

collected at the macro level. Studies by Balk (1994) and Kritz et al. (2000) obtained 

contextual gender equity indices by aggregating responses at the individual level, a typical approach among demographers. The work of Morgan and Niraula (1996) illustrates 

a slightly different approach: they chose two villages in Nepal with striking differences 

in women’s status as a theoretical measure of contextual gender equity. Research examining determinants of infant mortality (Andes, 1996; Sastry, 1996; Sastry, 1997) has 

linked individual data with community-level data, but studies of maternal health have 

rarely done so (although see Stephenson and Tsui, 2002). 

Sastry (1997) asserts that contextual analysis methods are of particular importance 

and relevance to policy makers, since omitted community variables can play a significant role in determining a particular outcome. The lack of research similar to Sastry’s is 

due to the limited availability of community data that can be easily and appropriately 

linked to household and individual surveys, whether through DHS cluster geocodes or 

through district codes available within DHS datasets. 

Study Site and Data Description 9

3

Study Site and Data 

Description

Nepal is a poor country: it is typically listed among the ten poorest countries in the 

world based on a per capita gross domestic product of $200–$250 per annum (Central 

Bureau of Statistics, 2001; NSAC, 1998). Although life expectancy has improved over 

time, it remains quite low at around 55 years in 1994 (NSAC, 1998). Nepal is one of 

the few countries in the world where a woman’s life expectancy is less than her male 

counterpart’s. Lower female life expectancy is a consequence of higher childhood mortality among girls and high maternal mortality. Indeed, Nepal has one of the highest 

maternal mortality rates in the world, estimated at about 850 to 1000 per 100,000 live 

births (RECPHEC, 1997).5 A country’s maternal mortality rate is an indicator of the 

overall health status of women, and such a high maternal mortality rate points to the 

human development deprivations facing women in Nepal (NSAC, 1998). Moreover, a 

World Bank report has concluded that 17 percent of the burden of disease in Nepal is 

a result of maternal and perinatal health problems (World Bank, 1996, cited by Hotchkiss, 2001), and almost 60 percent of households in Nepal report that they do not have 

adequate access to health care services (NSAC, 1998). Th ese statistics provide an indication of the low status of women in Nepal. 

Th is study focuses on currently married Nepali women and their experience with maternal health care services. Th e majority of Nepali women do not have access to or use 

professional health facilities and services during pregnancy (see Table 1). Moreover, the 

use of antenatal care varies considerably across the country. For example, the 2001 Nepal Demographic and Health Survey 

(NDHS) found that around 18 percent of urban mothers did not receive 

any antenatal care compared with 53 

percent of rural mothers. While only 

44 percent of mothers in the Terai 

lacked antenatal care, 56 percent of 

mothers in the hills and 69 percent 

in the mountains went without antenatal care (Ministry of Health et al., 

2002).

3.1 Nepal Demographic and Health Survey

Th e 2001 Nepal Demographic and Health Survey (NDHS) is the sixth in a series 

of national level population and health surveys conducted in Nepal and the second 

Table 1

Use of antenatal care in Nepal

Percentage of women who had a live birth in the 3–5 

years preceding the NDHS 2001, by use of antenatal care 

(N=3,283)

Frequency and timing of antenatal care Percent

Received antenatal care

At least once 49.1

At least four times 14.3

During the fi rst trimester 16.4

Source: Ministry of Health et al., 2002, Tables 9.1 and 9.2

Nepal has one 

of the highest 

maternal mortality 

rates in the world.

5 RECPHEC (1997) cites a study by the United Missionaries of Nepal (UMN) which reported a maternal mortality rate of 279 deaths per 100,000 live births, although this was based 

solely on hospital data.
10 Study Site and Data Description

6 It was important to limit the analysis to women who were usual residents of the community 

in order to avoid ascribing local contextual factors to women who did not normally live in the 

district where they were surveyed.

7 Approximately 4 percent of currently married women who had a recent birth resided in a 

household with another eligible women (i.e., a currently married woman who had a recent birth) 

who also participated in the survey. Strictly speaking these women are not independent observations as they share some household-level attributes, although they do not share individual 

attributes (age, parity, wanted child, educational attainment, relation to head of household, etc.). 

Th e analysis assumes that the households with multiple women participating in the survey were 

distributed randomly.

8 Other antenatal and maternal health outcomes also were considered in the analyses (see 

Appendix A).

9Th is measure was based on a recode of a DHS variable (M2N: Antenatal no one). A woman 

who received antenatal care during the last pregnancy was coded as 1 (N=1,586), and a woman 

who did not was coded as 0 (N=1,697). 

10 Th is measure was based on a recode of a DHS variable measuring the number of antenatal 

visits during pregnancy (M14: Number of ANC visits). All values 4 or higher were recoded as 

1 (N=468), while all other valid codes were relabeled as 0 (N=2,813). Note that 1,697 women 

reported no antenatal care visits (M14=0).

nationally representative comprehensive survey conducted as part of the worldwide 

Demographic and Health Surveys (DHS) project. A preliminary report based on the 

NDHS 2001 was issued in September 2001, and the fi nal report followed in April 2002 

(Ministry of Health et al., 2002). Briefl y, the NDHS 2001 is a nationally representative 

survey of 8,726 women aged 15–49 and 2,261 men aged 15–59. It includes information on fertility, family planning, infant and child mortality, maternal and child health, 

nutrition, and knowledge of HIV/AIDS. 

While the NDHS 2001 covers a wide variety of topical areas, this study focuses on 

the use of maternal health services.

3.2 Sample

To construct the study sample, all currently married women who had given birth during the past three years were selected from the NDHS 2001. Th e analysis was further 

limited to those women listed as a usual resident of the community in which they were 

surveyed.6 With some missing values, the current analysis is based on a sample of 3,283 

currently married women who had given birth within the past three years and who 

were usual residents of the community. Th e NDHS 2001 collected information on the 

utilization of antenatal care, delivery care, and postnatal care only for the last birth of 

these women.7

3.3 Outcome Measures

Th is report focuses on the determinants of antenatal health service use among Nepali 

married women included in the NDHS 2001. Th e analysis focuses on two dichotomous outcome measures:8

• Any antenatal care: A measure of whether a woman had received any antenatal 

care during her last pregnancy was constructed.9 Th e analysis is based on a sample 

size of 3,283 women, of whom 48 percent had received any antenatal care.

• Four or more antenatal care visits: A measure of whether a woman had received 

antenatal care four or more times during her last pregnancy was constructed.10

Th e analysis is limited to women who received some antenatal care (N=1,586). 

With some missing values, the analysis is based on a sample of 1,581 women, of 

whom 29 percent had made at least four antenatal care visits during their last 

pregnancy.

The majority of 

Nepali women do 

not have access to 

or use professional 

health facilities 

and services during 

pregnancy.
Study Site and Data Description 11

Table 2 provides sample size and 

percent distribution information for 

each outcome. 

3.4 Independent Variables

The analytical models include individ-

ual (Level 1) variables that recent lit-

erature has associated with behaviors 

related to maternal health (see Figure 

1). This section briefly describes these 

variables and discusses them in the 

context of the literature. Appendix B 

presents information on variable cod-

ing and recoding. 

Tables 3 through 6 summarize the 

independent variables of interest, based on the sample of currently married women who 

had a child in the three years preceding the NDHS 2001 and who were usual residents 

of the community. These variables are divided into four groups: geographic variables, 

control and empowerment measures, individual and partner characteristics, and house

-

hold characteristics. 

3.4.1 Geographic Variables (Table 3)

In addition to examining contextual factors, the study aims to investigate how the use 

of antenatal care varies by geographic region. The NDHS 2001 reports on the utili

-

zation of maternal health services by urban/rural location, Ecological Zone (Moun-

tain, Hill, and Terai), Development Region (Eastern, Central, Western, Mid-Western, 

and Far-Western), and sub-region (a 

combination of Ecological Zone and 

Development Region) (see Map 4). 

Geographic differences are quite evi-

dent in the contextual variables and, 

as shall be seen, in outcome measures 

as well. 

Many researchers include regional 

dummy variables in their models. For 

example, Magadi et al. (2000) found 

an association between frequency 

of antenatal care use and region of 

residence in Kenya. Glei et al. (2003) 

also found large differences in the 

likelihood of obtaining pregnancy 

care across regions in Guatemala, 

perhaps due to regional variations in 

belief systems or unmeasured charac

-

teristics of communities and health 

services. 

Table 3 presents the distribution 

of the study sample across Ecologi

-

cal Zones and Development Regions. 

These two regional breakdowns are 

often cited in the reports, policy doc-

uments, and planning documents is-

Table 2

Outcome measures

Percent distribution of currently married women who 

had a child in the three years preceding the NDHS 2001 

and who were usual residents of the community, by 

whether or not they received any antenatal care

Outcome variable Percent Sample size

Received any antenatal care N=3,283

 Yes 48.3

 No 51.7

Percent distribution of currently married women who 

had a child in the three years preceding the NDHS 

2001, who were usual residents of the community, and 

who received any antenatal care, by whether or not 

they made at least four antenatal care visits

Made at least four antenatal care visits N=1,581

 Yes 29.5

 No 70.5

Table 3

Geographic variables

Percent distribution of currently married women who 

had a child in the three years preceding the NDHS 2001, 

by geographic variables (N=3,283)

Variable Percent

Ecological Zone

 Mountain 15.0

 Hill 37.2

 Terai 47.8

Development Region

 Eastern 22.4

 Central 27.3

 Western 15.3

 Mid-Western 14.1

 Far-Western 20.9

Sub-region

 Eastern Mountain 3.5

 Central Mountain 4.9

 Western Mountain 6.6

 Eastern Hill 6.8

 Central Hill 9.1

 Western Hill 7.2

 Mid-Western Hill 5.6

 Far-Western Hill 8.5

 Eastern Terai 12.1

 Central Terai 13.3

 Western Terai 8.1

 Mid-Western Terai 5.9

 Far-Western Terai 8.4

Urban/rural location

 Urban 8.8

 Rural 91.2

Distance to nearest hospital (miles) 10.7
12 Study Site and Data Description

Source: NSAC, 1998

Mountain

Hills

Terai

Pokhara Kathmandu

3

8

13 7

12

6

11

10

5

2

1

4

9

1

2

3

4

5

6

7

= Eastern Mountain

= Central Mountain

= Western Mountain*

= Eastern Hills

= Central Hills

= Western Hills

= Mid-Western Hills

8

9

10

11

12

13

= Far-Western Hills

= Eastern Terai

= Central Terai

= Western Terai

= Mid-Western Terai

= Far-Western Terai

* Western, Mid-Western and Far-Western Mountain are combined to form the Western Mountain Sub-Region used by NDHS 2001.

Far-Western

Mid-Western

Western

Central

Eastern

50 0 50 100 Miles

Map 4

Ecological Zones, Development Regions, and DHS sub-regions in Nepal

sued by the government of Nepal and international non-governmental organizations. 

While separate analyses were run using the Ecological Zone and Development Region 

as regional dummy variables, this report only presents results from models based on 

sub-regions.

Sub-region. The sub-regional classification system cross-tabulates Nepal by Ecological Region and Development Region, although the NDHS 2001 combined data 

on three sub-regions (Far-Western Mountains, Mid-Western Mountains and Western 

Mountains) into a single “Western Mountains” sub-region.11 This sub-regional classification is becoming more popular in Nepal, because it offers both a finer level of analysis and arguably more homogeneity than other geographic breakdowns. This analysis 

uses the Far-Western Hills as the comparison group, since this area typically performs 

poorly on national development indicators.

Urban-rural location. According to the NDHS 2001, 82 percent of urban women 

received antenatal care versus just 46 percent of rural women, and 44.5 percent of 

urban women gave birth in a health facility versus only 6.6 percent of rural women. 

Indeed, much research in maternal, reproductive, and general health has found that 

health facilities and professional medical personnel tend to be concentrated in larger 

urban centers with greater economic resources and public infrastructure. Many studies 

of maternal health outcomes have found that urban women are more likely than rural 

women to use antenatal care, as is the case in Jordan (Obermeyer and Potter, 1991), 

Guatemala (Pebley et al., 1996), and Thailand (Raghupathy, 1996). However, the urban 

association with antenatal care does not always appear. In a study of antenatal care 

11 There are no DHS clusters in the original Western Mountain sub-region. Six rural clusters 

could not be included in the DHS due to security concerns (Ministry of Health et al., 2002, 

p.6).
Study Site and Data Description 13

in Nepal, Hotchkiss (2001) found that urban/rural residence was not significant after 

controlling for physical access to health care and other individual, household, and community characteristics. 

Sastry (1997) points out that an urban-rural comparison is a rather crude contextual measure that does not explain variations that might be evident within rural and 

urban areas. Considerable differences are found within rural areas of developing countries, where the majority of the population usually resides. This is especially so in Nepal, 

where 90 percent of the population lives in rural areas and where there is considerable 

regional variation. This analysis includes an urban/rural dummy variable, in which urban clusters are coded as 1. 

Access to health services. The accessibility of health services is often cited as a critical determinant of health care choice in the developing world (Timyan et al, 1993), 

where an increase in distance to the health facility is associated with less use. In Nepal 

accessibility is complicated further by the rugged terrain (Hotchkiss, 2001). Most doctors, hospitals, and health facilities in Nepal are concentrated in the main urban centers 

and in parts of the Eastern, Central, and Western Development Regions (see Map 5). 

Hotchkiss et al. (1998) suggests that in Nepal inadequate referral linkages, poor quality care, high out-of-pocket costs for consultations and transportation, high levels of 

illiteracy, and gender bias also are likely to contribute to poor utilization of health care. 

Magadi et al. (2000) found an association between access to antenatal care and its use 

in Kenya, but they did not find an association between access and the timing of the first 

antenatal visit. Pebley et al. (1996) found that distance to the nearest clinic in Guatemala is significantly and negatively related to both antenatal care and delivery assistance. In a recent study in Guatemala (Glei et al., 2003) no measures of access—including biomedical services available within the community and access to free care—were 

significantly related to pregnancy care, but distance to the capital city was related.

Map 5

Location of main hospitals in Nepal

Source: Compiled from maps and reports from the

Ministry of Health, Department of Health Services

and UNFPA, Nepal.

Pokhara Kathmandu

50 0 50 100 Miles
14 Study Site and Data Description

In addition to existing data on DHS cluster classifi cation, this study used GIS techniques to generate a crude distance measure refl ecting access to health services. A georeferenced point fi le of all signifi cant hospitals in the country was created, using data on 

the name and location of the main hospitals from the Ministry of Health’s annual reports, gazetteers, topographic maps, and in-country health reports (Ministry of Health, 

1995; Ministry of Health, 1996; Ministry of Health, 1998a; Ministry of Health, 1998b; 

Ministry of Health, 2001; Ministry of Health and UNFPA, 1995). Th ese hospitals 

generally were located in the main town of each district. Th is hospital point fi le was 

used to create a straight-line distance measure to all DHS clusters. A more appropriate 

measure of health accessibility would have been based on roads, altitude, and other data 

sources, but this was not practical. Distance was measured in miles and ranged from 0 

to 29 miles, with a mean of about 11 miles.

Th is measure provides a clear picture of which clusters are far from a main hospital 

and therefore relatively isolated. Hospitals tend to be located in the larger urban settlements throughout the country, especially in the main towns along the Nepal-India 

border (Terai) and in the Hill districts. Th erefore the measure may be a good proxy for 

distance to the nearest main town, even though it was conceived as the distance to the 

nearest hospital.12

3.4.2 Control and Empowerment Measures (Table 4)

Th is study includes measures of women’s empowerment along with traditional indicators of women’s status. Th e NDHS 2001 included three new and important variables 

based on women’s attitudes regarding wife beating, reasons to refuse sex with the husband, and involvement in decisionmaking.

Age. According to the NDHS 2001, 12.1 percent of women under age 20 gave birth 

in health facilities, compared with 8.9 percent of women aged 20–34 and 3.6 percent 

of women over 35 (Ministry of Health et al., 2002, Table 9.5, p.148). Th is is consistent 

with fi ndings from Th ailand (Raghupathy, 1996) and Peru (Elo, 1992), where younger 

women were more likely to accept modern health care and older women, with accumulated knowledge on maternal health care, were less likely to seek institutional care. In 

south India, Bhatia and Cleland (1995) found that mothers under age eighteen were 

less likely to receive antenatal care, but fi rst-order pregnancies were more likely to 

receive antenatal care. Women are generally considered at greater obstetric risk when 

they give birth before age 18 or after age 34 (Amini et al., 1996; Walsh et al., 1993).

Age was recoded into three categories following the breakdown used in the NDHS 

2001 report: under 20, 20–34, and over 35. Under 20 is the reference category.

Parity. Birth order, or parity, also is strongly associated with the use of antenatal care, 

with women more likely to seek care for fi rst pregnancies. Women giving birth to their 

fi rst child or to their fi fth or higher-order child are generally considered at greater obstetric risk (Amini et al., 1996; Walsh et al., 1993). Th e risks of pregnancy and delivery 

complications increase after the third and especially after the sixth birth (Dixon-Mueller and Wasserheit, 1991). 

Data from the NDHS 2001 suggests the importance of birth order in predicting 

antenatal care (Ministry of Health et al., 2002, Table 9.5, p.148). Because of their expe-

12 While the analysis could have used a district-level dummy for whether or not a hospital 

was present, this measure was crude and unlikely to be of analytic interest. First, individuals in 

a cluster may reside in a district without a hospital but still live very close to a hospital in a 

neighboring district. Second, a map of a hospital dummy would reveal that all but a handful of 

districts in the country lack a hospital; moreover, some of these districts were not included in 

the NDHS 2001 (i.e., no DHS clusters exist in those districts). Th us a map of a hospital dummy 

variable would show little or no variation across the country and therefore would have no predictive power analytically. 

Distance from each 

DHS cluster to the 

nearest hospital 

ranges from 0 to 29 

miles, with a mean 

of about 11 miles.
Study Site and Data Description 15

rience with pregnancy-related matters, 

older women and women with highorder births may not seek antenatal 

care. In Guatemala (Glei et al., 2003), 

Peru (Elo, 1992), Turkey (Celik and 

Hotchkiss, 2000), India (Bhatia and 

Cleland, 1995; Stephenson and Tsui, 

2002), and Thailand (Raghupathy, 

1996), women having their first child 

were more likely to receive antenatal 

care. Magadi et al. (2000) found that 

high-order births in Kenya were as-

sociated with a delayed first antenatal 

care visit. 

The total number of children ever 

born was recoded into four categories: 

1, 2–3, 4–5, and 6 or more births. The 

reference category is a parity of 1.

Wanted last child. Pregnancies that 

are mistimed or not wanted are as

-

sociated with late and irregular ante-

natal care compared with pregnancies 

that are wanted (Weller et al., 1987). 

In Kenya, Magadi et al. (2000) found 

that women who said their preg-

nancies were unwanted or mistimed 

made fewer antenatal care visits dur-

ing pregnancy and delayed their first 

antenatal care visit. 

Information on whether or not a 

woman wanted her last child was re-

coded into three categories signifying that the mother wanted the child then, wanted 

the child later, or did not want the child. The reference category is “did not want the 

child,” which included one-fourth of the sample. 

Relationship to head of household. The demographic literature increasingly recog-

nizes the influence of gender-based power dynamics within couples’ sexual relation-

ships on reproductive outcomes (Mason and Smith, 2000; Riley, 1997; Sen and Batli

-

wala, 2000). In a recent paper Larsen and Hollos (2003) find that the empowerment of 

women—as reflected in their socioeconomic and employment status, educational level, 

household organization, the dynamics of marital relations, and involvement in domes-

tic decisionmaking—is an important factor in research on demographic outcomes. A 

woman’s relationship to the head of the household sheds light on her position within 

the household.

Information regarding the respondent’s relationship to the household head was re

-

coded into four categories depending on whether the woman was: the head of the 

household, the wife of the household head, the daughter-in-law of the household head, 

or some other relation. The reference category is women who are household heads. 

Refusing sex. The NDHS 2001 asked women whether they would refuse sex with 

their husbands in four situations: if he had a sexually transmitted infection (STI), if he 

had sex with other women, if she had had a recent birth, or if she was tired or not in the 

mood. Reponses to these questions were combined to create a measure of the number 

of circumstances in which a woman would refuse sex with her husband. If the women 

stated she would refuse to have sex with her husband for all four reasons, she was coded 

Table 4

Control and empowerment measures

Percent distribution of currently married women who 

had a child in the three years preceding the NDHS 2001, 

by control and empowerment measures (N=3,283)

Variable Percent

Age

15–19 9.2

20–34 77.1

35+ 13.6

Parity 1 21.0

2–3 40.6

4–5 22.9

6+ 15.5

Wanted last child

Then 60.3

Later 14.7

Did not want 25.0

Relationship to head of household

Head 7.2

Wife 57.3

Daughter-in-law 29.7

Other 5.8

Refuse to have sex with husband:

Under all four circumstances 90.3

Under three or fewer circumstances 9.7

Wife beating is justified:

Under no circumstances 71.5

Under at least one circumstance 28.5

Decisionmaking 

No decisions 53.9

1–2 types of decisions 24.9

3–4 types of decisions 21.2

Problems getting health services 

No problems 10.7

1–2 problems 27.0

3–5 problems 41.3

6–7 problems 21.0

Woman owns land

Yes 5.7

No 94.3
16 Study Site and Data Description

as 1 (90.3 percent of the sample). All other values were coded as 0 (9.7 percent). In 

other words, almost 10 percent of women said that refusing sex was not justifi ed for at 

least one of the reasons specifi ed. 

Wife beating. Th e NDHS 2001 asked women fi ve questions about whether and 

under what circumstances wife beating was justifi ed, including going out without telling the husband, neglecting children, arguing with the husband, refusing sex with the 

husband, and burning food. Responses to these questions were combined to create a 

measure of the number of circumstances in which women felt wife beating was justifi ed. Women who said wife beating was never justifi ed were coded as 1, while women 

who said wife beating was justifi ed for at least one of the fi ve reasons were coded as 

0. Disturbingly, 28.5 percent of women felt wife beating was justifi ed under at least 

one circumstance. According to the NDHS 2001 report, “there appears to be a mixed 

association between women’s empowerment as measured by the number of reasons 

women believe that wife beating is justifi ed and their care seeking behavior” (Ministry 

of Health et al., 2002, p.153)

Decisionmaking. Increasingly researchers are recognizing that women’s participation in domestic decisionmaking aff ects their ability to make reproductive and maternal 

health decisions, particularly decisions regarding their fertility (Balk, 1994; Bloom et 

al., 2001; Dyson and Moore, 1983; Gage, 1995; Morgan and Niraula, 1996; Timyan et 

al., 1993). Th e NDHS asked women whether they were involved in making decisions 

in fi ve areas: health care, large household purchases, household daily needs, visits to 

family or relatives, and food to be cooked. For each question, if the woman was either 

the sole decision maker or made the decision jointly with a partner or another person, 

the value was coded as 1. If she was not involved in the decision, the value was coded 

as 0. Since women tended to make all decisions regarding food preparation, responses 

to just the fi rst four questions were combined to create a measure of women’s involvement in decisionmaking. Women were categorized as being involved in making 0, 1–2, 

or 3–4 types of decisions. Th e reference category is no decisions, which includes 53.9 

percent of the sample.

Problems getting medical help. Distance to the nearest health facility, lack of transportation, lack of knowledge about, and the perceived quality of services are all thought 

to be associated with the use of modern health care and seeking assistance from trained 

medical personnel (NoorAli et al., 1999; Paul, 1992; Paul and Rumsey, 2002; Sundari, 

1992). Paul and Rumsey (2002) note that lack of access to health care facilities refers 

to economic and sociocultural distance as well as physical distance. Seven questions in 

the NDHS 2001 asked about problems facing women who seek health care, including 

knowledge of where to go, getting permission to go, getting money for treatment, the 

distance to the health facility, transportation, unwillingness to go alone, and concern 

that there will be no female provider. Th e response to each question was coded as 1 if 

the woman felt the issue posed a “big problem.” Th ese values were summed to create a 

measure of how many problems women faced in getting medical help. Four categories 

were created: whether a woman considered 0, 1–2, 3–5, or 6–7 issues to be big problems. 

Th e reference category is no problems.

Land ownership. Th e fi nal variable in this empowerment grouping is whether a 

woman owns land or not. If the women owned land alone or jointly, this variable is 

coded as 1, otherwise 0.

3.4.3 Individual and Partner Characteristics (Table 5)

Women’s and partner’s education. Education can have an empowering eff ect on 

women, broadening their horizons, choices, and opportunities and “enabling women 

to take personal responsibility for their health and for that of their children” (Paul and 

Rumsey, 2002, p. 1757). Higher levels of maternal and head of household education 

Radio and television 

are an important 

source of maternal 

health information,

especially for 

women who are 

illiterate or have 

minimal schooling.
Study Site and Data Description 17

are associated with increased use of health care during pregnancy as well as having a 

modern delivery or a delivery by trained personnel (Bhatia and Cleland, 1995; Celik 

and Hotchkiss, 2000; Hotchkiss, 2001; Navaneetham and Dharmalingam, 2002; Ober-

meyer and Potter, 1991; Paul and Rumsey, 2002; Pebley et al., 1996; Raghupathy, 1996; 

Stephenson and Tsui, 2002). Women’s education is an important predictor of the use 

of antenatal care services and the knowledge and use of contraceptives (for studies in 

Nepal, see Joshi, 1994 and Tuladhar, 1987). In some studies the effect of education differentials persisted even after controlling for selected demographic variables and place 

of residence ( Jejeebhoy, 1995; Rodriguez, 1978; Tuladhar, 1987). However, neither 

Miles-Doan and Brewster (1998) in the Philippines nor Magadi et al. (2000) in Kenya 

found an association between education and the use of antenatal care after controlling 

for other covariates. A recent article by LeVine et al. (2004) implies that the effects of 

schooling on health behaviors are mediated through literacy skills. 

The NDHS 2001 includes information on both women’s and partners’ education. 

Rather than use years of completed schooling, this study looked at three educational 

attainment levels: no education, primary education, and secondary or higher education. 

For both the woman and the partner, no education is the reference category.

Radio and television. Electronic media can be an important source of information 

regarding the benefits of preventive care for 

maternal health (Navaneetham and Dharmal

-

ingam, 2002; Stephenson and Tsui, 2002). Na-

vaneetham and Dharmalingam (2002) suggest 

that exposure to electronic media can influ-

ence cultural barriers to using modern health 

care. Radio and television also can disseminate 

maternal health information to women who 

are illiterate or have minimal schooling. Two 

separate variables, which are not highly cor-

related with one another, are used to measure 

exposure to electronic media: whether or not 

the women listened to the radio daily (33.3 

percent) or watched television at least weekly 

(16.8 percent).

Work status. Working women who contrib

-

ute to household wealth are expected to have 

greater influence over household and individual 

decisionmaking, including resource allocation 

and maternal and child health care (Desai and 

Jain, 1994). That said, women in developing 

countries often work for the family and exert 

little influence over household and individual 

decisionmaking. 

Economic status of the household also may help determine the use of health services 

insofar as it reflects the ability of the household to pay for health care costs. Usually 

families belonging to a higher economic class are more aware of and have easier access 

to sources of health care (Feldman, 1983). Several studies have shown a relationship 

between the use of health care and the financial stability of the household (Celik and 

Hotchkiss, 2000; Pebley et al., 1996; also see Section 3.4.4 on household utilities). In a 

study of Nepali women, Tuladhar (1987) found the highest levels of knowledge of, use 

of, and access to maternal and family planning services among women engaged in nonfarm occupations. Thus, women engaged in wage employment are expected to be more 

likely to use antenatal health services (Kritz et al., 2000; United Nations, 1985). 

Women’s work status is classified as not working (14.4 percent), works in agriculture 

Table 5

Individual and partner 

characteristics

Percent distribution of currently married 

women who had a child in the three years 

preceding the NDHS 2001, by individual and 

partner characteristics (N=3,283)

Variable Percent

Woman’s education

None 73.1

Primary 14.1

Secondary and higher 12.9

Partner’s education

None 33.7

Primary 26.5

Secondary and higher 38.0

Don’t know 1.8

Listens to radio daily

Yes 33.3

No 66.7

Watches television weekly

Yes 16.8

No 83.2

Woman’s employment 

Not working 14.4

Agricultural or self-employed 80.2

Non-agricultural 5.1

Partner’s employment

Agricultural 53.7

Non-agricultural 43.1

Don’t know or missing 3.2
18 Study Site and Data Description

(80.2 percent), or works outside agriculture (5.1 percent). The reference category is 

does not work. The NDHS 2001 also provides information on the work status of the 

respondent’s partner. This is classified as works in agriculture (53.7 percent), works out-

side agriculture (43.1 percent), or work status is unknown (3.2 percent). The reference 

category is working in agriculture.

3.4.4 Household Characteristics (Table 6)

Utilities. A household’s socioeconomic status is related to the use of health facilities 

and trained medical personnel (Paul and Rumsey, 2002), so that measures of standard 

of living are likely to be associated with the use of maternal heath care. According to 

Navaneetham and Dharmalingam (2002), households with higher living standards are 

expected to be more modern and receptive to modern health care services. Magadi 

et al. (2000) found an association between socioeconomic status and the frequency 

of antenatal care as well as the timing of the first antenatal care visit in Kenya. In the 

Philippines, Miles-Doan and Brewster (1998) found an association between low socio-

economic status and underutilization of health services. Stephenson and Tsui (2002) in 

their work on reproductive health in Uttar Pradesh, India found the household-asset 

index (a proxy for household socioeconomic status) was significantly and positively as-

sociated with receiving antenatal care and giving birth in a medical institution. Celik 

and Hotchkiss (2000) found that measures of household wealth and resources were 

associated with use of antenatal care. 

To measure the standard of living of house

-

holds in Nepal, this study created an index 

based on a set of household utilities used by 

Raghupathy (1996). The NDHS 2001 gath-

ered information on a number of household 

utilities and resources including sources of 

drinking water, type of toilet facility, presence 

of electricity, type of flooring material, type of 

cooking fuel, and durable goods (bicycle, televi-

sion, telephone). Four of these are included in 

a utility index: the presences of piped water, a 

toilet or latrine, modern cooking fuel, and elec

-

tricity. Respondents are categorized depending 

on how many of these utilities are present in 

their household: none, 1, or 2 or more. The reference category is no utilities.

Religion. The dominant religion in Nepal is 

Hindu, and over 80 percent of the study sample 

is Hindu. A dummy variable based on religion 

is included in the analysis.

Ethnicity. Ethnicity is an important social factor in Nepal and can both facilitate 

and hinder use of maternal heath care. For example, while small in number there are 

Muslims in Nepal who may be less likely to use maternal care services. Navaneetham 

and Dharmalingam (2002) found in India that Muslim women, who have less au

-

tonomy to interact with males outside of their immediate families, are less likely to use 

antenatal services or delivery assistance if only a male doctor is available. 

The NDHS 2001 reported on an extensive range of ethnic groups (see Appendix B).

Following that example, these ethnic groups were first recoded into thirteen categories. 

Then the Gurung and Magar groupings were combined, as were other Hill and other 

Terai groupings, to make eleven ethnic categories: Brahmin, Chhetri/Thakuri/Rajput, 

Newar, Gurung/Magar, Tamang/Sherpa, Rai/Limbu, Muslim/Churaute, Tharu/Raj

-

Table 6

Household characteristics

Percent distribution of currently married 

women who had a child in the three years 

preceding the NDHS 2001, by household 

characteristics (N=3,283)

Variable Percent

Household utilities

None 65.9

1 utility 20.7

2+ utilities 13.4

Religion

Hindu 84.4

Non-Hindu 15.6

Ethnicity

Brahmin 9.3

Chhetri, Thakuri, and Rajput 22.4

Newar 3.7

Gurung and Magar 6.3

Tamang and Sherpa 7.1

Rai and Limbu 5.2

Muslim and Churaute 5.1

Tharu and Rajbanshi 8.9

Yadav and Ahir 2.8

Occupational 21.2

Other Hill and Terai 8.0
Study Site and Data Description 19

banshi, Yadav/Ahir, occupational groups, and other Hill and Terai groups. The Brahmin 

ethnic group, which is one of the highest caste groups in Nepal, is used as the reference 

category.

3.5 Summary

This study’s approach to modeling antenatal care is based on integrating individual 

and contextual data. Rather than use aggregate measures of individual attributes to 

create contextual measures, the study includes what the literature refers to as global 

measures of context (Lazarsfeld and Menzel, 1969, cited in Kreft and De Leeuw, 1998). 

The existence of geocodes for DHS clusters allows researchers to use GIS and related 

technologies to create unique hierarchical or multilevel datasets that can include global 

measures. Thus this project allows researchers to draw on numerous individual, or Level 

1, measures of women’s empowerment (e.g., involvement in decisionmaking, attitudes 

about wife beating and refusing sex with the husband, and land ownership) and women’s status (e.g., education and work). Moreover, the contextual modeling framework 

focuses explicitly on adding broader, Level 2 measures of women’s empowerment at the 

district level (i.e., GEM and GDI).

Analytical Methods 21

Th is study employs both logistic regression models, using SPSS (Statistical Package for 

the Social Sciences), and hierarchical generalized linear models, using HLM (Hierarchical Linear Model). Binomial logistic regression is a form of regression used when 

the dependent variable is dichotomous and the independent variables are of any type. 

Logistic regression applies maximum likelihood estimation after transforming the dependent variable into a logit (the natural log of the odds of the dependent variable 

occurring or not). In this way, logistic regression estimates the probability of a certain 

event occurring. Logistic regression calculates changes in the log odds of the dependent 

variable; it does not calculate changes in the dependent variable as does ordinary least 

squares (OLS) regression. Unlike OLS regression, logistic regression does not assume 

linearity of the relationship between independent variables and the dependent variable, 

it does not require normally distributed variables, and it does not assume homoscedasticity. In general, it also has less stringent requirements. 

As shown by Stephenson and Tsui (2002), hierarchical modeling techniques offer a mechanism for measuring the infl uence of community factors and unobserved 

community eff ects on health outcomes, while providing a robust method for analyzing 

multilevel data (Diez-Roux, 2001; DiPrete and Forrestal, 1994; Duncan et al., 1998; 

Goldstein, 1995). Due to the hierarchical structure of this data set, with women clustered in districts, a multilevel modeling structure is employed. OLS regression assumes 

that all observations are independent. In this study, however, women experiencing the 

outcomes are not independent, because they share common district characteristics. A 

multilevel modeling strategy accommodates the hierarchical nature of the data and 

corrects the estimated standard errors to allow for the clustering of observations within 

units (i.e., women within districts). Th e hierarchical analysis explicitly integrates two 

levels of data: 

• Individual, or Level 1, data from the NDHS 2001, and

• District, or Level 2, data from the gender-sensitive development index (GDI) and 

gender empowerment measure (GEM).

As the dependent variables of interest are dichotomous, a hierarchical generalized 

linear model (HGLM)—which is a special case of hierarchical linear models—was 

used.13

Th e study examines the infl uence of contextual and individual factors on the use of 

antenatal health care. Th e same set of predictor variables is used in both sets of analysis. 

Separate logistic regression models are fi tted for each of the outcome measures of interest. Th ey take the form:

log[pi1/1–pi1] = α0 + α1 Xij + α2 Yij + α3 Zj + ∈1ij

4

Analytical Methods

13 Since the occurrence and non-occurrence of these events are two categories in the dependent variable, a Bernoulli analysis is performed to suit the distribution of the dependent 

variable.

This study employs 

both logistic 

regression models 

and hierarchical 

generalized 

linear models.
22 Analytical Methods

The independent variables are classified into three groups: geographic/community, 

individual/household, and contextual factors. They are represented by the vectors X, Y, 

and Z, respectively, and αs represent the net effect of these variables on the probabilities 

of using health care. The term ∈1 represents unobserved determinants of antenatal care 

utilization and follows a logistic distribution.

The basic HLM formula for logistic regression is:

Level 1 model log[pij/(1–pij)] = 

β

0

 + β

1

xij

Level 2 model β

0j

 = β

0

 + uj

Combined model log[pij/(1–pij)] = β

0

 + β

1

xij + uj

where i = women, j = districts, and u is the random effect at level 2.14

The logistic regression and HGLM result tables include four models. Model 1 in-

cludes only geographic variables. Model 2 includes both geographic variables and the 

individual and household variables extracted from the 2001 NDHS. Model 3a builds 

on Model 2 by adding a single contextual variable: GDI. Model 3b parallels Model 3a 

but includes GEM instead of GDI.15

The success of the logistic regression and HGLM models can be assessed in a vari-

ety of ways. In the case of the logistic regression, the classification table shows correct 

and incorrect classifications of the dichotomous outcome variables, which are reported 

in the tables as percent correct prediction. Goodness-of-fit tests, such as model chisquare, provide a measure of model appropriateness. The –2 log likelihood (–2LL) statistic is called the scaled deviance and is used to assess the significance of the regression. 

The chi-square value for –2LL, or model chi-square, provides a significance test for 

a logistic model; that is, model chi-square measures the improvement in fit that the 

explanatory variables make compared with a null model. Model chi-square is a likeli-

hood ratio test that reflects the difference between error not knowing the values of the 

independent variables (initial chi-square) and error when the independent variables are 

included in the model (deviance). There is no accepted direct analog to R-squared in an 

OLS regression model, although Nagelkerke’s R-square, constrained to the range 0–1, 

is often reported. 

In the case of HGLM, the variance components of each successive model show the 

percentage of between variance that has been explained by the addition of variables in 

the model, as compared with the null model. The formula used here is:

 R2 Between = (variance of null model – variance of model)/variance of null model

In the logistic and HGLM models, the slope coefficients are not the rate of change 

in the dependent variable as X changes. Instead, the slope coefficient is interpreted as 

a rate of change in the “log odds” as X changes. A more intuitive interpretation of the 

logit coefficients, especially for dummy variables, is the “odds ratio”(OR)—which is 

Exp(B) in the tables. The exponent of B is the effect of the independent variable on the 

odds ratio, that is, the odds of the probability of an event divided by the probability of 

a non-event. For example, if Exp(B) equals 2, then a one-unit change in X would make 

the event twice as likely to occur. Negative coefficients generate odds ratios less than 

one. It is worth noting that the odds ratios for continuous independent variables tend 

to be close to one, but this does not indicate that the coefficients are insignificant.

14The classic texts on hierarchical linear and multilevel models are those by Goldstein (1995) 

and Raudenbush and Bryk (2002).

15 The researchers also calculated models using additional contextual models, but it was difficult to devise a clean set of uncorrelated contextual predictors that made sense to use in models 

for all of the outcome measures. Also, as noted earlier, both GDI and GEM are composite scores 

based on variables that are likely to be highly correlated with other measures associated with 

economic, social, and infrastructure development.
Results 23

Th ere is no universal explanation that applies to all places and 

times: the determinants of utilization of maternal health care services are not the same across socioeconomic and cultural contexts.

—Navaneetham and Dharmalingam (2002, p. 1849)

5.1 Any Antenatal Care

Th is study is concerned with exploring and better understanding factors that determine 

the use of antenatal services, especially the infl uence of geographic factors. Mapping 

the outcome variables based on the cluster and sub-regional geocodes is a useful fi rst 

step. Th e mapped distribution of outcomes is, to some extent, the pattern which the 

study seeks to explain. Two types of maps are presented: point maps of aggregate scores 

on maternal health outcomes for DHS clusters, and pie-chart maps at the sub-regional 

level based on data from the NDHS 2001. 

Mapping data values may be of limited use if the data are drawn from small samples 

for clusters that are not representative. However, the maps included here seek to reveal 

general patterns; mapping specifi c data values for each cluster is not the goal. Th e focus 

is on values that diverge from the national average by more than one standard deviation. 

Map 6 shows whether the rate of use of antenatal care—defi ned as making at least 

one antenatal visit—in each DHS cluster was at, above, or below the average for all 

clusters. Map 7 shows the proportion of women in each sub-region who made at least 

one antenatal care visit. 

As Map 6 illustrates, the rate of use of antenatal care is low in the Far-Western and 

Mid-Western Development Regions (particularly in the Hill and Mountain Ecological 

Zones) and, to a lesser extent, throughout the western side of the Eastern Development Region. Th e Eastern Development Region exhibits more variability: usage rates 

in many clusters are above the mean, while rates in many other clusters are below the 

mean. Th e rate of use of antenatal care appears to be considerably higher in the eastern 

half of the Eastern Development Region than in the western half, especially in the 

Terai. Usage rates are generally high in the main urban areas (Kathmandu and Pokhara 

clusters). 

Th roughout the Terai, most women received antenatal care, although the proportion does not exceed 70 percent in any sub-region (Map 7). In the Far-Western Hills, 

Mid-Western Hills, and Western Mountains, less than 25 percent of women received 

antenatal care. An interesting juxtaposition is found in the Mid-Western Development Region: approximately 65 percent of women in the Terai received antenatal care 

(the highest rate in the country), while only 23 percent of women in the neighboring 

sub-region in the Hills received antenatal care (one of the lowest rates in the country). 

Women seem to use antenatal care to the same degree within both the Eastern and the 

Central Development Regions, with the likelihood of using antenatal care decreasing 

5

Modeling Results

Approximately 65 

percent of women 

in the Terai received 

antenatal care (the 

highest rate in the 

country), while 

only 23 percent 

of women in the 

neighboring subregion in the Hills 

received antenatal 

care (one of the 

lowest rates in 

the country).
24 Results

Map 6

Usage rate for any antenatal care (women making at least one visit) by DHS cluster,

relative to the average for all clusters

Map 7

Proportion of women making at least one antenatal care visit by sub-region

Source: NDHS 2001

Pokhara Kathmandu

50 0 50 100 Miles

< –1 standard deviation

Within 1 standard deviation of mean

> 1 standard deviation

Source: NDHS 2001, Table 9.1, p. 140. 50 0 50 100 Miles

Note: Pie size is weighted by number of women.

None

At least one visit
Results 25

northwards (i.e., by Ecological Zone). 

In all models presented in main body of this report the geographic breakdown of 

Nepal is based on sub-regions.16

5.2 Models Predicting Any Use of Antenatal Care17

At 48.3 percent, the rate at which women use antenatal care—defi ned as making at 

least one visit—is low among the study sample of 3,283 Nepali women. To explore 

some of the factors that might be associated with use of antenatal care, both logistic 

regression and HGLM models were run. Th e results are presented in Tables 7 and 8, 

respectively.18 Th e results of both types of models are similar in directionality and the 

size of the log odds, so their fi ndings are interpreted together.

Model 1 includes geographic variables based on sub-region (with the Far-Western 

Hills serving as reference category), an urban dummy, and a continuous measure of 

distance to the nearest hospital. Th e overall performance of the model is signifi cant, and 

the association between independent variables is as expected. Th at is, the Far-Western 

Hills is the area of Nepal where pregnant women are least likely to receive antenatal 

care. Th e only sub-regions where the diff erence does not appear to be signifi cant in the 

logistic regression are the Western Mountain (which includes the Western, Mid-Western, and Far-Western Mountains) and Mid-Western Hills sub-regions. Th is confi rms 

the impression given by Maps 6 and 7. Even other remote areas of the country, the 

Eastern and Central Mountains, have odds ratios of 2.7 and 4.1, respectively, for use of 

antenatal care (although these sub-regions are not signifi cant in the HGLM model). 

Other sub-regions in the hills have odds ratios ranging from 3.4 to 5.7; that is, women 

living in these areas of Nepal are approximately 3 to 6 times more likely to use antenatal 

care than women living in the Far-Western Hills. Women living in the Terai, including 

those in the Far-Western Development Region, are consistently and signifi cantly more 

likely to use antenatal care than women in the Far-Western Hills. 

Looking at the other variables in Model 1, women living in urban areas are 2 to 3 

times more likely (depending on the model) to use antenatal care than those living in 

rural areas. Distance to the nearest hospital, which is a proxy for health service accessibility and infrastructure, has a negative coeffi cient. Th is implies that the further away 

women live from a hospital, the lower is their use of antenatal care.

For the most part the geographic associations observed in Model 1 hold up in Model 

2, although modifi ed after controlling for individual and household variables. After 

including these other covariates, the rural/urban distinction is no longer a signifi cant 

predictor. Adding GDI (Model 3a) or GEM (Model 3b) dampens all sub-regional 

odds ratios. Although substantial variations in the use of antenatal care by sub-region 

remain in the logistic Models 3a and 3b, the signifi cance of many sub-regions disappears in the HGLM Models 3a and 3b.

Model 2 includes all other covariates taken from the NDHS 2001. Among the logistic models, Model 2 is a considerable improvement over Model 1 in terms of overall 

performance measures. However, the R-squared between measures dips in the HGLM 

models.19 Th e percentage of women whom outcome measures would correctly predict 

increases from 64.7 percent in Model 1 to 72.6 percent in Model 2; the –2LL decreases; 

16 Models using Development Region were run, but the results are not reported here—and 

for the most part, the results regarding other covariates in the models are somewhat similar. 

Appendix C includes one example where antenatal care was modeled using Development Region dummy variables, with the Far Western Development Region serving as the reference 

category. 

17Th e analyses presented here are based on unweighted data. Th e analysis has been replicated 

using weights, and the substantive fi ndings do not change. 

18 Signifi cance at p < 0.001 ***, p <0.01 **, and p <0.05 *

The further away 

women live from 

a hospital, the 

lower is their use 

of antenatal care.
26 Results

Table 7

Logistic regression: Predictors of any use of antenatal care (N=3,283)

Background characteristic

Model 1 Model 2 Model 3a (GDI) Model 3b (GEM)

B Exp(B) B Exp(B) B Exp(B) B Exp(B)

Sub-region

Eastern Mountain 1.006*** 2.734 1.154*** 3.171 -0.036 0.964 0.384 1.468

Central Mountain 1.417*** 4.124 1.85*** 6.361 1.282*** 3.604 0.828** 2.288

Western Mountain 0.008 1.008 0.21 1.234 0.488 1.629 0.116 1.123

Eastern Hills 1.358*** 3.887 1.786*** 5.965 0.703* 2.02 0.767** 2.154

Central Hills 1.232*** 3.429 1.469*** 4.345 0.949*** 2.582 0.511 1.667

Western Hills 1.753*** 5.774 1.446*** 4.247 0.37 1.448 0.036 1.036

Mid-Western Hills 0.322 1.38 0.264 1.302 -0.198 0.82 -0.029 0.972

Eastern Terai 1.72*** 5.586 1.738*** 5.688 0.274 1.316 0.907*** 2.477

Central Terai 1.493*** 4.451 1.812*** 6.126 0.949*** 2.583 1.294*** 3.647

Western Terai 1.643*** 5.168 2.049*** 7.759 1.092*** 2.981 0.886** 2.426

Mid-Western Terai 2.186*** 8.901 2.612*** 13.623 1.93*** 6.891 1.62*** 5.052

Far-Western Terai 1.646*** 5.189 2.033*** 7.64 1.423*** 4.151 1.425*** 4.157

Urban 1.076*** 2.933 0.055 1.056 -0.124 0.883 -0.111 0.895

Distance to hospital -0.052*** 0.95 -0.036*** 0.965 -0.038*** 0.962 -0.038*** 0.963

Age

20–34 0.137 1.147 0.151 1.163 0.143 1.153

35+ -0.135 0.873 -0.137 0.872 -0.151 0.86

Parity

2–3 -0.242 0.785 -0.235 0.79 -0.207 0.813

4–5 -0.592*** 0.553 -0.575*** 0.563 -0.55*** 0.577

6+ -0.685*** 0.504 -0.697*** 0.498 -0.677*** 0.508

Want child

Then 0.144 1.154 0.15 1.162 0.132 1.142

Later 0.249 1.283 0.265 1.303 0.238 1.269

Refuse sex 0.373 1.453 0.411** 1.508 0.413** 1.512

Wife beating -0.213* 0.808 -0.205* 0.815 -0.157 0.855

Decisionmaking

1–2 0.149 1.161 0.165 1.18 0.125 1.133

3–4 0.337** 1.401 0.361** 1.434 0.343** 1.409

Problems getting services

1–2 -0.025 0.975 -0.055 0.947 -0.071 0.931

3–5 -0.099 0.905 -0.168 0.846 -0.203 0.816

6–7 -0.463* 0.629 -0.471* 0.624 -0.481* 0.618

Owns land -0.111 0.895 -0.102 0.903 -0.015 0.985

Relation to head of household

Wife 0.302 1.353 0.31 1.363 0.264 1.302

Daughter-in-law 0.326 1.385 0.355 1.426 0.328 1.388

Other 0.329 1.389 0.318 1.374 0.333 1.395

Women’s education

Primary 0.446*** 1.562 0.419*** 1.52 0.408** 1.504

Secondary 1.091*** 2.976 1.053*** 2.866 1.104*** 3.015

Partner’s education

Primary 0.049 1.051 0.028 1.028 0.033 1.034

Secondary & Higher 0.26* 1.297 0.266* 1.305 0.322** 1.38

Don’t know 0.59 1.804 0.587 1.798 0.702* 2.018

Listens to radio 0.433*** 1.542 0.455*** 1.577 0.453*** 1.573

Watches TV 0.496*** 1.642 0.418** 1.518 0.456** 1.578

Woman’s employment

Agricultural/self-employed -0.332* 0.718 -0.319* 0.727 -0.287* 0.75

Non-agricultural -0.432 0.649 -0.547* 0.579 -0.487 0.614

Partner’s employment

Non-agricultural 0.135 1.145 0.138 1.148 0.15 1.161

Unknown 0.222 1.248 0.209 1.232 0.14 1.15

Hindu -0.312 0.732 -0.266 0.767 -0.349 0.705

Ethnicity

Chhetri/Thakuri/Rajput -0.323 0.724 -0.312 0.732 -0.261 0.77

Newar -0.663* 0.515 -0.711* 0.491 -0.602 0.548

Gurung/Magar -1.448*** 0.235 -1.434*** 0.238 -1.398*** 0.247

Tamang/Sherpa -1.387*** 0.25 -1.408*** 0.245 -1.398*** 0.247

Rai/Limbu -1.276*** 0.279 -1.402*** 0.246 -1.364*** 0.256

Muslim/Churaute -0.938* 0.391 -0.926* 0.396 -0.98** 0.375

Tharu/Rajbanshi -1.207*** 0.299 -1.258*** 0.284 -1.226*** 0.294

Yadav/Ahir -1.044*** 0.352 -0.974** 0.378 -0.788* 0.455

Occupational -0.674*** 0.51 -0.696*** 0.498 -0.598** 0.55

Other Hills/Terai -0.709** 0.492 -0.705** 0.494 -0.527* 0.59

Utilities

1 0.205 1.228 0.172 1.188 0.074 1.077

2+ 0.484* 1.622 0.337 1.401 0.192 1.212

GDI 0.089*** 1.093

GEM 0.134*** 1.143

Constant -0.895*** 0.409 -0.849 0.428 -2.443*** 0.087** -1.627 0.197

-2 log likelihood 4059.76 3494.23 3451.44 3422.37

Nagelkerke R2 0.183 0.365 0.376 0.386

% correct prediction 64.7 72.6 73 73.1

Model chi-square (df) 484.91(14)*** 1050.44(56)*** 1093.23(57)*** 1122.30(57)***

Block chi-square (df) 484.91(14)*** 565.53(42)*** 42.79(1)*** 71.86(1)***

 Significance at p < 0.001 ***, p <0.01 **, and p <0.05 *
Results 27

Table 8

HGLM: Predictors of any use of antenatal care (N=3,283)

Background characteristic

Model 1 Model 2 Model 3a (GDI) Model 3b (GEM)

Coeff Exp (B) Coeff Exp (B) Coeff Exp (B) Coeff Exp (B)

Sub-region

Eastern Mountain 0.793 2.21 1.271 3.565 0.167 1.182 0.595 1.813

Central Mountain 1.129 3.092 2.038** 7.679 1.539* 4.66 1.242 3.462

Western Mountain -0.373 0.689 -0.16 0.853 0.12 1.128 -0.171 0.843

Eastern Hills 1.278* 3.588 1.999*** 7.38 0.944 2.571 1.101 3.007

Central Hills 1.335** 3.8 1.635** 5.127 0.993 2.7 0.691 1.996

Western Hills 1.68** 5.365 1.488** 4.429 0.502 1.653 0.292 1.34

Mid-Western Hills 0.1 1.105 -0.003 0.997 -0.385 0.68 -0.224 0.799

Eastern Terai 1.545** 4.688 1.736** 5.673 0.406 1.501 1.058 2.882

Central Terai 1.503** 4.495 1.855** 6.391 1.08 2.945 1.436** 4.202

Western Terai 1.46* 4.307 1.987** 7.295 1.21 3.353 1.081 2.948

Mid-Western Terai 2.172*** 8.775 2.776*** 16.05 2.126** 8.379 1.921** 6.827

Far-Western Terai 1.817* 6.152 2.271** 9.689 1.66** 5.26 1.705* 5.501

Urban 0.692*** 1.998 -0.18 0.835 -0.235 0.791 -0.212 0.809

Distance to hospital -0.065*** 0.937 -0.047*** 0.954 -0.047*** 0.954 -0.046*** 0.955

Age

20–34 0.211 1.235 0.211 1.234 0.205 1.227

35+ -0.053 0.949 -0.059 0.943 -0.062 0.94

Parity

2–3 -0.322* 0.725 -0.314* 0.731 -0.306* 0.736

4–5 -0.622*** 0.537 -0.614*** 0.541 -0.607*** 0.545

6+ -0.742*** 0.476 -0.737*** 0.478 -0.731*** 0.482

Want child

Then 0.199 1.22 0.197 1.218 0.195 1.215

Later 0.276 1.317 0.273 1.314 0.272 1.313

Refuse sex 0.256 1.291 0.285 1.33 0.288 1.334

Wife beating -0.142 0.868 -0.137 0.872 -0.129 0.879

Decisionmaking

1–2 0.202 1.223 0.2 1.221 0.194 1.214

3–4 0.275* 1.317 0.279* 1.322 0.278* 1.321

Problems getting services

1–2 -0.21 0.811 -0.216 0.806 -0.217 0.805

3–5 -0.267 0.766 -0.284 0.753 -0.287 0.75

6–7 -0.612** 0.542 -0.617** 0.539 -0.61** 0.544

Owns land -0.059 0.943 -0.055 0.946 -0.038 0.963

Relation to household head

Wife 0.075 1.078 0.083 1.086 0.077 1.08

Daughter-in-law 0.13 1.138 0.141 1.152 0.137 1.147

Other relation 0.141 1.151 0.145 1.156 0.146 1.157

Women’s education

Primary 0.449*** 1.566 0.442*** 1.556 0.443*** 1.557

Secondary and higher 1.158*** 3.182 1.15*** 3.159 1.158*** 3.185

Partner’s education

Primary 0.037 1.038 0.033 1.033 0.037 1.037

Secondary and higher 0.382** 1.465 0.383** 1.467 0.391** 1.478

Don’t know 0.736* 2.087 0.732* 2.08 0.743* 2.102

Listens to radio 0.439*** 1.551 0.444*** 1.559 0.446*** 1.562

Watches TV 0.452** 1.572 0.436** 1.546 0.441** 1.554

Women’s employment

Agricultural/self-employed -0.189 0.828 -0.18 0.835 -0.183 0.833

Non-agricultural -0.257 0.774 -0.305 0.737 -0.308 0.735

Partner’s employment

Non-agricultural 0.143 1.154 0.143 1.153 0.145 1.157

Unknown 0.196 1.217 0.19 1.209 0.184 1.201

Hindu -0.113 0.893 -0.126 0.881 -0.132 0.877

Ethnicity

Chettri/Thakuri/Rajput -0.333 0.717 -0.331 0.718 -0.315 0.73

Newar -0.744* 0.475 -0.748* 0.473 -0.742* 0.476

Gurung/Magar -1.524*** 0.218 -1.534*** 0.216 -1.517*** 0.219

Tamang/Sherpa -1.405*** 0.245 -1.394*** 0.248 -1.39*** 0.249

Rai/Limbu -1.595*** 0.203 -1.618*** 0.198 -1.6*** 0.202

Muslim -1.044** 0.352 -1.057** 0.347 -1.046* 0.351

Tharu/Rajbansi -1.394*** 0.248 -1.403*** 0.246 -1.384*** 0.251

Yadav/Ahir -0.99** 0.372 -0.988** 0.372 -0.955** 0.385

Occupational -0.522* 0.594 -0.532* 0.587 -0.508* 0.601

Other Hills/Terai -0.539 0.583 -0.549* 0.578 -0.515 0.598

Utilities

1 -0.001 0.999 0.001 1.001 -0.011 0.989

2+ 0.345 1.412 0.289 1.335 0.278 1.321

GDI 0.079** 1.082

GEM 0.109*** 1.115

Intercept -0.618 0.539 -0.652 0.521 -2.054* 0.128 -1.32 0.267

Variance comp

Null 

Model Model 1 Model 2 Model 3a Model 3b

Tau (Level 2 

variance) 1.24 0.651*** 0.71*** 0.601*** 0.564***

R2 between 0.475 0.427 0.515 0.545

Significance at p < 0.001 ***, p <0.01 **, and p <0.05 *
28 Results

and the model chi-square is highly signifi cant with 56 variables in the logistic model. 

A number of independent variables are associated with the use of antenatal care. 

Age is not a signifi cant predictor, but parity is negatively associated with antenatal care. 

When a pregnancy is a fourth or higher order birth, women are less likely to use antenatal care. Th e relationship is consistent: as parity level increases, women are less likely 

to use antenatal care compared with fi rst-time and lower parity mothers. Th is association holds up when either GDI or GEM is added to the model (Models 3a and 3b).

Turning to individual empowerment variables, women who believe that no circumstances justify wife beating are less likely to use antenatal care, at least according to the 

logistic model. Th is somewhat perplexing association is documented in the NDHS 

2001 report (Ministry of Health et al., 2002). However, women who are involved in 

decisions about health care, purchases, daily needs, and family visits are 1.3 to 1.4 times 

more likely to use antenatal care than women who are not involved in such decisionmaking. Women who report six or seven “big” problems in securing access to health 

services are less likely to use antenatal care than women who report no such problems; 

the odds ratio in the HGLM is 0.54.

Among more traditional women’s status variables, women’s education is strongly associated with use of antenatal care. Compared with women who have no education, 

women with primary education were 1.5 times more likely to use antenatal care, and 

those with secondary or higher levels of education were 3 times more likely. Partner’s 

education level also was moderately signifi cant with an odds ratio of 1.3 to 1.5. Women’s 

work status has a seemingly paradoxical association: women who were not employed 

were more likely to use antenatal care than those who worked in agriculture or were 

self-employed (although this is not signifi cant in the HGLM). Perhaps as noted earlier, 

this may be explained by the fact that women often work for the family and exert little 

infl uence over household and individual decisionmaking. Partner’s work status was not 

signifi cant in determining the use of antenatal care.

Women who regularly listened to the radio or watched television were 1.5 to 1.6 

times more likely to use antenatal care than women who did not. According to the 

logistic model, women living in households with two or more utilities were 1.6 times 

more likely to use antenatal care than women living in households without access to any 

utilities (piped water, a toilet or latrine, modern cooking fuels, and electricity); however, 

utilities were not signifi cant in the HGLM. Lastly, Brahmin women (the reference 

category) were more likely than women of any other ethnic group to use antenatal care, 

including other higher caste groups such as Newar. Odds ratios were lowest (ranging 

from 0.2 to 0.3) for the Gurung/Magar, Tamang/Sherpa, Rai/Limbu, and Th aru/Rajbanshi. Being Hindu was not a signifi cant predictor of the use of antenatal care.

Model 3a adds one additional covariate: GDI, which is based on gender diff erences 

in life expectancy, sex ratio, literacy and schooling, and income. Model 3a provides no 

real improvement over Model 2, although the overall model performance measures 

are strong and the GDI variable is highly signifi cant, implying that women are more 

likely to use antenatal care if they live in districts with higher GDI scores. Th e HGLM 

model includes summary measures that allow for the identifi cation of the importance 

of contextual factors. In Table 8 the Level 2 variance components show that there 

is signifi cant variation across districts. Th e inclusion of contextual variables improves 

overall explained variance in the use of antenatal care compared with Model 2. Indeed, 

the HGLM results suggest that adding GDI to individual and household variables 

increases explained variance for predicting the use of antenatal care by 9 percentage 

points.

19 Th is is not a concern since it is always a possibility when there are many dichotomous 

variables in a model. Moreover, the directionality and the signifi cance of individual covariates 

are consistent with the study’s theoretical expectations and model.

Women are more 

likely to use antenatal care if they 

live in districts with 

higher GDI scores.
Results 29

Including GDI changes the associations between some of the other covariates and 

use of antenatal care. As noted above, including GDI (Model 3a) or GEM (Model 3b) 

dampens all sub-regional odds ratios. In addition, while substantial variations in use 

of antenatal care by sub-region remain in the logistic models, the significance of most 

sub-regions disappears in the HGLM models. There are minor differences in which 

sub-regions remain significant between GDI and GEM models. 

In the logistic models, some individual covariates are significant in Model 3a that 

were not in Model 2, although the HGLM models are almost identical. In the logistic 

model, women who said they would refuse sex with their husbands under all four cir

-

cumstances were more likely to use antenatal care than women who said they would not 

refuse sex in at least one circumstance (odds ratio = 1.5). 

Model 3b replaces GDI with GEM, which is based on women’s economic and po-

litical participation. Among the logistic models, Model 3b is better than Model 3a 

based on comparative model performance measures (–2LL), although it, too, is only a 

modest improvement over Model 2. HGLM results from Model 3b imply a 12-per

-

centage point increase in explained variance for predicting antenatal care after includ

-

ing GEM. The GEM measure is highly significant, suggesting that women are more 

likely to use antenatal care if they live in districts with higher GEM scores. Among the 

logistic models, refusing sex is the only covariate that becomes significant in Model 3b 

compared with Model 2, while household utilities lose significance. The main effect of 

including GEM in the model is the dampening of the significance of the sub-regional 

variables, especially compared with Model 2. In both the logistic and HGML models, 

the odds ratios of the sub-regions that remain significant are halved in some cases. In 

other words, including GEM in the model reduces sub-regional differences in the use 

of antenatal care. For example, the odds of using antenatal care in the Terai sub-regions 

are 5–16 times higher than in the Far-Western Hills according to Model 2, but only 

2–6 times higher according to Model 3b. 

In summary, models that include contextual variables, either GDI or GEM, provide 

a better overall explanation for the variation in use of antenatal care by married Nepali 

women. In models with contextual variables, there is some sub-regional variation in 

antenatal care, especially within the Terai, and a significant negative association with 

distance to nearest main hospital (the greater the distance to a hospital, the less likely 

a woman is to use antenatal care services). Regarding individual and household covari

-

ates, married women are less likely to use antenatal care as their parity increases, if 

they report many problems accessing health care, and if they are not Brahmin. Mar-

ried women are more likely to use antenatal care if they make more decisions in the 

household, are educated, have partners who are educated, listen to the radio, and watch 

television.

5.3 Four or More Antenatal Care Visits 

Map 8 illustrates geographic variations in the proportion of women who make exten-

sive use of antenatal care, defined as four or more visits, relative to the national average. 

The map is based only on those women who reported using any antenatal care. The 

average value for all DHS clusters was 27.1 percent, with a standard deviation of 28.1 

percent. This makes it impossible for any cluster to fall in the below-average category, 

which is defined as less than one standard deviation below the mean. 

The map does reveal where above-average proportions of women made extensive 

use of antenatal care during their pregnancy. Kathmandu (Central Hills) falls in this 

category, although few other clusters in the Central Development Region do. Most 

women in Pokhara (Western Hills) made at least four antenatal visits, as did women in 

a number of other clusters throughout the Western Hills. Women in the eastern half of 

the Eastern Development Region are most likely to have made four or more antenatal 
30 Results

care visits, in sharp contrast to the Far-Western Development Region where not a 

single cluster falls in the above-average category. 

5.4 Models Predicting Extensive Use of Antenatal Care

As previously noted, 48 percent of the 3,283 women in the study sample received antenatal care. Of these 1,586 women, only 29.5 percent made at least four antenatal visits, 

the minimum recommended by the National Safe Motherhood Program in Nepal. 

The same set of predictors as before is used to explore which women are likely to make 

extensive use of antenatal care. Logistic regression results are presented in Table 9, and 

HGLM results in Table 10. Discussion of the results is combined.

Model 1 reveals that women who receive antenatal care in the Far-Western Hills 

(the reference category) differ little from women elsewhere in Nepal as to whether they 

make four or more antenatal care visits. According to the logistic model, in only three 

sub-regions of the country are women more likely to make extensive use of antenatal 

care: odds ratios range from 2.3 to 3.0 in the Eastern Terai, Central Hills, and Western 

Hills. In the HGLM, only one sub-region is marginally different: women in the Western Hills are 3 times more likely to make extensive use of antenatal care than those in 

the Far-Western Hills. Among women who have attended antenatal care facilities at 

least once, urban women are 2 to 3 times more likely than rural women to make four or 

more visits. Distance to the nearest hospital is not significant in this model, presumably 

because women in this sample, all of whom had made at least one antenatal care visit, 

had already proved their ability to overcome accessibility issues.

Model 2 uncovers some modest associations. The geographic variables are generally 

weaker, with no significant differences in the HGLM model. The urban dummy variable is no longer significant after the individual-level covariates have been added. Age 

Map 8

Usage rate for extensive antenatal care (women making at least four visits) by DHS cluster,

relative to the average for all clusters

Source: NDHS 2001

50 0 50 100 Miles

< –1 standard deviation

Within 1 standard deviation of mean

> 1 standard deviation
Results 31

Table 9

Logistic regression: Predictors of four or more antenatal care visits (N=1,586)

Background 

characteristic

Model 1 Model 2 Model 3a (GDI) Model 3b (GEM)

B Exp(B) B Exp(B) B Exp(B) B Exp(B)

Sub-region

Eastern Mountain 0.192 1.211 -0.195 0.823 -1.316* 0.268 -1.091* 0.336

Central Mountain -0.022 0.978 -0.043 0.957 -0.501 0.606 -1.165* 0.312

Western Mountain -0.614 0.541 -0.463 0.629 -0.329 0.72 -0.604 0.547

Eastern Hills 0.425 1.529 0.363 1.437 -0.671 0.511 -0.725 0.485

Central Hills 0.884* 2.42 0.541 1.718 -0.299 0.742 -0.768 0.464

Western Hills 1.114** 3.047 0.801* 2.227 -0.209 0.811 -0.599 0.549

Mid-Western Hills -0.564 0.569 -0.591 0.554 -1.041 0.353 -0.916 0.4

Eastern Terai 0.833* 2.301 0.747 2.111 -0.546 0.579 -0.186 0.83

Central Terai 0.043 1.044 0.305 1.356 -0.468 0.626 -0.281 0.755

Western Terai 0.458 1.58 0.7 2.013 -0.158 0.854 -0.377 0.686

Mid-Western Terai 0.006 1.006 0.057 1.059 -0.495 0.61 -0.842 0.431

Far-Western Terai 0.268 1.308 0.447 1.564 -0.102 0.903 -0.165 0.847

Urban 1.106*** 3.022 0.326 1.386 0.15 1.162 0.127 1.136

Distance to hospital -0.008 0.992 0.007 1.007 0.009 1.009 0.005 1.005

Age

20–34 0.507* 1.661 0.511* 1.668 0.499* 1.646

35+ 0.618 1.855 0.596 1.815 0.636 1.889

Parity

2–3 -0.416* 0.659 -0.431* 0.65 -0.418* 0.658

4–5 -0.685** 0.504 -0.678** 0.508 -0.642* 0.526

6+ -0.705 0.494 -0.73* 0.482 -0.713 0.49

Want child

Then 0.157 1.17 0.143 1.154 0.147 1.159

Later 0.426 1.532 0.428 1.535 0.44 1.552

Refuse sex 0.466 1.594 0.586* 1.797 0.614 1.848

Wife beating 0.02 1.02 0.044 1.045 0.095 1.099

Decisionmaking

1–2 0.152 1.164 0.173 1.189 0.144 1.155

3–4 0.293 1.34 0.316 1.372 0.325 1.384

Problems getting services

1–2 -0.227 0.797 -0.275 0.76 -0.301 0.74

3–5 -0.187 0.83 -0.268 0.765 -0.323 0.724

6–7 -0.217 0.805 -0.231 0.794 -0.273 0.761

Owns land -0.06 0.942 -0.063 0.939 -0.043 0.958

Relation to household head

Wife 0.375 1.456 0.393 1.481 0.389 1.476

Daughter-in-law 0.555 1.742 0.599 1.82 0.622 1.862

Other 0.469 1.599 0.479 1.614 0.536 1.709

Women’s education

Primary -0.005 0.995 -0.03 0.97 -0.038 0.963

Secondary 0.237 1.268 0.216 1.241 0.236 1.266

Partner’s education

Primary -0.084 0.92 -0.057 0.945 -0.056 0.946

Secondary and higher 0.344 1.41 0.41* 1.507 0.427* 1.532

Don’t know 0.635 1.888 0.713 2.041 0.793 2.21

Listens to radio 0.016 1.016 0.056 1.058 0.052 1.054

Watches TV 0.383* 1.467 0.283 1.328 0.339 1.403

Women’s employment

Agricultural/self employed -0.413* 0.662 -0.378 0.685 -0.403* 0.669

Non-agricultural -0.351 0.704 -0.433 0.649 -0.438 0.645

Partner’s employment

Non-agricultural 0.232 1.261 0.234 1.264 0.247 1.28

Unknown -0.083 0.921 -0.182 0.834 -0.159 0.853

Hindu -0.491 0.612 -0.473 0.623 -0.531 0.588

Ethnicity

Chhetri/Thakuri/Rajput 0.417 1.517 0.429 1.536 0.503* 1.654

Newar 0.214 1.238 0.266 1.305 0.25 1.284

Gurung/Magar -0.33 0.719 -0.299 0.741 -0.253 0.777

Tamang/Sherpa -0.563 0.57 -0.425 0.654 -0.378 0.685

Rai/Limbu -0.479 0.619 -0.552 0.576 -0.461 0.631

Muslim/Churaute -1.17* 0.31 -1.076* 0.341 -1.171* 0.31

Tharu/Rajbanshi -0.759* 0.468 -0.756* 0.469 -0.727* 0.484

Yadav/Ahir -0.545 0.58 -0.427 0.653 -0.276 0.759

Occupational -0.271 0.763 -0.203 0.816 -0.088 0.916

Other Hills/Terai -0.75* 0.472 -0.682* 0.505 -0.586* 0.556

Utilities

1 0.572*** 1.771 v0.565*** 1.76 0.505** 1.658

2+ 0.712*** 2.038 0.639** 1.895 0.557* 1.746

GDI 0.076*** 1.079

GEM 0.118*** 1.125

Constant -1.427*** 0.24 -2.405** 0.09 -3.926*** 0.02 -3.217*** 0.04

-2 log likelihood 1783.43 1559.1 1538.49 1528.57

Nagelkerke R2 0.119 0.292 0.306 0.313

% correct prediction 73.7 76.9 77.7 77.6

Model chi-square(df) 363.06(56)*** 138.72(14)*** 383.67(57)*** 393.52(57)***

Block chi-square(df) 224.34(42)*** 138.72(14)*** 20.61(1)*** 30.53(1)***

Significance at p < 0.001 ***, p <0.01 **, and p <0.05 *
32 Results

Table 10

HGLM: Predictors of four or more antenatal care visits (N=1,586)

Background characteristic

Model 1 Model 2 Model 3a Model 3b

Coeff Exp (B) Coeff Exp (B) Coeff Exp (B) Coeff Exp (B)

Sub-region

Eastern Mountain 0.23 1.258 -0.041 0.96 -1.275 0.279 -0.965 0.381

Central Mountain -0.535 0.586 -0.41 0.663 -0.845 0.429 -1.402 0.246

Western Mountain -0.727 0.483 -0.535 0.585 -0.317 0.729 -0.624 0.536

Eastern Hills 0.295 1.343 0.386 1.47 -0.761 0.467 -0.716 0.489

Central Hills 0.898 2.455 0.569 1.766 -0.325 0.722 -0.751 0.472

Western Hills 1.104* 3.017 0.867 2.38 -0.23 0.795 -0.578 0.561

Mid-Western Hills -0.624 0.536 -0.627 0.534 -1.122 0.326 -0.968 0.38

Eastern Terai 0.58 1.787 0.611 1.843 -0.789 0.454 -0.26 0.771

Central Terai 0.006 1.006 0.298 1.347 -0.539 0.583 -0.263 0.769

Western Terai 0.559 1.749 0.867 2.38 -0.021 0.979 -0.257 0.773

Mid-Western Terai 0.073 1.075 0.158 1.171 -0.511 0.6 -0.833 0.435

Far-Western Terai 0.404 1.498 0.53 1.7 -0.122 0.885 -0.154 0.858

Urban 0.827*** 2.286 0.171 1.186 0.098 1.103 0.101 1.106

Distance to hospital -0.016 0.984 -0.003 0.997 0 1 0 1

Age

20–34 0.485* 1.624 0.492* 1.635 0.479* 1.614

35+ 0.595 1.812 0.589 1.803 0.604 1.829

Parity

2–3 -0.443** 0.642 -0.445** 0.641 -0.431* 0.65

4–5 -0.7** 0.497 -0.692** 0.501 -0.663** 0.515

6+ -0.729* 0.482 -0.733* 0.481 -0.714 0.49

Want child

Then 0.178 1.195 0.169 1.184 0.173 1.189

Later 0.429 1.536 0.427 1.532 0.437 1.548

Refuse sex 0.568* 1.764 0.626* 1.871 0.636* 1.888

Wife beating 0.077 1.08 0.091 1.095 0.112 1.118

Decisionmaking

1–2 0.164 1.178 0.171 1.186 0.158 1.171

3–4 0.344 1.41 0.352 1.422 0.36 1.434

Problems getting services

1–2 -0.319 0.727 -0.338 0.713 -0.346 0.708

3–5 -0.346 0.708 -0.374 0.688 -0.386 0.68

6–7 -0.334 0.716 -0.325 0.723 -0.314 0.73

Owns land -0.022 0.978 -0.032 0.968 -0.011 0.99

Relation to household head

Wife 0.475 1.608 0.474 1.607 0.463 1.589

Daughter-in-law 0.671* 1.956 0.684* 1.981 0.689* 1.991

Other relation 0.593 1.809 0.587 1.799 0.608 1.837

Women’s education

Primary 0.016 1.016 0.001 1.001 -0.002 0.998

Secondary and higher 0.296 1.345 0.282 1.325 0.293 1.34

Partner’s education

Primary -0.17 0.844 -0.151 0.86 -0.135 0.873

Secondary and higher 0.329 1.39 0.363 1.437 0.382 1.464

Don’t know 0.721 2.056 0.754 2.125 0.79 2.202

Listen to radio -0.008 0.992 0.011 1.011 0.011 1.011

Watches TV 0.246 1.279 0.213 1.237 0.24 1.272

Women’s employment

Agricultural -0.379 0.685 -0.362 0.696 -0.383 0.682

Non-agricultural -0.415 0.661 -0.456 0.634 -0.465 0.628

Partner’s employment

Non-agricultural 0.263 1.3 0.257 1.293 0.262 1.299

Unknown -0.114 0.893 -0.165 0.848 -0.159 0.853

Hindu -0.467 0.627 -0.465 0.628 -0.498 0.608

Ethnicity

Chettri/Thakuri/Rajput 0.503* 1.654 0.501* 1.65 0.544* 1.723

Newar 0.307 1.359 0.322 1.38 0.301 1.351

Gurung/Magar -0.355 0.701 -0.347 0.707 -0.303 0.738

Tamang/Sherpa -0.441 0.644 -0.388 0.678 -0.372 0.689

Rai/Limbu -0.641 0.527 -0.674 0.51 -0.622 0.537

Muslim -1.057* 0.347 -1.052* 0.349 -1.06* 0.347

Tharu/Rajbansi -0.576 0.562 -0.603 0.547 -0.579 0.56

Yadav/Ahir -0.234 0.791 -0.22 0.802 -0.155 0.857

Occupational -0.147 0.864 -0.135 0.873 -0.065 0.937

Other Hills/Terai -0.964** 0.381 -0.919** 0.399 -0.846* 0.429

Utilities

1 0.426* 1.531 0.439** 1.55 0.423* 1.526

2+ 0.629** 1.876 0.594** 1.811 0.573** 1.774

GDI 0.083*** 1.087

GEM 0.122*** 1.13

Intercept -1.378** 0.252 -2.512** 0.081 -4.055*** 0.017 -3.284*** 0.037

Variance comp Null Model Model 1 Model 2 Model 3a Model 3b

Tau (Level 2 

variance) 0.72 0.461*** 0.444*** 0.32*** 0.245*

R2 between 0.36 0.383 0.556 0.66

Significance at p < 0.001 ***, p <0.01 **, and p <0.05 *
Results 33

is signifi cant: women aged 20-34 are 1.6 times more likely than those under 20 years to 

extensively use antenatal care. Th ere is also a negative association with parity: women 

experiencing their fi rst pregnancy are more likely than others to make four or more antenatal visits. Women also are almost twice as likely to make four antenatal visits if they 

are the household head’s daughter-in-law, according to the HGLM model.

Th e logistic models also show a positive association for women who watch TV regularly, a negative association for those who work in agriculture, and a negative association for various ethnic groups: Muslim, Th aru/Rajbanshi, and the other Hills and Terai. 

Muslim women are about one-third as likely as Brahmin women to attend four or more 

antenatal visits. Of the variables discussed in this paragraph only the ethnic diff erences 

are signifi cant in HGLM Model 2.

One of the strongest associations found in both the logistic and HGLM models 

concerns modern utilities. Women living in households with one utility are 1.5–1.7 

times more likely to make extensive use of antenatal care than women in households 

lacking utilities. Women in households with two or more utilities are 1.9-2.0 times 

more likely to use antenatal services as those without utilities.

Model 3a is a slight improvement on Model 2, and GDI is signifi cant. As Table 10 

shows, the Level 2 variance components are signifi cant. Compared with Model 2, the 

inclusion of GDI as a contextual variable improves overall explained variance by 17 

points among women who attend antenatal care a minimum of four times.

For the most part, the same set of variables discussed in Model 2 are signifi cant 

predictors in Model 3a. However, the inclusion of GDI eliminates the signifi cance of 

sub-regional associations (although in the logistic Model 3a women from the Eastern 

Mountains are signifi cantly less likely to make four antenatal care visits than women 

in the Far Western Hills). Adding GDI also means that TV watching and women’s 

employment are no longer signifi cant. 

Model 3b includes GEM, and the HGLM results show a 28-point increase in explained variance for predicting extensive use of antenatal care compared with Model 2, 

although the model is marginally signifi cant.20 Th e geographic pattern changes again 

in the logistic model, with women in both the Eastern and Central Mountains onethird as likely to use antenatal care as women in other mountain areas. Th ere are no 

sub-regional diff erences in the HGLM formulation. Women employed in agriculture 

were two-thirds as likely as women who did not work to make extensive use of antenatal care, and Chhetri women were 1.6–1.7 times more likely to do so than Brahmin 

women. Th ere are no other major diff erences between the HGLM models including 

GDI or GEM. 

It is not surprising to fi nd a reduced set of signifi cant associations in this analysis, 

given that it attempts to diff erentiate between women who made more or less use of 

antenatal care and excludes those who never received antenatal care. Th e inclusion of 

GDI or GEM accounts for most of the small sub-regional variations, and other geographic variables were never signifi cant after the inclusion of individual covariates. In 

addition to parity, there is a modest age eff ect, with women aged 20–34 more likely 

than younger women to make at least four antenatal visits. An empowerment variable 

(women’s attitudes toward refusing sex) also was signifi cant: women who hold strong 

views were more likely than others to make extensive use of antenatal care. Other positive associations were observed for women who were a daughter-in-law of the head of 

household, Chhetri, and who lived in households with a greater number of modern 

utilities. Negative associations were found among some ethnic groups, most notably 

Muslim, Th aru/Rajbansi, and other Hill/Terai groups.

Modeling was used to explore other contextual variables, but more often than not 

the variables added were not signifi cant and did not change the substantive fi ndings. 

20 Th is is not a concern, as discussed in footnote 12.

Women who hold 

strong views on the 

right to refuse sex 

were more likely 

than others to make 

extensive use of 

antenatal care.
34 Results

For example, Appendix D presents a model with an additional contextual variable: 

the average number of antenatal care visits reported for each pregnancy in the district, 

which was derived from a government report (Ministry of Health, 2001). Because this 

is district-level data, it can be mapped (see Map 9). Note that the Level 2 variance 

component is not significant in this example, which is presented as Model 4 (see Table 

D.1). The additional variable is expected to be associated with the frequent use of antenatal care by women in the NDHS 2001 sample. This contextual variable is marginally 

significant, and moderates the effect of GEM. 

Map 9

Average number of antenatal care visits by district

Source: NDHS 2001

50 0 50 100 Miles

Less than 2

2–4

4 or more
Discussion 35

6.1 Overall Analytical Findings

Given our interest in women’s empowerment, we have been encouraged by the fi ndings. Using the NDHS 2001 enabled the study to draw on a larger set of women’s 

empowerment measures—some of which appear to be important predictors of the use 

of antenatal care—that go beyond the traditional women’s status variables of education 

and work. At the same time the study has been able to situate women in broader gender 

empowerment contexts at the district level. 

Models that include the contextual variables, GDI or GEM, seem to provide a better overall explanation for the variation in the use of antenatal care by married Nepali 

women, although in many instances coeffi cients and odds ratios do not change compared with models that do not exclude Level 2 variables. After controlling for GDI or 

GEM, evidence remains for some sub-regional variation in the use of antenatal care, 

especially among Terai areas, and for the signifi cant impact of distance to nearest main 

hospital (the greater the distance to a hospital, the less likely a woman is to use antenatal care services). Regarding individual and household covariates, married women are 

less likely to use antenatal care as their parity increases, if they report many problems 

regarding access to medical care, and if they are not Brahmin. Married women are more 

likely to use antenatal care if they make more decisions in the household, if they are 

educated, if their partner is educated, and if they regularly listen to the radio or watch 

television. 

Th e models also were able to detect the infl uence of contextual factors on whether 

women who received any antenatal care made four or more visits. All women in this 

smaller sample received antenatal care, so the goal of the investigation was to determine 

who was most likely to make extensive use of antenatal services and whether gender 

context matters. Th at made fi nding signifi cant diff erences based on the covariates in 

the model quite challenging. Including GDI or GEM accounts for most of the small 

sub-regional variations, and other geographic variables were never signifi cant after the 

inclusion of individual covariates. In addition to parity, there are modest age and empowerment-related eff ects. Positive associations were observed for women who were 

daughters-in-law of the head of household, were Chhetri, and who lived in households with more modern utilities. Negative associations were found among some ethnic 

groups, most notably Muslim, Th aru/Rajbansi, and other Hill and Terai groups.

6.2 Implications for Research and Policy

Th is macro-micro level analysis of the use of antenatal care in Nepal contributes to 

the existing literature and opens up new directions for research. Paying close attention 

to processes operating at various spatial scales can help us better understand gender 

diff erences in health. Policies and programs conceived without consideration for local 

context and place will have limited impact, unless they are informed by data that appreciates the vital connection between women’s health and women’s status across scales.

6

Discussion

Married women 

are more likely to 

use antenatal care 

if they make more 

decisions in the 

household, if they 

are educated, if their 

partner is educated,

and if they regularly 

listen to the radio 

or watch television.
36 Discussion

This analysis has been preliminary: there are theoretical, methodological, and data 

issues that have only been briefly touched upon. Some may question how the study 

has treated hierarchies. The study model employs a straightforward, two-level structure 

based on individual women living within districts. Some variables that strictly speaking 

are measured at the cluster level—such as distance to the nearest hospital and urban/

rural location—are treated as individual factors. The study also blends variables at the 

individual and household levels. Thus it could be argued that we should have developed 

and made operational a more complex three- or four-level model that situates women 

within households and within clusters, as well as within districts. If the study had taken 

a traditional approach, which aggregates individual and household characteristics to 

create cluster-level variables, we might have thought more carefully about a three-level 

structure. Instead, however, the study’s strategy was to explore the availability of “global” 

variables that would demonstrate a significant strength of a spatial perspective and the 

use of geographic information systems (GIS); clusters were linked to non-DHS de

-

rived variables for geographic contexts of interest. 

While it was possible to generate a measure for distance to the nearest main hospital 

for each cluster, it would have been more difficult to justify the feasibility, reliability, 

and, most importantly, validity of other distance and/or density measures derived at 

the cluster level. Moreover, the search for possible global variables at higher geographic 

levels, such as the district, showed that more complete and reliable data was available at 

those levels than the cluster level. Other variables created at the cluster level also likely 

would be highly correlated with one another and therefore of limited analytical value 

(these might include, for example, distance to major road, density of road network, 

whether served by a bus route, distance to main urban center, distance to hospital, den

-

sity of health personnel, even latitude and elevation).21

The models used here are quite basic when considered from the perspective of the 

emerging application of hierarchical linear modeling. They do not fully explore crosslevel interactions or the application of different forms of random and fixed models. 

These are important directions to pursue in the future.

6.3 DHS Geocodes and Geographical Information Systems

While this study did not fully exploit DHS geocodes, in the future researchers will in-

creasingly employ geographic information systems and related technologies to explore 

the possibility of integrating an array of different data sets with geocoded surveys such 

as DHS. Researchers may begin to analyze DHS data in new ways to explore new ques-

tions, especially questions relating to the significance of the geographic context. Within 

the next decade both the quantity and quality of geospatial data available to researchers 

will significantly increase. In some countries rich geospatial data may already exist, and 

they may include attribute information relevant to DHS-based research. Of course, 

researchers need to be cautious and avoid creating and linking data together across 

scales without thinking through the theoretical model and analytic plan. For example, 

adding additional geographic or contextual levels could result in multiple embedded 

data levels (e.g., individuals, within households, within clusters, within village develop-

ment committees, within districts). Such complexity can create modeling challenges 

and methodological problems related to sample size issues at the different levels, which 

might well preclude the identification of significant salient variables. 

21 Since this application focuses on antenatal care, it did not warrant including “global” envi-

ronmental variables. There is much climate, land use, and other environmental data available for 

small raster grids, however, and it would be feasible to link DHS cluster geocodes to these raster 

cells and expand the number of variables included in a model at the cluster level.
References 37

Amini, S.B., P.M. Catalano, L.J. Dierker et al. 1996. Births to teenagers: Trends and 

obstetric outcomes. Obstetrics and Gynecology 87:668–674.

Andes, N. 1996. Socioeconomic, medical care, and public health contexts affecting infant mortality: A study of community-level differentials in Peru. Journal of Health and 

Social Behavior 30:386–397.

Axinn, W.G., and J.S. Barber. 2001. Mass education and fertility transition. American 

Sociological Review 66(4):481–505.

Axinn, W.G., and S.T. Yabiku. 2001. Social change, the social organization of families, 

and fertility limitation. American Journal of Sociology 106(5):1219–1261.

Balk, D. 1994. Individual and community aspects of women’s status and fertility in rural 

Bangladesh. Population Studies 48:21–45. 

Bhatia, J.C., and J. Cleland. 1995. Determinants of maternal health care in a region of 

south India. Health Transition Review 5(2):127–142.

Bloom, S.S., D. Wypij, and M. Das Gupta. 2001. Dimensions of women’s autonomy 

and the influence of maternal health care utilization in a North Indian city. Demography

38(1):67–78.

Celik, Y., and D. Hotchkiss. 2000. The socioeconomic determinants of maternal health 

care utilization in Turkey. Social Science and Medicine 50:1797–1806.

Central Bureau of Statistics [Nepal]. 1996. Nepal Living Standards Survey, 1996. Kathmandu, Nepal: Central Bureau of Statistics.

Central Bureau of Statistics [Nepal]. 2001. Statistical Yearbook of Nepal. Kathmandu: 

Central Bureau of Statistics.

Chacko, E. 2001. Women’s use of contraception in rural India: A village-level study. 

Health and Place 7:197–208.

Degraff, D.S., R.E. Bilsborrow, and D.K. Guilkey. 1997. Community-level determinants of contraceptive use in the Philippines: A structural analysis. Demography

34(3):385–398.

Desai, S., and D. Jain. 1994. Maternal employment and changes in family dynamics: 

The social context of women’s work in south India. Population and Development Review

20(1):115–136.

Diez-Roux, A.V. 1998. Bringing context back into epidemiology: Variables and fallaReferences
38 References

cies in multilevel analysis. American Journal of Public Health 88:216–222.

Diez-Roux, A.V. 2001. Investigating neighborhood and area effects on health. American Journal of Public Health 91:1783–1789. 

DiPrete, T.A., and J.D. Forrestal. 1994. Multilevel models: Method and substance. Annual Review of Sociology 20: 331–357.

Dixon-Mueller, R. 2003. Female empowerment and demographic processes: moving 

beyond Cairo. In IUSSP contributions to gender research. Paris: IUSSP, pp. 89–108.

Dixon-Mueller, R., and J. Wasserheit. 1991. The culture of silence: Reproductive tract infections among women in the third world. New York, NY: International Women’s Health 

Coalition.

Duncan, C., K. Jones, and G. Moon. 1998. Context, composition and heterogeneity: 

Using multilevel models in health research. Social Science and Medicine 46:97–117.

Dyson, T., and M. Moore. 1983. On kinship structure, female autonomy, and demographic behavior in India. Population and Development Review 9(1):35–60.

Elo, I.T. 1992. Utilization of maternal health-care services in Peru: The role of women’s 

education. Health Transition Review 2(1):49–69.

Entwisle, B., J.B. Casterline, and H.A.A. Sayed. 1989. Villages as context for contraceptive behavior in rural Egypt. American Sociological Review 54(6):1019–1034.

Entwisle, B., A.I. Hermalin, P. Kamnuansilpa et al. 1984. A multilevel model of family 

planning availability and contraceptive use in rural Thailand. Demography 21:559–574.

Entwisle, B., W.M. Mason, and A.I. Hermalin. 1986. The multilevel dependence of contraceptive use on socioeconomic development and family planning program strength. 

Demography 23(2):199–216.

Feldman, S. 1983. The use of private health care providers in rural Bangladesh: A response to Claquin. Social Science and Medicine 17:1887–1896.

Gage, A.J. 1995. Women’s socioeconomic position and contraceptive behavior in Togo. 

Studies in Family Planning 26(5):264–277.

Glei, D.A., N. Goldman, and G. Rodriguez. 2003. Utilization of care during pregnancy in rural Guatemala: Does obstetrical need matter? Social Science and Medicine

57(12):2447–2463.

Goldstein, H. 1995. Multilevel statistical models London, UK: Edward Arnold.

Hermalin, A.I. 1985. Use of individual and area data in examining family planning 

programmes. In Population research methodology volume 7: Contraception and family planning. New York: UNFPA, pp. 305-317.

Hirschman, C., and P. Guest. 1990. Multilevel models of fertility determination in four 

Southeast Asian countries: 1970 and 1980. Demography 27(3):369–396.

Hotchkiss, D.R. 2001. Expansion of rural health care and the use of maternal services 

in Nepal. Health and Place 7:39–45.

Hotchkiss, D.R., J. Rous, K. Karmacharya et al. 1998. Household health expenditure 
References 39

in Nepal: Implications for health care financing reform. Health Policy and Planning

13(4):371-383.

Jejeebhoy, S.J. 1995. Women’s education, autonomy and reproductive behaviour: Experience 

from developing countries. Oxford, UK: Clarendon Press.

Joshi, A. 1994. Maternal schooling and child health: Preliminary analysis of the intervention mechanisms in rural Nepal. Health Transition Review 4:1–28.

Kreft, I., and J. De Leeuw. 1998. Introduction to multilevel modeling. Thousand Oaks, 

CA: Sage Publications.

Kritz, M.M., P. Makinwa-Adebusoye, and D.T. Gurak. 2000. The role of gender context 

in shaping reproductive behavior in Nigeria. In Women’s empowerment and demographic 

processes: Moving beyond Cairo, ed. H.B. Presser and G. Sen. New York, NY: Oxford 

University Press Inc. pp. 237–260.

Larsen, U., and M. Hollos. 2003. Women’s empowerment and fertility decline among 

the Pare of Kilimanjaro region, Northern Tanzania. Social Science and Medicine 57:1099–

1115.

Lazarsfeld, P.F., and H. Mendel. 1969. On the relation between individual and collective properties. In A sociological reader on complex organizations, ed. A. Etzioni. New 

York, NY: Holt, Rinehart and Winston. pp. 499–516

LeVine, R.A., S.E. LeVine, M.L. Rowe et al. 2004. Maternal literacy and health behavior: A Nepalese case study. Social Science and Medicine 58(4):863-877.

Magadi, M.A., N.J. Madise, and R.N. Rodrigues. 2000. Frequency and timing of antenatal care in Kenya: Explaining the variations between women of different communities. Social Science and Medicine 51:551–561.

Miles-Doan, R., and K.L. Brewster. 1998. Type of employment, use of prenatal services 

and contraceptive practices in the Philippines. Studies in Family Planning 29:69–77.

Ministry of Health [Nepal]. 1995. Annual report. Department of Health Services 2050/51 

(1993/94). Kathmandu, Nepal: Ministry of Health and HMG Nepal.

Ministry of Health [Nepal]. 1996. Nepal Family Health Survey (NFHS). Kathmandu, 

Nepal: Ministry of Health and HMG Nepal. 

Ministry of Health [Nepal]. 1998a. National reproductive health strategy 2054 (1998).

Kathmandu, Nepal: Family Health Division, Department of Health Services, Ministry 

of Health; and HMG Nepal.

Ministry of Health [Nepal]. 1998b. Annual report. Department of Health Services 2053/54 

(1996/97). Kathmandu, Nepal: Ministry of Health and HMG Nepal.

Ministry of Health [Nepal]. 2001. Annual report. Department of Health Services 2056/57 

(2000/01). Kathmandu, Nepal: Ministry of Health and HMG Nepal.

Ministry of Health [Nepal], New ERA, and ORC Macro. 2002. Nepal Demographic and 

Health Survey 2001. Calverton, Maryland, USA: Ministry of Health Family Health Division, New ERA, and ORC Macro.

Ministry of Health [Nepal] and United Nations Population Fund (UNFPA). 1995. 
40 References

Atlas of population distribution and health facilities with regional/district FP/MCH profiles. 

Kathmandu, Nepal: Ministry of Health, HMG Nepal, and UNFPA.

Morgan, S.P., and B.B. Niraula. 1996. Gender inequality and fertility in two Nepali 

villages. Population and Development Review 21:541–561.

Navaneetham, K., and A. Dharmalingam. 2002. Utilization of maternal health care 

services in Southern India. Social Science and Medicine 55:1849–1869.

Nepal South Asia Center (NSAC). 1998. Nepal human development report, 1998. Kathmandu, Nepal: NSAC.

NoorAli, R., S. Luby, and M.H. Rahbar. 1999. Does use of a government service depend on distance from the health facility? Health Policy and Planning 14(2):191–197.

Obermeyer, C.M., and J. Potter. 1991. Maternal health care utilization in Jordan: A 

study of patterns and determinants. Studies in Family Planning 22:177–187.

Paul, B.K. 1992. Health search behavior of parents in rural Bangladesh: An empirical 

study. Environment and Planning A 24, 963–973.

Paul, B.K., and D.J. Rumsey. 2002. Utilization of health facilities and trained birth 

attendants for childbirth in rural Bangladesh: An empirical study. Social Science and 

Medicine 54:1755–1765.

Pebley, A.R., N. Goldman, and G. Rodriguez. 1996. Prenatal and delivery care and 

childhood immunization in Guatemala: Do family and community matter? Demography 33(2):231–247. 

Presser, H.B. 1997. Demography, feminism, and science-policy nexus. Population and 

Development Review 23(2):295–331.

Raghupathy, S. 1996. Education and the use of maternal health care in Thailand. Social 

Science and Medicine 43:459–471.

Raudenbush, S.W. and A.S. Bryk. 2002. Hierarchical linear models: Applications and data 

analysis methods (2nd ed). Thousand Oaks, CA: Sage Publications.

Resources Center for Primary Health Care (RECPHEC). 1997. Health in Nepal: Realities and challenges. Kathmandu, Nepal: RECPHEC.

Riley, N.E. 1997. Gender, power, and population change. Population Bulletin 52(1):1-

48.

Rodriguez, G. 1978. Family planning availability and contraceptive practice. International Family Planning Perspective and Digest 4(4):100–115.

Sastry, N. 1996. Community characteristics, individual and household attributes, and 

child survival in Brazil. Demography 33(2):211–229.

Sastry, N. 1997. What explains rural-urban differentials in child mortality in Brazil? 

Social Science and Medicine 44:989–1002.

Sen, G. and S. Batliwala. 2000. Empowering women for reproductive rights. In Women’s 

empowerment and demographic processes: moving beyond Cairo, ed. H.B. Presser and G. 

Sen. New York: Oxford University Press, pp. 15–36.
References 41

Sen, G., A. Germain, and L.C. Chen, ed. 1994. Population policies reconsidered: Health, 

empowerment, and rights. Cambridge, MA: Harvard Center for Population and Development Studies.

Smith, H.L. 1989. Integrating theory and research on institutional determinants of 

fertility. Demography 26:171–184.

Stephenson, R., and A.O. Tsui. 2002. Contextual influences on reproductive health 

service use in Uttar Pradesh, India. Studies in Family Planning 33:309–320.

Sundari, T.K. 1992. The untold story: How the health care systems in developing countries contribute to maternal mortality. International Journal of Health Services 22:513–

528.

Timyan, J., S.J. Griffey-Brechin, D.M. Measham et al. 1993. Access to care: More than 

a problem of distance. In The health of women: A global perspective, ed. M. Koblinsky, J. 

Timyan, and J. Gay. Boulder, CO: Westview Press, pp. 217–234.

Tuladhar, J.M. 1987. Effects of family planning availability and accessibility on contraceptive use in Nepal. Studies in Family Planning 18(1):49–53.

United Nations. 1985. Women’s employment and fertility: A comparative analysis of world 

fertility results from 38 developing countries. New York, NY: United Nations Publications.

Walsh, J.A., C.N. Feifer, A.R. Measham et al. 1993. Maternal and prenatal health. In 

Disease Control Priorities in Developing Countries, ed. D.T. Jamison, W.H. Mosely, A.R. 

Measham et al. Oxford, UK: Oxford University Press, pp.363–390.

Watkins, S.C. 1993. If all we knew about women was what we read in demography, 

what would we know? Demography 30(4):551–577.

Weller, R.D., I.W. Eberstein, and M. Bailey. 1987. Pregnancy wantedness and maternal 

behavior during pregnancy. Demography 24:407–412.

Appendix A 43

Two other antenatal health outcome measures were considered for inclusion in this 

report: 

• Use of antenatal care during the first trimester, and 

• Receipt of at least two tetanus toxoid injections. 

Antenatal care during the first trimester: A dichotomous outcome classifying 

whether or not a pregnant women had received antenatal care during the first trimester 

was created, based on DHS variable M13$1 (TIMING OF FIRST ANTENATAL 

CHECKUP IN MONTHS). A dummy variable labeled ANTU4MTH was created, 

and a value of 1 was assigned when the month was coded as 1, 2 or 3. A value of 0 was 

assigned in all other instances. The sample size for analysis is 1,581. Almost one-third 

of all women who received antenatal care did so during the first trimester.

At least 2 tetanus toxoid injections: This dichotomous variable is based on data contained in the DHS variable M1$1 (NUMBER OF TETANUS TOXOID INJECTIONS BEFORE BIRTH). It was recoded as a series of three dummy variables to 

reflect the number of times tetanus toxoid (TT) injections were given, matching the 

breakdown used in the 2001 NDHS report (Ministry of Health et al., 2002): (a) where 

a women did not receive a TT injection was coded as 1, all others 0; (b) where a women 

received one injection was coded as 1, all other 0; and, (c) where a women received two 

or more TT injections was coded as 1, all others 0. The third dummy, labeled TT2, is 

used as the outcome measure. 

Two shots are recommended for a first pregnancy, one shot for each subsequent 

pregnancy, and five shots offers lifetime protection. While antenatal care visits are important in the provision of TT injections, such injections can be administered during 

other health visits. Thus not all women who had antenatal care received a TT injection, and not all women who received a TT injection had antenatal care. Therefore, the 

sample for analysis includes all respondents (N=3,279), not just women who received 

antenatal care. Forty-three percent of women had received at least two TT injections.

Table A.1 shows the distribution of women in the study sample for these two outcomes. 

Appendix A

Other Antenatal and Maternal 

Outcomes

Table A.1

Other antenatal and maternal outcomes

Percent distribution of currently married women who had a child 

in the three years preceding the NDHS 2001, by additional outcome 

measures

Outcome variable Percent Sample size

ANC visit during first trimester N=1,581

 Yes 32.3

 No 67.7

Received tetanus toxoid (TT) injections

at least twice N=3,279

 Yes 43.5

 No 56.5
44 Appendix A

Map A.1 shows whether the usage rate for antenatal care during the first trimester 

in each DHS cluster is at, above, or below the average for all clusters. It is based on data 

only from those women who received some antenatal care (48.3 percent of the sample). 

Usage rates are below average in clusters in the Far-Western Development Region, 

although many of the clusters within the Far-Western Terai are within one standard 

deviation of the mean. The pattern elsewhere is quite varied, but use of early antenatal 

care appears to be lowest in remote areas and among the highest in major urban areas.

Map A.2 compares each cluster’s tetanus toxoid immunization rate (defined as at 

least two injections) with the average for all clusters. The mean is reasonably high at 

69.7 percent, with a standard deviation of 30 percent. Thus clusters with a high immunization rate fall within one standard deviation of the mean and do not show up in 

a separate category on the map. The map does show clusters where the immunization 

rate is very low (that is, less than one standard deviation below the mean). Interestingly, 

all sub-regions contain at least one DHS cluster that falls into this category. A clearer 

regional pattern emerges from the map of aggregated data at the sub-regional level 

(Map A.3).

Tables A.2 and A.3 present logistic regression model results for first trimester antenatal care and TT injections, respectively. 

The contextual variables GDI and GEM are not significant in the models predicting antenatal care during the first trimester. It is worth noting that this model is calibrated for the reduced sample, i.e., only those women who reported using antenatal 

care during their pregnancy. The most significant covariates across the models are the 

sub-regional dummy variables for the Central Hills (Kathmandu) and Western Hills 

(Pokhara), where women are about 3 times more likely than those in the Far-Western 

Hills to seek antenatal care during the first trimester. While this emphasizes the domiMap A.1

Usage rate for antenatal care during the first trimester by DHS cluster,

relative to the average for all clusters

Source: NDHS 2001

50 0 50 100 Miles

< –1 standard deviation

Within 1 standard deviation of mean

> 1 standard deviation
Appendix A 45

Map A.2

Tetanus toxoid immunization rate (at least two injections) by DHS cluster,

relative to the average for all clusters

Source: NDHS 2001

50 0 50 100 Miles

Within 1 standard deviation of mean

> 1 standard deviation

Map A.3

Number of tetanus toxoid injections women received, by sub-region

Source: NDHS 2001, Table 9.4, p. 146. 50 0 50 100 Miles

Note: Pie size is weighted by number of women.

1

None

2+
46 Appendix A

Table A.2

Logistic regression: Predictors of antenatal care during the first trimester, using sub-region (N=1,586)

Background characteristic

Model 1 Model 2 Model 3a (GDI) Model 3b (GEM)

B Exp(B) B Exp(B) B Exp(B) B Exp(B)

Sub-region

Eastern Mountain 0.679 1.972 0.507 1.661 0.36 1.433 0.485 1.624

Central Mountain 0.946* 2.575 1.098* 2.998 1.033* 2.809 1.07* 2.915

Western Mountain 0.643 1.902 0.809 2.246 0.829 2.291 0.805 2.237

Eastern Hills 0.515 1.674 0.586 1.797 0.45 1.569 0.559 1.748

Central Hills 1.361*** 3.899 1.116*** 3.053 1.008* 2.739 1.083* 2.953

Western Hills 1.328*** 3.773 1.101*** 3.007 0.968* 2.632 1.066* 2.903

Mid-Western Hills 0.205 1.227 0.314 1.369 0.25 1.284 0.305 1.356

Eastern Terai 1.027** 2.793 0.764 2.146 0.597 1.817 0.741 2.098

Central Terai 0.459 1.582 0.457 1.58 0.359 1.433 0.443 1.558

Western Terai 0.991* 2.695 0.915* 2.497 0.808 2.243 0.888 2.43

Mid-Western Terai 1.033** 2.81 0.879* 2.409 0.805 2.236 0.856 2.355

Far-Western Terai 0.241 1.273 0.251 1.285 0.182 1.2 0.236 1.266

Urban 0.445** 1.561 -0.026 0.974 -0.054 0.948 -0.031 0.969

Distance to hospital -0.005 0.995 0.006 1.006 0.006 1.006 0.006 1.006

Age 

20–34 0.433* 1.542 0.433* 1.542 0.433 1.542

35+ 0.63 1.877 0.627 1.872 0.63 1.878

Parity

2–3 -0.054 0.947 -0.056 0.946 -0.054 0.947

4–5 -0.243 0.784 -0.243 0.784 -0.242 0.785

6+ -0.487 0.614 -0.49 0.612 -0.488 0.614

Want child

Then -0.176 0.838 -0.178 0.837 -0.177 0.838

Later -0.027 0.973 -0.027 0.973 -0.027 0.973

Refuse sex 0.029 1.029 0.041 1.042 0.032 1.032

Wife beating -0.136 0.873 -0.133 0.876 -0.134 0.875

Decisionmaking

1–2 0.177 1.194 0.18 1.197 0.177 1.193

3–4 0.028 1.028 0.03 1.03 0.028 1.029

Problems getting services

1–2 -0.302 0.739 -0.31 0.734 -0.304 0.738

3–5 -0.1 0.905 -0.111 0.895 -0.103 0.902

6–7 -0.097 0.908 -0.099 0.905 -0.098 0.907

Owns land 0.135 1.145 0.137 1.147 0.136 1.145

Relation to household head

Wife -0.298 0.742 -0.297 0.743 -0.298 0.742

Daughter-in-law -0.177 0.838 -0.173 0.841 -0.176 0.838

Other -0.07 0.932 -0.07 0.933 -0.069 0.934

Women’s education

Primary -0.149 0.862 -0.152 0.859 -0.15 0.861

Secondary 0.34 1.405 0.335 1.398 0.34 1.404

Partner’s education

Primary -0.255 0.775 -0.25 0.779 -0.254 0.775

Secondary and higher 0.351* 1.42 0.358* 1.431 0.353* 1.423

Don’t know 0.468 1.596 0.476 1.61 0.471 1.602

Listens to radio 0.123 1.13 0.129 1.137 0.124 1.132

Watches TV -0.028 0.972 -0.044 0.957 -0.03 0.971

Women’s employment

Agricultural or self-employed -0.261 0.771 -0.257 0.773 -0.26 0.771

Non-agricultural -0.326 0.722 -0.338 0.713 -0.328 0.72

Partner’s employment

Non-agricultural 0.281* 1.325 0.279* 1.322 0.281* 1.325

Unknown 0.304 1.356 0.293 1.34 0.302 1.353

Hindu -0.192 0.825 -0.188 0.829 -0.193 0.825

Ethnicity

Chhetri/Thakuri/Rajput 0.16 1.173 0.163 1.177 0.162 1.176

Newar 0.425 1.53 0.428 1.533 0.426 1.53

Gurung/Magar 0.278 1.32 0.284 1.328 0.281 1.324

Tamang/Sherpa 0.212 1.237 0.234 1.264 0.219 1.245

Rai/Limbu -0.01 0.99 -0.018 0.983 -0.009 0.991

Muslim/Churaute 0.256 1.292 0.265 1.304 0.256 1.292

Tharu/Rajbanshi 0.41 1.507 0.406 1.501 0.41 1.507

Yadav/Ahir -0.426 0.653 -0.414 0.661 -0.42 0.657

Occupational 0.26 1.297 0.268 1.307 0.265 1.303

Other Hills/Terai 0.392 1.48 0.399 1.491 0.396 1.486

Utilities

1 -0.086 0.917 -0.086 0.918 -0.088 0.915

2+ 0.302 1.352 0.292 1.34 0.297 1.346

GDI 0.01 1.01

GEM 0.003 1.003

Constant -1.595*** 0.203 -1.644* 0.193 -1.837* 0.159 -1.664* 0.189

-2 log likelihood 1920.2 1822.28 1821.82 1822.25

Nagelkerke R2 0.059 0.14 0.14 0.14

% correct prediction 68.9 71.3 71.1 71.3

Model chi-square(df) 68.76(14)*** 166.68(56)** 167.15(57)*** 166.71(57)***

Block chi-square(df) 68.76(14)*** 97.93(42)*** 0.46(1) .03(1)

Significance at p < 0.001 ***, p <0.01 **, and p <0.05 *
Appendix A 47

Table A.3

Logistic regression: Predictors of at least two tetanus toxoid injections, using sub-region (N=3,279)

Background characteristic

Model 1 Model 2 Model 3a (GDI) Model 3b (GEM)

B Exp(B) B Exp(B) B Exp(B) B Exp(B)

Sub-region

Eastern Mountain 0.845*** 2.327 0.969** 2.636 1.316** 3.727 1.5100** 4.527

Central Mountain 0.974*** 2.648 0.627 1.873 0.774 2.169 1.3320** 3.789

Western Mountain -0.027 0.974 0.141 1.151 0.064 1.066 0.2146 1.239

Eastern Hills 1.380*** 3.977 1.626*** 5.084 1.947*** 7.006 2.3110*** 10.084

Central Hills 1.260*** 3.526 1.260*** 3.526 1.445*** 4.243 1.9784*** 7.231

Western Hills 1.297*** 3.658 0.342 1.408 0.655 1.925 1.3046*** 3.686

Mid-Western Hills 0.777*** 2.176 1.103*** 3.014 1.247*** 3.482 1.3509*** 3.861

Eastern Terai 2.352*** 10.503 2.050*** 7.769 2.464*** 11.756 2.5962*** 13.412

Central Terai 1.956*** 7.073 1.495*** 4.460 1.743*** 5.716 1.8731*** 6.508

Western Terai 1.734*** 5.665 1.303*** 3.680 1.576*** 4.837 2.0654*** 7.889

Mid-Western Terai 2.265*** 9.628 1.688*** 5.408 1.873*** 6.509 2.3169*** 10.145

Far-Western Terai 2.077*** 7.980 2.016*** 7.510 2.180*** 8.848 2.4063*** 11.093

Urban 0.564*** 1.757 -0.089 0.915 0.014 1.014 0.1263 1.135

Distance to hospital -0.051*** 0.950 -0.029*** 0.972 -0.028** 0.972 -0.0260** 0.974

Age 

20–34 0.344 1.411 0.342 1.407 0.3536 1.424

35+ -0.062 0.940 -0.061 0.941 -0.0409 0.960

Parity

2–3 -0.098 0.907 -0.097 0.908 -0.1078 0.898

4–5 -0.407** 0.665 -0.407* 0.666 -0.4214* 0.656

6+ -1.016*** 0.362 -1.007*** 0.365 -1.0073*** 0.365

Want child

Then -0.148 0.862 -0.146 0.864 -0.1380 0.871

Later -0.020 0.980 -0.020 0.980 -0.0184 0.982

Antenatal care 3.128*** 22.818 3.157*** 23.504 3.2659*** 26.203

Refuse sex -0.394 0.674 -0.418* 0.658 -0.4477** 0.639

Wife beating 0.053 1.055 0.051 1.053 0.0208 1.021

Decisionmaking

1–2 0.123 1.130 0.118 1.125 0.1411 1.152

3–4 0.067 1.069 0.068 1.071 0.0610 1.063

Problems getting services

1–2 -0.380* 0.684 -0.371* 0.690 -0.3519 0.703

3–5 -0.379 0.685 -0.359 0.698 -0.3102 0.733

6–7 -0.209 0.812 -0.198 0.820 -0.2037 0.816

Owns land 0.209 1.233 0.207 1.230 0.1570 1.170

Relation to household head

Wife -0.659* 0.517 -0.653** 0.520 -0.6528** 0.521

Daughter-in-law -0.585 0.557 -0.585* 0.557 -0.6013* 0.548

Other -0.449 0.639 -0.433 0.649 -0.4404 0.644

Women’s education

Primary -0.108 0.898 -0.097 0.907 -0.0755 0.927

Secondary 0.075 1.077 0.091 1.095 0.1030 1.109

Partner’s education

Primary -0.079 0.924 -0.076 0.927 -0.0812 0.922

Secondary and higher 0.533*** 1.704 0.526*** 1.693 0.4784*** 1.614

Don’t know 0.070 1.072 0.062 1.063 -0.0129 0.987

Listens to radio 0.230* 1.258 0.220 1.246 0.2054 1.228

Watches TV -0.018 0.982 0.004 1.004 -0.0007 0.999

Women’s employment

Agricultural/self employed 0.226 1.254 0.225 1.252 0.2016 1.223

Non-agricultural 0.226 1.254 0.275 1.316 0.3331 1.395

Partner’s employment

Non-agricultural -0.137 0.872 -0.136 0.873 -0.1544 0.857

Unknown 0.253 1.287 0.261 1.298 0.2914 1.338

Hindu 0.275 1.317 0.271 1.312 0.2826 1.327

Ethnicity

Chhetri/Thakuri/Rajput -0.234 0.791 -0.240 0.787 -0.2983 0.742

Newar 0.365 1.441 0.373 1.452 0.4041 1.498

Gurung/Magar -0.503* 0.605 -0.514* 0.598 -0.5610* 0.571

Tamang/Sherpa -0.397 0.673 -0.407 0.666 -0.4270 0.652

Rai/Limbu -0.950*** 0.387 -0.914** 0.401 -0.8939** 0.409

Muslim/Churaute 1.107** 3.025 1.109** 3.031 1.0857* 2.962

Tharu/Rajbanshi -0.478 0.620 -0.463 0.629 -0.4436 0.642

Yadav/Ahir 0.535 1.707 0.513 1.670 0.3483 1.417

Occupational 0.449 1.567 0.451* 1.570 0.3534 1.424

Other Hills/Terai 0.924*** 2.520 0.923*** 2.517 0.7853** 2.193

Utilities

1 -0.309* 0.734 -0.303* 0.738 -0.2384 0.788

2+ -0.598** 0.550 -0.549* 0.577 -0.4144 0.661

GDI -0.026 0.975

GEM -0.0902*** 0.914

Constant -1.2428*** 0.289 -2.209*** 0.110 -1.782** 0.168 -1.727** 0.178

-2 log likelihood 4089.95 2596.27 2683.19 2657.81

Nagelkerke R2 0.164 0.572 0.573 0.579

% correct prediction 67.30 82.10 82.70 82.00

Model chi-square(df) 427.91(14)*** 1894.19(57)*** 1834.67(58)*** 1860.05(58)***

Block chi-square(df) 427.91(14)*** 1393.39(43)*** 3.38(1) 28.76(1)***

Significance at p < 0.001 ***, p <0.01 **, and p <0.05 *
48 Appendix A

nance of the two major cities, note that the urban variable is not significant in Models 

2, 3a, and 3b. Other covariates of significance in Models 2 and 3a include an age effect: 

women aged 20–34 are more likely to seek antenatal care visit during the first trimester 

than women under age 20. It is possible that women were seeking confirmation of the 

pregnancy during this first visit. While women’s status and empowerment variables 

are not significant, having a partner with at least a secondary level of education (odds 

ratio = 1.4) and a partner who is employed in non-agricultural work (odds ratio = 1.3) 

are both significant.

In the models predicting TT injections, the sub-regions are highly significant for the 

most part. Women in various sub-regions are 2 to 10 times more likely than women 

in the Far Western Hills to have had two TT injections, with the exception of women 

in the Western Mountain sub-region. These geographic relationships hold across all 

models, with odds ratios generally lower in Model 2 but significantly higher in Models 

3a and 3b. Women who received antenatal care were over 20 as likely to have received 

two TT injections as women who did not receive antenatal care. Other significant predictors in Model 2 include parity, problems getting health care, being the wife of the 

household head, partner’s education, listening to the radio, and living in a household 

with modern utilities. Women of some ethnic groups were significantly less likely to 

receive two TT injections than Brahmin women (e.g., Gurung/Magar and Rai/Limbu), 

but others were more likely (e.g., Muslim and Hill/Terai groups). Adding GDI and 

GEM modify the results of Model 2 on the margin: listening to the radio is no longer 

significant but refusal of sex becomes significant. Model 3b is the better of the two 

contextual models. GDI is not significant in Model 3a. 
Appendix B 49

To construct the sample, all currently married women (V501 = 1) who had given birth 

during the past three years (V238 = >0) were selected. The analysis was limited to those 

women listed as a “usual resident” (V135 = 1). This generated an initial sample of 3,285 

currently married women who had given birth within the past three years and who were 

usual residents of the community in which they were interviewed.

Variables relevant to the analysis of the primary outcome measures of interest were 

extracted. The listing below begins with these outcome measures, followed by individual, household, and community variables.22 Items used in the analysis are highlighted 

in BOLD.

B.1 Outcome Measures

ANTENATAL 

A dichotomous outcome measure was constructed based on whether or not the woman 

had received any antenatal care during her last pregnancy. This measure was based on a 

recode of variable M2N (antenatal no one), in which a woman who received antenatal 

care during her last pregnancy was coded as 1 (N=1,586) and a woman who did not 

receive care was coded as 0 (N=1,697). Analysis is based on N=2,383.

ANTNUM4

A dichotomous outcome measure was constructed based on whether or not the woman 

had received antenatal care at least four times during her last pregnancy. This measure 

was based on a recode of variable M14 (number of antenatal visits for pregnancy). All 

values 4 or higher were recoded as 1 (N=468), and all other valid codes were recoded as 

0 (N=2,813). Note that 1,697 women reported no antenatal care visits (M14=0). These 

women are not included in logistic regression and HGLM models for this outcome 

measure. Instead the analyses is limited to women who received some antenatal care 

(N=1,586). Analysis is based on N=1,581.

B.2 Predictors

B.2.1 Geographic variables

ZONES

The analytical models used sub-regions as the primary demarcation of place within 

Nepal. In total there are thirteen sub-regions: Eastern Mountain, Central Mountain, 

Western Mountain, Eastern Hills, Central Hills, Western Hills, Mid-Western Hills, 

Far-Western Hills, Eastern Terai, Central Terai, Western Terai, Mid-Western Terai, 

and Far-Western Terai. The variable SREF3 (sub-region) was used to create dummy 

Appendix B

NDHS Variables in the Analysis

22 Other variables used in data manipulation and exploratory analysis are not listed.
50 Appendix B

variables for these regions, which were labeled respectively as ZONEEM, ZONECM, 

ZONEWM, ZONEEH, ZONECH, ZONEWH, ZONEMWH, ZONEFWH, 

ZONEET, ZONECT, ZONEWT, ZONEMWT, ZONEFWT. Far Western Hills 

(ZONEFHW) is used as the reference category.

DEVELOPMENT REGIONS

The variable V024 (region) was used to create regional dummies for the five Development Regions: EASTERN, CENTRAL, WESTERN, MIDWEST, FARWEST. The 

reference category is the Far-Western Development Region. 

ECOLOGICAL REGIONS

The variable SREG1 (ecological region) was used to create dummy variables for the 

three main ecological regions of Nepal: MOUNTAIN, HILL, TERAI. Mountain is 

used as the reference category.

URBAN 

The variable V025 (urban place of residence) was recoded, with rural as 0 and urban as 

1. 

CLD2HOSP

Nepal DHS cluster geocodes were used together with locational data on hospitals 

throughout Nepal (derived from the Ministry of Health’s annual reports) to calculate 

distances between a cluster and the nearest main hospital. Given the data available, 

these are straight-line distance measured in miles—an assumption that may be more 

tenable in the Terai than other areas of Nepal. Distances ranged from 0 to 29 miles. 

B.2.2 Individual Characteristics

RESPONDENT’S AGE

The variable V013 (age in 5-year groups) was recoded into three dummy variables to 

be consistent with the 2001 NDHS report: (a) under 20 years equals 1, all others are 0 

(AGEU20); (b) 20–34 years equals 1, all others are 0 (AGE2034); and (c) 35 years or 

more equals 1, all others are 0 (AGE35P). AGEU20 is used as the reference category.

PARITY

The variable V201 (total number of children ever born) was recoded as four dummy 

variables: (a) parity equals 1 if a woman had one child ever born, all others are 0 (PARITY1); (b) parity equals 2 or 3 if a woman had two or three children ever born, all 

others are 0 (PARITY23); (c) parity equals 4 or 5 if a woman had four or five children 

ever born, all others are 0 (PARITY45), and, (d) parity equals 6 or more if a woman 

had six or more children ever born, all others are 0 (PARITY6). PARITY1 is used as 

the reference category.

WANTED LAST CHILD

V367 provides information on whether or not the woman wanted the last child. This 

variable was recoded into three dummy variables: (a) wanted then equals 1, all others 

are 0 (WANTTHEN); (b) wanted later equals 1, all others are 0 (WANTLATE); and 

(c) wanted no more equals 1, all others are 0 (NOTWANT). NOTWANT is used as 

the reference category.

B.2.3 Control and empowerment variables

REFUSE SEX

Four variables include data on whether a woman would refuse sex with her hus-
Appendix 

B 51

band: V633A (if the husband has an STD); V633B (if a husband has other women); 

V633C (if the woman had a recent birth); and V633D (if a women was tired or not 

in the mood). In all instances “don’t know” was recoded as 0 to create dummy vari-

ables in which “yes” equals 1 and all others are 0. The dummy variables were labeled as 

REFSTD, REFWOMEN, REFBIRTH, and REFTIRED, respectively. Next a new 

variable (REFUSE) was created; it is equivalent to the sum of V633A(REFSTD) + 

V633B(REFWOMEN) + V633C(REFBIRTH) + V633D(REFTIRED) and ranges 

from 0 to 4. Finally, a single dummy variable was created from REFUSE: women who 

would refuse to have sex for all four reasons are coded as 1, all others are 0. This variable 

was labeled REFUSE4.

WIFE BEATING

Five questions (V744A through V744E) asked about whether and under what circum

-

stances a woman thought wife beating was justified. They address going out without 

telling the husband, neglecting children, arguing with the husband, refusing sex with 

the husband, and burning food. Dummy variables were recreated for these five vari

-

ables, where “yes” equals 1 and all others (including “don’t know”) are 0. They were 

labeled EMPGOOUT, EMPNEGCH, EMPARGUE, EMPREFSX, and EMP

-

BURNF. Next was the creation of a new variable (BEATING) that is the sum of 

V744A(EMPGOOUT) + V744B(EMPNEGCH) + V744C(EMPARGUE) + 

V744D(EMPREFSX) + V744E(EMPBURNF); it ranges from 0 to 5. Finally, a single 

dummy variable based on BEATING was created: women who said no reason justified 

wife beating are coded as 1, all others (that is, women who felt at least one of the five 

reasons justified wife beating) are 0. This variable was labeled BEAT.

DECISIONS

Five questions (V743A-V743E) asked about decisionmaking. For each question, wom

-

en were coded as 1 if they were the sole decisionmaker or made the decision jointly 

with a partner or another person (values 1–3); all others (who did not contribute to 

the decision) were coded as 0. The five questions were: final say on health care (V743A, 

EMPHEALTH), final say on large household purchases (V743B, EMPPURCH), fi

-

nal say on household daily needs (V743C, EMPNEEDS), final say on visits to family 

or relatives (V743D, EMPVISIT), and final say on food to be cooked (V743E, EMP-

FOOD).

Because women tend to make all decisions on food preparation, a variable DECISION2

was created that that is the sum of V743A(EMPHEALTH), V743B(EMPPURCH), 

V743C(EMPNEEDS), and V743D(EMPVISIT). Its value ranges from 0 to 4. From 

this variable, three dummy variables were created: (a) the woman makes no decisions 

equals 1, all others are 0 (DECIS0); (b) the woman makes 1-2 decisions equals 1, all 

others are 0 (DECIS12); and (c) the woman makes 3-4 decisions equals 1, all others 

are 0 (DECIS34). DECIS0 is used as the reference category.

PROBLEMS

Seven questions (V467A through V467G) asked about the problems women face when 

seeking health care: For each question, dummy variables were created in which “a big 

problem” was coded as 1, and all others as 0. The variables created were (a) know where 

to go (MEDHLP1), (b) getting permission to go (MEDHLP2), (c) getting money 

for treatment (MEDHLP3), (d) the distance to the health facility (MEDHLP4), (e) 

having to take transportation (MEDHLP5), (f ) not willing to go alone (MEDHLP6), 

and, (g) concern that there will be no female provider (MEDHLP7). Then a new vari

-

able (NPROBS1) equal to the number of problems was created (V467A + V467B 

+ ... + V467G); it ranges in value from 0 to 7. Finally, NPROBS1 was recoded into 

four dummy variables: (a) zero problems equals 1, all others equal 0 (NPROBS0); (b) 
52 Appendix B

1–2 problems equals 1, all others equal 0 (NPROBS12); (c) 3–5 problems equals 1, 

all others equal 0 (NPROBS35), and, (d) 6–7 problems equals 1, all others equal 0 

(NPROBS67). NPROBS0 is used as the reference category.

OWN LAND

Variable S618 signifies whether or not a woman owns land. If a women owned land 

alone or jointly, it was recoded as 1, all other responses were coded as 0. The variable 

name assigned was OWNLAND.

RELATION TO HEAD OF HOUSEHOLD

Variable V150 (relationship to household head) was used to create a series of four 

dummy variables: (a) head equals 1, all others 0 (HEAD); (b) wife equals 1, all others 0 

(WIFE); (c) daughter-in-law equals 1, all others 0 (DINLAW); and (d) other relationships equal 1, all others 0 (OTHERHH). HEAD is used as the reference category.

B.2.4 Other Individual and Household Characteristics

WOMEN’S EDUCATION

Variable V106 (highest educational level for respondent) was used to create three dummy variables: (a) no education equals 1, all others 0 (RESNOED); (b) primary education equals 1, all others 0 (RESRIED); and (c) secondary education and higher equals 

1, all others 0 (RESSECED). RESNOED is used as the reference category.

PARTNERS EDUCATION

Variable V701 (educational level of respondent’s partner) was recoded to create three 

dummy variables: (a) no education equals 1, all others 0 (PARTNOED); (b) primary 

education equals 1, all others 0 (PARTPRIE); and (c) secondary education and higher 

equals 1, all others 0 (PARTSECE).PARTNOED is used as the reference category.

LISTENS TO RADIO

Variable S114 (listens to radio everyday) was relabeled as RADIO_D. Values of 1 indicate that the woman listens to the radio daily; other responses were coded as 0.

WATCHES TV

Variable S115 (watches TV at least once a week) was relabeled as TV_D. Values of 1 

indicate that the woman watches TV at least once per week; other reponses were coded 

as 0.

WOMEN’S WORK STATUS

Variable V717 (respondent’s occupation) was recoded to create three dummy variables: 

(a) not working equals 1, all others 0 (NOTWORK); (b) working in agriculture and/or 

self employed equals 1, all others 0 (AGSELFEM); and (c) working as professional, 

technical, manager, clerical and sales, services, skilled and unskilled manual equals 1, all 

others 0 (NONAGWK). NOTWORK is used as the reference category.

PARTNER’S WORK STATUS

Variable V705 (occupation of respondent’s partner) was recoded to create three dummy variables: (a) working in agriculture and/or self employed equals 1, all others 0 

(PARAGRIC); (b) working as professional, technical, manager, clerical and sales, services, skilled and unskilled manual equals 1, all others 0 (PARNONAG); and (c) missing and unknown equals 1, all others 0 (PARUNKN). PARAGRIC is used as the 

reference category.
Appendix 

B 53

RELIGION

The variable V130 reports on religious groups. A dummy variable was created to indi-

cate whether or not the respondent was Hindu. V130 was recoded so that Hindu equals 

1, all others 0 (HINDU).

ETHNICITY

V131 reports on numerous ethnic groups with

-

in Nepal (see Table B.1). Following the 2001 

NDHS report, V131 was recoded to create 

thirteen ethnic groupings (ETHNIC). Gu

-

rung and Magar were combined, as were other 

Hill and other Terai groups, to create a variable 

labeled ETHNIC2. From ETHNIC2 eleven 

ethnic group dummies were created, where a 

value of 1 equals a specific ethnic group and all 

others are coded as 0. This generated variables 

for Brahmin (BRAHMIN), Chhetri, Thakuri, 

and Rajput (CHTARAJ), Newar (NEWAR), 

Gurung and Magar (GURMAG), Tamang 

and Sherpa (TAMSHER), Rai and Limbu 

(RAILIMBU), Muslim and Churaute (MUS-

LIM), Tharu and Rajbanshi (THARRAJB), 

Yadav and Ahir (YADAHIR), occupational 

groups (OCCUP), and Hill and Terai origin 

groups (HILTERAI). BRAHMIN is used as 

the reference category.

UTILITIES

Several questions were asked about house

-

hold utilities and resources, including V113 

(sources of drinking water), V116 (type of 

toilet facility), V119 (has electricity), V127 

(type of material for floor), and V161 (type 

of cooking fuel used). Then (a) V113 (sourc-

es of drinking water) was recoded so that 

“piped into house/yard” equals 1, all others 0 

(H20HOUSE); (b) V116 (type of toilet facil-

ity) was recoded so that “toilet/latrines” equals 

1, no facility equals 0 (TOILET2); (c) V161 

(type of cooking fuel) was recoded so that 

LPG/natural gas, biogas, and kerosene equal 

1, all others 0 (FUELMAT); and (d) V119 

was relabeled ELECTRIC. A basic utilities 

variable, UTILITY, was created; it is equal to 

the sum of TOILET2, H20HOUSE, FUEL-

MAT, and ELECTRIC. UTILITY was then 

recoded into three dummies: (a) no utilities 

equals 1, all others 0 (UTILZERO); (b) one 

utility equals 1, all others 0 (UTILONE); and (c) two or more utilities equals 1, all oth-

ers 0 (UTIL2P). UTILZERO is used as the reference category.

Table B.1

Ethnicity in Nepal

V131 Code for Ethnicity

(ETHNIC2)

DHS Code

(ETHNIC2)

1 Yadav Ahir 10

2 Kayastha3

3 Kumhar 11

4 Baniya 13

5 Dhobi 11

6 Sundhi Kalwar 11

7 Kurmi 11

8 Brahman1

9 Rajput2

10 Tharu9

11 Teli 13

12 Kushwaha 13

13 Musalman8

14 Haluwai 11

15 Malaha 13

16 Rajbanshi9

17 Dhimal 11

19 Marwadi 13

20 Bangali 13

21 Dhanuk 11

23 Dushad 11

24 Chamar 11

25 Khatwe 11

26 Bhumihar 11

27 Kewat 11

29 Kanu 11

30 Tarai others 13

31 Brahman1

32 Chhetri2

33 Thakuri2

34 Sanyashi2

35 Newar3

36 Limbu7

37 Rai7

38 Gurung4

39 Thakali 12

40 Tamang6

41 Magar5

42 Danuwar 11

44 Majhi 11

45 Sunuwar 11

46 Gaine 11

47 Chepang 11

48 Kumhal 11

55 Thami 11

56 Damai 11

57 Kami 11

58 Sharki 11

59 Badi 11

60 Pahadi others 12

61 Sherpa6

62 Mugrali/Humli/Kar bhote6

96 Others 12

Appendix C 55

Appendix C

Logistic Model Results for Antenatal 

Care using Development Region Dummy 

Variables
56 Appendix C

Table C.1

Logistic regression: Predictors of any use of antenatal care, using Development Region (N=3,283)

Background characteristic

Model 1 Model 2 Model 3a (GDI) Model 3b (GEM)

B Exp(B) B Exp(B) B Exp(B) B Exp(B)

Development Region

Eastern 0.72*** 2.054 0.521*** 1.684 -0.831*** 0.436 -0.259 0.772

Central 0.62*** 1.859 0.627*** 1.872 0.057 1.058 0.036 1.037

Western 0.926*** 2.524 0.561*** 1.752 -0.326 0.722 -0.715*** 0.489

Mid-Western 0.268* 1.307 0.081 1.085 -0.2 0.819 -0.365 0.694

Urban 1.365*** 3.916 0.368 1.444 0.048 1.05 0.002 1.002

Distance to hospital -0.049*** 0.952 -0.03*** 0.971 -0.033*** 0.967 -0.033*** 0.967

Age 

20–34 0.1 1.105 0.09 1.094 0.138 1.148

35+ -0.172 0.842 -0.181 0.834 -0.159 0.853

Parity -0.213 0.808 -0.202 0.817 -0.174 0.84

2–3

4–5 -0.568*** 0.566 -0.549*** 0.578 -0.513*** 0.599

6+ -0.651*** 0.522 -0.695*** 0.499 -0.603** 0.547

Want child 0.091 1.095 0.123 1.131 0.114 1.121

Then

Later 0.231 1.26 0.235 1.265 0.212 1.237

Refuse sex 0.194 1.214 0.229 1.258 0.279 1.321

Wife beating -0.187* 0.83 -0.174 0.84 -0.172 0.842

Decisionmaking

1–2 0.181 1.198 0.182 1.199 0.132 1.142

3–4 0.335** 1.398 0.299* 1.349 0.353** 1.424

Problems getting services

1–2 -0.109 0.897 -0.118 0.889 -0.174 0.841

3–5 -0.301 0.74 -0.308 0.735 -0.412* 0.662

6–7 -0.725*** 0.484 -0.619*** 0.538 -0.734*** 0.48

Owns land 0.054 1.055 0.051 1.052 0.113 1.12

Relation to household head

Wife 0.288 1.334 0.33 1.391 0.212 1.236

Daughter-in-law 0.329 1.389 0.366 1.442 0.325 1.384

Other 0.386 1.471 0.386 1.471 0.367 1.443

Women’s education

Primary 0.597*** 1.816 0.501*** 1.65 0.467*** 1.596

Secondary 1.138*** 3.119 1.001*** 2.722 1.105*** 3.019

Partner’s education

Primary 0.061 1.063 0.025 1.026 0.017 1.017

Secondary and higher 0.219 1.245 0.228* 1.256 0.318** 1.375

Don’t know 0.424 1.528 0.395 1.484 0.69* 1.995

Listens to radio 0.44*** 1.552 0.465*** 1.592 0.425*** 1.529

Watches TV 0.535*** 1.707 0.405** 1.499 0.555*** 1.742

Women’s employment

Agricultural/self–employed -0.424** 0.654 -0.321* 0.725 -0.427** 0.653

Non-agricultural -0.442 0.642 -0.511* 0.6 -0.535* 0.586

Partner’s employment

Non-agricultural 0.089 1.093 0.101 1.107 0.074 1.076

Unknown 0.096 1.101 0.091 1.095 -0.001 0.999

Hindu -0.444 0.641 -0.462 0.63 -0.352 0.703

Ethnicity

Chhetri/Thakuri/Rajput -0.327 0.721 -0.357 0.7 -0.286 0.751

Newar -0.446 0.64 -0.57 0.565 -0.616* 0.54

Gurung/Magar -1.12*** 0.326 -1.313*** 0.269 -1.229*** 0.293

Tamang/Sherpa -1.23*** 0.292 -1.328*** 0.265 -1.518*** 0.219

Rai/Limbu -1.015*** 0.363 -1.222*** 0.295 -1.258*** 0.284

Muslim/Churaute -0.403 0.668 -0.802* 0.448 -0.462 0.63

Tharu/Rajbanshi -0.091 0.913 -0.605** 0.546 -0.515* 0.598

Yadav/Ahir -0.517 0.596 -0.74* 0.477 -0.2 0.819

Occupational -0.342 0.71 -0.615*** 0.541 -0.356 0.7

Other Hills/Terai -0.239 0.788 -0.579* 0.56 -0.014 0.986

Utilities

1 0.193 1.213 0.135 1.144 -0.025 0.975

2+ 0.404* 1.498 0.265 1.303 0.008 1.008

GDI 0.113*** 1.12

GEM 0.178*** 1.195

Constant -0.163 0.85 0.393 0.717 -1.785*** 0.168 -0.932 0.394

-2 log likelihood 4256.48 3656.67 3554.09 3494.47

Nagelkerke R2 0.112 0.316 0.348 0.365

% correct prediction 58.7 71.1 72.3 73.7

Model Chi-square(df) 288.19(6)*** 886.00(48)*** 990.58(49)*** 1050.20(49)***

Block Chi-square(df) 288.19(6)*** 597.81(42)*** 104.579(1)*** 164.20(1)***

Significance at p < 0.001 ***, p <0.01 **, and p <0.05 *
Appendix D 57

Appendix D

HGLM Predictors of Four or 

More Antenatal Visits (Model 4)
58 Appendix 

D

Table D.1

HGLM: Predictors of four or more antenatal 

visits, Model 4, using average number of 

antenatal care visits in district (N= 1,586)

Background characteristic Coeff Exp (B)

Sub-region

Eastern Mountain -0.804 0.447

Central Mountain -1.072 0.342

Western Mountain -0.611 0.543

Eastern Hills -0.505 0.603

Central Hills -0.795 0.451

Western Hills -0.205 0.815

Mid-Western Hills -0.984 0.374

Eastern Terai -0.244 0.783

Central Terai 0.042 1.043

Western Terai 0.152 1.164

Mid-Western Terai -0.391 0.676

Far-Western Terai 0.015 1.015

Urban 0.132 1.141

Distance to hospital 0.003 1.003

Age

20–34 0.489* 1.631

35+ 0.598 1.818

Parity

2–3 -0.436** 0.647

4–5 -0.674** 0.509

6+ -0.711 0.491

Want child

Then 0.156 1.168

Later 0.434 1.543

Refuse sex 0.671** 1.957

Wife beating 0.124 1.132

Decisionmaking

1–2 0.148 1.16

3–4 0.356 1.427

Problems getting services

1–2 -0.352 0.703

3–5 -0.387 0.679

6–7 -0.354 0.702

Owns land -0.011 0.989

Relation to household head

Wife 0.479 1.614

Daughter-in-law 0.702* 2.018

Other relation 0.63 1.878

Women’s education

Primary -0.003 0.997

Secondary and higher 0.299 1.349

Partner’s education

Primary -0.137 0.872

Secondary and higher 0.394 1.483

Don’t know 0.801 2.227

Listens to radio

Watches TV 0.008 1.008

Women’s employment 0.254 1.29

Agricultural/self-employed -0.371 0.69

Non-agricultural -0.498 0.608

Partner’s employment

Non-agricultural 0.257 1.294

Unknown -0.15 0.861

Hindu -0.488 0.614

Ethnicity

Chettri/Thakuri/Rajput 0.548* 1.729

Newar 0.255 1.29

Gurung/Magar -0.322 0.724

Tamang/Sherpa -0.38 0.684

Rai/Limbu -0.659 0.517

Muslim -1.065* 0.345

Tharu/Rajbansi -0.592 0.553

Yadav/Ahir -0.177 0.838

Occupational -0.067 0.935

Other Hills/Terai -0.851** 0.427

Utilities 1 0.402* 1.495

2+ 0.537* 1.711

GDI1

GEM 0.076* 1.079

Average # of ANC visits 0.702* 2.018

Intercept -4.24*** 0.014

Variance comp Null Model Model 4

Tau (Level 2 

variance) 0.72 0.213 R2

 between 0.704

Significance at p

< 0.001 ***, p< 0.01 **, and p< 0.05*