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MICROECONOMETRIC ANALYSIS ON DETERMINANTS OF ANTENATAL CARE IN BANGLADESH: A FINITE MIXTURE

MODELLING APPROACH

Manhal Mohammad Ali†

†Department of Economics, East West University, Plot A/2, main road, Jahurul Islam City, Aftabnagar, Dhaka – 1212, Bangladesh. Email: manhal.ali@ewu.bd.edu

Abstract

Estimation of health care demand or utilisation depends on the empirical

specification used in the analysis. This paper estimates antenatal care (ANC)

utilization in the case of Bangladesh and studies its determinants using a finite

mixture model (FMM) of count data models. Bangladesh, a developing country but

with poor and limited resources have made improvements in reducing maternal

mortality yet it still has a long way to go towards achieving Millennium

Development Goals 5 (MCG5). Existing literature points that greater utilization of

ANC reduces improves health outcomes and therefore reduces maternal and infant

mortality. Unlike standard specifications of count data models FMM as the ability to

distinguish between different classes of users of ANC. (for example low frequent

users and high frequent users). This estimation method was applied to the

Bangladesh Demographic and Health Survey dataset 2007 and it was found that a

two component FMM based on negative binomial density - 1 provides better fit to

ANC utilization than other standard specifications and variants of FMM. The study

found that population of users are divided into two types or latent classes, one that

makes low use of ANC and another makes regular or frequent uses. Among the

determinants, women’s education, type of residence, wealth status were found to be

significant determinants of ANC demand amongst the classes, with age, husband’s

education and autonomy were found to be significant determinants amongst low

intensity users and regional divisions were found be important predictors amongst

high intensity users. Pertinent proposals for health policies and reforms in the

health care sector of Bangladesh are also presented and discussed.

JEL classification: I10; C50

Keywords health care utilization, count data, finite mixture model, heterogeneity

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1. INTRODUCTION

The lack of prenatal or antenatal care (ANC) (care given to mothers before or during

birth) can significantly improve the health of both mothers and children (Holian,

1989; Gertler et al. 1993). Lack of such care is commonly associated with premature

delivery, maternal and infant mortality and postnatal complications (Habibov, 2011:

Magadi et al. 2000). This is one area that merits attention in the case of Bangladesh

as the state of maternal health care in terms of its utilisation is very poor. It is

reported that 50% and 21% of pregnant women in Bangladesh make 1 and 4 ANC

visits respectively, against 4 visits recommended by World Health Organization

(NIPORT 2009). The median number of visits was 3.1 in 2007. About 63% of the

pregnant women were not informed about the signs of the pregnancy complications.

Although the utilization of ANC visits in terms of median visits in Bangladesh has

increased over time with a subsequent fall in maternal mortality, utilization still

remains low (Hossain, 2010). As Koblinsky et al. (2008) mentions, although the use

of skilled birth attendants has increased over the last 15 years, it remains less that

20% as of 2007, and utilization remains particularly low for poor, uneducated rural

women. Bangladesh is on its way to achieve Millennium Development Goals 5 (MDG

5) of reducing maternal mortality by three quarters between 1990 and 2015

however the annual rate of decline needs to triple. Given this backdrop, the purpose

of this paper is to study the socioeconomic, demographic and other determinants of

utilization of ANC services in Bangladesh using count models or count data

regressions, and produce some recommendations or policy implications based on

the outcome of the study. Count data models or regressions is appropriate when the

variable of interest is a non – negative integer valued count for example 0, 1, 2 etc.

Examples include number visits to doctor in a given period, number of cigarettes

smoked, number of drugs dispensed etc.

There exits many studies that have looked and studied into the determinants of

ANC in developing countries. Simkhada et al. (2007) provides a systematic review

on the existing literature. The attempt of this paper is to add to the existing

literature by modelling the counts of ANC visits made by ever married women in

Bangladesh using finite mixture model (FMM) analysis or approach. By using this

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approach this paper attempts to study and evaluate different range of determinants

of ANC use measured in counts of visits made to rage of providers that provide ANC

services to the maternal population, accommodating the possibility that the

population may be divided into distinct groups or classes that have differential

utilization of ANC or maternal health care and therefore different socioeconomic

aspects. FMM and latent class analysis, a closely related area has received increasing

attention in statistics literature primarily because of the number of cases where

such distributions are encountered (for applications see Lindsay, 1995).

Econometric applications of FMM for health care demand or utilization includes Deb

and Trivedi (1997), Deb and Holmes (2000), Deb and Trivedi (2002), Sarma and

Simpson (2006) and recently Singh and Ladusingh (2010).

A common feature of non–negative integer count variables like number of ANC

visits or doctor visits etc is that they contain large proportion of zero observations,

for example those who make no use of health care or make no visits to health care

provider, as well as a long right hand tail of individuals who make heavy use of

health care. Typically count data models or regressions are applied when the

distribution of the dependent variable, in this case ANC visits as a measure or

indicator of its utilization, is skewed. The feature of data or variable exhibiting high

frequency of zero observations is called over–dispersion what are also known as

‘excess–zeros’. Empirical evidence suggests that over–dispersion is a common

feature of health or medical care utilization data (Deb and Trivedi, 1997). Count

variables require estimation by count regression models since ordinary least

squares would produce biased results (Long and Freese, 2006). Aforementioned, the

dependent variable, ANC visits, is the number of visits made to receive ANC or, the

number of times, or counts, when utilization of antenatal health care has occurred.

When there is over–dispersion, the variance of the variable is greater than the mean

and a simple Poisson regression, the classical model for count variables will not be

valid model because of its equi–dispersion property (see Jones, 2011). It is expected

that data will exhibit over–dispersion since the variance of antenatal utilization is

greater than its mean (mean = 2.178, variance = 7.20). Common count data models

that handle over–dispersion include negative binomial regression model, hurdle or

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two part model and zero inflated models1. The hurdle or two–part specification

makes the distinction between participants and non–participants. The motivation

for this specification comes from the principal–agent theories. It assumes that

participation decision and the positive observations of the count data are generated

by two separate probability functions. In the first stage a standard binary logit or

probit model is used to model participation decision – the hurdle. In the second

stage a standard count data is applied the most common being negative binomial,

allowing for the fact that the count data is truncated at zero.

Among the commonly used models for count data, Deb and Trivedi (1997)

propose the FMM approach as an alternative in the empirical modelling of health

care utilization. In addition to discrete count data, FMM can also be applied to model

continuous data for instance health care expenditures, as done by Deb and Holmes

(2000). The population of users of ANC may be divided into two or more distinct

latent classes of groups that have different levels of utilization. One may be frequent

user and the other an infrequent user group. In addition to over–dispersion, FMM

allows this population heterogeneity which splits the population into different latent

classes having or making different uses of health care. For instance one group can be

relatively healthy implying less use and another group relatively ill implying heavy

use of care. In the context of ANC care use, each individual in an underlying sub–

population differs in terms of socioeconomic and demographic characteristics

resulting in heterogeneity and differential use of ANC, as also discussed by Deb and

Holmes (2000) and d’Uva (2006). The common count models aforementioned does

not allow for such heterogeneity between sub–groups of users. Not only FMM

accommodates heterogeneity, but the model can also serve as an approximation to

any true, but unknown probability density (Deb and Holmes, 2000). There exists a

fair amount of studies that have looked into determinants of utilization or demand

of maternal and/or ANC services in Bangladesh, for instance: Iftekher (2010) and

Kamal (2009). To the best of my knowledge, there exists no previous study to model

ANC utilization using FMM in a developing country including Bangladesh if not also

developed countries, and therefore not taking into account the underlying

unobserved heterogeneity as discussed above. In the case of ANC utilization in

1 See Jones (2011) basic description of these models. A more formal treatment, including its formulation, properties and estimation is provided by Winkelmann (2004) and Cameron and Trivedi (1997)

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Bangladesh, Iftekher (2010) uses negative binomial and truncated negative

binomial. Fan and Habibov (2009) and Habibov (2011) use zero inflated and

negative binomial models for the case of Tajikistan and Azerbaijan respectively.

Arthur (2012) uses an ordered logistic regression model to study antenatal

utilization in Ghana using Ghana Demographic and Health Survey therefore not

taking into account the problem of over–dispersion. The aim of this paper is to study

the socioeconomic and demographic and other determinants of ANC by

differentiating users in this case married women, by fitting a FMM accounting for

heterogeneity in uses. The findings of the study are to help in formulating

government health care policies and reforms, providing specific suggestions to

increase use of antenatal care thereby reducing maternal and infant mortality.

The remainder of the paper is organized as follows. Section 2 provides

information on data set. Section 3 and 4 provides methodology, estimation methods,

model selection statistics and empirical results. Section 5 finally concludes with

some suggestions for policy making and reforms.

2. DATA

The date set used for this study comes from BDHS 2007. The 2007 BDHS employs a

nationally representative sample that covers the entire population in private

dwelling units in Bangladesh. The survey used the sampling frame from the census

enumeration areas (EAs) with population and household information from the 2001

population census. EAs were used a Primary Sampling Units (PSU) for the survey.

The survey is based on two–stage stratified sample of households. In the first stage

of sampling 361 PSUs were selected, done independently for each stratum (with 22

strata’s obtained by dividing into economic regions) with probability proportional

to PSU size in terms of households. A household listing was carried out in all

selected PSUs. The resulting lists of households were used as the sampling frame for

the selection of households in the second stage using equal probability sampling

technique. In this was around 10,000 households were selected. The survey was

designed to obtain completed interviews of respondents who were ever–married

aged women from 10–49. BDHS is a part of long standing worldwide demographic

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and health surveys program which is developed to assist developing countries to

collect data on demographic, socioeconomic factors, reproductive, maternal and

child health, nutrition and epidemiology factors like malaria and HIV/AIDS. BDHS

are organized by MEASURE DHS. More information on data, DHS and BDHS can be

accessed at http//:www.measuredhs.com.

The dependent variable of interest, ANC visits is non–negative integer count

variable i.e. y = 0,1,2,3 etc which serves as a measure of frequency of utilization. In

addition to asking women whether they saw anyone to receive ANC, BDHS records

information about the frequency of ANC utilization, which are recorded as counts or

number of visits made. The respondents or women were asked if they saw anyone

for ANC during pregnancy period. The respondent had the opportunity to answer

‘no one’ or ‘someone’. The definition of someone in this question includes a wide

range of ANC providers in Bangladesh such as: MBBS doctors, nurses or midwives,

auxiliary midwives, family welfare visitors and assistants, skilled birth attendants,

health assistants, unqualified doctors and relatives. For the women who did receive

care, BDHS then asks the follow-up question: “How many times did you receive ANC

for this pregnancy?” The responses to this question vary from 1 to 20 visits.

Definitions of determinants or explanatory variables are provided in table 1. This

study uses socioeconomic and demographic determinants that includes age, division

or region, religion (whether Muslim or non-Muslim), wealth index that is given in 5

quintiles: poorest, poorer, middle, richer, richest, education, husband’s education, an

accessibility factor namely whether the respondent lives in rural or urban

household, a measure of women’s status in the household measured autonomy on

own health care and whether the women was or were pregnant with a male child.

Two most recent systematic reviews on the literature found that the above

socioeconomic and demographic factors, pregnancy characteristics, accessibility are

strong determinants of ANC utilization in developing countries (Simkhada et al.

2007; Say and Raine, 2007)

Table 1. Definitions of variables used in regression analysis (n = 10996)

Variables Definition

Dependent variable Antenatal visits Number of antenatal visits during pregnancy

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Independent variables Age A continuous variable that indicates current age of women in years.

Education A continuous variable that indicates women's education in years.

Muslim A binomial discrete variable. If Muslim = 1, otherwise = 0.

Husband's education A continuous variable that indicates husband's education in years

Rural A binomial discrete variable. If women resides in rural area = 1, otherwise = 0.

Poorest households A binomial discrete variable. If women are from poorest household = 1, otherwise = 0.

Poorer households A binomial discrete variable. If women are from poorer households = 1, otherwise = 0.

Middle income households A binomial discrete variable if women are from middle income households = 1, otherwise = 0.

Richer households A binomial discrete variable. If women are from richer households = 1, otherwise = 0.

Richest Household A binomial discrete variable. If women are from richest households = 1, otherwise = 0.

Barisal A binomial discrete variable. If women resides in Barisal = 1, otherwise = 0.

Chittagong A binomial discrete variable. If women resides in Chittagong = 1, otherwise = 0.

Dhaka A binomial discrete variable. If women resides in Dhaka = 1, otherwise = 0.

Khulna A binomial discrete variable. If women resides in Khulna = 1, otherwise = 0.

Rajshahi A binomial discrete variable. If women resides in Rajshahi = 1, otherwise = 0.

Sylhet A binomial discrete variable. If women resides in Sylhet = 1, otherwise = 0.

Autonomy: Have final say on own health care.

A binomial discrete variable. If women has autonomy on won health care = 1, otherwise = 0.

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Male A binomial discrete variable. If pregnant with male child = 1, otherwise = 0.

Other important determinants of health care use or demand are insurance related

variables however BDHS 2007 does not contain insurance on such variables hence

they are not part of this study.

3. METHODS

Recent work in health economics has advocated a more a robust and flexible

approach amongst the classes of count data models: the finite mixture approach to

health care utilization. The FMM based on standard count models (Poisson or

negative binomial) allow for modelling of unobserved heterogeneity in a non-

parametric way across individuals, splitting the population into different groups. In

this approach the unobservable heterogeneity is treated as a discrete random

variable where each category of the variable represents an unobservable ‘type’ of

individual (Deb and Trivedi, 1997). For instance in a two-point or component finite

mixture model there would be two types, such as ‘healthy’ and ‘ill’ individuals as

interpreted in Deb and Trivedi (1997) who applies FMM to model health care

utilisation by Medicare beneficiaries in US. These two types are two latent

populations that reflect unobservable differences in frailty across the populations.

In an FMM, the population is assumed to be made up of C distinct populations in

proportions , , , where , , , The C-point

finite mixture model or the density and is given by

where the mixing probabilities are estimated along with all other parameters,

denoted as . Also,

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The component distribution in a C-point finite mixture negative binomial model

(FMMNB) are specified as

,

,

, , ,

,

,

, ,

where, , , are the latent classes, , exp( ) and , ,

Inserting the value of , in (2) implies

,

,

,

,

,

Equation (2) is also gives the negative binomial density. Of the densities available in

the statistical literature for econometric models of count data, the family of negative

binomial densities is the most flexible. This study looks at two cases where one case

and in another case . The latter corresponds to negative binomial

density – 1 (NB1) and the former corresponds to negative binomial density – 2

(NB2) (Winkelmann, 2004; Cameron and Trivedi, 1997). In addition the study also

uses the Poisson density to estimate and evaluate several FMM’s. Moreover NB1 and

NB2 estimations are also considered for the purpose of model comparison or

selection. Also in this paper we consider FMMs with only two points of support i.e. C

= 2. The results of Deb and Trivedi (1997) suggest that the two-point or two-

component FMM is sufficiently flexible to explain health care counts quite well with

one group as frequent or high users of care and the second group as infrequent or

low users. Estimations of FMM and its variants are carried out using maximum

likelihood using Newton-Rhapson algorithm procedure. For the basis of

comparisons or model selection two information criteria namely Akaike information

criteria (AIC) and Bayesian information criterion (BIC) are used along with log

likelihood. The AIC is given by:

AIC = - 2logL + 2K,

and the BIC by:

BIC = - 2logL + K log (N)

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where log L denotes the maximized log-likelihood value, K is the number of

parameters in the model and N is the sample size. Models with smaller AIC and BIC,

are preferred. All FMM estimation and count regressions are carried out by

adjusting for sample survey design. BDHS uses a complex multi stage survey design

based on stratification and clustering. Statistical or econometric analysis of surveys

with such a complex design will render the regression results biased unless

estimations are adjusted for the sample survey design namely stratum, clusters, and

sample weights (Levy and Lemeshow, 2008).

4. RESULTS

Table 1 provides summary statistics of variables used in regression analysis. The

mean number of ANC visits made by respondents or women was 2.18 with a

minimum of 0 and maximum of 20 visits. The education variable measures

education of women in years or years in education which has a mean of 4.52 years.

This indicates low level of education attainment and inequality of opportunities in

Bangladesh where 35% of the women reported 0 years of education. About 90% of

the respondents were Muslims and the rest non-Muslims. The educational

attainment of husbands is also very depressing with mean of 5.15 years in education

and with 35% having no education at all. A larger proportion of the sample dwell in

rural areas, 64% of women reported having autonomy on own health care and 53%

of the pregnant women are born with male child.

Table 2. Descriptive statistics of variables

Variables

Dependent variable Mean S.D. Min Max

Antenatal visits 2.18 2.68 0 20

Independent variables Age 30.62 9.33 15 49

Education 4.52 4.40 0 17 Muslim 0.90 0.30 0 1 Rural 0.62 0.48 0 1 Husband's education 5.15 5.00 0 19

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Poorest households 0.16 0.37 0 1 Poorer households 0.18 0.39 0 1 Middle income households 0.19 0.39 0 1 Richer households 0.20 0.40 0 1 Richest Household 0.27 0.44 0 1 Barisal 0.13 0.34 0 1 Chittagong 0.18 0.34 0 1 Dhaka 0.21 0.41 0 1 Khulna 0.16 0.36 0 1 Rajshahi 0.19 0.29 0 1 Sylhet 0.13 0.34 0 1 Autonomy: Have final say on health 0.64 0.48 0 1 Male 0.533 0.50 0 1

Figure 1 below provides a histogram of the dependent count variable: ANC visits

made by women. As the figure shows a substantial portion of observations (38%)

are reported to have made so visits with only 77% making 3 or less visits, against 4

visits recommended by World Health Organization. Using ordinary least squares

and Poisson regression with its equi-dispersion to model utilization ANC will thus

not be valid as the figure shows presence of over-dispersion.

Figure 1. Histogram of antenatal care visits.

0.1

.2.3

.4

Fra

ction

0 5 10 15 20Antenatal care visits

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Table 3 provides the model selection statistics. As the table shows, based on

entire sample of study, the log likelihood and both AIC and BIC measures support

FMM with NB1 density with 2 components or classes (FMMNB1 – 2). Based also on

evidence and statistics, Deb and Trivedi (1997) found that FMMNB1 – 2 model as

the best, allowing interpretation of health care utilization to be made in two distinct

components or classes: healthy and ill. Based on statistics and indicators given in

table 3, FMMNB1 – 2 was compared with hurdle two-part NB1 (HNB) model. The

latter model performs better than FMMNB1 – 2 in terms of likelihood, AIC and BIC

measures, however the statistics or indicators don’t significantly or substantially

differ. Moreover, when BIC indicator for FMMNB1 – 2 was adjusted for the sample

size, the indicator is supportive of estimation using FMMNB1 – 2 rather than HNB

specification. In addition because all users of care are represented by one equation

in the two-part HNB model, it does not account for heterogeneity in utilization

between subgroups of users. If users are drawn from distinct groups then HNB will

not be able to adequately present demand or utilization of health care for instance

ANC. Thus in what follows the empirical results on determinants of ANC utilization

is obtained using FMMNB1 – 2. In align with Deb and Trivedi (1997), this allows

interpretation of having the population or users of ANC divided into two

components or classes i.e. one group that make have use of ANC (frequent users)

and another that make low uses (infrequent users)

Table 3. Model selection

Model Log

likelihood AIC BIC

FMM - 2 Negative binomial - 1 (FMMNB1) -8430.78 16935.57 17175.91

FMM - 2 Negative binomial - 2 (FMMNB2) -8471.77 17017.54 17257.88

FMM - 2 Poisson -8526.63 17123.26 17350.61

NB1 -8616.764 17269.53 17386.45

NB2 -8616.76 17269.53 17386.45 FMM - 2 indicates FMM with 2 components

Table 4 presents the parameter estimates and average marginal effects (AME) of the

determinants of ANC using FMMNB1 – 2 for the two components or groups of users

of ANC. Table 4 shows that important number of socioeconomic and demographic

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covariates or explanatory variable are statistically significant determinants of ANC

utilization or demand, especially for component or group 1. It is apparent that

effects of some of the covariates are different for the two components or sub-groups

of users. The standard count data models cannot uncover such patterns because it

assumes all users come from the same population. The interpretation of at the

bottom of table is that it represents the proportion of observations in classes or

components 1 and 2 which is statistically significant at 1 percent level. From the

table there are about 79% in group 1 and 21% in group 2. These components or

classes are latent so it is helpful to give some interpretation. Table 5 provides the

sample average estimates of the fitted mean and other summary statistics for the

two latent classes or components. Table 5 and the summary statistics make explicit

the implication of the FMM. The first latent class or component categorized as low or

infrequent users comprising of 79% has a relatively low number of ANC visits with a

value of 1.74 visits. The second component categorized as high or frequent users

comprising of 21% has a relatively high number of ANC visits of around 3.5 visits.

The probability weighted average of the two classes is

which is close to the overall sample average of 2.18. The variation of

the second component is relatively greater than the first indicating that there is

some overlap between the two distributions. There also exists substantial variation

or differences from percentile values across the components. Figure 2 provides

histograms of the distributions of the fitted means for the two components, where

one can see that the second component experiences more ANC visits. The parameter

estimates are the over-dispersion parameters where the estimates are

significantly different from zero, reinforcing the stylized fact that medical care

utilization counts are over-dispersed relative to the Poisson model.

From table 4 effects of some of the covariates as measured by AME varies across

the two components or latent classes corresponding to low users and high users

with a statistically significant impact. For low users of ANC, current age although in

magnitude small, is statistically significant determinant of ANC use or demand

where as age increases by one year, antenatal visits decreases by -0.028. Women’s

education given in years of education is statistically significant for both high and low

users suggesting that education makes individuals more knowledgeable about

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preventive and maternal care leading to higher probability of ANC visits. Amongst

low group an increase in education by one year leads to increase in ANC visits by

0.114 compared to 0.094 for high group users. The effect of religious beliefs is

statistically significant for high group users where being from a Muslim household

leads to lower probability of making ANC visits. The effect of women dwelling in

rural households has a statistically strong significant effect amongst the low users

but a weakly significant effect for high intensity users. This raises accessibility issues

implying that one reason for low utilization of ANC is lack of proper or nearby health

facilities or health care providers in rural areas2. Husband’s or partner’s education

also plays a statistically significant role in utilization ANC amongst the low group of

users but not amongst the higher group. Overall from education variables, lower

education leads to inequalities in opportunities and inequalities regarding

utilization of ANC in Bangladesh. From the wealth quintiles, women’s affordability

perspectives are important predictors of ANC use. Women from richer or richest

households tend to use more ANC than those from lower wealth quintiles. All the

wealth quintiles are statistically significant predictors of ANC visits amongst low

group or low intensity users. Women’s from richest household is a statistically

significant determinant of ANC utilization for both low intensity and high intensity

users, whereas a woman from poorer households is weakly significant determinant

amongst high intensity users. The other wealth category namely middle income

household was dropped along with regional variable Sylhet from the regression

analysis due to perfect multicollinearity. There is presence of regional disparities

regarding the use of ANC however it is statistically insignificant amongst low users

but relationships between regions or divisions and ANC is somewhat significant

amongst high group users: the probability of using ANC is higher and statistically

significant amongst households living in Barisal, Khulna and Rajshahi. Women’s

autonomy is associated with higher probability of use of ANC and is statistically

significant amongst low group but not for high users. Women pregnant with male

child however have no statistical significant effects amongst the latent classes.

2 The latest BDHS dataset, BDHS 2007 does not have supply side covariates like presence and/or distance to health facilities therefore it was not possible to include them in this study.

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Table 4. Parameter estimates and marginal effects (FMMNB1 – 2)

Component - 1 Component - 2

Low users: average (1.74) High users: average (3.47)

79% 21%

std. error AME std. error AME

Constant 0.462* (0.246)

0.585* (0.343)

Age -0.020*** (0.005) -0.028 0.010 (0.009) 0.030 Education 0.081*** (0.008) 0.114 0.032** (0.015) 0.094 Muslim -0.057 (0.100) -0.081 -0.311** (0.139) -1.025 Rural -0.329*** (0.067) -0.484 -0.235† (0.149) -0.704 Husband's education 0.032*** (0.008) 0.044 0.005 (0.010) 0.015 Poorest households -0.371** (0.127) -0.464 -0.132 (0.125) -0.367 Poorer households -0.230* (0.119) -0.300 -0.234† (0.152) -0.632 Richer households 0.270** (0.117) 0.411 0.085 (0.288) 0.251 Richest Household 0.484*** (0.099) 0.775 0.313* (0.180) 0.988 Barisal -0.234 (0.215) -0.301 0.602** (0.262) 2.201 Chittagong -0.145 (0.203) -0.194 0.481 (0.339) 1.624 Dhaka -0.110 (0.202) -0.149 0.372 (0.308) 1.205 Khulna 0.058 (0.212) 0.083 0.588* (0.331) 2.146 Rajshahi 0.022 (0.202) 0.030 0.801*** (0.244) 3.103 Autonomy: Have final say on health 0.150** (0.064) 0.206 -0.121 (0.118) -0.354 Male

0.020

(0.054)

0.028

0.091

(0.115)

0.264

1.763*** (0.195)

2.29E-06** (9.88E-06)

0.789*** (0.042)

0.211*** (0.042)

Log likelihood -8430.783

N (observations) 4894 *** 1% level significance; ** 5% level significance; *10% level significance

†15% level significance Robust sample adjusted standard errors in parentheses

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In summary, from results presented in table 4 and 5, FMM has the interpretation

that data are generated by two classes of individuals, first which accounts for 79% of

the population who are relatively low users of ANC visits and the second that

accounts for about 21% of the population who are high users of ANC visits and thus

unobservable characteristics influencing ANC utilization must be take into account.

From table 5, socioeconomic and demographic factors like age, women’s education,

income and rural residency, autonomy and husband’s education are significant

determinants of demand or ANC amongst the low users of ANC. Women’s education,

rural residency and some wealth quintiles are also significant determinants of ANC

utilization amongst high intensity users. Regional factors are important significant

determinants for high intensity users but not for low intensity group. The results

are in line with Grossman’s (1972) theory of human capital where education makes

a person more efficient in the use of services in this case maternal health care

services, leading to more health conscious behaviour thereby leading to improved

maternal health. According to Grossman’s model, higher income also leads to more

utilization of health care and vice-versa and the effect is stronger and robustly

significant amongst low intensity users rather than high intensity users.

Table 5. Distribution of fitted means from finite mixture component densities

Statistic Component

1 Component

2

Mean 1.74 3.47

S.D 1.66 1.71

Minimum 0.23 0.92

Maximum 11.18 14.29

Percentile 10 0.45 1.80

25 0.63 2.28

50 1.11 3.02

75 2.23 4.16

99 7.86 9.24

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Figure 2 Fitted values distribution, FMMNB1 – 2

5. CONCLUSION

The present study attempts to explore ANC utilization in Bangladesh by FMM in

particular by FMMNB1 – 2 model and finds that population of users are divided into

two distinct latent classes, one being the high group user and another the low user

group which accounts for majority of the population. There is strong support for the

hypothesis of population being divided into two distinct sub-users of ANC, one with

low mean and low variance and another with higher mean and higher variance.

Therefore from the results of the analysis, it is clear that the different latent classes

possess different socioeconomic and demographic characteristics and thus

unobserved characteristics influencing ANC must be taken into account. This should

assist in the preparation of perspective policies for more efficient utilization of

0.2

.4.6

.8

Density

0 5 10predicted mean: component1

0.1

.2.3

.4

Density

0 5 10 15predicted mean: component2

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resources. Standard count models that assumes all users come from the same

population cannot unravel such heterogeneity.

From the results of the analysis, women’s age, education, rural or types of

residency, wealth or income, husband’s education are significant determinants of

ANC utilization or demand amongst low group users in Bangladesh from the sample

data provided by BDHS. Women’s education and rural residency are also significant

determinants of ANC amongst high group users thus underlying their robustness.

Amongst the wealth quintiles, households from poorer and richest group are

significant determinants of ANC use amongst high group users, albeit weakly

significant for poorer households. Also, women living in certain areas or divisions of

the country are significant predictors of ANC use amongst high intensity users. The

empirical evidence presented in this study has important implications for health

policy in Bangladesh.

Overall the results suggest that to increase utilization, accessibility, and equality

of ANC use and to achieve MDG 5 of reducing maternal mortality, policy makers

need to adopt measures by allowing for more MBBS doctors, doctors on residency

programs or on internships, satellite, community or mobile clinics to operate in

rural areas. Policy makers or government can also allow other programs such NGO

organization, non government and not-for-profit organizations, and state based

funded programs to operate in rural areas to disseminate more knowledge about

maternal and infant health, early warnings or signs of pregnancy complications.

Such health education and health promotion programs should and can address the

issues of ANC in a comprehensive way that encompasses improved information,

education and empowerment of women on the benefits of its usage, since a key

obstacle in ANC utilization is lack of education. Another pertinent and pragmatic

solution is to improve the quality and number of academic educational activities for

instance primary and secondary schools, especially in rural areas as well for non-

academic educational activities like health and family education activities, active

welfare or social community groups etc. Evidence also suggests that poverty and

income are significant determinants of ANC use especially amongst low intensity

users. Hence this warrants a greater budgetary allocation and public expenditure to

improve maternal and infant health, implementation of targeted social assistance

program that delivers benefits to the poorest families, and increasing overall

19

economic activities of the nation to generate higher GDP for instance establishment

of businesses, jobs, trade etc. However such income growth and pro poor policies

needs to be implemented effectively in tandem with policies aimed at resolving

rural/urban regional inequalities in accessing ANC.

Finally, there are certain limitations to this study and scope for extensions. Firstly

the study does not take into account quality and components of ANC, data on its

costs and effectiveness. The study also does not take into account other covariates

like pregnancy and maternal related characteristics, although it has taken into

account whether women was pregnant with male child and women’s autonomy.

Nevertheless the findings of the study taking into account the dichotomy between

low and high intensity users of care will provide impetus and create a baseline in

regards to study and assess the determinants of ANC utilization in Bangladesh and

other developing countries.

Acknowledgements I would like to thank Dr. M Sekander Hayat Khan and three

other anonymous referees for their helpful comments on BDHS dataset. All

remaining errors are mine.

REFERENCES

1. Arthur, E., 2012. Wealth and antenatal care use: implications for maternal health

care use in Ghana, Health Economics Review, 2(14), pp. 1-8.

2. Cameron, A, C., and Trivedi, P, K., 1998. Regression analysis of count data,

Cambridge University Press, Cambridge, UK.

3. d’Uva, T, B., 2006. Latent class models for utilization of health care, Health

Economics, 15(4), pp. 329-343.

4. Deb, P., and Holmes, A, M., 2000. Estimates of use and costs of behavioural health

care: a comparison of standard and finite mixture models, Health Economics,

9(6), pp. 475-489.

20

5. Deb, P., and Trivedi, P, K., 2002. The structure of demand for health care: latent

class versus two-part models, Journal of Health Economics, 21(4), pp. 601-625.

6. Deb, P., and Trivedi, P, K., 1997. Demand for medical care by the elderly: a finite

mixture approach, Journal of Applied Econometrics, 12(3), pp. 313-336.

7. Fan, L., and Habibov, N, N., 2009. Determinants of maternity health care

utilization in Tajikistan: learning from national living standards survey, Health

and Place, 15(4), pp. 952-960.

8. Gertler, P., Rahman, O., Feifer, C., and Ashley, D., 1993. Determinants of

pregnancy outcomes and targeting of maternal health services in Jamaica, Social

Science and Medicine, 37(20, pp. 199-211.

9. Grossman, M., 1972. On the concept of human capital and demand for health,

Journal of Political Economy, 80(2), pp. 223-255.

10. Habibov, N, N., 2011. On the socio-economic determinants of antenatal care

utilization in Azerbaijan: evidence and policy implications for reforms, Health

Economics, Policy and Law, 6(2), pp. 175 – 203.

11. Holian, J., 1989. Infant mortality and health care in Mexican communities, Social

Science and Medicine, 29(5), pp. 677-679.

12. Hossain, K., A.H.M., 2010. Utilization of antenatal care services in Bangladesh: an

analysis of levels, patterns, and trends from 1993 to 2007, Asian-Pacific Journal

of Public Health, 22(4), pp. 395-406.

13. Iftekhar, M, H., 2010. Inequality in the utilization of maternal care and the

impact of macroeconomic policy: evidence from Bangladesh. HEDG working

paper 10/08, York, UK.

14. Jones, A., 2011, Applied Econometrics for Health Economists: A practical guide,

2nd edition, Radcliffe Publishing, Oxford, UK.

15. Kamal, S, M., 2009. Factors affecting utilization of skilled maternity care services

among married adolescents in Bangladesh, Asian Population Studies, 5(2), pp.

153-170.

16. Koblinsky, M., Iqbal, A., Mridha, M, K., Chowdhury, M, E., and Botlero, R., 2008.

Reducing maternal mortality and improving maternal health: Bangladesh and

MDG 5, Journal of Health, Population and Nutrition, 26(3), pp. 280-294.

17. Levy, P., and Lemeshow, S., 2008. Sampling of populations: Methods and

Applications, 4th edition, Wiley, New York.

21

18. Lindsay, B, J., Mixture Models: Theory, Geometry and Applications, NSF-CBMS

Regional Conference Series in Probability and Statistics, Vol. 5, IMS-ASA.

19. Long, J, S., and Freese, J., 2006. Regression Models for Categorical Dependent

Variables Using Stata, College Station, STATA Press, Texas, US.

20. Magadi, M, A., Madise, N, J., Rodrigues, R, N., 2000. Frequency and timing of

antenatal care in Kenya: explaining variations between women of different

communities, Social Science and Medicine, 51(4), pp. 551-561.

21. National Institute of Population Research and Training (NIPORT), Mitra and

Associates, and Macro International, 2009. Bangladesh Demographic and Health

Survey 2007. Dhaka, Bangladesh and Calverton, Maryland, USA: National

Institute of Population Research Training, Mitra and Associates, and Macro

International.

22. Say, L., and Raine, R., 2007. A systematic review of inequalities in the use of

maternal health care in developing countries: examining the scale of the problem

and the importance of the context, Bulletin of the World Health Organizations,

85(10) pp. 812-819.

23. Sarma, S., and Simpson, W., 2006. A microeconometric analysis of Canadian

health care utilization, Health Economics, 15(3) pp. 219-239.

24. Simkhada, B., van Teijingen, E., Porter, M., and Simkhada, P., 2008. Factors

affecting the utilization of antenatal care in developing countries: systematic

review of the literature, Journal of Advanced Nursing, 61(3), pp. 244-260.

25. Singh, C., H., and Ladusingh, L., 2010. Inpatient length of stay: a finite mixture

modelling analysis, 10(2), pp. 119-126.

26. Winkelmann, R., 2008. Econometric Analysis of Count Data, 5th edition, Verlag

Berlin Heidelberg, Germany.