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Determinants of AntenatalCare Utilization in NigeriaRifkatu Nghargbu and Olanrewaju Olaniyan

n° 32

1

July 2

019

Working Paper No 321

Abstract

The study examines the determinants of antenatal

care Utilization in Nigeria. Determinants of

antenatal care utilization were categorized into

economic and non-economic determinants.

Estimates of the determinants of antenatal care

utilization were derived from two-part model

analysis using five rounds of Nigerian Demographic

and Health Survey (NDHS) from 1990 to 2013.

Previous studies have used one or two rounds of

surveys to estimate the determinants of antenatal

care utilization using logit or count data (poison,

negative binomial) model. However, health care

utilization consist of two parts of decisions; the first

is to either utilize health care or not while the second

is the frequency of utilization. Estimation using logit

model takes care of the first part while the count data

takes care of the second part. Using one of the

models does not estimate the two components of

decisions. This study is different from other studies

in three ways; the use of five rounds of surveys, two-

part model analysis and the inclusion of variables

not found in other studies. Results from the two-part

model analysis shows that economic and non-

economic variables were statistically significant at

1% and 5% respectively. Economic variables

include; income, price and supply factors. These

were measured by wealth, employment, health

insurance, “distance and transport to health

facilities”, "no provider" and "no female provider".

Non-economic variables were age, education, birth

order, region, ethnicity, marital status and religion.

The implication of these results reveals that more

has to be done in terms of policy to influence

economic and non-economic variables to improve

antenatal care utilization in Nigeria.

This paper is the product of the Vice-Presidency for Economic Governance and Knowledge Management. It is part

of a larger effort by the African Development Bank to promote knowledge and learning, share ideas, provide open

access to its research, and make a contribution to development policy. The papers featured in the Working Paper

Series (WPS) are those considered to have a bearing on the mission of AfDB, its strategic objectives of Inclusive

and Green Growth, and its High-5 priority areas—to Power Africa, Feed Africa, Industrialize Africa, Integrate

Africa and Improve Living Conditions of Africans. The authors may be contacted at workingpaper@afdb.org.

Rights and Permissions

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purposes is forbidden. The WPS disseminates the findings of work in progress, preliminary research results, and

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Working Papers are available online at https://www.afdb.org/en/documents/publications/working-paper-series/

Produced by Macroeconomic Policy, Forecasting, and Research Department

Coordinator

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Correct citation: Nghargbu R. and O. Olaniyan (2019), Determinants of Antenatal Care Utilization in Nigeria, Working Paper Series

N° 321, African Development Bank, Abidjan, Côte d’Ivoire.

1

Determinants of Antenatal Care Utilization in Nigeria

Rifkatu Nghargbu1 and Olanrewaju Olaniyan2

JEL classification: I12, I15, I18

Keywords: Antenatal care, utilization, two-part model, wealth, determinants.

1Rifkatu Nghargbu is a Lecturer, Department of Economics, Usmanu Danfodiyo University Sokoto, Sokoto-Nigeria

and former Visiting Research Fellow, Macroeconomic Policy, Forecasting, and Research Department, African

Development Bank (corresponding author, rifnghargbu@gmail.com).

2 Olanrewaju Olaniyan is a Professor in the Department of Economics and Director, Centre for Sustainable Development,University of Ibadan, Ibadan, Nigeria.

2

1. Introduction

Antenatal care is one of the vital maternal health care services worldwide, because pregnancy

complications are important source of maternal mortality and morbidity. Although global

proportion of women attending antenatal care once during pregnancy have increased to 83% for

the period 2007–2014, only 64% of pregnant women received the recommended minimum of four

antenatal care visits worldwide. (WHO, 2015). This suggests that there is global need for more

antenatal care utilization. In Africa, over two-third of women (69%) have at least one antenatal

visits during pregnancy but majority do not attend the required minimum number of four visits

(WHO).

In Nigeria, the utilization of antenatal care is still very low especially in the rural areas and

the northern part of the country. Less educated as well as poor people also have poor utilization

level. Although the rural population is six times larger than the urban, only 50% attended antenatal

care (see table A in the appendix). On the other hand, majority of women who attend antenatal

care do not attain the required number of visits recommended by the World Health Organization

(WHO). This has resulted in high maternal mortality rate of over

500 per 100,000 live births accounting for 13% of the global maternal deaths. Also over

36,000 women die in pregnancy or at child birth each year (http://www.thisdaylive.com/articles/

nigeria-accounts-for-13-global-maternal-mortality-rates/183394).

Regular antenatal care attendance ensures proper monitoring of the health of the mother

and child throughout pregnancy to enhance their optimal health outcomes. It also exposes pregnant

women to counseling and education about their own health and the health of their children.

Antenatal care can be more effective when it is sought early in pregnancy and continues until

delivery. The advantage of starting antenatal care early especially within the first three months of

pregnancy is that a woman's baseline health will be assessed (NDHS report 1990). This helps in

detecting any abnormality and aids the health workers in taking necessary actions concerning the

woman's health. Obstetricians usually recommend that antenatal visits by pregnant women should

be carried out every month at the beginning of the pregnancy until the 7th month, fourth nightly

in the 8th month and weekly until birth (NDHS report, 2008). However, the minimum standard

of WHO for antenatal visits is at least four before delivery. This is in line with the antenatal care

policy in Nigeria; it is termed the focused antenatal care (FANC). FANC emphasizes quality of

3

care instead of focusing on the number of visits, (NDHS report, 2008). The schedule for the four

antenatal visits states that the first visit should occur by the end of 16 weeks of pregnancy, the

second should be between 24 and 28 weeks of pregnancy, the third is at 32 weeks, while the fourth

should be undertaken at 36 weeks of pregnancy. In cases were complications occurs, and for

women with basic needs, additional visits should be undertaken. Antenatal care contents includes

tetanus toxiod injections, test for complications, weight and height measurement, urine and blood

sample test, anti-malaria drugs and iron tablets or syrup (NDHS report, 2003). The essence of the

antenatal care contents is to avert neonatal tetanus, malaria, and maternal anemia, which are the

major causes of neonatal and maternal mortality (NDHS report, 2003)

Given the importance of antenatal care, this study sets out to investigate the determinants

of antenatal care utilization using two-part model to estimate the economic and non-economic

determinants of antenatal care attendance and number of visits. In the model, wealth, employment

status, “distance and transport to health facility” as well as insurance status were estimated as

income and price variables which are economic variables. Education, residence, age of respondent,

religion, ethnicity, birth order and marital status are demographic and social variables which are

non-economic. Other economic variables include; "no provider" and "no female provider" which

are supply variables. There are few limitations of the study basically on the five sets of NDHS

data. Not all the variables in the model are available in the sets of data. Variables such as insurance

status, "distance to health facility", "transport to health facility ", "no provider and no female

provider" and ethnicity are not available in 2003, 1999, and 1990.

2. Empirical literature

Most studies on utilization of antenatal care categorize the determinants as socioeconomic and

demographic factors (Babalola and Fatusi, 2009; Adamu, 2011; Goland et al, 2012; Jat et al,;

2011; Nwosu et al, 2012; Dairo and Owoyokun, 2010; Owoo and Lambon-Quayefio, 2013;

Nketiah-Amponsah, et al, 2012; Edgard-Marius et al 2015; Dahiru and Oche, 2015; Babalola (2014).

Socio-economic factors are education, wealth status, income, religion, and marital status while

demographic factors include age, ethnicity and residence. These studies found that education,

ethnicity, residence, age at birth of last child, respondent's age and wealth are significant in the use

of antenatal care in Nigeria, India, Ghana and Benin. Education is associated with knowledge and

skills that influences the ability to understand health risks and access to health care.

4

Some studies used DHS and National HIV/AIDS reproductive survey data to estimate

logistic regression to establish a significant relationship between household wealth, education and

ethnicity, religion, distance to health facility, partners education, employment and marital status

(Babalola and Fatusi (2009; Goland et al 2012; Nketiah-Amponsah et al, 2012; Babalola, 2014;

Edgard-Marius et al, 2015; Author, 2012; Fagbamigbe and Ademudia, 2016). They compared the

influence of wealth, ethnicity, age, marital status and insurance on antenatal care utilization. The

studies found that, women from ethnic minority and poor household had an almost tenfold risk of

not receiving antenatal care as compared to women from ethnic majority living in a non-poor

household. Some of the studies used descriptive statistics (Jat et al, 2011; Dairo and Owoyokun,

2010). The results shows that socioeconomic factors specially respondent's education were the

most important factors associated with the use of antenatal care at community and district level.

Some studies used the count data models to investigate the determinants of antenatal care service

utilization in Nigeria (Nwosu et al 2012). Results shows that Women education beyond primary

school increases significantly the likelihood that a pregnant woman would complete at least four

antenatal visits before delivery. Also household wealth status and region has significant positive

effect on the number of visits before delivery.

Other studies where cross sectional at village, State, community and district levels (Awusi

et al 2009; Emelumadu et al; 2016; Onasoga et al, 2012; Ononokpono, 2015; Titaley et al 2010).

The studies were carried out in Emevor village of Delta State, Anambra State, Osun State and Oyo

State of Nigeria, as well as six villages in Indonesia. Results show that socioeconomic and

demographic factors such residence, religion, age, marital status, education, knowledge, and

distance affects antenatal care utilization. In Delta State, 43% non-utilization of antenatal care

were due to lack of motivation, non-accessibility, culture and negative role played by husbands

as well as ignorance of older women on increased risks of pregnancy with age. In Anambra State,

Socioeconomic factors influenced women choice of place of delivery for maternal health care

services but had no significance on the timing of first ANC visits and number of visits.

Two-part model analysis was used to estimate the determinants of demand for antenatal

care utilization in Columbia (Ortiz, 2007). Findings shows that region and age of mothers

influenced antenatal care utilization. Younger mothers from pacific region have lower probability

of attending the first visit. However once the first visit has been established, other factors order

than age and region determine the number of subsequent visits. Studies in Ghana used 2008 Ghana

5

(DHS) data to investigate the effect of wealth on antenatal care utilization in Ghana (Eric Arthur,

2012; Owoo and Lambon-quayefio, 2013). Results show that wealth has a significant influence on

antenatal care use in Ghana. Education, age, number of living children, transportation, regions and

health insurance are other factors that were found to influence the use of antenatal care in Ghana.

This study fills gap by estimating economic and non-economic determinants of antenatal

care utilization in Nigeria using two-part model. The first part is the logit while the second is the

negative binomial model. This is because the two-part model represents the characteristics of

health care utilization decision which has two components; to utilize or not and the frequency of

utilization. The first part establishes the determinants of first visit while the second estimates the

determinants of the second and more visits or frequency of visits. According to Ortiz (2007) the

"determinants of the first visit and frequency of visits might be different, partly because of the

agency problem in terms of the demand inducement. In these models, the patient takes the decision

of attending the first medical visit but further consultations are decided by both patient and medical

doctor where each one maximizes his utility function and takes advantage of some information

asymmetry problems which is overcome by using the two-part model on health care utilization".

Therefore estimation using logit model takes care of the first part while the negative binomial

model takes care of the second part of the decision. Further, some variables not found in the

literature such as “transport to health facility”, “no provider” and “no female provider” which are

important price and supply side variables are included in the utilization model. Other additional

value to the literature is the use of five rounds of survey for the analysis.

3. Methodology

3.1 Theoretical framework

Following Grossman (1972) and its extensions, the demand for antenatal care is a derived demand

to enhance the stock of good health of pregnant women. The quantity of antenatal care demanded

is related to its own shadow price and the price of other goods as well as other maternal

characteristics. Maternal characteristics affect both taste and health productive efficiency of the

woman. These characteristics include; wealth status, education, marital status, health insurance

status, area of residence, age, religion, employment status and region. For instance, the more

wealthy and educated a woman is, the more she is able to afford the health care needed to improve

6

the efficiency of her health and the health of her child. Also, the utilization of antenatal care

promotes good health during pregnancy which in turn improves utility. We assume that the price

variables and the maternal characteristics constitute the economic and the non-economic

determinants of antenatal care utilization.

Assume that the ith pregnant woman has a utility function (U) where

iiii XZHUU ,, ...................................................................................... (1)

Hi is the stock of health for the pregnant woman at age t, Z is a vector of all other goods consumed,

and Xi is a vector of characteristics of the ith pregnant woman that influence preferences for

antenatal care utilization.

Optimization of the utility function forms a demand function as follows;

)2(..........................................................................................,,,,

iiiiZCCi SYXIPPDD

Equation 2 is estimated as the antenatal care utilization model. The D1 stands for

demand/utilization of antenatal care. Empirically, it is measured as antenatal use and frequency of

antenatal visits. The Pc stands for the price of antenatal care, empirically; there is no data on how

much is paid. The price is therefore measured by access cost which in the data is represented by

proxies such as "distance to health facility", "transport to health facility" and insurance status.

These variables are also represented by proxies in the data because the numerical values of distance

and transport cost is not in the data, therefore, proxies like "distance to health facility" being a big

problem and not being a big problem is used. Respondents who have transport to health facilities

as big problem are affected negatively by the access cost of antenatal care. This variable is a good

proxy for the price of antenatal care because, a respondent has to transport herself to the nearest

health care center and this involves paying a certain price. Also, “distance and transport” to health

facility determine the ease of financial and physical access to health care. The data for distance to

health facility” is made available in the NDHS for 2003, 2008 and 2013, the data for 1990 and

1999 is not available. In addition, "transport to health facility" is made available in NDHS for

2003 and 2008, there was no data for other years. Pz in the model represents the price of other

goods, it was not estimated due to lack of data. I is the income of the ith pregnant woman which

is measured in the empirical model as wealth status and employment status. The I in the model

stands for the income of the respondent, empirically, income in terms of numerical value is not

captured in the DHS data. However, proxy for long run income of the household is captured using

7

asset/wealth index which represents 1wealth status. Pc, and I are economic variables because they

are very important in the analysis of theory of demand. X is the vector of characteristics of ith

pregnant woman which influences her health care consumption but are not theoretically recognized

in the theory of demand. They are; education, marital status, area of residence, age, religion,

ethnicity, employment status and region. The variables are referred to as non-economic variables.

Education in the theoretical model also represent a vector of characteristics of the ith pregnant

woman that determine the efficiency of health production. In additional to these variables, the

study includes other variables which also determine antenatal care utilization. These are supply

side variables such as "no provider" and "no female provider". In the questionnaire, respondents

were asked if “no health care provider” and no female care provider was a “big problem” or “not

a big problem”. Those respondents who agreed to the fact that no provider and no female providers

is a big problem are affected by the supply variable.

In terms of apriori expectation, wealth index is expected to be positively related to the

utilization model; in the second case, wealth index is transformed to dummies and the richest

wealth index is used as the reference category. It is expected that the richest wealth quintile will

have higher level of utilization than other category of wealth index. The a priori expectation of

region and ethnicity is not categorically indicated in the theoretical model, region and ethnicity are

subjective and depends on country, however for this study, given the literature for Nigeria it is

expected that respondents from the southern part of the country and the Yoruba ethnic groups are

more likely to utilize any of the health care than respondents from the northern part of the country

and other ethnic groups. Region and ethnicity in the regression are represented by dummies with

the South West and Hausa ethnic group as the reference category. Residence in the empirical model

is an indicator variable for current residence in the rural-urban context with "rural" as the reference

category. The apriori expectation for residence based on the literature is that respondents from the

rural areas are less likely to utilize any of the health care compared to respondents from the urban

areas. Also it is expected that respondents who are insured and employed are more likely to utilize

antenatal care health care compared to those who are not employed. Respondent's age influences

utilization positively. In terms of religion, based on the literature, Christians are more likely to

1 Wealth status was measured by household wealth index. In the DHS wealth index was determined through

Principal Component Analysis derived from Factor Analysis which was based on household assets such as type of

(flooring, water supply, electricity, radio, television, refrigerator and type of vehicle)

8

utilize antenatal care compared to other religions. The Y in the theoretical model is the

characteristics of the respondent that determine the efficiency of the health production. In this

respect, the demand for health care which provides utility to an individual improves health

(Grossman, 1972). Education is therefore the characteristics of individual that improves the

efficiency of health production. It is represented in the empirical model as education of respondents

and that of their partners. Education in the empirical model is regressed as dummies; "no

education" for those who do not have formal education, then primary, secondary and higher

education for those that have formal education in each of the levels, respectively. Education is

positively related to health care utilization, women with higher education are expected to have a

higher probability of utilizing health care than respondents without education. This also applies to

partner's education.

3.2 Estimation technique and data

In estimating the demand for antenatal care, the study used the two-part model approach. This

involves first identifying women who have attended at least one antenatal visit against those who

never did. This is the first part and it is estimated using logit model. After this, the study also try

to investigate the effect of having attended antenatal care more than once and this is estimated

using the negative binomial model. The essence of this is to examine the importance of attending

antenatal care many times. The two-part model also shows the impact of the explanatory variable

at each stage of decision in utilization of antenatal care; this involves the decision to go for

antenatal care and the decision to have the recommended number of antenatal visits. The two-part

model is outlined below according to the parts;

First part

In the first part, the logit model is regressed, it specified as;

)15,..(.....................................................................1

lnlog 110 kik

i

i

i XXP

PPit

P represents the probability that a woman attends antenatal care. The responses are coded as 1 if

a woman goes for antenatal care and 0 if otherwise.

9

The second part

In part 2, the negative binomial model is estimated which is presented as;

)16,..(........,..................................................expexp0Pr ijij eXbisitsantenatalv

Where Xij is a row vector of K of i individual characteristics, b is a set of parameter to be estimated

and ei is the error term. The negative binomial measures frequency of visits of antenatal care. The

model is estimated using five rounds of data; 1990, 1999, 2003, 2008 and 2013 NDHS data. The

survey was conducted by the National Population Commission of Nigeria based on two-stage

probability sampling. Information is provided on the reproductive health of women aged 15 to 49.

4. Results and discussion

4.1 Descriptive Statistics

Table B in the appendix presents the mean and standard deviation of the variables regressed in the

antenatal care utilization model. The mean and standard deviations of the dummy variables in the

regression are expressed in decimals and interpreted in percentages. Antenatal care which is the

dependent variable is expressed based on the two-part model. In the logit regression the percentage

of women who attended antenatal care at least once was 66%, 65%, 58%, and 66% in 1999 to

2013, respectively. The negative binomial regression shows that respondents achieved at least 4 to

5 visits within the time period. The two-part model is better than the other models because it shows

the two aspects of decisions taken in antenatal care utilization in terms of percentage of people that

achieved the first visit and the number of visits they undertook. 58% of women used antenatal

care with a mean value of 5 visits. This shows that more than half of the women sampled during

this period attended antenatal care and had 5 visits on the average. In 2008, 58% attended antenatal

care with 4 visits on the average, while in 2003, 1999 and 1990, over 60% attended antenatal care

with an average of 4, and 5 visits respectively. Antenatal care use and the frequency of visits did

not vary significantly between the years. Antenatal care statistics is quite impressive showing that

utilization is above 60% with at least 4 visits, on the average.

In addition, 20 to 27% belong to the poorest and poorer wealth quintile, while the richer

quintile has between 16 to 19%. An average of 23% to 39% of respondent are not employed for

the years. The variable “distance and transport to health facility” shows that over 32% and 41% of

the respondents view “distance to health facility” as a big problem in 2013 and 2008, respectively.

10

53% and 40% view “transport to health facility” as a big problem. Also, in 2008, 37% and 23%

view “no provider” as a big problem. While 71% in 2003 view “no female provider” as a big

problem. The average age of the respondents ranges from 34 to 36 for all rounds of survey. Also,

about 50% to 60% of women had no education. This also applies to “partner’s education”. An

average of 47% of men had “no education” while 22% had “primary” or “secondary education”.

Also, majority of respondents sampled are from northwest and northeast; about 20 to 32%.

4.2 Two-part model regression results

Table 3 presents the results of two-part regression analysis.

11

Table 5: Two-part Regression Model for Antenatal Care Utilization (1990- 2013)

NDHS 2013 NDHS 2008 NDHS 2003 NDHS 1999 NDHS 1990 First

Logit

model

Second

NB

model

First

Logit

model

Second

NB

model

First

Logit

model

Second

NB

model

First

Logit

model

Second

NB

model

First

Logit

model

Second

NB model

Coef/std

error

Coef/st

d error

Coef/st

d error

Coef/st

d error

Coef/std

error

Coef/st

d error

Coef/std

error

Coef/st

d error

Coef/st

d error

Coef/std

error

ECONOMIC VARAIBLES

Income variables Wealth index (ref: richest) Poorest -1.86***

(0.14) -0.69***

(0.04) -1.67***

(0.15) -0.78***

(0.05) -2.86***

(0.369) -0.65***

(0.08) -1.34**

(0.29) -0.59***

(0.08) -2.33***

(0.17) -0.77***

(0.048)

Poorer -1.33***

(0.13)

-0.33***

(0.03)

-1.19***

(0.15)

-0.38***

(0.04)

-2.63***

(0.36)

-0.52***

(0.07)

-0.88***

(0.279)

-0.23***

(0.073)

-2.30***

(0.167)

-0.82***

(0.052)

Middle -0.87***

(0.13)

-0.10***

(0.02)

-0.70***

(0.15)

-0.04

(0.03)

-1.88***

(0.355)

-0.14**

(0.063)

-0.58**

(0.27)

-0.06

(0.06)

-1.64***

(0.16)

-0.35***

(0.04)

Richer -0.46***

(0.13)

-0.02

(0.02)

-0.48***

(0.15)

0.02

(0.03)

-1.28***

(0.35)

0.07

(0.05)

-0.12

(0.29)

0.06

(0.05)

-0.91***

(0.16)

-0.11***

(0.03)

Employment (reference: employed) not employed -0.35***

(0.044)

-0.11***

(0.019)

-0.14***

(0.05)

-0.12***

(0.03)

-0.26**

(0.10)

-0.12***

(0.05)

-0.26**

(0.13)

-0.18***

(0.05)

-0.39***

(0.07)

-0.19***

(0.03)

Price variables Distance to health facility (ref: not a big problem) big problem -0.55***

(0.05) -0.36***

(0.02) -0.15**

(0.0) -0.10*** (0.04)

-0.42** (0.17)

0.28*** (0.07)

Transport to health facility (ref: not a big problem) big problem -0.21***

(0.07)

0.004

(0.04) -0.08

(0.17)

-0.03

(0.09)

Small problem -0.04

(0.16)

0.06

(0.09)

Insurance status No insurance -1.06***

(0.35) -0.05

(0.03) -1.19**

(0.50) 0.02

(0.05)

Supply variables No provider (ref: not a big problem)

big problem -0.11**

(0.06)

-0.07***

(0.028)

No female provider (ref: not a big problem) big problem -0.30***

(0.06) -0.06

(0.04) -0.42***

(0.11) -0.29*** (0.06)

NON-ECONOMIC VARIABLES

12

Age 0.02*** (0.01)

0.01*** (0.002)

0.008* (0.005)

.002*

0.03** (0.011)

0.02*** (0.005)

0.004 (0.013)

0.002 (0.005)

0.003** (0.006)

-0.001 (0.003)

Respondent's education (ref: higher)

No education -1.78*** (0.28)

-0.28*** (0.03)

-1.90*** (0.318)

-0.34*** (0.046)

-1.49 (0.814)

-0.25*** (0.08)

-1.48 (1.20)

-0.37*** (0.09)

0.34 (0.65)

0.16** (0.08)

Primary -1.13***

(0.275)

-0.03

(0.03)

-1.28***

(0.32)

-0.04

0.04

-0.75

(0.81)

0.009

(0.07)

-0.45

(1.09)

0.01

(0.01)

0.79

(0.65)

0.25***

(0.07)

Secondary -0.80*** (0.27)

-0.03 (0.02)

-0.84*** (0.32)

-0.018 (0.03)

-0.59 (0.81)

0.039 (0.06)

0.09 (1.08)

0.02 (0.07)

1.11* (0.659)

0.15** (0.07)

Partner's education (ref: higher) No education -0.95***

(0.10)

-0.38***

(0.02)

-0.94***

(0.11)

-0.47

(0.040)

-0.80***

(0.24)

-0.40***

(0.07)

-1.76***

(0.39)

-0.56**

(0.25)

-1.51***

(0.35)

-0.36***

(0.05)

Primary -0.30***

(0.10)

-0.06**

(0.03)

-0.32***

(0.11)

-0.10***

(0.04)

-0.423*

(0.241)

-0.19***

(0.057)

-1.205***

(0.406)

-0.11

(0.16)

-1.06***

(0.358)

-0.16***

(0.04)

Secondary -0.30*** (0.10)

-0.05** (0.019)

-0.25** (0.11)

-0.10*** (0.03)

-0.078 (0.246)

-0.035 (0.049)

-0.967** (0.447)

0.14 (0.1459)

-1.10*** (0.36)

-0.08** (0.04)

Birth order Birth order -0.05***

(0.01) -0.02***

(0.01) -0.02*

(0.014) 0.005

(0.007) -0.06**

(0.028) -0.03**

(0.01) 0.03

(0.034) 0.004

(0.01) -0.397**

(0.20) -0.04

(0.066)

marital status (ref: married ) Single -0.09

(0.12) 0.04

(0.04) -0.10

(0.14) 0.11*

(0.06) -0.091

(0.118) 0.037

(0.039) 0.622*

(0.433) 0.38***

(0.123) -0.397**

(0.196) -0.04

(0.066)

Ethnicity (ref. Hausa) Igbo 1.03***

(0.30) 0.26***

(0.04) 0.64**

(0.29) 0.18***

(0.06)

Ijaw/izon -1.25***

(0.14)

-0.45***

(0.07)

-0.56***

(0.17)

-0.17**

(0.08)

Kanuri/beriberi -0.35**

(0.16)

-0.09

(0.08)

-0.44***

(0.11)

-0.29***

(0.082)

Tiv -1.10*** (0.17)

-0.42*** (0.08)

-0.19 (0.15)

-0.12 (0.08)

Yoruba 1.01***

(0.182)

0.29***

(0.04)

0.77***

(0.167)

0.33***

(0.057)

Others 0.22***

(0.07)

0.14***

(0.03)

0.51***

(0.07)

0.27***

(0.04)

Region (ref: south west) North central -0.12

(0.14)

-0.48***

(0.03)

-0.68***

(0.15)

-0.57***

(0.04)

-2.02***

(0.35)

-0.70***

(0.06) -1.23***

(0.27)

-0.50***

(0.05)

-1.22***

(0.13)

-0.74***

(0.04)

Northeast 0.18 (0.14)

-0.57*** (0.04)

-0.86*** (0.15)

-0.64*** (0.05)

-1.88***

(0.336)

-1.22***

(0.066) -2.20***

(0.26) -0.90***

(0.072) -1.44***

(0.13) -0.77***

(0.04)

Northwest -0.74***

(0.14)

-0.90***

(0.04)

-1.97***

(0.16)

-1.22***

(0.06)

-2.53***

(0.34)

-1.28***

(0.07) -2.49***

(0.27)

-0.96***

(0.084)

-0.29**

(0.14)

0.06**

(0.03)

Southeast 0.08

(0.27)

-0.22***

(0.04)

-0.58*

(0.32)

-0.33***

(0.05)

1.221*

(0.69)

-0.48***

(0.07) -1.19***

(0.35)

-0.37***

(0.05)

South south -1.16***

(0.14)

-0.46***

(0.04)

-1.25***

(0.17)

-0.44***

(0.05)

-2.4***

(0.40) -0.55***

(0.08)

13

Residence (ref: rural ) Urban 0.36***

(0.06) 0.08***

(0.02) 0.71***

(0.07) 0.24***

(0.02) 0.18

(0.13) 0.05

(0.04) 1.16***

(0.17) 0.25***

(0.05) 0.59***

(0.11) 0.27***

(0.03)

Religion (ref. Christianity ) Islam -0.04

(0.09)

0.05**

(0.03)

-0.21**

(0.082)

-0.005

(0.03)

-0.83***

(0.16)

-0.06

(0.05)

-0.49**

(0.26)

-0.02

(0.05)

-1.03***

(0.10)

-0.31***

(0.03) Traditionalist -0.752***

(0.20)

-0.26**

(0.10)

-0.58***

(0.15)

-0.24***

(0.08)

-0.84**

(0.37)

-0.13

(0.16)

-0.83***

(0.194)

-0.13**

(0.05)

-0.04

(0.21)

0.08

(0.08)

_cons 5.03*** (0.47)

2.29*** (0.07)

5.62*** (0.60)

0.19*** (0.02)

6.02***

(1.04) 2.07***

(0.16) 5.72***

(1.12) 2.41*** (0.157)

4.53*** (0.662)

2.26*** (0.10)

No of observations 18187 15096 3497 2839 7468

Prob >chi2 0.0000 0.0000 0.0000 0.0000 0.000 Pseudo R2 0.3446 0.387 0.375 0.454 0.365

*significance at 10% **significance at 5% ***significance at 1% # missing values

14

Results on economic variables shows that wealth index was significant at 1%. The coefficients for

the logit and the negative binomial model shows that the size of the coefficients increases as one

moves from a lower to higher wealth index. This was more pronounced in 2003 with coefficient

as -2.86 and 2008 when the coefficient for the negative binomial model is -0.78. The results implies

that wealth is a major determinant of antenatal care use and number of antenatal visits. The

respondent's employment status in the model was significant at 1%. This is more pronounced in

1990 when the coefficients of the logit and negative binomial model was -0.39 and -0.19

respectively. Respondents not employed had 39% and 19% likelihood of not attending antenatal

care and the required number of visits. “Distance to health facility" and "transport to health facility"

which represented the price for accessing antenatal care in the model, were also statistically

significant at 1%. Women who found "distance to health facility" and "transport to health facility"

as a big problem had lower probability of deciding to go for antenatal care as well as undertaking

the required number of antenatal visits compared to those that do not see these factors as a big

problem. This is more pronounced in 2013 when the coefficients for both models were -0.55 and

-0.36, respectively.

The results on the variables "no provider" and "no female provider" were also significant

at 1% for most of the years. This implies that women who viewed "no provider" and "no female

provider" as a big problem were less likely to utilize antenatal care compared to those who do not

view it as a problem. The results obtained on non-economic variables shows that respondent’s

educational status is positively and significantly correlated with antenatal care use and the

frequency of antenatal visits. With higher education as the reference category, the signs and

coefficients for other educational status were negative and significant. This implies that women

with "no education" and other lower educational status compared to the reference category were

less likely to go for antenatal care than women with higher education. This is more evident in

2008 with coefficient -1.90 and -0.34 for the logit and negative binomial model respectively.

Other non-economic variables that were significant at 1% include ethnicity, region,

residence, age and religion. The results on ethnicity using “Hausa” as the reference category,

shows that Hausa women and those from minority tribes were less likely to report for examination

when pregnant and less likely to meet up with the requirements for antenatal care visits compared

to the Yoruba and the Igbo women. In line with ethnicity, regional differences in antenatal care

utilization were also observed. Given the South West as the reference category, women from other

15

regions were less likely to utilize antenatal care compared to South West; this is in line with Nwosu

et al (2012). Age of the respondent in line with priori expectation has positive sign and it is

significant, implying that the probability of deciding to go for antenatal care and sustaining regular

visits increased with the age of the respondents, this is in contrast with the study by (Awusi et al

2009) older women were more likely to go for antenatal care compared to younger women.

4.3 Discussion

Based on the results, key variables are crucial in considering the frequency of antenatal care visits

and antenatal care use because of higher magnitude the variables possess compared to others in

the two-part model. These variables include, Wealth, education, region "distance to health facility"

and residence. Although other variables are statistically significant their magnitude in terms of the

size of the coefficients are less. The significance of wealth is in line with the literature (Babalola

and Fatusi, 2009; Goland et al 2012; Nketiah-Amponsah et al, 2012; Babalola, 2014; Edgard-

Marius et al, 2015; Author, 2012; Fagbamigbe and Ademudia, 2016; as well as Owoo and

Lambon-quayefio, 2013). The statistical relationship and the magnitude of wealth index in the

antenatal care utilization model shows that wealth represent household ability to pay for health

services. In this case, higher wealth and thereby higher budget by individual households and

government will influence women to seek more health care. Another reason why wealth is

significant despite free antenatal care services in most public hospitals is that, some health

providers extort women who came for antenatal care. Few charges were collected before

women can access free care. At other times, some corrupt health personnel extort illegal fees

from patients.

Education is an important correlate with good health. Better educated persons tend to have

healthier lifestyles and are expected to be more efficient producers of health (Grossman 1972).

Also, they have knowledge of the effects of different health care measures and with the ability to

use this information more effectively. They are expected to be able to determine which health care

measures should be undertaken at different situations. As such, educated women know the

importance of antenatal care. This also explains why antenatal care is underutilized by women

with no education. Education was also associated with higher income and affordability of services.

However, the result for 1990 survey was conflicting with other results. This may be due to the fact

that the individuals interviewed for higher education were very few compared to the other

16

categories of educational attainment during the 1990 survey. However, other categories of

education were not significant in 2003 and 1999. In addition to respondent's education, partner's

education is also found to be significant for some of the years. This means that the education of

husbands was also important in determining antenatal care utilization. This is more evident in 1999

with coefficient -1.76 and -0.56 for the two models. To buttress the role of partner's decision in the

utilization of antenatal care by a woman in 1999, about 38% of women who were single as at the

time of the survey had lower probability of visiting health facilities frequently compared to women

who were married. The findings on education is in line with other studies (Babalola and Fatusi,

2009; Adamu, 2011; Goland et al, 2012; Jat et al,; 2011; Nwosu et al, 2012; Dairo and Owoyokun,

2010; Owoo and Lambon-Quayefio, 2013; Nketiah-Amponsah, et al, 2012; Edgard-Marius et al

2015), However, 2003, 1999, and 1990 at the lower educational category had exceptions.

The positive sign and level of significance for “residence” suggest that the location in terms

of rural-urban settlement greatly determines the probability of a woman's decision on antenatal

care utilization. If women residing in the rural areas were to move to the urban areas, their

probability of intensifying the use of antenatal care will increase. This is more evident in 1999

when the coefficients was 1.16 for the logit model and in 1990 when the coefficient was 0.27.

Distance and transport represents access cost of antenatal care which had negative impact because

a pregnant woman might not seek health care if the marginal cost of access or the price of the

health care is too high. So long as the marginal cost of "transport to health facility" and "distance

to health facility" is too high relative to income, she will view it as a big obstacle/problem to seek

health care. Travel time is also a cost associated with "distance to health facility" and "transport to

health facility". These variables are significant because, majority of the population live in rural

areas and health care facilities as well as good road infrastructure are concentrated in cities. This

reasoning also explains why residence is significant in most studies. The results on distance and

transport is in line with Author (2012) but at variance with Nketiah-Amponsah et al (2012).

“Employment status” which is one of the economic variables emphasized the role of

income and wealth in antenatal care utilization. Women without employment do not earn income

as such they have less probability of attending antenatal care and undertaking frequent visits.

Insurance status in the model was not significant based on the 2013 and 2008 binomial model

results. This may be because very few people have health insurance policy in Nigeria. Majority of

people with health insurance are in the public sector especially at the federal level. The effect of

17

insurance can be felt by extending insurance coverage to majority of the citizens through

community health insurance policies. In 2013 and 2008, women without insurance were less likely

to attend antenatal care, this is more noticeable in 2008 with coefficient of -1.19. "No provider"

and "no female provider" may be associated with the attitude of the doctors/health workers over

absenteeism at the health facilities especially in rural areas. This may also be an indication of

insufficient workforce in the health facilities. Age of the respondent in line with priori expectation

has positive sign and it is significant, implying that the probability of deciding to go for antenatal

care and sustaining regular visits increased with the age of the respondents. This is in contrast with

the study by (Awusi et al 2009) older women were more likely to go for antenatal care compared

to younger women, the magnitude was more in 2003.

However, Age was not significant for both models in 1999 and for negative binomial in

1990. The level of significance also varies between years; at 1%, 5% and 10%, respectively.

Religion is also a factor that determines antenatal care utilization. The results show that

“Christians” which is the reference category are more likely to utilize antenatal care compared to

other religions. This is evident in 1990, when the coefficient for the traditional religion was -1.03

and -0.31 for both models. Birth order is also significant with negative sign for most of the results,

this suggest that, the probability of antenatal care utilization is decreasing with birth order. Women

with more children have less probability of deciding to go for antenatal care as well as having at

least 4 visits. This also is in line with other studies (Babalola and Fatusi, 2009; Adamu, 2011;

Goland et al, 2012; Jat et al,; 2011; Nwosu et al, 2012; Dairo and Owoyokun, 2010; Owoo and

Lambon-Quayefio, 2013; Nketiah-Amponsah, et al, 2012; Edgard-Marius et al 2015; Awusi et al

2009; Emelumadu et al; 2016; Onasoga et al, 2012; Ononokpono, 2015)

Although most of the studies obtained similar findings, these studies used the logit and

poison or descriptive statistics, in estimating antenatal care utilization, which is different from

what was used in this study. The difference lies in the fact that a variable may influence the first

visit but may not influence the frequency of antenatal visits therefore, in considering which

variable is important at each stage of decision, the two-part model is appropriate. For instance, the

2013 results show that in some regions; the North Central, South East and South South were not

significant for the logit model but significant for the negative binomial model. This implies that

the decision to go for antenatal care or not is not a major issue in 2013, although the decision to

attend antenatal care had improved in 2013 more should be done to improve the frequency of visits

18

especially among women to meet up with the WHO minimum standard for number of visits before

delivery. Another example is if wealth is assumed as the variable of interest, and wealth in the

logit model is significant while in the negative binomial it is not significant, it means the decision

to go for antenatal care is determined by wealth. But once the contact is made with the health care

provider, the health care provider or other factors then will determine the frequency of visits and

not wealth any more. This is typical of the findings by Ortiz (2007) and Nunez and Chi (2013),

where the patient first determines and takes the decision to use health care, but subsequently, the

health care provider now determines the frequency of visits. In this study region and mother's age

are both significant for the first antenatal visit and frequency of visits. This is in contrast to the

findings by Ortiz (2007) in Columbia.

5. Conclusions and policy recommendations

Following the results obtained from the study, the following conclusions are drawn. Economic and

non-economic factors such as wealth, employment status, “distance and transport to health

facilities”, insurance status, "no provider and no female provider" education, region, residence,

religion, age and birth order are significant in antenatal care utilization in Nigeria. This cut across

all the surveys with few exceptions especially in 1990. Although most variables are significant for

the logit and negative binomial model, it is not in all cases. As such the two-part model analysis is

relevant as it shows the significance of each variable in explaining the determinants of the first

antenatal visit and the frequency of visits. Secondly, wealth, education, region, "distance to health

facilities" and residence have higher magnitude compared to all other variables in the model

The study recommends that interventions that will reduce access costs of “distance and

transport to health facilities” should be enhanced. This can be achieved by establishing more public

health facilities and equipping them especially in rural areas. The results also suggest that

improvement in antenatal care utilization will require increased investment in women’s education

as well as that of their partners at all levels. Programs that target improvement in women's wealth

status should also be a priority of the government if they want to increase antenatal care utilization

in Nigeria. This can be achieved by providing employment opportunities to women. This will

require training in income generating skills as well as strengthening of their entrepreneurial

capacity through training and provision of credit facilities.

19

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22

Appendix

Table A: antenatal care utilization by socio-economic factors

2013 NDHS REPORT 2008 NDHS REPORT 2003 NDHS REPORT 1999 NDHS REPORT 1990 NDHS REPORT

Socioeconomic

characteristics Doctor

TN/W

No

ANC

No. of women

Doctor TN/W

No

ANC

No. of women

Doctor TN/W

No

ANC

No. of

women

Doctor TN/W

No

ANC

No. of

women

Doctor TN/W

No

ANC

No. of

women

RESIDENCE

Urban 83.2 10.6 7,278 82.7 15 1,144 78.8 11.8 5,330 83.5 10.3 984 84.3 11.1 1,714

Rural 43.7 46.7 13,189 47.8 46 2,766 41.6 46.9 12,305 55.9 37.2 2,563 49 41.1 6,399

REGION

North Central 65.5 26 2,890 73.8 25.3 575 57.4 26.2 2,525 76.2 20.2 788

North East 42.6 40.8 3,434 47.3 47.1 862 36.5 51.2 2,751 25.9 54.1 629 36.4 54.7 1,924

North West 39.7 55.4 7,445 36.9 59 1,341 28.7 67.1 5,372 49.4 65.1 649 45.2 52.4 2,242

South East 86.3 4.2 1,719 96.2 0.8 222 75.1 7.4 1,603 77.3 7.7 777 64.5 19.6 2,422

south West 87.3 5.7 2,002 91.9 2.3 544 84.2 5.7 2,310 106 3.5 704 85.5 7.7 1,525

South South 71.5 20.6 2,977 72.1 16.8 367 66.2 18.8 3,075

EDUCATION

No education 33.6 57.7 9,794 35.9 59.6 1,989 27.5 63.7 8,017 38.6 54.4 1,714 43.4 47.9 5,091

Primary 68.2 20.5 3,915 72 20.3 918 62 23.1 4,012 81.6 10.6 868 67.4 15 1,212

Secondary 84.6 8.4 5,475 87.5 8.1 862 79.9 7.9 4,557 91.2 3.4 827 74.2 8 459

More than

secondary

96.4

1.1 1,283 98.1

1.7

143

93.6

1.2

1,050 94.2

0.8

138

78.8

2.6

521

WEALTH QUINTILE

Lowest 22.5 69.4 4,699 34 59.7 852 20.4 71 4,074

Second 41 47.8 4,588 37.3 58.1 846 35.8 52.7 3,916

Midle 64.5 25.3 3,902 56.5 37.2 808 56.8 27.9 3,350

Fourth 82.5 10.3 3,674 77.1 18 735 75.2 13.1 3,204

Highest 92.6 3.1 3,604 95.8 1.8 670 89.8 2.9 3,091

Source: Extracted from NDHS report

23

Table B: Descriptive Statistics

NDHS 2013 NDHS 2008 NDHS 2003 NDHS 1999 NDHS 1990 Variable Definition mean SD mean SD mean SD mean SD mean SD

Antenatal care Logit model 0 if no visit, 1 if respondent had 1 or more visits. 0.663

0.473

0.583 0.493 0.656

0.475

0.660

0.474

0.662 0.473

Negative binomial model Antenatal visits from 1 and above 5.32

6.14 4.405 5.643 5.169

6.129

4.746

4.889

5.042 5.164

ECONOMIC VARIABLES Income variables

Wealth index (ref: richest) Poorest 1 if respondent belong to poorest 20% of

respondent; 0 if otherwise 0.240

0.472

0.274 0.446 0.248

0.432

0.201

0.401

0.212 0.408

Poorer 1 if respondent belong to poorer 20% of

respondent; 0 if otherwise 0.229

0.420

0.246 0.431 0.219

0.413

0.203

0.402

0.190 0.392

Middle 1 if respondent belong to middle 20% of

respondent; 0 if otherwise 0.205

0.404

0.202 0.402 0.202

0.402

0.202

0.401

0.201 0.401

Richer 1 if respondent belong to richer 20% of respondent;

0 if otherwise 0.186

0.389

0.160 0.367 0.186

0.389

0.196

0.397

0.197 0.398

Employment status (ref: employed) not employed 1 if not employed; 0 if employed 0.235 0.424 0.293 0.455 0.292 0.455 0.392 0.488 0.268 0.443

PRICE VARIABLES

Distance to health facility (ref: not a big problem)

big problem 1 if distance is a big problem; 0 if otherwise 0.329 0.470 0.412 0.492

Transport to health facility (ref: not a big problem) big problem 1 if transport is a big problem; 0 if otherwise 0.395 0.489 0.529

0.499

Small problem 0.198 0.399

Insurance status (ref: insured)

No insurance 1 if not insured, 0 if insured 0.984 0.127 0.987 0.114

SUPPLY VARIABLES

No provider (ref: not a big problem)

Big problem 1 if no provider is a big problem; 0 if otherwise 0.372 0.483

No female provider (ref: not a big problem)

Big problem 1 if no female provider is a big problem; 0 if

otherwise

0.227 0.419 0.718

0.450

NON-ECONOMIC VARIABLES Age Age of respondent 15 to 49 35.99 8.073 35.69 8.110 35.68 8.114 34.89 7.876 34.30 7.774 Respondent's education (ref: higher) No education 1 if no education; 0 otherwise 0.510 0.500 0.559 0.497 0.582 0.493 0.562 0.496 0.669 0.471 Primary 1 if has primary education; 0 otherwise 0.234 0.424 0.238 0.426 0.237 0.425 0.253 0.435 0.234 0.424 Secondary 1 if has secondary education; 0 if otherwise 0.203 0.403 0.161 0.368 0.143 0.350 0.145 0.352 0.084 0.277

24

Partner's education (ref: higher) No education 1 if no education; 0 if otherwise 0.429 0.495 0.475 0.499 0.476 0.499 0.456 0.498 0.555 0.497 Primary 1 if has primary education; 0 if otherwise 0.211 0.408 0.222 0.415 0.247 0.431 0.263 0.440 0.269 0.443 Secondary 1 if has secondary education ; 0 if otherwise 0.24 0.43 0.205 0.404 0.177 0.382 0.175 0.380 0.131 0.338 Birth order/number of children Birth order Birth order 1 and above 3.526 2.379 3.565 2.418 3.686 2.481 3.385 2.280 3.506 2.338 Marital status (ref: married ) Single 1 if single, 0 if married 0.071 0.256 0.067 0.249 0.070 0.255 0.061 0.240 0.065 0.247 Ethnicity (ref. Hausa)

Igbo 1 if Igbo; 0 if otherwise 0.111 0.314 0.106 0.308

Ijaw/izon 1 if Ijaw/izon; 0 if otherwise 0.038 0.190 0.031 0.175

Kanuri/beriberi 1 if Kanuri/beriberi; 0 if otherwise 0.015 0.122 0.033 0.178

Tiv 1 if Tiv; 0 if otherwise 0.016 0.126 0.028 0.166

Yoruba 1 if Yoruba; 0 if otherwise 0.113 0.316 0.106 0.308

Others 1 if Others; 0 if otherwise 0.292 0.455 0.326 0.469

Region (ref south west)

North Central 1 if from North Central; 0 if otherwise 0.135

0.341

0.178 0.383 0.163

0.370

0.214

0.410

North East 1 if from North East; 0 if otherwise 0.202 0.402 0.231 0.422 0.238 0.426 0.190 0.393 0.243 0.429 North West 1 if from North West; 0 if otherwise 0.325 0.468 0.276 0.447 0.288 0.453 0.160 0.367 0.212 0.409 South East 1 if from South East; 0 if otherwise 0.095 0.293 0.088 0.284 0.109 0.312 0.216 0.411 0.279 0.448 South South 1 if from South South; 0 if otherwise 0.122 0.327 0.114 0.318 0.101 0.301

Residence (ref: rural)

Urban 1 if from urban; 0 if otherwise 0.326 0.469 0.251 0.434 0.361 0.480 0.291 0.454 0.337 0.473 Religion (ref. Christianity )

Islam 1 if respondent practice Islam; 0 if otherwise 0.578 0.494 0.560 0.496 0.566 0.496 0.176 0.381 0.499 0.500 Traditionalist 1 if respondent practice Traditional religion; 0 if

otherwise 0.013 0.115

0.022 0.148 0.025 0.157

0.496 0.500

0.028 0.165

Poorest Muslim 1 if in the poorest category of Muslims; 0 if

otherwise 0.212

0.409

0.199 0.399 0.141

0.348

0.019

0.135

0.093 0.291

Richest Muslim 1 if in the richest category of Muslims; 0 if

otherwise 0.045

0.208

0.039 0.195 0.050

0.217

0.051

0.220

Wealth and residence

Poorest rural 1 if among the poorest from rural; 0 if otherwise 0.23 0.418 0.262 0.440 0.225 0.417 0.192 0.394 0.203 0.402 Richest rural 1 if among the richest from rural; 0 if otherwise 0.02 0.13 0.028 0.166 0.031 0.173 0.066 0.249 0.023 0.148