contextual determinants of maternal health care service utilization in nigeria

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Contextual Determinants of MaternalHealth Care Service Utilization in NigeriaDorothy Ngozi Ononokpono PhD a , Clifford Obby Odimegwu PhD b ,Eunice Imasiku MSc c & Sunday Adedini PhD da Department of Sociology and Anthropology, University of Uyo,Uyo, Nigeria, and Demography and Population Studies Programme ,University of the Witwatersrand , Johannesburg , South Africab Demography and Population Studies Programme , University of theWitwatersrand , Johannesburg , South Africac Department of Geography, School of Natural Sciences, Universityof Zambia, Lusaka, Zambia, and Demography and Population StudiesProgramme , University of the Witwatersrand , Johannesburg , SouthAfricad Demography and Social Statistics Department, Obafemi AwolowoUniversity, Ile-Ife, and Demography and Population StudiesProgramme , University of the Witwatersrand , Johannesburg , SouthAfricaAccepted author version posted online: 05 Aug 2013.Publishedonline: 04 Oct 2013.

To cite this article: Dorothy Ngozi Ononokpono PhD , Clifford Obby Odimegwu PhD , Eunice ImasikuMSc & Sunday Adedini PhD (2013) Contextual Determinants of Maternal Health Care Service Utilizationin Nigeria, Women & Health, 53:7, 647-668, DOI: 10.1080/03630242.2013.826319

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Women & Health, 53:647–668, 2013Copyright © Taylor & Francis Group, LLCISSN: 0363-0242 print/1541-0331 onlineDOI: 10.1080/03630242.2013.826319

Contextual Determinants of Maternal HealthCare Service Utilization in Nigeria

DOROTHY NGOZI ONONOKPONO, PhDDepartment of Sociology and Anthropology, University of Uyo, Uyo, Nigeria, and

Demography and Population Studies Programme, University of the Witwatersrand,Johannesburg, South Africa

CLIFFORD OBBY ODIMEGWU, PhDDemography and Population Studies Programme, University of the Witwatersrand,

Johannesburg, South Africa

EUNICE IMASIKU, MScDepartment of Geography, School of Natural Sciences, University of Zambia, Lusaka, Zambia,

and Demography and Population Studies Programme, University of the Witwatersrand,Johannesburg, South Africa

SUNDAY ADEDINI, PhDDemography and Social Statistics Department, Obafemi Awolowo University, Ile-Ife,

and Demography and Population Studies Programme, University of the Witwatersrand,Johannesburg, South Africa

Despite the high maternal mortality ratio in Nigeria, the use ofmaternal health care services is very poor. Attempts to explainthis situation has focused on individual level factors and theinfluence of community contextual factors have not receivedmuch attention. This study examined the relation of communityfactors to the use of antenatal care in Nigeria, and explored

Received November 28, 2012; revised June 7, 2013; accepted July 13, 2013.This research was partially funded by the African Doctoral Dissertation Research

Fellowship offered by the African Population and Health Research Center (APHRC) in part-nership with the International Development Research Centre (IDRC), and Council for theDevelopment of Social Science Research in Africa (CODESRIA). The authors would liketo thank the University of the Witwatersrand Strategic Planning and Resource AllocationCommittee (SPARC) for its support for this study. The authors also thank MEASURE DHSfor permission to use the 2008 Nigeria DHS data.

Address correspondence to Dorothy Ngozi Ononokpono, PhD, Department of Sociologyand Anthropology, University of Uyo, Uyo, 52001, Nigeria, and Demography and PopulationStudies Programme, University of the Witwatersrand, Johannesburg, South Africa. E-mail:[email protected]

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648 D. N. Ononokpono et al.

whether community factors moderated the association betweenindividual characteristics and antenatal care visits. Data weredrawn from the 2008 Nigeria Demographic and Health Surveyamong 16,005 women aged 15–49 years who had had theirlast delivery in the five years preceding the survey. Results frommulti-level models indicated that living in communities with ahigh proportion of women who delivered in a health facility wasassociated with four or more antenatal care visits. Residence inhigh-poverty communities decreased the likelihood of antenatalcare attendance. Living in communities with a high proportionof educated women was not significantly related to antenatalcare visits. Community factors acted as moderators of the associ-ation between educational attainment and antenatal care atten-dance. Improvement in antenatal care utilization may therefore beenhanced by targeting poverty reduction programs and increasinghealth facility delivery in disadvantaged communities.

KEYWORDS antenatal care visits, community, maternal healthcare service, multi-level models, Nigeria

INTRODUCTION

Maternal health is a global concern. More than 358,000 women worldwidedie each year during pregnancy and in the postpartum period; about 99%of these deaths occur in developing regions, with sub-Saharan Africa andsouthern Asia accounting for 87% of maternal deaths (Brown & Small, 2012;Kistiana, 2009). The maternal mortality ratio in sub-Saharan Africa is 500 per100,000 live births (United Nations Population Fund [UNFPA], 2012). TheWorld Bank estimate of maternal mortality ratio of 630 per 100,000 live birthsin Nigeria (World Bank, 2013) is an indication of the poor maternal healthsituation in the country.

Most often, the high maternal mortality has been attributed to inade-quate or non-use of maternal health care services, such as antenatal, delivery,and postnatal care (Onah, Ikeako, & Iloabachine, 2006). In Nigeria, the useof maternal health care services differs significantly between north and south,with the latter faring better than the former (Doctor, 2011). Hence, the pooruse of antenatal care (ANC) largely contributes to the overall high maternalmortality ratio in the country. The ANC policy in Nigeria also follows thelatest World Health Organization (WHO) approach (Focused Antenatal Care)that seeks to promote safe pregnancies. The updated approach recommendsat least four ANC visits for women without complications and emphasizes thequality of care necessary during each visit (National Population Commission[NPC] & Inner City Fund [ICF] Macro, 2009). The number of ANC visits awoman makes during pregnancy is important in preventing complications

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Maternal Health Care Service in Nigeria 649

and adverse maternal health outcomes (Ikamari, 2004). ANC visits also pro-vide an opportunity for pregnant women to identify complications associatedwith pregnancy and benefit from other interventions including counseling onhealthy lifestyles and the management of complications (Kistiana, 2009).

Despite the call of the International Conference on Population andDevelopment (ICPD) to improve the use of maternal health care servicesand reduce maternal mortality, more than half the women in Nigeria (55%)attend fewer than the four recommended ANC visits (NPC & ICF Macro,2009). Babalola and Fatusi (2009) found that this level of use of ANC inNigeria was lower than in other countries in Africa. Attendance was as highas 88% for Benin, 83.4% for Cameroun, and 91.9% for Ghana.

Several studies have established the relationship between demo-graphic and socio-economic characteristics and ANC. Studies in Nepal andBangladesh have shown that predisposing and enabling characteristics suchas educational level, autonomy, household structure, household wealth, andplace of residence were significantly related to the use of ANC (Haque, 2009;Matsumura & Gubhaju, 2001). Current use of contraception and the frequentvisit of health workers to respondents were significantly associated with useof ANC (Abedine, Islam, & Hossain, 2008). Family size and access to a healthcare facility have been found to be strongly related to the use of maternalhealth care (Chakraborty et al., 2003). Studies in Nigeria have revealed thatthe perceived quality of care, religion, ethnicity, income-yielding occupa-tions, and saturation of mass media were also significantly related to theuse of antenatal care (Awusi, Anyanwu, & Okeleke, 2009; Babalola & Fatusi,2009; Iyaniwura & Yussuf, 2009). Adamu (2011) found that the utilizationof maternal health care service varied across the regions of Nigeria, and thateducation and family wealth index were strongly related to service utilizationin all the regions.

Maternal health care utilization is not only related to individualchoice or characteristics but also to a large extent depends on the socio-cultural arrangements of communities (Shaikh Haran, & Hatcher, 2008).Understanding the role of community factors in studies of maternal healthcare service utilization is important because decisions to seek health carecan be related to the characteristics of the community in which a womanlives (Mackian, 2003). Moreover, social ecological perspectives empha-sized the contribution of multiple relations of physical socio-cultural andenvironmental conditions to health behavior (Stokols, 1996).

A few previous studies have documented the relation of contextual fac-tors to the use of maternal health care (e.g., Babalola & Fatusi, 2009; Gage,2007; Stephenson et al., 2006; Tiwari, 2010). Other studies have rarely exam-ined all the contextual dimensions of communities and have thus failedto assess how these often overlapping dimensions of communities couldbe related to individual health care behavior. Therefore, examining otherdomains of communities such as community poverty and community massmedia exposure which were not considered in some previous studies would

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help to test more theories regarding community variables. To the best of theauthors’ knowledge, these previous studies have largely ignored the mod-erating effects of community factors on the association between individualfactors and ANC visits. Thus, examining the moderating effects of communityfactors would be useful in identifying community factors that might mitigatethe effects of individual level factors on health outcomes (Vu, 2005).

The present study sought to examine the separate effects of communityfactors and the moderating effects on the association between individualfactors and ANC visits in Nigeria. Specifically, the study sought to answer thefollowing important research questions:

Do community factors have a significant relation to maternal health careservice use in Nigeria? To what extent do community factors moderatethe association between individual factors and antenatal care visits?

METHODOLOGY

Study Sample

The study drew data from a cross-sectional survey, the 2008 NigerianDemographic and Health Survey (NDHS) (NPC & ICF Macro, 2009). The2008 NDHS provided information on population and health indicators at thenational and state levels. Nigeria is made up of 36 states and a federal capi-tal territory (FCT; Abuja). All the states and the FCT of Abuja were selectedto be in the sample. Each state is subdivided into local government areas(LGAs). Each LGA is divided into localities which are then subdivided intocensus enumeration areas (EAs). In the 2008 NDHS, the EAs were groupedby states, by LGAs within the state, and by localities within an LGA. Thesample frame for the survey was lists of EAs developed from the 2006 pop-ulation census. The primary sampling unit (PSU), which is referred to as thecluster (civil administrative unit), was selected from the lists of EAs.

The sample for the survey was selected using a stratified, two-stagecluster design, composed of 888 clusters: 286 in the urban and 602 in therural areas (NPC & ICF Macro, 2009). A weighted probability sample of36,800 households was selected in the survey and a minimum of 950 inter-views were completed for each state. For each cluster, a listing of householdsand mapping was done, and the lists of households were used as the sam-pling frame for the selection of households in the second stage. All privatehouseholds were listed, and on average 41 households were selected ineach cluster, by equal probability systematic sampling. All women aged15–49 years were interviewed and a total sample of 33,385 women aged15–49 years was selected. In these analyses, the sample consisted of the16,005 women who had last given birth in the five years preceding thesurvey. In the selection of the sample, only the last birth was considered;

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all other previous births were excluded. The preference for the last deliv-ery was occasioned by the fact that information on maternal health care forthe last birth tended to be more accurate than that given for previous births(Kistiana, 2009).

VARIABLES AND DEFINITIONS

The outcome variable was ANC visit, defined as the number of ANC visits awoman had during her last pregnancy. An adequate number of ANC visitswas considered to be four or more. This was a binary variable coded as 1, ifa woman had 4 or more antenatal care visits, and 0 if she had no visits orfewer than four visits. The individual variables1 included maternal age at lastbirth, education, religion, ethnic origin, occupation, a woman’s autonomy,and household wealth index. Maternal age at delivery was calculated bysubtracting the century month code (CMC) of the date of birth of the childfrom the CMC of the date of birth of the respondent. Maternal age wascategorized as 15–24, 25–34, and 35–49 years. Education was defined asthe highest level of education attended by the respondent and categorizedas: no education, primary (Grades 1–6), secondary (Grades 7–12), andhigher education (tertiary). Religion was measured as the religious affiliationof the respondent and categorized as Muslim, Christian, and traditionalreligion/others. Ethnicity was categorized as Hausa/Fulani/Kanuri (a mergerof Hausa, Fulani, and Kanuri ethnic groups was done due to the smallnumber of Fulani and Kanuri women in the sample), Igbo, Yoruba, andNorthern/Southern (all the minority groups were merged due to the smallnumber of the groups in the sample and because they seemed to have hadthe same health and development indicators). Occupation was measuredas the respondents’ occupation and re-grouped into formal employment(professional/technical/managerial/clerical/sales/services/skilled manualworkers), agricultural employment, unskilled manual workers, and unem-ployed. Informal employment (private paid jobs) was included in the formalcategory.

Woman’s autonomy was measured as the degree of decision-makingoccurring on a woman’s own health care. The household wealth index wasthe DHS wealth index measured as a standardized composite variable madeup of quintiles. This was determined through principal component anal-ysis (from factor analysis) and based on household assets (e.g., type offlooring, water supply, electricity, radio, television, refrigerator, and type ofvehicle). The index was constructed by assigning a factor score to each of thehousehold assets. Each household was assigned a score for each asset, andindividuals were then ranked according to the total score of the household inwhich they live (NPC & ICF Macro, 2009). The household wealth index of thesample was then categorized into five quintiles. Each quintile represented a

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relative measure of a household’s socioeconomic status (Rutstein & Johnson,2004).

The community-level variables included region of residence, communityhospital delivery, community women’s education, community mass mediaexposure, and community poverty. Region of residence was defined as anyone of the geopolitical zones with administrative boundaries categorized as:North Central, North East, North- West, South East, South-South, and SouthWest. Community hospital delivery was defined as the proportion of womenthat gave birth in a health facility in the PSU. This measure was dividedinto three tertiles and categorized as low, medium, and high. Communitywomen’s education was the proportion of women with secondary and highereducation in the PSU. Community mass media exposure was the proportionof women exposed to mass media (radio and television) in the PSU. The pro-portion was divided into three tertiles and categorized as low, medium, andhigh. Community poverty was constructed based on the household wealthindex variable and defined as the proportion of women from the poor andpoorest households or wealth quintiles in the PSU. The measure was dividedinto three tertiles and categorized as low, medium, and high.

The community variables were constructed by aggregating the individ-ual characteristics at the community level (cluster or PSU). The cluster is anadministrative unit used as a proxy for the community (Koening et al., 2003).A total of 886 (PSUs) were represented in the study, each cluster compris-ing a minimum of 80 households. The measurements related to communitywomen’s education, community hospital delivery, community poverty, andcommunity mass media exposure were constructed based on individual mea-sures of educational attainment, place of delivery (health facility), householdwealth index and media exposure. These variables were aggregated at thePSU and divided into tertiles, defined as low, medium, and high. In construct-ing the community variables, the index woman was excluded to preventinter-correlation between individual and community variables (Do, 2008).

STATISTICAL METHODS

The distribution of respondents by key variables was assessed and expressedas percentages. At the bivariate level, frequencies and cross-tabulations wereused to identify the distributions of the outcome variables by selected back-ground characteristics. The chi square test of association was used to testthe statistical significance of these bivariate distributions. Sample weightsprovided in the DHS data were applied for the univariate and bivariateanalyses to adjust for non-response and over-sampling of some areas. Forall analyses, the Stata 11.1 software package was used. Multi-level logis-tic regression was used to assess the relation of measured individual andcommunity level factors. Multi-level analysis was considered appropriate, toaccount for the hierarchical nature of the NDHS data (Antai, 2009) and to be

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able to estimate the relation of community-level variables on the outcomevariable. A two-level multi-level logistic regression model was applied con-sisting of two sub-models at level 1 and level 2. Individuals (level 1) werethus nested within communities (level 2). The level 1 model representedthe relationships of the individual-level variables to the outcome, while thelevel 2 model examined the relation of community-level factors. A two-levelmulti-level model for a dichotomous outcome used a binomial sampling anda logit link (Vu, 2005). In the level 1 model, the outcome variable Yij forindividual i living in community j is written as follows:

Probability(Yij = 1|B) = �ij

Level 1 variance = [�ij

(1 − �ij

)]∗

Predicted log odds ηij = log[�ij/

(1 − �ij

)]

ηij = β0j +∑

βqj Xqij (1)

q=1

where �ij was the probability that the ith individual in the jth communityhad the value 1 (“1” indicated that the event occurred); β0j was the level1 intercept, βqj was the level 1 coefficients; Xqij was level 1 independent qfor the ith individual within the jth community.

In the level 2 model, each of the level 1 coefficients, βqj defined in thelevel 1 model became an outcome (Vu, 2005) and could be expressed asfollows:

βqj = γ q0 + γ q1W1j + γ q2W2j + ............. + γ qsqWsqj + uqj

Sq

= γ q0 +∑

γqsWsj + uqj (2)

s=1

where γ qs (q = 0,1, . . . . . . . . . . . . . . . . . . .. Sq) were level 2 coefficients;Wsj were level 2 predictors and uqj is level 2 random effects.

All the level 2 random effects were assumed to have normal distributionwith mean of zero and variance of τqq (Vu, 2005). A comparison of thevariance component (τqq) of the intercept (β0) with its standard error gavean indication of variations among communities in the use of antenatal careservice.

Overall, four models containing variables of interest were fitted for theoutcome variable. The first model, called the “empty” or “null” model, wasfitted without explanatory variables. It decomposed the total variance intoindividual and community components. The empty model was used to deter-mine the overall difference between communities and individuals in ANC

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visits. The second model, referred to as the “individual” model included onlyindividual characteristics. This was to allow the assessment of the associationbetween the outcome variable and individual characteristics. A third modelwas also estimated which contained only the community characteristics toallow the assessment of the relation of the community variables to the out-come variable. The fourth model was generated and included explanatoryvariables at both the individual and community levels. This model allowedthe assessment of the relation of both individual and community variablesto ANC visit. The variables were retained in each model if a significant (p <

0.05) variance component was observed or if they were biologically impor-tant (e.g., maternal age). The fifth model (final model) was used to estimatewhether community factors moderated the association between individualfactors and the outcome variable.

Moderating effects indicated that the magnitude of the associationbetween some individual variables and ANC visits changed as a functionof some community variables. To estimate the moderating effects of com-munity factors, an interaction term was introduced into the model. Thisincluded the cross-level interaction between community poverty and edu-cational attainment. These variables were significantly and positively relatedto health outcomes in previous studies (Gage, 2007). In the final model,only significant (p < 0.05) community and individual-level variables wereretained. Maternal age at last delivery was retained in the model based on itsbiological importance. Educational attainment and community poverty wereexcluded from the model to prevent collinearity.

In the multi-level models, fixed effects referred to the average relationof individual and community covariates on ANC visits and were expressed asodds ratios (OR) and 95% confidence intervals. The random effects were themeasures of variation in ANC visit across communities. Put differently, ran-dom effects were measures of the differences between communities in therelation of the explanatory variables to the outcome variable. The ratio of thevariance at the community level to the total variance was the intra-class cor-relation coefficient (ICC). The precision was measured by the standard error(SE) of the independent variables (Antai, 2009). The results of random effects(which were the measures of variation) were expressed as Variance PartitionCoefficient (VPC). The VPC was calculated based on the linear thresholdmodel method or latent variable method that converted the individual levelvariance from the probability scale to the logistic scale, on which the com-munity level variance was expressed (Merlo et al., 2006). By using the linearthreshold model, the unobserved individual outcome variable followed alogistic distribution with individual level variance σ 2

e equal to π 2/3 (equal to3.29). The VPC corresponded to the intra-class correlation coefficient (ICC)2

which was a measure of general clustering of the individual outcome ofinterest in the communities. The maximum likelihood was evaluated by inte-grating the random effects using the adaptive Gaussian quadrature (AGQ;

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Gutierrez, 2007) available in Stata (version 11.1), while the likelihood ratio(LR) statistic was used to test the null hypothesis that the community levelvariance was equal to zero.

ETHICAL CONSIDERATION

This study comprised analyses of existing survey data with all identifier infor-mation removed. The conduct of the survey was approved by both theEthics Committee of the Opinion Research Corporation Macro InternationalIncorporated (ORC Macro Inc., Calverton, Maryland, USA), at Calverton andthe National Ethics Committee in the Federal of Ministry of Health (Abuja,Nigeria). Written and signed informed consent was obtained from all the par-ticipants before participation in the survey, and information was collectedanonymously and confidentially (NPC & ICF Macro, 2009, 528).

RESULTS

Descriptive Information of Respondents

Among female participants, 37% were in the age group 15–24 years, whereasthe largest proportion (45%) belonged to age group 25–34 years (Table 1).Women in the age group 35–49 years constituted the smallest percentageof the sample population. A significant percentage (45%) of women had noeducation. However, 26% and 23% of women had attained secondary orprimary education, respectively. The lowest proportion (6%) of the samplepopulation had higher education. For the entire sample, 54% were Muslims,44% were Christians. A significant proportion (30%) of the women wereunemployed, while 41% worked in the formal sector. The distribution ofethnic origins of the sample reflected the dominance of Hausa/Fulani/Kanuri40%, Igbo 12%, and Yoruba 15%. The minority ethnic groups from Northernand Southern Nigeria made up 34% of the sample. A significant proportion(45%) of women were in the two poor wealth quintiles, while the lowestproportion were in the richest wealth quintile.

One-third of the women resided in North West, while about 14% and16%, respectively, lived in North Central and North East. The lowest pro-portion of women lived in South East. A significant 43% of the study samplelived in communities with a low proportion of educated women, while 40.3%resided in communities with a low proportion of women that had given birthin a health facility. Women who lived in communities with a high propor-tion of poor households and a high proportion of those exposed to massmedia accounted for 40.1% and 34.7% of the study population, respectively.A substantial 41.1% of the study sample resided in communities with a lowproportion of women from different ethnic groups.

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TABLE 1 Percentage Distribution of Women by Background Characteristics Controlling forAntenatal Care (ANC) Visits, Nigeria, 2008 Demographic and Health Survey (DHS)

All Women ANC Visits

Characteristics (%) (n)≤3 visits

(%)4 or morevisits (%) (P-value)

Maternal age (years) at last birth15–24 36.9 6476 57.3 42.7 0.00125–34 44.7 7847 43.9 56.135–49 18.4 3238 53.2 46.8Educational attainmentNo education 45.4 7969 76.8 23.2 0.001Primary 22.8 4004 40.1 59.9Secondary 25.9 4542 20.1 79.9Higher 5.9 1045 5.9 94.1OccupationUnemployed 30.4 5312 61.6 38.4 0.001Formal employment 41.4 7235 39.1 61.0Agricultural employment 17.2 3005 58.6 41.4Unskilled manual workers 10.9 1910 50.7 49.3ReligionMuslims 54.3 9482 65.3 34.7 0.001Christians 44.0 7685 30.5 69.5Traditional/Others 1.7 297 64.6 35.4Ethnic originHausa/Kanuri/Fulani 39.6 6924 76.7 23.3 0.001Igbo 11.6 2033 19.7 80.3Yoruba 15.0 2627 9.5 90.5Northern/Southern minority 33.7 5887 46.0 54.0Women’s autonomy (decisions over

own health)Wife alone 8.8 1450 31.2 68.8 0.001Wife/husband 33.1 5477 35.5 64.5Husband alone/others 58.2 9634 62.2 37.8Household wealth indexPoorest1 23.1 4059 83.8 16.2 0.001Poor2 22.2 3898 69.1 30.9Middle3 19.0 3332 46.1 53.9Rich4 18.2 3187 25.7 74.3Richest5 17.6 3084 7.6 92.4Region of residenceNorth Central 14.3 2516 47.9 52.1 0.001North East 15.6 2745 67.1 32.9North West 30.4 5337 78.0 22.0South East 9.1 1599 22.0 78.0South South 13.1 2303 35.8 64.2South West 17.4 3061 10.5 89.5Community women’s educationLow6 42.6 7487 78.3 21.7 0.001Medium7 29.0 5097 37.6 62.4High8 28.3 4976 19.1 80.9Community hospital deliveriesLow 40.3 7072 81.6 18.4 0.001Medium 27.4 4807 39.2 60.8

(Continued)

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TABLE 1 Percentage Distribution of Women by Background Characteristics Controllingfor Antenatal Care (ANC) Visits, Nigeria, 2008 Demographic and Health Survey (DHS)(Continued)

All Women ANC Visits

Characteristics (%) (n)≤3 visits

(%)4 or morevisits (%) (P-value)

High 32.4 5682 18.1 81.9Community povertyLow 30.5 5356 13.5 86.5 0.001Medium 29.4 5166 46.5 53.5High 40.1 7039 78.3 21.7Community mass media exposureLow 32.9 5779 74.5 25.6 0.001Medium 32.4 5683 49.5 50.5High 34.7 6098 28.2 71.8

1Poorest denotes the first wealth quintile in the NDHS household wealth index.2Poor is the second wealth quintile.3Middle refers to the third wealth quintile.4Rich is the fourth wealth quintile.5Richest is the fifth wealth quintile.6Low refers to the lowest tertile (first).7Medium is the middle tertile (second).8High refers to the upper tertile (third).

The bivariate results (Table 1) showed that women aged 25-34 yearswere more likely to have four or more ANC visits than older women(35–49). Education was significantly related to ANC visits. Having four ormore antenatal care visits was more likely among women with higher andsecondary education compared to those who had had no education. Womenwho were in formal employment were more likely to have four or moreANC visits compared to the unemployed. Religion was significantly associ-ated with ANC visits, with Muslims exhibiting the lowest levels of attendingfour or more ANC visits compared to Christians. A higher proportion ofwomen from Igbo, Yoruba and Northern/Southern minority ethnic groupsattended four or more ANC visits compared to Hausa/Fulani/Kanuri women.Women who reported taking decisions jointly with their husbands over theirown health were less likely to attend four or more ANC visits compared towomen who made decisions alone. Women in the richest wealth quintilewere more likely to have four or more ANC visits, relative to those in thepoorest household wealth quintile.

The lowest proportion of ANC visits was found among women in NorthWest and North East compared to North Central. Women from South West,South South, and South East had the highest proportion. Women who lived incommunities with a high proportion of educated women, a high proportionof facility delivery and a high proportion who were exposed to mass mediahad a higher frequency of four or more ANC visits. In contrast, the lowest

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TABLE 2 Percentage Distribution of Community Variables in the PrimarySampling Units (PSUs)

Variables Percent

Community women’s educationLow1 46Medium2 29High3 25Community hospital deliveriesLow 43Medium 28High 29Community PovertyLow 25Medium 30High 45Community mass media exposureLow 37Medium 32High 31

Note: The community variables were constructed by aggregating the individual variablesat the PSU, and the measure was divided into three tertiles: low, medium and high.Statistically, tertiles refer to 3-quantiles and are defined as any two points that devidean ordered distribution into three parts, each containing a third of the population.1Low refers to the first tertile (lowest tertile).2Medium is the second tertile (middle tertile).3High refers to the third tertile (upper tertile).

proportion of ANC attendance was observed among women who lived incommunities with a high level of poverty.

Description of Community Characteristics

A sizeable percentage (46% and 43%) of communities had a low proportionof educated women and a low proportion of women who had given birthin a health facility, respectively (Table 2). Over one third were communi-ties with a low proportion of women (a large proportion of women in thelowest tertile) who were exposed to mass media. A substantial 45% werecommunities with high levels of poverty.

MULTI-LEVEL ANALYSIS

The variation in the likelihood of having four or more ANC visits acrosscommunities was significant (τ = 11.071, p = 0.001; Table 3). As shown inmodel 1, the intra-community correlation coefficient (ICC) was 77%. Model2 included the individual variables and results indicated that maternal ageat last delivery was significantly associated with ANC visits. Women aged25–34 years were more likely to attend four or more ANC visits compared to

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Tab

le3

Multi

leve

lLo

gist

icReg

ress

ion

Odds

Rat

ioof

the

Effec

tsof

Indiv

idual

and

Com

munity

Fact

ors

on

Ante

nat

alCar

e(A

NC)

Vis

its,

Nig

eria

,20

08D

emogr

aphic

and

Hea

lthSu

rvey

(DH

S)

Model

1M

odel

4Em

pty

Model

2M

odel

3In

div

idual

and

Model

5m

odel

Indiv

idual

variab

les

Com

munity

variab

les

com

munity

Com

munity

×In

div

idual

Odds

Rat

ioO

dds

Rat

ioO

dds

Rat

ioO

dds

Rat

io

Var

iable

s(9

5%CI)

(95%

CI)

(95%

CI)

(95%

CI)

Fixe

def

fect

sIn

div

idu

alc

ha

ract

eris

tics

Ma

tern

ala

gea

tla

stbi

rth

(yea

rs)

15–2

41.

00—

1.00

1.00

25–3

41.

22∗

(1.0

2–1.

45)

1.18

(0.9

9–1.

41)

1.15

(0.9

6–1.

36)

35–4

90.

98(0

.80–

1.20

)0.

92(0

.75–

1.14

)0.

86(0

.69–

1.06

)

Ed

uca

tion

ala

tta

inm

ent

No

educa

tion

1.00

—1.

00−

Prim

ary

2.92

∗∗∗

(1.9

3–4.

42)

2.57

∗∗∗

(1.8

1–3.

65)

Seco

ndar

y/H

igher

5.88

∗∗∗

(3.1

0–11

.14)

5.15

∗∗∗

(2.9

8–8.

91)

Eth

nic

Ori

gin

Hau

sa1.

00—

1.00

1.00

Igbo

9.61

∗∗∗

(4.0

3–22

.88)

2.97

∗∗(1

.37–

6.43

)3.

63∗∗

(1.6

3–8.

01)

Yoru

ba

32.9

9∗∗∗

(9.8

7–11

0.32

)4.

52∗∗

∗(2

.23–

9.16

)5.

85∗∗

∗(2

.75–

12.4

3)N

orth/So

uth

min

ority

4.30

∗∗∗

(2.4

8–7.

47)

2.41

∗∗∗

(1.6

1–3.

60)

2.91

∗∗∗

(1.8

8–4.

51)

(Con

tin

ued

)

659

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Tab

le3

Multi

leve

lLo

gist

icReg

ress

ion

Odds

Rat

ioof

the

Effec

tsof

Indiv

idual

and

Com

munity

Fact

ors

on

Ante

nat

alCar

e(A

NC)

Vis

its,

Nig

eria

,20

08D

emogr

aphic

and

Hea

lthSu

rvey

(DH

S)(C

onti

nu

ed)

Model

1M

odel

4Em

pty

Model

2M

odel

3In

div

idual

and

Model

5m

odel

Indiv

idual

variab

les

Com

munity

variab

les

com

munity

Com

munity

×In

div

idual

Odds

Rat

ioO

dds

Rat

ioO

dds

Rat

ioO

dds

Rat

io

Var

iable

s(9

5%CI)

(95%

CI)

(95%

CI)

(95%

CI)

Occ

upa

tion

Unem

plo

yed

1.00

—1.

001.

00Fo

rmal

emplo

ymen

t1.

58∗∗

∗(1

.24

–2.

02)

1.57

∗∗∗

(1.2

3–1.

99)

1.61

∗∗∗

(1.2

6–2.

06)

Agr

icultu

ralem

plo

ymen

t0.

95(0

.74

–1.

22)

0.94

(0.7

3–1.

22)

0.95

(0.7

3–1.

22)

Unsk

illed

man

ual

work

ers

1.64

∗∗(1

.18

–2.

27)

1.76

∗∗∗

(1.2

6–2.

47)

1.89

∗∗(1

.34–

2.68

)

Wom

an

’sa

uto

nom

yW

ife

alone

1.00

—1.

001.

00W

ife/

husb

and

1.87

∗∗(1

.30–

2.70

)2.

04∗∗

∗(1

.41–

2.97

)2.

04∗∗

∗(1

.40–

2.96

)H

usb

and

alone/

oth

ers

1.17

(0.8

7–1.

58)

1.41

∗(1

.02–

1.95

)1.

37∗

(0.9

9–1.

89)

Hou

seh

old

wea

lth

ind

exPoore

st1.

00−

1.00

1.00

Poore

r2.

25∗∗

∗(1

.59–

3.19

)1.

92∗∗

∗(1

.42–

2.59

)1.

97∗∗

∗(1

.44–

2.67

)M

iddle

5.10

∗∗∗

(2.7

0–9.

33)

3.12

∗∗∗

(2.0

2–4.

82)

3.28

∗∗∗

(2.1

0–5.

14)

Ric

her

11.3

8∗∗∗

(4.7

6–27

.19)

4.89

∗∗∗

(2.7

4–8.

76)

5.46

∗∗∗

(2.9

6–10

.05)

Ric

hes

t46

.12∗∗

∗(1

2.42

–171

.32)

15.5

5∗∗∗

(6.2

6–38

.62)

3.28

∗∗∗

(6.7

1–44

.52)

Reg

ion

ofre

sid

ence

North

Cen

tral

1.00

1.00

1.00

North

Eas

t0.

59∗∗

(0.3

7–0.

96)

1.23

(0.7

8–1.

93)

0.23

∗∗∗

(0.1

1–0.

48)

North

Wes

t0.

16∗∗

∗(0

.08–

0.32

)0.

33∗∗

∗(0

.18–

0.60

)0.

26∗∗

∗(0

.12–

0.79

)So

uth

Eas

t1.

93∗∗

(1.1

2–3.

34)

1.16

(0.5

3–2.

55)

0.07

∗∗∗

(0.0

3–0.

19)

South

South

0.79

(0.4

9–1.

27)

0.61

∗(0

.38–

1.00

)0.

07∗∗

(0.1

2–0.

79)

South

Wes

t7.

81∗∗

∗(3

.76–

16.2

3)4.

09∗∗

∗(2

.02–

8.32

)0.

17∗∗

∗(0

.08–

0.39

)

660

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Model

1M

odel

4Em

pty

Model

2M

odel

3In

div

idual

and

Model

5m

odel

Indiv

idual

variab

les

Com

munity

variab

les

com

munity

Com

munity

×In

div

idual

Odds

Rat

ioO

dds

Rat

ioO

dds

Rat

ioO

dds

Rat

io

Var

iable

s(9

5%CI)

(95%

CI)

(95%

CI)

(95%

CI)

Com

mu

nit

yw

omen

’sed

uca

tion

Low

1.00

1.00

—M

ediu

m2.

99∗∗

∗(1

.81–

4.97

)1.

32(0

.87–

1.99

)H

igh

3.44

∗∗∗

(1.8

5–6.

39)

0.86

(0.5

1–1.

46)

Com

mu

nit

yh

ospi

tald

eliv

erie

sLo

w1.

001.

001.

00M

ediu

m5.

06∗∗

∗(2

.79–

9.19

)4.

04∗∗

∗(2

.30–

7.09

)4.

81∗∗

∗(2

.64–

8.76

)H

igh

5.82

∗∗∗

(2.9

3–11

.55)

4.23

∗∗∗

(2.2

4–8.

00)

4.99

∗∗∗

(2.5

7–9.

71)

Com

mu

nit

ypo

vert

yLo

w1.

001.

00—

Med

ium

0.35

∗∗∗

(0.2

2–0.

57)

0.84

(0.5

6–1.

28)

Hig

h0.

09∗∗

∗(0

.04–

0.20

)0.

36∗∗

∗(0

.20–

0.64

)

Com

mu

nit

ym

ass

med

iaex

posu

reLo

w1.

001.

001.

00M

ediu

m2.

62∗∗

∗(1

.70–

4.03

)2.

33∗∗

∗(1

.54–

3.54

)2.

43∗∗

∗(1

.59–

3.72

)H

igh

3.94

∗∗∗

(2.2

4–6.

93)

3.16

∗∗∗

(1.8

5–5.

40)

3.03

∗∗∗

(1.8

2–5.

03)

(Con

tin

ued

)

661

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14

Tab

le3

Multi

leve

lLo

gist

icReg

ress

ion

Odds

Rat

ioof

the

Effec

tsof

Indiv

idual

and

Com

munity

Fact

ors

on

Ante

nat

alCar

e(A

NC)

Vis

its,

Nig

eria

,20

08D

emogr

aphic

and

Hea

lthSu

rvey

(DH

S)(C

onti

nu

ed)

Model

1M

odel

4Em

pty

Model

2M

odel

3In

div

idual

and

Model

5m

odel

Indiv

idual

variab

les

Com

munity

variab

les

com

munity

Com

munity

×In

div

idual

Odds

Rat

ioO

dds

Rat

ioO

dds

Rat

ioO

dds

Rat

io

Var

iable

s(9

5%CI)

(95%

CI)

(95%

CI)

(95%

CI)

Com

mu

nit

ypo

vert

y×

edu

cati

onLo

wpove

rty

×N

one/

prim

ary

(RC)

1.00

Low

pove

rty

×Se

c/hig

her

educa

tion

2.74

∗∗∗

(1.6

9–4.

43)

Med

ium

pove

rty

×Se

c/hig

her

educa

tion

2.44

∗∗(1

.39–

4.27

)

Hig

hpove

rty

×Se

c/hig

her

educa

tion

1.18

(0.6

4–2.

20)

Ran

dom

effe

cts

par

amet

ers

Em

pty

Indiv

idual

Com

munity

Indiv

idual

/Com

munity

Com

munity

∗ Indiv

idual

Com

mu

nit

yle

vel

Var

iance

11.0

71∗∗

∗2.

969∗∗

∗2.

550∗∗

∗2.

149∗∗

∗2.

240∗∗

∗(S

E)

(3.9

03)

(1.0

24)

(0.7

19)

(0.6

62)

(0.6

94)

VPC

=IC

C(%

)77

47.4

43.6

39.5

40.5

(PCV

)(%

)Ref

eren

ce73

.276

.980

.679

.7

Log-

like

lih

ood

−815

5.00

25−6

806.

1056

−759

1.73

26−6

636.

2953

−667

9.03

39

Mod

elfi

tst

ati

stic

sA

IC16

316.

013

650.

215

215.

513

336.

613

420.

1B

IC16

339.

213

795.

615

339.

013

581.

413

657.

3

Note

:The

empty

model

conta

ins

no

variab

les

butpar

titio

ns

the

varian

cein

totw

oco

mponen

tpar

ts.AIC

=Aka

ike

info

rmat

ion

crite

rion,BIC

=Bay

esia

nin

form

atio

ncr

iterion,

CI

=Confiden

ceIn

terv

al,

PCV

=Poportio

nal

chan

gein

varian

ce,

RC

=re

fere

nce

cate

gory

,SE

=st

andar

der

ror,

VPC

=va

rian

cepar

titio

nco

effici

ent.

Log-

likel

ihood

=This

isth

elo

g-lik

elih

ood

ofth

efitted

model

.The

valu

eofth

elo

g-lik

elih

ood

(−66

36.2

953)

inM

odel

4in

dic

ates

abet

ter

fit.

Sign

ifica

nce

leve

l:∗∗

∗ p<

0.00

1;∗∗

p<

0.01

;∗ p

<0.

5.

662

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those aged 15–24 years. Women with primary and secondary/higher educa-tion were significantly more likely to have four or more ANC visits (2.9 timesand 5.8 times more likely respectively) compared to those with no education.Relative to Hausa/Fulani/Kanuri women, women from Igbo, Yoruba, andNorthern/Southern minority ethnic groups were 9.9 times, 32.9 times, and4.3 times, respectively, more likely to make 4 or more ANC visits. Womenin formal employment and unskilled manual workers were almost twice aslikely to have four or more ANC visits compared to those with no employ-ment. Attending four or more ANC visits was more likely among womenwho made joint decisions with their husbands. In line with expectations, theodds of having four or more ANC visits were higher for women from therichest, richer and middle income households relative to women from thepoorest households. Compared to the empty model, the variation in havingfour or more ANC visits was significant across communities (τ = 2.969; p< 0.001). The ICC was 47.4%, indicating that the clustering of ANC visitsacross communities was related to the composition of the communities byindividual-level characteristics.

As shown in model 3, all the community variables were positively andsignificantly associated with ANC visits. The likelihood of having four ormore ANC visits was higher among women who resided in South Westand South East relative to those who resided in the North Central region.Conversely, living in North East and North West was associated with lowerodds. Further, women who lived in communities with a high proportion ofwomen with secondary and higher education, a high proportion of womenwho gave birth in a health facility, and a high proportion of women whowere exposed to mass media were respectively 3.4 times, 5.8 times, and3.9 times more likely to attend ANC at least four times compared to thoseliving in disadvantaged communities. Community poverty was significantlyassociated with ANC visits. Living in communities with a high proportion ofwomen who were from poor households was associated with lower odds ofattending four or more ANC visits. Compared to model 2, the variation inANC visits across communities remained significant (τ = 2.550, p = 0.001).The ICC decreased to 43.6 %, indicating that the clustering of ANC visitsbetween communities was related to the community characteristics.

Model 4 contained both the individual and community variables. Therelation of education, occupation, ethnic origin, woman’s autonomy andhousehold wealth index to ANC attendance remained significant, but witha slight reduction in odds for education, ethnic origin and household wealthindex variable categories. The odds of having four or more ANC visits werehigher for women with secondary/higher education and those who belong toIgbo, Yoruba, and Northern/Southern minority ethnic groups. Women fromthe richest wealth quintiles were 15.5 times more likely to have made four ormore ANC visits. Women in formal employment, and those who made jointdecisions with their husbands on health care were 1.6 times and two times,

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respectively more likely to attend ANC. Whereas the likelihood of havingfour or more ANC visits was 67% lower for women from North West and39% lower for those from South-South, the odds were higher for womenfrom South West compared to North Central.

Further, living in communities with a high proportion of women whohad given birth in a hospital or health facility, and a high proportion ofwomen exposed to mass media was associated with a higher likelihoodof having made four or more ANC visits. However, living in communi-ties with a high proportion of women from poor households decreasedthe odds by 64%. Comparatively, the variance at the community level inmodel 4 remained significant (τ = 2.149; p <0.001). The ICC decreased to39.5%, indicating that the inclusion of community variables was importantfor obtaining a better explanatory model. The clustering of the likelihood ofhaving four or more ANC visits at the community level was related to com-munity factors and partly to individual characteristics. Overall, the model fitstatistics AIC and BIC revealed that the community characteristics increasedthe fit of the multi-level models in explaining the variations in ANC visitsacross communities. The model with smaller values of AIC and BIC had abetter fit.

The final model (model 5) showed that community factors had mod-erating effects on the association of individual factors with ANC visits.The cross-level interaction between community poverty and educationalattainment was significant for some of the categories. Among women withsecondary/higher education, the odds of making four or more ANC visitswere significantly increased by residence in low poverty communities (OR =2.74, Confidence Interval [CI] = 1.69–4.43) and medium poverty communities(OR = 2.44, CI = 1.39–4.27).

DISCUSSION

Our results indicated that community factors were associated with ANC visitsand also acted as moderators of the association between individual factorsand antenatal care visits. The finding that residing in South West signifi-cantly increased the odds of making four or more ANC visits could reflectregional differences in socio-economic development, thus suggesting theneed for regional specific interventions that promote the use of ANC. Thestudy showed a strong and positive association between community hos-pital delivery and ANC attendance. This finding is consistent with studieselsewhere (Stephenson et al., 2006) and could reflect service availability andhealth practices of other women. In a community in which a large propor-tion of women delivered in a health facility, the practice may become anorm that other women may emulate (Stephenson et al., 2006). Communitypoverty was negatively associated with ANC visits and may suggest a lack of

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community resources and low financial autonomy. Community mass mediaexposure was strongly and positively related to ANC visits suggesting greaterawareness of maternal health care services in the community which in turnmay increase the use of maternal health care. Community poverty moderatedthe association between educational attainment and ANC visits. The odds ofmaking four or more ANC visits among women with secondary/higher edu-cation significantly increased as a function of the level of poverty within acommunity. Thus community interventions aimed at increasing ANC visitsshould target poverty reduction programs. The significant variation in ANCvisits across communities implies the presence of factors that were not con-trolled for in this analysis, hence the need for further research to investigatethese factors.

The finding that education, employment and household wealth indexwere significantly and positively associated with making four or more ANCvisits is consistent with other studies (Dhaher et al., 2008; Islam & Odland2011) and may reflect higher socio-economic status which is associated withbetter health outcomes (Antai, 2009). This points to the need to empowerwomen educationally and economically.

LIMITATIONS AND STRENGTHS OF THE STUDY

This study had some limitations that are noteworthy. First, the definitionof relevant “groups” is a great challenge in multi-level analyses. This studyused PSUs as a proxy for the community, and as Boco (2010) noted, usingthe NDHS primary sampling unit as the community may have biased resultstoward a functioning population as a result of endogeneity and selectioneffects. Second, potential confounding bias was possible for variables thatwere not measured and thus not controlled in the analyses. For instance,perceptions of the use of health care services and actual distance to a healthfacility were not measured and thus not controlled in the analyses. Third, thestudy may have involved some recall bias, given that the events took placefive years before the survey, and those participants who used health careservices or had better outcomes may have had different recall than thosewho did not. Fourth, the cross-sectional design of the study did not permitdetermining the temporal relations of the individual and community variablesto the dependent variable and hence limited the potential causal inferencesthat could be drawn. Finally, the community variables were constructed byaggregating the individual level characteristics at the community level, andthis could have resulted in making inferences at a higher level based oninformation from data collected at a lower level (Boco, 2010).

These limitations notwithstanding, the study remains valuable. The find-ings represent a further step toward an understanding of the relation of

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community factors and their moderation of the association between indi-vidual factors and ANC visits. An examination of the moderating effectsof community factors allowed for more complete tests of relevant theo-ries, suggesting which aspects of communities are potential targets for policymanipulation.

CONCLUSION

The findings of this study indicated that community factors were associatedwith ANC visits and also acted as moderators of the association betweenindividual factors and ANC visits. Future interventions aimed at improvingANC visits in Nigeria may be enhanced by targeting not only disadvantagedindividuals but also disadvantaged communities. Specifically, interventionsshould focus on poverty reduction programs, and encourage health facil-ity delivery. To be optimally effective, regional specific interventions thatallow better targeting of health policy and need-based resources to be chan-neled appropriately should be taken into consideration. It is important forpolicy makers to use the most effective media to convey health programsparticularly to women living in disadvantaged communities.

NOTES

1. Both the individual and household variables are referred to as ‘individual-level variables’.2. The intra-class correlation was calculated as: ρ = (σ 2

μ / (σ 2μ + π 2/3) (Snijders & Bosker, 1999).

REFERENCES

Abedin, S., R. Islam, and T. Hossain. 2008. Antenatal care during pregnancy: A studyon Naogaon district of Bangladesh. The Soc Sci 3(8):537–41.

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contextual determinants of maternal health care service utilization in nigeria

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