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Background: The maternal, newborn and child health care continuum require that mother/child pair should receive the full package of antenatal, intrapartum and postnatal care in order to derive maximum benefits. Continuity of care is a challenge in sub-Saharan Africa. In this study, we investigate the patterns and factors associated with dropout in the continuum of maternity (antenatal, delivery and postnatal) care in Nigeria. Method: Using women recode file from the 2013 Nigeria Demographic and Health Survey, we analysed data on 20,467 women with an index birth within 5 years prior to data collection. Background characteristics and pattern of dropouts were summarised using descriptive statistics. The outcome variable was dropout which we explored in three stages: antenatal, antenatal-delivery, delivery-6 weeks postnatal visit. Multilevel logistic regression models were fitted to identify independent predictors of dropout at each stage. Measure of effect was expressed as Odds Ratio (OR) with 95 % confidence interval (CI). Results: Overall, 12,392 (60.6 %) of all women received antenatal care among whom 38.1 % dropout and never got skilled delivery assistance. Of those who received skilled delivery care, 50.8 % did not attend postnatal visit. The predictors of dropout between antenatal care and delivery include problem with getting money for treatment (OR = 1.18, CI: 1.04-1.34), distance to health facility (OR = 1.31, CI: 1.13-1.52), lack of formal education, being in poor wealth quintile (OR = 2.22, CI: 1.85-2.67), residing in rural areas (OR = 1.98, CI: 1.63-2.41). Regional differences between North East, North West and South West were significant. Between delivery and postnatal visit, the same factors were also associated with dropout. Conclusion: The rate of dropout from maternity care continuum is high in Nigeria and driven by low or lack of formal education, poverty and healthcare access problems (distance to facility and difficulty with getting money for treatment). Unexpectedly, dropouts are high in South east and South south as well as in the Northern regions. Intervention programs focusing on community outreach about the benefits of continuum of maternal healthcare package should be introduced especially for women in rural areas and lower socio-economic strata.
According to the World Bank, Nigeria is a lower-middle-income country with a population of 178.5 million people as at year 2014. There is wide geographical, ethnic, and health diversity as reflected in many of her demographic and health indices. Administratively, the country comprised of 36 states and a Federal Capital grouped into 6 geo-political zones: North West, North East, North Central, South East, South West and South- South. The population is young with 46 % being under 15 years. There are 3 tiers of government (Local, State and Federal) with each tier playing active roles in maternal and child health (MCH) care programmes. Total fertiility rate as at 2013 was 5.5 with 23 % of women aged 15–19 years having began childbearing [7]. The life expectancy at birth is 52 years. Under-5 mortality declined from 201 deaths per 1000 live births in 2003 to 128 per 1000 live births in 2013 while the maternal mortality ratio was 576 maternal deaths per 100, 000 live births [7]. There have been several programmes to promote MCH in the country but the introduction of the Midwives Service Scheme (MSS) in 2009 was a landmark initiative. The MSS was initiated by the National Primary Health Care Development Agency to address the acute shortage of skilled birth attendants in rural areas. The data for this study was extracted from the individual women recode data file for the 2013 Nigeria Demographic Health Survey (NDHS). NDHS 2013 is the fifth round of a nationally representative survey conducted to monitor population and reproductive health among Nigerians. Sampling and data collection techniques of the NDHS 2013 are described in the full published report [7]. The NDHS 2013 used a stratified 3-stage cluster design to select eligible respondents. The primary sampling units, referred to as clusters in this study were enumeration areas selected from a sampling frame prepared for the 2006 population and housing census. With a fixed sample of 45 households per 904 clusters (rural – 532; urban – 372), a total of 40, 680 households were selected and 38, 948 women aged 15–49 years successfully interviewed. Analysis was restricted to women with an index birth within 5 years preceeding the survey. The dependent variable in this study was dropout from maternity care continuum which was considered in 3 stages. Stage 1(model I) is the level of antenatal care (ANC) at which dropout was coded ‘1’ for those who did not receive antenatal care and ‘0’ for those who did. ANC here means at least one visit with a doctor, nurse or midwife providing care. In stage 2 (model II), those who got antenatal care but did not received skilled delivery assistance (from doctor, nurse/midwife) were deemed to be dropout and coded 1 (and otherwise 0). At the third stage (model III), those who got skilled delivery assistance but without the 6 week postnatal care were deemed to have dropped out and subsequently coded 1 (and otherwise 0). Antenatal care was derived from response to questions 408 and 409 asking women concerning their last birth within 5 years before the NDHS 2013. The questions were “(1) did you see anyone for antenatal care for this pregnancy?” [Yes/NO]. Those who answered in the affirmative were further asked “whom did you see?” For this study, those who saw a doctor, nurse/midwife were classified as having received antenatal care. Skilled delivery was ascertained from responses to question 433 which was “who assisted with the delivery of (NAME)?” Those that were assisted by doctor, nurse/midwife were deemed to have received skilled delivery assistance. Status of sixth week postnatal checkup was derived from question 442 (“In the two months after (NAME) was born, did any healthcare provider check on his/her health…..”). Women who gave a ‘yes’ response to the question were categorized as having received the 6th week postnatal checkup. For each model, we controlled for other covariates which are known to be associated with maternal healthcare utilisation [8, 9]. These included: maternal age, birth order, education, household wealth status, partner’s education, decision making authority (own health and visits to friends/families), rural/urban residence,geo-political zone and problems in accessing health care (getting permission to go to health facility, getting money for treatment and consultation, distance to health facility, fear of going alone, and attitude of health workers). We fitted mutilevel logistic regression models for each stage of dropout from the maternity care continuum described above to identify the associated independent factors. The multilevel model serves 2 purposes. First, it enabled us to control for dependence in data collected among respondents who live in the same neighbourhood (clusters). Secondly, the multilevel model allowed us to control for the effect of unmeasured/unobserved or latent variables at the cluster (community) level. Intentionally, no contextual variables were derived for inclusion in the analyses because investigation of contextual variables was not our primary interest in this study. Measure of fixed effects were expressed as Odds Ratio (OR) with their 95 % conficence interval (CI). Where: Yij = log odds of dropout at any stage of the maternity care continuum for woman i in cluster (community) j 0j = intercept for individual-level model (average risk of dropout at any stage in cluster j) Xikj = covariates (education, age group, wealth index, etc) Kj = coefficients for the individual level covariates eij = error terms for the individual-level model We estimated the intra-cluster correlation (ICC) in the dependent variable for stages I to III. The likelihood ratio test was used to check the significance of the random effects (intra-community correlation). The ICC capture the proportion of the total variation in risk of dropout that is attributable to differences between cluster.
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