The study involved a cross-sectional secondary analysis of data from the 2018 Nigeria Demographic and Health Survey (NDHS) and a literature review of transdisciplinary approaches to addressing health inequities.

A systematic search of PubMed and Google scholar was performed for studies that explored transdisciplinary approaches to addressing inequity in access to health care services. Key search terms used were: 1) Health inequity* OR health for all OR universal health coverage; 2) essential health services OR essential services OR health services OR maternal care services OR childcare services OR family planning services OR nutrition services; and 3) transdisciplinary approach* OR Multi-sectorial approach* OR Policy approach* OR Asset-based strategy*. Wildcards and truncations were used to capture words that could have multiple endings. Search was restricted to studies that were reported in English, and basically, studies conducted in low- and middle-income countries to capture approaches that can be easily contextualized in Nigeria. There was no restriction to the publication period.

One author screened the title and abstracts of the studies from the search result to identify eligible studies. Full-text screening was performed by two authors and disagreement was resolved by referring to a third author and through discussions and a consensus. Results of the screening were presented in Microsoft Office Excel. Data were extracted from selected studies and include study title, study link, lead author, contact details of the lead author, study location (country), aim of the study, study design, outcome measure, barriers/challenges to fair access to health care, and recommended trans-sectorial approaches to addressing inequities in access to essential health care services.

The sampling frame that was used for the 2018 NDHS is the Population and Housing Census of the Federal Republic of Nigeria conducted in 2006 by the National Population Commission. The NDHS survey is conducted every five years. Nigeria is administratively divided into states which are stratified into geopolitical zones. Each state is further divided into local government areas (LGAs), and each LGA is divided into localities. The primary sampling unit for the 2018 NDHS, referred to as cluster, is defined based on census enumeration areas (a subdivision of an administrative locality).

The survey adopted a two-staged stratified sampling approach. Stratification was achieved by separating each of the 37 states into rural and urban areas, resulting in a total of 74 sampling strata. Samples were selected independently in every stratum. Lower administrative level stratifications were achieved by sorting the sampling frame before sample selection according to administrative order and by using a probability that is proportional to size selection during the first sampling stage. For the first sampling stage, 1400 census enumeration areas (EAs) or clusters were selected with probability proportional to the size of the EA. This is to ensure that the survey precision was comparable across clusters. A listing of households in each selected EA was conducted and this served as a sampling frame for the selection of households in the second sampling stage. For the second sampling stage, a systematic probabilistic sampling was conducted to select a fixed number of 30 households in every cluster resulting in a total sample size of 41,668 households. Given the non-proportional allocation of the sample to the different states and the possible differences in response rates, sampling weights were calculated, added to the data file, and applied so that the results of the survey would be representative at the national level as well as the domain level. These sampling weights were calculated based on sampling probabilities separately for each sampling stage and for each cluster.

Data collection for the 2018 NDHS was conducted from August 14, 2018, to December 29, 2018. The survey was successfully conducted in 1389 clusters after 11 clusters with deteriorating security conditions during the fieldwork were dropped. These areas were Zamfara (4 clusters), Sokoto (3 clusters), Katsina (2 clusters), Borno (1 cluster), and Lagos (1 cluster). Five questionnaires were used for the survey: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire. Information obtained from the woman’s questionnaire includes but is not limited to, demographic characteristics (including age, education, media exposure), birth history, knowledge, use and source of family planning methods, antenatal, delivery, and post-natal care, vaccination, and childhood illnesses. The biomarker questionnaire was used to obtain information on anthropometric characteristics of children (height and weight measurements), biomarkers for anemia, malaria, and genotype.

For the current study, we included all the women and their child(ren) whose information was obtained during the interviews. We extracted data on family planning, maternal care, and childcare. Indicators for family planning were unmet need, met need and demand satisfaction. Maternal care indicators include antenatal care from a skilled provider, number of antenatal visits, protection against neonatal tetanus, delivery done by a skilled provider, delivery done in the health facility, postnatal check within the first 2 days of birth, use of intermittent preventive therapy for malaria in pregnancy (IPTp), and anemia prevalence and use of insecticide-treated nets (ITN) in pregnancy. Childcare indicators include basic vaccination status, age-appropriate vaccination status, advice or treatment sought for acute respiratory illnesses (ARI), advice or treatment sought for fever, oral rehydration solution (ORS)/zinc given for diarrhea, nutritional status (measured using anthropometric indices such as height-for-age, weight-for-height, and weight-for-age), anemia prevalence, use of ITN, blood taken for testing for fever, and treatment for fever with artemisinin-based combination treatment. Table 1 summarizes the description of each of the selected indicators. These indicators were considered as outcome variables for the study.

The independent variables considered in the study include geopolitical zone of

residence, maternal education attainment, and household wealth index. Geopolitical zone of residence was stratified according to the administrative zones of northcentral, northeast, northwest, southeast, southwest, and south-south zones. Maternal educational level was defined as having no education, primary education, secondary education, and more than secondary education. The household wealth index was presented in five quintiles (lowest, second, middle, fourth, and highest quintiles) and was derived from a measurement of the household dwelling unit, such as a source of drinking water, type of toilet facilities, materials used for flooring, external walls, and roofing, and ownership of durable goods.

The distributions of the independent variables—geopolitical zone of residence, maternal educational attainment, and household wealth index—were summarized using descriptive statistics. The distributions were expressed in frequencies and proportions across the different categorical levels of the independent variables. We examined measure of association between the independent variables and the outcome indicators using bivariate analysis. Due to the dichotomous nature of the outcome indicators, we assumed they follow a Bernoulli distribution. As a result, we used multi-level mixed effect generalized linear modelling with Newton-Raphson (maximum likelihood) optimization, binomial variance function and the logit link function to fit logistic regression models for each of the outcome indicators. We used multilevel analysis due to the stratified nature of the data. To account for the clustering effect, we adopted the mixed-effect logistic regression approach. We applied Bayesian Information Criterion to assess the goodness of fit of the models. All analyses were adjusted to account for the aggregate nature of the datasets. Measures of association were exponentiated and presented as odds ratios (ORs) with their corresponding 95% confidence intervals, with a p-value of <0.05 considered statistically significant. Data analysis was done using Stata statistical software.

Out of the 41,668 households that were sampled, 40,666 were occupied. Of the occupied households, 40,427 were successfully interviewed, resulting in a response rate of 99%. In the interviewed households, 42,121 women aged 15 - 49 were identified for individual interviews. 41,821 women completed interviews with a response rate of 99%. Data for these women and their child(ren) were included in the analysis.

Table 2 presents the distribution of women with unmet needs, met needs and demand satisfied for family planning across the six geopolitical zones, educational attainment levels, and wealth quintiles, and the results of the logistic regression analyses. From the logistic regression, the odds of having an unmet need for family planning are relatively similar across the six geopolitical zones, with women residing in the South-south region having approximately 1.5 times higher odds of having an unmet need than women residing in the other zones (OR 1.49; 95% CI 1.33, 1.67). Women in the Southeast (OR 2.02; 95% CI 1.80, 2.27) and South-west regions (OR 2.80; 95% CI 2.52, 3.10) have more than two times higher odds of having their need for family planning met than women residing

*P-value < 0.005 indicating statistical significance.

in other regions; with women in the North-east (OR 0.54; 95% CI 0.48, 0.62) and Northwest zones (OR 0.38; 95% CI 0.34, 0.42) having significantly lower odds. The demand for family planning was approximately twice more likely to be satisfied in the South-east (OR 2.02; 95% CI 1.83, 2.22) and South-south (OR 2.00; 95% CI) than in other zones.

Women across all educational attainment levels have relatively similar odds of having their need for family planning not met. However, the odds of having their needs met and the demand for family planning satisfied consistently increased as level of educational attainment increased, with women who attained higher than secondary educational level having about nine times (OR 9.09; 95% CI 8.14, 10.15) and six times (OR 6.28; 95% CI 5.75, 6.86) higher odds of having their needs met and demand satisfied respectively than women with no primary education. In the same vein, women across all wealth quintile levels had relatively similar odds of having their family planning need not met. However, with an increasing measure of wealth, the odds of having their needs met and demand satisfied increased; with women within the highest wealth quintile having approximately ten times (OR 10.72; 95% CI 9.36, 12.27) and seven times (OR 6.64; 95% CI 6.12, 7.21) higher odds of having their needs met and demand satisfied respectively than women within the lowest wealth quintile.

Table 3 presents the distribution of women, across the six geopolitical zones, educational attainment levels, and wealth quintiles, who had their antenatal care provided by a skilled provider, ≥4 antenatal visits, recent protection against neonatal tetanus, delivery done by a skilled provider, delivery done in the health facility, post-natal check in the first 48 hours following birth, used IPTp and ITN during pregnancy, and diagnosed of anemia in pregnancy.

Women who reside in Southeast and Southwest zones had consistently higher odds of having their antenatal care provided by a skilled provider, of attending ≥ 4 antenatal visits, recently protected against neonatal tetanus, had their delivery done by a skilled provider and in a health facility, and had post-natal check 2 days after birth than women residing in the Northern and South-South regions. The odds of using ITN in pregnancy were found to be approximately three times higher in women residing in the Northwest region (OR 3.60; 95% CI 2.97, 4.37) than in women residing in other regions; with women in the southern region having significantly lesser odds of making use of ITN in pregnancy. The odds of a woman being diagnosed with anemia during pregnancy were found to be relatively similar across all the six zones.

With the increasing level of educational attainment and measure of wealth, index is a correspondingly higher odds of a woman having their antenatal care provided by a skilled provider, of attending ≥ 4 antenatal visits, recently protected against neonatal tetanus, had their delivery done by a skilled provider and in a health facility, and had post-natal check 2 days after birth. However, the odds of