Study design, data and area
The study is a secondary analysis of 2018 Nigerian Demographic and Health Survey (NDHS) data. NDHS is a cross-sectional population-based and nationally representative survey routinely collected in five years’ intervals in Nigeria. Nigeria is administratively grouped into six geopolitical zones (Northcentral, Northeast, Northwest, Southeast, Southsouth and Southwest) with an average of 6 states per geo-political zone and the federal capital territory (FCT) as the administrative headquarter [14]. Each state is further divided into local government areas that serve as the lowest and the closest administrative cadre of government for the people. The 36 states and FCT are shown in the study area map in Fig. 1.
Sampling strategy and participants
The sampling frame of the 2018 nationally representative NDHS was obtained from the list of rural and urban enumeration areas collated by the National Population and Housing Census (NPHC) in Nigeria. A two-stage stratified random sampling design was used in the 2018 NDHS, where 1400 enumeration areas consisting of 820 rural and 580 urban strata were selected using probability proportional to size at the first sampling stage. Hence the difference in the number of urban and rural strata. Equal probability systematic sampling was then used to select the same number of households (30 households per enumeration area) in the second sampling stage. A total of 41,821 (22,658 in rural and 19,163 in urban) women participants were interviewed in the cross-sectional survey that achieve a 99% response rate [14]. 21,447 women who had at least one ANC visit and whose information were at least non-missing in one of the maternity CoC pathway made up the weighted sample size of the study. The survey also collected information on women’s demographics, socioeconomic and health-related characteristics that includes the key measures of the maternity continuum of care (ANC, SBA and PNC) investigated in this study.
Outcome variables
Outcomes of interest in this study are the maternity continuum of care received during pregnancy (ANC), childbirth (use of SBA) and post-delivery (PNC). A postpartum woman is regarded to have completed the three gamut of care if she received the recommended 4 or more ANC contacts in a healthcare facility during pregnancy, move on to utilize SBA i.e., delivery assisted by at least a doctor, nurse or midwive and subsequently received postnatal checkup within the first 48 h after childbirth [14]. The combined outcome was based on the WHO recommendation of at least 4 ANC visits and the use of SBA at birth, especially in low-resource settings of the lower-middle-income countries [11, 12]. We measured PNC within the first two days after birth which has been reported in the 2018 NDHS due to most maternal morbidity and mortality that occur at the time and therefore highlighted PNC (within two days) as an important measure in the maternity CoC model [33]. We avoided the adaptation of the recently recommended 8 ANC contacts since the DHS framework was designed on a minimum of 4 ANC visits as the optimal number of ANC visit and also; because the strategy to implement the 8 ANC visits was recently devised in the orientation package for healthcare providers in Nigeria after most of the respondents have had the indexed childbirth [14, 35, 36]. The outcome variable was obtained from the combination of responses to the following questions:
-
1. How many times did you receive antenatal care during this pregnancy?
-
2. Who assisted with the delivery of (NAME)?
-
3. Did anyone check on your health after you left the facility i.e., the place of delivery?
Three sets of dichotomous variables were extracted, such that; a positive response to question ‘1’ is 4 or more ANC and negative response is ANC visit less than 4 (0, 1, 2, 3), response to question ‘2’ that delivery was assisted by doctor/nurse/midwife is a positive response and otherwise a negative response and similarly positive response to question ‘3’ is ‘Yes’ and ‘No’ is the negative response. The sequence of maternity continuum of care was drawn from the combination of positive responses. Hence, positive response to; question 1 indicate ANC (4 +) visits, question 2 indicate ANC (4 +) visits and SBA use and question 3 indicate maternity CoC completion in this study i.e., when ANC (4 +), SBA and PNC were all received.
Explanatory variables
Independent variables included in this study were based on similar factors considered by previous studies that investigated the maternity continuum of care [3, 5, 30,31,32,33, 37]. This can be defined under the broad categories as; socio-demographic characteristics, maternal health and birth factors, quality of pregnancy care received, economic status and physical and autonomy factors [13, 38, 39].
Socio-demographic characteristics
These includes maternal age (15–24, 25–34, 35–49 years), place of residence (urban, rural), educational level (none, primary, secondary, tertiary), marital status (never married, married, cohabiting, divorced/widowed/separated) husband educational level (none, primary, secondary, tertiary), geopolitical zone (northcentral, northeast, northwest, southeast, south-south, southwest).
Maternal health and birth factors
These are birth-related and women health-seeking characteristics. Which are; wanted last pregnancy (wanted then, wanted later, wanted no more), birth order (1, 2, 3 and 4 +), covered by health insurance (no, yes), the timing of first ANC visit (first, second and third trimester), institutional delivery (yes, no), delivery by caesarian section mode (yes, no), childbirth sex (male, female), child-size at birth (very small, smaller than average, average, larger than average, very large).
Quality of pregnancy care received
These are factors assessing pregnancy care which are; status of blood pressure measured during pregnancy (yes, no), urine sample taken during pregnancy (yes, no), blood sample taken during pregnancy (yes, no), iron-folic acid tablet taken during pregnancy (yes, no), number of tetanus toxoid vaccine taken during pregnancy (0, 1, 2 +), provider of ANC (no one/traditional birth attendant, community health ‘extension’ worker, auxiliary nurse/midwife, skilled nurse/midwife, doctor).
Economic status
Employment type (not-working/manual/clerical, agricultural, sales, services, professional/ managerial/technical/), Wealth index (poor, average, rich), Media access (no, yes).
Healthcare accessibility and autonomy factors
Distance to health facility (no problem, big problem), Person who usually decides on respondent’s healthcare (respondent alone, both, spouse alone), Person who usually decides on how respondent’s earnings are spent (partner alone, joint decision, respondent alone).
Statistical analysis
Descriptive statistics of the background characteristics and outcomes were reported in frequency and percentages. Missing data were reported for at least 1% of the observation and otherwise negligible i.e., less than 1%. Three sequences of maternity CoC model defined under the space that; postpartum women received at least 4 ANC visits during pregnancy was coded as 1 and 0 otherwise – model 1, continued from ANC (4 +) to use SBA at childbirth was coded as 1 and 0 otherwise – model 2 and completed the three key CoC which is from ANC (4 +) to SBA and to PNC after childbirth was equally coded as 1 and 0 otherwise – model 3 were fitted.
Initially, model selection was carried out to assess the set of maternal factors/characteristics associated with the maternity CoC model (models 1, 2 and 3). This was carried out using the backward stepwise logistic regression for models 1 and 2 and backward stepwise complementary log–log regression for model 3 due to the rare outcome and since the probability of completing the three key maternity continuum of care is small (less than 10%). The backward regression started with the full model and at each model step, the variable whose removal significantly reduced the log likelihood (-2logL) was returned and retained in the model and otherwise removed. All the independent variables were given an equal chance of selection and variable inclusion was considered at p < 0.10.
Bivariate and Multivariate analysis that includes all the significant variables retained in the stepwise regression (final models 1, 2 and 3) were performed to determine the likelihood and significance of each of the predictor variables and the combined set of the predictors respectively. The respective unadjusted and adjusted odds ratio were reported for the binary logistic regression analysis of models 1 and 2 while the unadjusted and adjusted e(form) or exp(b) equivalent of the odds ratio was reported in the multivariable complementary log–log analysis of model 3. Data were weighted with the women’s sample weight indices included in the NDHS data and the svyset command was used to adjust for unequal group/population sizes due to the complex survey design. Bivariable and multivariable statistical analysis were performed at 10% and 5% level of significance (95% confidence level) respectively, using Stata version 16.0 (Stata Corp, Texas, USA). Variable (Union type) that causes multicollinearity (variance inflation factor > 5) was subsequently removed from the multivariate analysis.
The multivariable regression analysis
The multiple binary logistic regression and the complementary log–log modeled the odds of optimal ANC uptake and continuation to the use of SBA and PNC as a binary response [P(\({Y}_{i}=0\)), P(\({Y}_{i}=1)]\) [40, 41]. The multiple logistic model which equates the function of the odds to a linear combination of the regression terms and the predictors is generally expressed as:
$${Y}_{i}= \mathrm{ln}\left(\frac{P}{1-P}\right)= {\beta }_{0}+ {\beta }_{1}{X}_{1i}+\dots + {\beta }_{p}{X}_{pi}+ \varepsilon$$
(1)
$$E\left({Y}_{i}\right)={P}_{i}=\frac{\mathrm{exp}\left({\beta }_{0}+{\beta }_{1}{x}_{1i}+\cdots +{\beta }_{p}{x}_{pi}\right)}{1+\mathrm{exp}\left({\beta }_{0}+{\beta }_{1}{x}_{1i}+\cdots +{\beta }_{p}{x}_{pi}\right)}$$
(2)
where: \(\mathrm{ln}\left(\frac{P}{1-P}\right)\) is the log odds (P is the probability of success and 1-P is the failure probability).
\({\beta }_{0}\) is the logistic regression constant.
\({\beta }_{1}+\dots + {\beta }_{p}\) are the px1 vector of regression coefficient or estimates of the multiple predictors.
\({X}_{i1}+\dots +{X}_{ip}\) are the nxp matrix of explanatory variables predicting the log odds in the model.
When the probability of success “P” is very large or very small (less than 10%) leading to asymmetrical S-shape compared to the symmetric logistic curve [42], the use of the complementary log–log model becomes more appropriate (accurate) as it’s in rare CoC outcome. The complementary-log–log model is generally stated as:
$${Y}_{i}=\mathrm{log}\left\{-\mathrm{log}\right.\left.\left[1-\pi \left(\mathrm{x}\right)\right]\right\}={\beta }_{0}+{\beta }_{1}{X}_{1i}+\cdots +{\beta }_{p}{X}_{pi}+\varepsilon$$
(3)
$$\mathrm E\left(Y_i\right)=\pi\left(\mathrm x\right)=1-\exp\left[-\exp\left(\beta_1X_{1i}+\cdots+\beta_pX_{pi}\right)\right]$$
(4)
where log{-log[1-π(x)]} is the complementary log–log transformation with binary response (0, 1).