Study population

This was a cross-sectional study conducted from April to May 2021 in the Nabdam district in the Upper East Region of Ghana. The study population comprised of mothers with a live birth within the past year. The inclusion criteria were mothers attending child welfare clinics in the Nabdam district and who were at least 18 years old at the time of the study.

Study setting

The Nabdam district located in the Upper East region in northern Ghana has an estimated population of 51,861 [19]. The district lies between latitudes 100 47″ and 100 57″ north and longitudes 00 31″ and 10 15″ west. Approximately, 9995 women are of reproductive age and 970 deliveries were conducted in 2020 [20]. The district has an estimated 100 midwives and community health nurses who offer antenatal care across several healthcare facilities. These healthcare facilities include two clinics, four health centres and 18 Community-based Health Planning and Services (CHPS) compounds [20].

Sampling strategy

We used a convenient sampling approach to sample mothers who met our inclusion criteria. Trained research assistants sampled mothers from child welfare clinics in the Nabdam district and administered structured questionnaires during clinic hours. Mothers responded to questions related to their socio-demographic and obstetric-related characteristics (e.g., parity, planned pregnancy, early ANC) when pregnant with their current child. Prior to data collection for our main study, the survey instrument was pre-tested on ten mothers with a live birth in the past year in Bolgatanga East district. Additionally, trained research assistants reviewed and extracted information from the maternal and child health record book, which contains records of some socio-demographic characteristics and obstetric indicators for the mother when she was pregnant.

Sample size

The minimum sample size required for the study was estimated using Epi info version 7.1. The sample size was estimated with the assumption that 50% of pregnant women had early antenatal care since the prevalence of early ANC was unknown in the study setting (i.e., Nabdam district) [20]. The minimum sample size estimated was 407 (including a non-response rate of 10%) using a 5% margin of error with a 95% confidence interval. During data collection, 541 mothers were invited to participate, but 22 mothers were ineligible. Therefore, 519 mothers took part in our study.

Primary outcome

The primary outcome was early ANC. Early antenatal care was defined as pregnant women who had antenatal contact with the healthcare provider at a gestational age of less than 12 weeks (i.e., within first trimester) [5]. Early antenatal care was assessed by reviewing the maternal and child health record book, which records the number of antenatal care contacts and the trimester of pregnancy when each contact was made with a healthcare provider when the mother was pregnant. The value of “1” and “0” were assigned to mothers who had early ANC and those who did not respectively.

Exposures

The exposures of interest include mother and partners’ highest educational level (secondary or higher education, less than secondary), planned pregnancy (yes, no); mothers’ age (18–19 years, ≥ 20 years), and employment status (employed. unemployed). A partner was either a husband or a boyfriend to the mother when she was pregnant. Being employed was either formal or informal. Exposures such as mother and partners’ education, mothers’ age, mothers’ education, and employment status were self-reported measures assessed using the maternal and child health record book, whilst planned pregnancy was self-reported during the survey about their last pregnancy.

Covariates

The covariates were: marital status (single, married); place of residence (urban, rural); health insurance status (insured, uninsured); and parity (≥ 4 children, 0–3 children). Other covariates include household size (i.e., a continuous variable) and whether the coronavirus 2019 (COVID-19) pandemic affected my ANC attendance (yes, no).

Marital status, place of residence and parity were assessed using the maternal and child health record book, whilst household size, health insurance status and whether COVID-19 pandemic affected my ANC attendance were self-reported during the survey. The variable selection for our study was guided on prior knowledge of existing literature [12, 13, 15, 17].

Data analysis

Descriptive statistics were used to describe our study population. Characteristics of study participants were presented in proportions for categorical variables and mean and standard deviation for continuous variables.

We conducted our analyses with five generalized estimating equation (GEE) models to achieve our study objectives using adjusted prevalence ratios (aPRs) [11, 21, 22]. The first model was used to assess the relationship between partners’ education and early ANC, whilst controlling for mothers’ education, planned pregnancy, mother’s age, employment status, parity, marital status, place of residence, health insurance status, household size and whether COVID-19 pandemic affected a mother’s ANC attendance. All potential confounders were specified as a priori, as we believe there is a biological plausibility that these variables might be associated with both the exposure and the outcome of interest.

The second model was used to assess whether the relationship between a mother’s education and early ANC was modified by partners’ education by introducing an interaction term (mothers’ education*partners’ education) into the model. Effect modification was assessed on the multiplicative and additive scales, whilst adjusting for planned pregnancy, mother’s age, employment status, parity, marital status, place of residence, health insurance status, household size and whether COVID-19 pandemic affected a mother’s ANC attendance.

The third model was used to characterize whether the association between planned pregnancy and early ANC was modified by partners’ education by introducing an interaction term between planned pregnancy and partners’ education (planned pregnancy*partners’ education). Effect modification was also assessed on the multiplicative and additive scales, whilst controlling for mothers’ education, mother’s age, employment status, parity, marital status, place of residence, health insurance status, household size and whether COVID-19 pandemic affected a mother’s ANC attendance.

A fourth model assessed whether the association between a mother’s age and early ANC varies by the level of a partner’s education. We introduced an interaction term between mothers’ age and partners’ education (mothers’ age*partners’ education) into the model and effect modification was assessed on the multiplicative and additive scales, whilst adjusting for mothers’ education, planned pregnancy, employment status, parity, marital status, place of residence, health insurance status, household size and whether COVID-19 pandemic affected a mother’s ANC attendance.

The fifth model assessed whether the association between employment status and early ANC was modified by partners’ education on the multiplicative and additive scales. This was done by introducing an interaction term between employment status and partner’s education (employment status*partners’ education), whilst controlling for mothers’ education, planned pregnancy, mother’s age, parity, marital status, place of residence, health insurance status, household size and whether COVID-19 pandemic affected a mother’s ANC attendance.

Effect modification on the additive scale was assessed using the relative excess risk due to interaction (RERI) as this is the most appropriate additive measure, which is of public health importance [23,24,25]. The RERI and corresponding 95% confidence intervals (CIs) were assessed using the “MOVER” approach proposed by Zou [26]. RERI was presented using prevalence ratio to estimate the risk ratio as our outcome was common and risk could not be directly determined from our study. An estimate greater than zero signified positive effect modification, while an estimate less than zero signified negative effect modification [27].

Our results on effect modification were presented according to STROBE (Strengthening the reporting of observational studies in epidemiology) recommendations [28]. We also included results on effect estimates of our exposures across the strata of another factor (i.e., partners’ education) as recommended by Knol and VanderWeele [27]. The format we present our results will therefore allow readers to obtain sufficient information needed to assess effect modification [27]. The data analyses were conducted using SAS version 9.3 (SAS Institute, Cary, NC).

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