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Background: Antenatal care (ANC) is a recommended intervention to lessen maternal and neonatal mortality. The increased rate in ANC coverage in most Sub-Saharan African countries is not considerably reducing the maternal and neonatal mortality. This disconnection has raised concerns to study further the trend and determinants of the ANC timing and quality. We aimed to assess the determinants and trend of the timing, the adequacy and the quality of antenatal care in Rwanda. Method: A population-based cross-sectional study design. We used data from the 2010,2015 and 2020 Rwanda demographic and health surveys (RDHS). The study included 18,034 women aged 15–49 years. High quality ANC is when a woman had her first ANC visit within 3 months of pregnancy, had 4 or more ANC visits, received services components of ANC during the visits by a skilled provider. Bivariate analysis and multivariable logistic regression were used to assess the ANC (timing and adequacy), the quality of the content of ANC services and the associated factors. Results: The uptake of antenatal services increased in the last 15 years. For instance, the uptake of adequate ANC was 2219(36.16%), 2607(44.37%) and 2925(48.58%) respectively for 2010;2015 and 2020 RDHS. The uptake of high quality ANC from 205(3.48%) in 2010 through 510(9.47%) in 2015 to 779(14.99%) in 2020. Women with unwanted pregnancies were less likely to have timely first ANC (aOR:0.76;95%CI:0.68,0.85) compared to planned pregnancies, they were also less likely to achieve a high-quality ANC (aOR: 0.65;95%CI:0.51,0.82) compared to the planned pregnancies. Mothers with a secondary and higher education were 1.5 more likely to achieve a high-quality ANC (aOR:1.50;95%CI:1.15,1.96) compared to uneducated mothers. Increasing maternal age is associated with reduced odds of update of ANC component services (aOR:0.44;95%CI:0.25,0.77) for 40 years and above when referred to teen mothers). Conclusion: Low-educated mothers, advanced maternal age, and unintended pregnancies are the vulnerable groups that need to be targeted in order to improve ANC-related indicators. One of the credible measures to close the gap is to strengthen health education, promote family planning, and promote service utilization.
This study is a cross-sectional study using secondary data from three waves of Rwanda demographic and health survey (RDHS). The three waves include RDHS 2010, RDHS 2015 and RDHS 2020. The RDHS is a cross-sectional study using a stratified two-stage sampling design in which rural and urban place of residence are regarded as strata [15, 28, 29]. The census enumeration areas are considered as clusters and a full list of all households was later used as a sampling frame to choose which households should be interviewed. A nationally representative household sample is finally collected. The response rate has been high, above 99% for women across the three waves. The RDHS collects data on maternal and child health services covering a period within the preceding 5 years of the survey. Details on sampling design, sample size, study instruments, data collection, informed consent, and other associated procedures can be found elsewhere [15]. The RDHS data are accessible from the Measure DHS website at http://dhsprogram.com/data/available-datasets.cfm. For the purpose of this study, the 2010, 2015, and 2020 RDHS individual recode (IR) datasets were merged based on established guidelines for managing DHS data. Women aged 15 to 49 years’ old who had a live birth in the five years prior to each survey and answered questions about ANC were included in this sample. Women with missing values or invalid responses to the key exposure, outcome, and possible confounders, such as “don’t know”, were removed. 18,034 of the 41,802 women who took part in the survey met the requirements for inclusion. More information is available in Fig. 1. Flow chart of analytic sample selection The outcome variables of this study were (i) timing of first ANC visit; (ii) adequacy of ANC visits; (iii) services components of ANC; and (iv) High quality ANC (all quality indicators of ANC). ANC visits are crucial for preventing pregnancy-related issues, providing maternal and fetal health counseling, and preparing for birth in a health-care institution [30]. WHO recommends the first ANC visit should occur within the first trimester of gestation and at least four visits during the pregnancy. According to these guidelines, the outcome variables are dichotomous and are categorized as:(a) Timing of first ANC attendance (within 12 weeks of gestation = timely, beyond 12 weeks = delayed) and (b) adequacy of ANC attendance (frequency of 4 or more visits). There is no formal definition to help qualifying the (c) services components of ANC visits. For the purpose of analysis, the third outcome variable (c) was classified as either received or not received based on whether a woman had all five components of her ANC visits. This included receiving urine test, blood pressure measurement, blood sample test, tetanus injection, and iron and folic acid tablets. The choice to define this dependent variable this way is founded on the presumption that all the five components are crucial for quality pregnancy care [31]. The fourth outcome (d) High quality ANC is a composite of the first three and the receipt of ANC services by a skilled provider. A woman who had timely first ANC visit, had 4 or more ANC visits, received services components of ANC during the visits by a skilled provider was categorized as “yes” received high-quality ANC and “no” otherwise. A skilled ANC provider was considered as a medical doctor, a nurse or a midwife. The choice of this model was adapted from Bollini P and colleagues who proposed indicators to help measure the quality of ANC [32]; and referred to a recent study in India [33]. Various determinants of ANC utilization were examined as explanatory variables for their relevance in the uptake of ANC. These factors were adapted from Andersen’s behavioral model for healthcare use [34]. Many studies have made use of this model to investigate the determinants of antenatal care utilization [34–36]. These factors were: Age, type of place of residence (urban, rural), province, woman’s education level, employment status, wealth index, husband education level, husband employment status, access to media, involvement in health decision, birth order, place of antenatal care, perceived distance to health facility, the ease of getting money for treatment and child wantedness. Numerical values like age, birth order and years of education attended were grouped into categories. Women’s age in years was tabulated into groups (15–19 years, 20–24 years, 25–29 years, 30–34 years, 35 and above); birth order of the baby into four categories (1st,2nd,3rd,4th and above); women’s and husbands’ education were classified as ‘no education’, ‘primary’, ‘secondary’ or ‘higher’ education. Access to media is a composite variable obtained from three variables (frequency of listening to radio/watching TV/reading newspapers) and is classified into not at all, less than once a week and at least once a week. The household wealth index was constructed using principal component analysis from items related to possession of durable assets, access to utilities and infrastructure, and housing characteristics. Each woman was ranked into five categories (poorest, poorer, middle, richer and richest) based on a household asset score, comprising 20% of the population [37, 38]. These five categories were later used to obtain three categories (poor, middle and rich). All the statistical analyses were conducted using Stata v14.0 [39]. Descriptive statistics for the sociodemographic characteristics of the study participants were generated by means of frequency and percentage as shown in Table 1.We used chi-square tests to identify demographic and socio-economic factors associated with each outcome variable. Crude odds ratios were generated by means of bivariate analyses to determine the odds of each outcome variable with explanatory variables. Potential factors with p = 0.8, using Pearson correlation test), the variable that was most correlated with the outcome variable of interest was retained. To account for clustering, stratification, and sample weight, we weighted all analyses using the survey module “svyset” stata commands. Sociodemographic characteristics of study participants
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