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Background: In Rwanda, a majority of pregnant women visit antenatal care (ANC) services, however not to the extent that is recommended. Association between socio-demographic or psychosocial factors and poor utilization of antenatal care services (≤2 visits during the course of pregnancy irrespective of the timing) among recently pregnant women in Rwanda were investigated. Methods: This population-based, cross sectional study included 921 women who gave birth within the past 13 months. Data was obtained using an interviewer-administered questionnaire. For the analyses, bi-and multivariable logistic regression was used and odds ratios were presented with their 95% confidence intervals. Results: About 54% of pregnant women did not make the recommended four visits to ANC during pregnancy. The risk of poor utilization of ANC services was higher among women aged 31 years or older (AOR, 1.78; 95% CI: 1.14, 2.78), among single women (AOR, 2.99; 95% CI: 1.83, 4.75) and women with poor social support (AOR, 1.71; 95% CI: 1.09, 2.67). No significant associations were found for school attendance or household assets (proxy for socio-economic status) with poor utilization of ANC services. Conclusion: Older age, being single, divorced or widowed and poor social support were associated with poor utilization of ANC services. General awareness in communities should be raised on the importance of the number and timing of ANC visits. ANC clinics should further be easier to access, transport should be available, costs minimized and opening hours may be extended to facilitate visits for pregnant women.
This cross-sectional population based study was conducted in the Northern province and in Kigali city, the capital and largest city in Rwanda. Kigali city has urban, semi-urban and rural areas, whereas the Northern Province is predominantly rural. The target population was women who gave birth within the past 13 months. The selection process was based on the total population of about 2,865,000 inhabitants from 4 791 villages.[13]. The sample size was calculated based on the estimated prevalence of hypertensive disorders during pregnancy (10%) [17–19], as hypertension is one of the major factors to be investigated within this research programme, and was the least prevalent among study outcomes. The desired level of precision was set at 0.025 and considering the effect of multi-stage sampling; a design effect of 1.5 was used. Adding 10% to the sample size to take care of possible non-responses, the total sample size was calculated to be 922 women. The selection process was based on a total population of about 2,865,000 inhabitants from 4 791 villages [13]. In three steps, villages (the smallest administrative entity in Rwanda), households and study participants were randomly selected in the five districts of the Northern Province and in three districts within Kigali City. Firstly, out of 4 791 villages, it was decided to select in total 48 villages (equal to 1%). The villages were then randomly selected proportionate to total number of villages in each district by using Epi-Info random function. Approximately 20% of Rwandan population lives in urban areas [6]. In order to mirror the country’s rural-urban divide, 20% of the villages were selected from urban areas. Secondly, households from each village were selected based on the total number of the households in each selected village (proportionate to size). With the help of community health workers who keep records of maternal pregnancies and childbirths, women who gave birth within the past 13 months were identified, and finally the women to be interviewed were randomly selected among eligible women in each household, if more than one were present. In case of no eligible woman in the household, the closest household with an eligible woman was approached. If an excess number of households with an eligible woman were at hand in a village, lottery decided which ones to include. In case of fewer eligible women in the village than envisaged in the study, the closest village was approached and the same data collection procedures were used to obtain the remaining number of eligible women. All selected women but one gave their consent to participate (overall response rate was 99.9%.), and some internal non-responses were at hand. Data collection took place between July and August of 2014. A structured questionnaire was developed, which included questions relating to ANC attendance and procedures, delivery procedures, pregnancy and delivery complications and cost of ANC services. The questionnaire was translated into Kinyarwanda, the Rwandan national language and pre-tested but no major changes were made on the questionnaire after the pilot study apart from a few adjustments in wording. Twelve well-trained interviewers who were all women belonging to a pool of interviewers at the School of Public Health, University of Rwanda were selected. These were nurses, midwives and clinical psychologists. Face-to-face interviews were performed and four supervisors (first author and three colleagues) guided the interviewers. The supervisors ensured that all selected households were contacted and reviewed the questionnaires before the team left the village. For the protection of the interviewed women in the households and for confidentiality purposes, only one person in each household was interviewed. The School of Public Health, College of Medicine and Health Sciences, University of Rwanda, was the lead implementer of the study. Data entry was performed by four skilled personnel selected from a permanent cohort of data entry clerks from the School of Public Health under the supervision of a data entry manager. Number and timing of antenatal care visits was used as the dependent variable and two different variables were tried in the initial analyses. The first one used ≤2 visits at ANC clinic during the course of pregnancy irrespective of the timing as the poor ANC services utilization. For comparison reasons, the second one used ≤2 visits plus those who made 3 visits but none during first trimester as the poor ANC services utilization. Participants’ age was categorized into 15–30 years and 31–46 years age groups. The number of people in the household was described as a three-category variable (1–3 people, 4–6 people and 7 or more) then, a dichotomized variable was created where the first two categories were combined and the latter one considered the exposed. Marital status was dichotomized into married or cohabitating and then single, divorced or widowed were brought together in the exposure category. Women’s relationship with household head was assessed as being the wife, daughter, daughter in law, other family relationship and no relationship, further dichotomized into the wife or any other relationship as the exposure category. Ever attended school was responded with yes/no with the latter as the exposure category. Total household monthly income was made into a three-category variable as more than 36,000 FRW (60USD), between 17,501 and 35,999 FRW (30–60 USD) and less than 17,500 FRW (30 USD) later dichotomized into ≤17, 500FRW and ≥17, 501FRW. Social support was defined as having a family member, a relative or a friend who could lend support to the woman if any problem would arise. This item was responded to with yes/no, with the latter as the exposure category. Partner’s age was categorized into ≤ 40 and 41–70 age groups. Then, identical techniques were used to categorize partner’s level of education and the total household monthly income as described above for participants. A composite variable of assets in the household was used as a proxy for socio-economic status of the household. Assets in the household included a radio, a television set, a refrigerator, a bicycle, a motorcycle, a car, a mobile phone and a computer. It was later dichotomized into having at least one of the items or having none of the items. Having none of the items constituted the very poor subjects. Frequency and prevalence (n and %) were used to describe participants’ characteristics. Two variables were tried as dependent variables, i.e., poor ANC utilization, ≤2 visits to ANC (n = 122) irrespective of when in the course of the pregnancy, and a composite variable of those with ≤2visits plus those with 3 visits but none in the 1st trimester (n = 317). Associations between socio-demographic and psychosocial factors and poor utilization of antenatal care services were calculated by use of bi- and multivariable logistic regression models. As the results were rather similar for the two dependent variables in the bivariate analyses, it was decided to use the strongest criteria for poor ANC attendance (≤2 visits) in the further analysis to identify the groups of women most at risk of low ANC attendance. Those socio-demographic and psychosocial factors that displayed statistical significance in the bivariate analyses were entered into the regression model in a stepwise fashion. For theoretical reasons, ever-attended school and assets in the household (proxy for socio-economic status) were further tried in the multivariable analyses, as these have been important predictors in other studies. If two variables were highly correlated (r ≥ 0.40), one was excluded. Using this approach, pregnant women’s relationship with household head, the sex of the household head and husband’s age were not included in the final model as these variables were highly correlated with marital status. Also, parity and number of previous pregnancies were excluded because they were highly correlated with the number of people in the household. The fit of the final model was assessed by using Nagelkerke R-Square test. Finally, potential interactions between variables in the final model were tested but no statistically significant interactions were present. All measures of association are presented as odds ratios (OR) with their 95% confidence interval (95% CI). All the analyses were performed using Statistical Package of Social Sciences version 22.0 for windows (SPSS, Armonk, NY, USA).
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