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Background: Despite the widespread use of antenatal care (ANC), its effectiveness in low-resource settings remains unclear. In this study, self-reported health-related quality of life (HRQoL) was used as an alternative to other maternal health measures previously used to measure the effectiveness of antenatal care. The main objective of this study was to determine whether adequate antenatal care utilization is positively associated with women’s HRQoL. Furthermore, the associations between the HRQoL during the first year (1– 13 months) after delivery and socio-economic and demographic factors were explored in Rwanda. Methods: In 2014, we performed a cross-sectional population-based survey involving 922 women who gave birth 1–13 months prior to the data collection. The study population was randomly selected from two provinces in Rwanda, and a structured questionnaire was used. HRQoL was measured using the EQ-5D-3L and a visual analogue scale (VAS). The average HRQoL scores were computed by demographic and socio-economic characteristics. The effect of adequate antenatal care utilization on HRQoL was tested by performing two multivariable linear regression models with the EQ-5D and EQ-VAS scores as the outcomes and ANC utilization and socio-economic and demographic variables as the predictors. Results: Adequate ANC utilization affected women’s HRQoL when the outcome was measured using the EQ-VAS. Social support and living in a wealthy household were associated with a better HRQoL using both the EQ-VAS and EQ-5D. Cohabitating, and single/unmarried women exhibited significantly lower HRQoL scores than did married women in the EQ-VAS model, and women living in urban areas exhibited lower HRQoL scores than women living in rural areas in the ED-5D model. The effect of education on HRQoL was statistically significant using the EQ-VAS but was inconsistent across the educational categories. The women’s age and the age of their last child were not associated with their HRQoL. Conclusions: ANC attendance of at least four visits should be further promoted and used in low-income settings. Strategies to improve families’ socio-economic conditions and promote social networks among women, particularly women at the reproductive age, are needed.
This cross-sectional population-based survey involved women who gave birth between 1 and 13 months prior to the data collection. The study population was randomly selected from all districts in Kigali City and the Northern Province. Kigali City has three districts, and the Northern Province has five districts. Kigali City has a population of 1,135,428, and most residents live in areas with urban characteristics (i.e., housing, economic activities, and access to infrastructures), while the Northern Province has 1,729,927 inhabitants living mainly in rural areas [23]. The total population in the selected provinces represents 27.2% of the country’s total population [23]. Kigali City and the Northern Province were chosen not only because of logistical reasons but also because these areas are considered to reflect the situation in other provinces based on various surveys conducted in Rwanda showing no major regional differences in the reproductive health indicators [24]. The sample size was calculated based on the estimated prevalence of hypertension as a pregnancy complication (p = 10%) [25], a precision rate of 5%, and projected non-response rates of 10 and 5% to account for the design effect. Three-stage sampling was conducted to identify the households to include in the study. First, in eight districts (three in Kigali City and five in Northern Province), 48 primary sampling units, i.e., villages, which represent the lowest administrative unit in Rwanda, were randomly selected from a total of 3918 villages in the two provinces, corresponding to 1.2% of the total number of villages [26]. Twenty percent of the villages were selected from urban areas, and 80% of the villages were selected from rural areas to reflect the rural-urban proportions in Rwanda. Second, the number of households selected in each village was decided according to the proportion to the total number of households in each village. In each village in Rwanda, community health workers maintain records of women expecting to deliver and women with newborns and infants less than 1 year of age. Third, these records were used to randomly select the households to be visited, and the list was reduced to only include households with a woman who fulfilled the main inclusion criterion, i.e., delivery within 1 to 13 months before the survey. If a village did not have the desired number of households fulfilling the inclusion criterion, the remaining households were selected from a neighboring village. In total, 922 women were selected and invited to participate. All women agreed to participate. A questionnaire comprising socio-economic and demographic factors, health conditions, and the use of maternal health services was developed in English and translated into Kinyarwanda by a professional translator with experience in translating medical and public health questionnaires in Rwanda. The data collection was performed by the University of Rwanda, School of Public Health between July and August of 2014. In addition to four PhD students who were responsible for leading the fieldwork and their five supervisors, 12 female data collectors were hired. The data collectors had previous training in nursing or a related subject (minimum of 6 years of post-primary school) and experience in population-based data collection. The data collectors received a 4-day training, including 1 day of piloting in one village (not included in the sampled area). Data entry was performed by four experienced data clerks trained on using an SPSS data-entry template (SPSS version 22.0) [27] and supervised by a data manager. The dependent variable is the HRQoL, which was measured according to the EQ-5D praxis. The following two methods [28] were used: (1) The EQ-5D-3L descriptive system (designated EQ-5D) comprises the following five dimensions: mobility, self-care, usual activities, pain or discomfort, and anxiety or depression. The respondents selected among three options (i.e., no problems, some problems, severe problems) in response to each dimension. Each response provides a combination (e.g., 1, 1, 2, 1, and 2) that has a corresponding value, often called a score [28]. Given the lack of an official version of the EQ-5D-3L in any language spoken in Rwanda, we translated the English version of the EQ-5D-3L into Kinyarwanda and obtained retrospective permission to use the translated questionnaire from EuroQol. To calculate the EQ-5D scores, we used the weights used in a population study conducted in the UK [29]. (2) Using the visual analogue scale (designated EQ-VAS), the women were asked to express how good or poor their health was on the day of the interview by indicating a point on a scale from 0 to 100. A score of 100 represented the best imaginable health state, while a score of 0 represented the worst imaginable health state. The main independent variable was adequacy of antenatal care utilization, which is a binary indicator of whether a woman had received antenatal care according to Rwandan guidelines. This variable was constructed using Kessner’s index [30] as a prototype and adapted to the Rwandan antenatal care guidelines. The Rwandan antenatal care policy was developed based on the 2002 WHO guidelines, which suggested four focused antenatal visits for normal pregnancies [31]. However, most Rwandan couples do not adhere to this recommendation. For example, in 2014/15, only 45% of women completed four visits, and the average month of initiation of the first antenatal visit was the fifth month of gestation [24]. The following two categories of this variable were constructed: The women’s socio-economic and demographic characteristics were divided into the following groups: residence was divided into two groups (i.e., rural and urban) according to the location of the village; age was divided into groups in 5-year intervals; educational level was divided into four groups (i.e., some primary school, completed primary school, lower secondary or vocational education, and upper secondary or higher education); and marital status was divided into four groups (i.e., married, cohabitating, separated/divorced/widowed, and unmarried/single). A household wealth index was constructed by performing principal component analysis and using information regarding housing characteristics (i.e., materials used to construct walls, source of water, type of cooking fuel, and connection to electricity) and ownership of durable assets (i.e., mattress, iron, TV, mobile phone, and computer). The household scores were standardized based on a normal distribution with a mean of zero and a standard deviation of one [32]. The households were ranked according to these scores and divided into the following five equal groups: lowest, second, middle, fourth, and highest wealth quintiles. The social support variable was constructed from seven questions regarding the respondents’ access to four main types of social support, i.e., emotional, tangible, or instrumental, informational and appraisal support, which are often referenced in the medical literature [33, 34]. The responses were dichotomized, given the values 0 (never) or 1 (sometimes, often or always) and summed for all respondents. Thus, the respondents were assigned social support values ranging between zero and seven. Finally, the values were grouped into the following two categories: poor social support (access to 0–4 types of social support) and good social support (access to 5–7 types of social support). The average EQ-5D scores and 95% confidence intervals were calculated in relation to all independent variables. First, a bivariate analysis was performed using independent t-tests and one-way analyses of variance to identify the significant differences in the mean HRQoL values among different groups of independent variables at a 0.05 significance level. Second, two multivariable linear regression models were constructed using the EQ-5D scores and EQ-VAS scores as the outcomes and the adequacy of ANC utilization and socio-economic and demographic variables as the predictors. All predictors included in the bivariate analysis were considered in the initial model, except for the variable “Number of children” because this variable had a high proportion of missing values (31.4%), which led to biased model results. A backward stepwise procedure was performed at a p value of 0.05 to identify the variables that remained significant in the final model. The co-linearity among the covariates was assessed by performing a variance inflation factor (VIF). None of the covariates presented a VIF value above the maximum acceptable value of 10. The data analysis was performed using STATA 13 [35]. Following Pullenayegum et al. [36], the ordinary least squares with heteroskedasticity-consistent (robust) standard errors method was used as a simple and valid estimation method when many individuals achieved the upper bound of one as in our EQ-5D data and because our sample size can be considered large.
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