Socioeconomic inequalities in modern contraceptive use among women in Benin: a decomposition analysis – BMC Women’s Health

Data source and study design

Data for this study was obtained from the 2017–2018 BDHS. Specifically, we used data from the individual recode (IR) file of the BDHS. BDHS is part of several surveys obtained from the Monitoring and Evaluation to Assess and Use Results Demographic and Health Surveys (MEASURE DHS) Program, which contain information on some issues on population, health and nutrition including contraception use. The DHS is a comparatively nationally representative survey conducted in over 85 low-and-middle-income countries worldwide [20]. DHS employed a descriptive cross-sectional design. The survey adopted a two-stage sampling design. The first stage was characterised by the selection of clusters across urban and rural locations from the entire nation. These made up enumeration areas for the study. The second stage involved the selection of households from the predefined clusters. Details of the methodologies employed in the various rounds of the surveys can be found in the final reports [14]. Standardized structured questionnaires were used to collect data from the respondents on health indicators including contraceptive use. We included 7,360 sexually active women of reproductive age who had complete observations on variables of interest in the final analysis. The dataset used is free and available at https://dhsprogram.com/data/available-datasets.cfm. This paper was drafted with reference to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.

Variables

Outcome variable

Utilisation of modern contraceptive method was the outcome variable in the study which was derived from the question: “Which method type of contraceptive does [NAME] currently use?”. Responses to this question were “no method” =0, “folkloric method” =1 “traditional method” =2 and “modern method”=3. For this study, women who responded no method, folkloric, and traditional method were categorised as “0” which represented “other methods” while the modern method was labelled “1” [6, 21, 22, 23, 24].

Independent variable

Household wealth index was considered the main explanatory variable. The wealth index was calculated by the BDHS using several indicator variables. These indicators included elements about the household’s possessions, traits, ownerships, and utility services, including floor and roof materials, the presence of technological items like televisions, cell phones, and refrigerators, the availability of a source of drinking water for cooking and washing, the type of cooking fuel used, restrooms, and land and livestock ownership, among other things. After standardizing the variables, principal component analysis was performed on these indicator variables, and the wealth index was constructed using the determined factor loadings. Afterwards, the wealth index was sorted and divided into five portions, each of 20%. We used the categories of wealth index that were generated by the DHS as follows: poorest, poorer, middle, richer, and richest in the BDHS dataset.

Covariates

Nine other variables were considered for this study. These variables included age (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49), marital status (not married and married), religion (Christianity, Islam, and other), employment status (not working and working), number of live births (no birth, one birth, two births, three births, and four or more births), exposure to mass media (no and yes), educational level (no education, primary, and secondary/higher), place of residence (urban and rural), and ethnicity (Adja and related, Bariba and related, Dendi and related, Fon and related, Yoa, loka and related, Betamaribe and related, Peulh and related, and Yoruba and related). The explanatory variables considered in this study were selected based on their association with modern contraceptive use [1, 2, 13, 16].

Statistical analyses

Missing observations on variables of interest were dropped during the analysis. The prevalence of modern contraceptive use by sociodemographic factors was determined using univariate and bivariate analysis (Table 1). We used binary. logistic regression analysis to examine the factors associated with the utilisation of modern contraceptives. Model I and II were the bivariable and multivariable binary regression analyses, respectively. The results in Model I were presented as crude odds ratio (cOR) with their respective 95% confidence interval (CI), whilst the results in Model II were presented as adjusted odds ratio (aOR

SDGs, Targets, and Indicators

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 3: Good Health and Well-being
• SDG 5: Gender Equality
• SDG 10: Reduced Inequalities

2. What specific targets under those SDGs can be identified based on the article’s content?

• SDG 3.7: By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes.
• SDG 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferences.
• SDG 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Utilization of modern contraceptive methods (indicator for SDG 3.7 and SDG 5.6)
• Socioeconomic disparities in women’s usage of modern contraception (indicator for SDG 10.2)

Table: SDGs, Targets, and Indicators

Behold! This splendid article springs forth from the wellspring of knowledge, shaped by a wondrous proprietary AI technology that delved into a vast ocean of data, illuminating the path towards the Sustainable Development Goals. Remember that all rights are reserved by SDG Investors LLC, empowering us to champion progress together.

Source: bmcwomenshealth.biomedcentral.com

Join us, as fellow seekers of change, on a transformative journey at https://sdgtalks.ai/welcome, where you can become a member and actively contribute to shaping a brighter future.