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Background: Institutional delivery is essential in reducing maternal morbidity and mortality. We investigated the prevalence of institutional delivery and associated factors among women in Ghana. Methods: National representative data from the 2017-2018 Ghana Multiple Indicator Cluster Survey was used for the analysis. The study included 3466 women, ages 15-49 y, who had a live birth in the last 2 y. Descriptive statistics were used to assess the prevalence of institutional delivery while multivariate logistic regression was used to assess the relationship between our variables of interest and institutional delivery. Results: The prevalence of institutional delivery among women in Ghana was 77.89% (95% confidence interval [CI] 75.29 to 80.50). High-income households (adjusted odds ratio [aOR] 2.13 [95% CI 1.36 to 3.35]), attending antenatal care at least four times (aOR 2.37 [95% CI 1.54 to 3.65]) and knowing one’s human immunodeficiency virus status (aOR 1.41 [95% CI 1.08 to 1.84]) were associated with higher odds of institutional delivery. Living in rural areas (aOR 0.43 [95% CI 0.27 to 0.67]), multiparity (aOR 0.59 [95% CI 0.41 to 0.85]) and no health insurance (aOR 0.57 [95% CI 0.44 to 0.74]) were associated with lower odds of institutional delivery. Conclusions: The government of Ghana may need to focus on increasing health insurance utilization and antenatal care attendance in order to increase the coverage of institutional delivery.
We analysed data from the 2017–2018 MICS for Ghana. The MICS is a national representative household population-based survey.27 A total of 3466 women, ages 15–49 y, with a live birth within the last 2 y were included in the study. The MICS uses a two-stage sampling procedure. The first stage involves selection of census enumeration areas from each sampling strata using a probability proportional to the number of households in each enumeration area. In the second stage, households are selected from enumeration areas using systematic random sampling. A description of the MICS sampling design and data collection procedures has been published.27 Institutional delivery was the outcome variable of interest. The outcome variable was binary and coded as 1 for women who delivered at a health facility and 0 for those who were reported not to have delivered at a health facility. The predictor variables were age, marital status, education, household wealth, place of residence, attended antenatal care, parity, access to media, insurance status, know human immunodeficiency virus (HIV) status and number of sulfadoxine–pyrimethamine (SP) doses. SP was used to assess the intermittent preventive treatment regime in relation to institutional delivery. Age was categorized as 15–24, 25–34 and 35–49 y while marital status was categorized as never married and married/cohabitation. The other variables were categorized as follows: education (no formal education, primary education, secondary or higher education), health insurance status (no insurance, insurance), parity (primiparous, multiparous), attended antenatal care (0–3, ≥4), place of residence (rural, urban), know HIV status (yes, no) and number of SP doses (0–1, ≥2). Wealth quintiles were used to construct the household wealth variable. The upper two, middle and lower two wealth quintiles were used to represent high income, middle income and poor households, respectively. Access to media was also dichotomized as ‘yes’ for women who reported having access to any of the following: read the newspaper/magazine, listen to radio, watch television or use the internet at least once a week or almost every day, and ‘no’ for otherwise. Our variable selection was informed by previous studies11,28–30 and data available in the MICS.27 Descriptive statistics were used to assess the prevalence and characteristics of the study population. Bivariate and multivariable logistic regression models were used to assess the relationship between all our predictor variables of interest and the outcome variable. We accounted for clustering and stratification and applied sampling weights to account for the complex sampling design. A p-value <0.05 was considered statistically significant. Descriptive statistics and logistic regression analysis were done using SAS version 9.3 (SAS Institute, Cary, NC, USA).
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