ArticleGlobal health, epidemiology and genomics2026
Bayesian Spatial Conditional Logistic Regression Modelling of Overweight/Obesity Prevalence and Determinants Among Women in Ghana, Accounting for Contraceptive Use.
Article in Global health, epidemiology and genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Bayesian Spatial Conditional Logistic Regression Modelling of Overweight/Obesity Prevalence and Determinants Among Women in Ghana, Accounting for Contraceptive Use.Global health, epidemiology and genomics · 2026Article
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Abstract
introductionOverweight/obesity (OW/OB) poses a major public health challenge among reproductive-aged women across most LMICs, including Ghana. The continuous rise in maternal average BMI coupled with fragile healthcare system makes surveillance and interventions to reverse the trend in Ghana as well as most sub-Saharan African (SSA) countries, a pertinent issue.
objectiveThis study identifies risk factors associated with OW/OB among reproductive-aged women in Ghana, accounting for spatial autocorrelation and the potential confounding effect of modern contraceptive use (MCU). It also provides updated information on the distribution of OW/OB risk. MCU was used as a stratification variable to control for potential confounding related to contraceptive-induced metabolic or behavioural influences.
methodsThe recent cross-sectional Ghana Demographic and Health Survey (2022 GDHS) data, made up of 7054 women aged 15-49 years, was used in this study. The Bayesian spatial conditional logistic regression model was developed to determine significant factors.
resultsAbout 36.9% of the women were overweight/obese (OW = 22.5% and OB = 14.4%). A significantly high maternal OW/OB rate (> 50) was observed in the Ashanti and Greater Accra regions and the least prevalence (< 20%) in the North-East region. Regional disparity rates indicate that middle and southern Ghana are associated with higher burden compared to northern Ghana. The Bayesian conditional logistic model with convoluted CAR prior emerged as the best model for studying maternal OW/OB risk while accounting for MCU with the minimum WAIC and DIC values. Significantly higher risk of OW/OB burden is linked to high education (primary [aOR = 1.87, 95% CI:2.27-6.50]; secondary [aOR = 2.02, 95% CI: 2.42-7.60]; and higher [aOR = 2.05, 95% CI: 1.59-2.64]), increase in age (20-29 [aOR = 3.34, 95% CI: 2.67-4.17]; 30-39 [aOR = 6.07, 95% CI: 4.66-7.89]; and 40-49 [aOR = 7.22, 95% CI: 5.40-9.65]), being married (aOR = 1.81, 95% CI: 1.49-2.19), urban residency (aOR = 1.41, 95% CI: 1.23-1.61) and the number of children (aOR = 1.06, 95% CI: 1.03-1.11).
conclusionThe spatial heterogeneity in OW/OB risk provided evidence for regional targeted preventive strategies, particularly in the Ashanti and Ahafo regions, which are significant hotspots for high OW/OB risk, to help reduce the public health burden of maternal OW/OB in Ghana. This could contribute to ensuring the quality health of women (SDG 3).
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