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Spatial analysis of the prevalence of obesity and overweight among women in Ghana
OBJECTIVE: Identifying hot spots for the overweight aids in effective public health interventions due to the associated public health burden and morbidities. This study, therefore aimed to explore and determine the spatial disparities in the overweight/obesity prevalence among women in Ghana. The st...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813332/ https://www.ncbi.nlm.nih.gov/pubmed/33455932 http://dx.doi.org/10.1136/bmjopen-2020-041659 |
Sumario: | OBJECTIVE: Identifying hot spots for the overweight aids in effective public health interventions due to the associated public health burden and morbidities. This study, therefore aimed to explore and determine the spatial disparities in the overweight/obesity prevalence among women in Ghana. The study also aims at modelling the average body mass index (BMI) values using the spatial regression and the performance compared with the standard regression model. DESIGN: This is a cross-sectional study using data from the 2014 Ghana Demographic and Health Survey (GDHS). SETTING: The study was set in Ghana. PARTICIPANTS AND METHODS: Data on 4393 non-pregnant women aged 15–49 years from the 2014 GDHS. Both global (Moran’s I) and the local indicators for spatial dependence were examined through the mapped BMI values across the country by clusters. An estimated spatial lag model was used to explain the spatial differences in the average body sizes of women. RESULTS: The overall prevalence of overweight/obesity among reproductive women in Ghana was 35.4%, and this was highly prevalent among educated women (p<0.001), those from wealthy households (p<0.001) and dwelling in an urban setting (p<0.001). Significant clustering (Moran’s I=0.3145, p<0.01) of overweight/obesity was observed with hot spots (clustering) in Greater Accra, Central, Western and Ashanti regions. The spatial lag model was the best fit based on the Likelihood ratio test and the Akaike information criterion and Bayesian information criterion values. The mean age of women and household wealth were significant factors accounting for the increase in the average cluster body size (BMI) of women and the spatial differences. CONCLUSION: The prevalence of overweight/obesity was high and spatially clustered in the southern, middle and coastal regions. Geographic specific and effective public health interventions and strategies are needed to address the growing morbidity burden associated with the rise in the average body sizes of reproductive women. |
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