Association of overweight and obesity with hypertension, diabetes and comorbidity among adults in Bangladesh: evidence from nationwide Demographic and Health Survey 2017–2018 data

Abstract

ObjectivesThe study aimed to determine the association of overweight and obesity with hypertension, diabetes and comorbidity among the adults of Bangladesh.Study designThis study used cross-sectional data from the nationally representative Bangladesh Demographic and Health Survey conducted in 2017–2018. The main outcome variables were hypertension, diabetes and comorbidity. Comorbidity was defined as the coexistence of hypertension and diabetes. Overweight and obesity, as measured by body mass index, were the main explanatory variables. The strength of the association was determined using the adjusted multiple logistic regression models.SettingRural and urban areas in Bangladesh.ParticipantsThe study included a total of 11 881 adults (5241 men and 6640 women) aged 18 years or older.ResultsThe prevalence of hypertension, diabetes and comorbidity among the sample population were 28.5%, 9.9% and 4.5%, respectively. Among the respondents, 20.1% were overweight and 4.1% were obese. The risk of hypertension was 2.47 times more likely in the overweight group (adjusted OR (AOR) 2.47; 95% CI 2.22 to 2.75) and 2.65 times more likely in the obese group (AOR 2.65; 95% CI 2.16 to 3.26) compared with the normal or underweight group. Adults who were overweight and obese had 59% (AOR 1.59; 95% CI 1.37 to 1.84) and 88% (AOR 1.88; 95% CI 1.46 to 2.42) higher odds of having diabetes, respectively, than normal or underweight adults. Moreover, the risk of comorbidity was 2.21 times higher in overweight adults (AOR 2.21; 95% CI 1.81 to 2.71) and 2.86 times higher in obese adults (AOR 2.86; 95% CI 2.09 to 3.91) compared with normal or underweight adults.ConclusionsUsing large-scale nationally representative data, we found that overweight and obesity were significantly associated with hypertension, diabetes and comorbidity. So, nationally representative data can be used for programme planning to prevent and treat these chronic conditions.