Determinants of maternal morbidity during pregnancy in urban Bangladesh: Negative binomial regression approach

AimTo investigate the prevalence of maternal morbidity during pregnancy and its determinants among the women from urban areas of Bangladesh. MethodsThe secondary data were used and extracted from the latest Bangladesh Urban Health Survey (BUHS) 2013. Several statistical models: Poisson, negative binomial (NB) and mixed Poisson were adapted and compared to explore the best model for investigating potential determinants of maternal morbidity. Pearson chi-square statistic was used for the detection of overdispersion in the data. ResultsOverall 13.5% of the urban women in Bangladesh suffered from at least two pregnancy complications. The study detected the overdispersion existing in the maternal morbidity count data and found the NB regression as the best choice for analyzing the data because of its smallest Akaike information criterion. Administrative division (Rangpur: p=0.003, IRR=1.34, 95% CI: 1.11 to 1.63; Sylhet: p=0.006, IRR=1.42, 95% CI: 1.11 to 1.82), wanted pregnancy (p<0.001, IRR=0.80, 95% CI: 0.71 to 0.90), place of delivery (p<0.001, IRR=0.60, 95% CI: 0.54 to 0.66) and wealth index (Rich: p<0.001, IRR=0.74, 95% CI: 0.66 to 0.84) were found to be statistically significant determinants for maternal morbidity during pregnancy among the urban women in Bangladesh. ConclusionsThe urban women in Bangladesh with unwanted pregnancy, from poor/middle income group; and living in Rangpur and Sylhet divisional cities have higher risk of maternal morbidity during pregnancy. The women already suffering from major pregnancy related complications visit health centre to give birth. Study findings may help the government and relevant authorities to take necessary steps for reducing maternal morbidity and mortality due to pregnancy related complications.