|Socio-economic factors explain differences in public health-related variables among women in Bangladesh: a cross-sectional study|
||Md. Mobarak H Khan and Alexander Kraemer
||BMC Public Health, 2008, 8:254
Health care utilization
||Worldwide one billion people are living in slum communities and experts projected that this number would double by 2030. Slum populations, which are increasing at an alarming rate in Bangladesh mainly due to rural-urban migration, are often neglected and characterized by poverty, poor housing, overcrowding, poor environment, and high prevalence of communicable diseases.
Unfortunately, comparisons between women living in slums and those not living in slums are very limited in Bangladesh. The objectives of the study were to examine the association of living in slums (dichotomized as slum versus non-slum) with selected public health-related variables among women, first without adjusting for the influence of other factors and then in the presence of socio-economic variables.
Methods: Secondary data was used in this study. 120 women living in slums (as cases) and 480 age-matched women living in other areas (as controls) were extracted from the Bangladesh Demographic and Health Survey 2004.
Many socio-economic and demographic variables were analysed. SPSS was used to perform simple as well as multiple analyses.
P-values based on t-test and Wald test were also reported to show the significance level.
Results: Unadjusted results indicated that a significantly higher percent of women living in slums came from country side, had a poorer status by household characteristics, had less access to mass media, and had less education than women not living in slums.
Mean BMI, knowledge of AIDS indicated by ever heard about AIDS, knowledge of avoiding AIDS by condom use, receiving adequate antenatal visits (4 or more) during the last pregnancy, and safe delivery practices assisted by skilled sources were significantly lower among women living in slums than those women living in other areas. However, all the unadjusted significant associations with the variable slum were greatly attenuated and became insignificant (expect safe delivery practices) when some socio-economic variables namely childhood place of residence, a composite variable of household characteristics, a composite variable of mass media access, and education were inserted into the multiple regression models.
Taken together, childhood place of residence, the composite variable of mass media access, and education were the strongest predictors for the health related outcomes.
Conclusions: Reporting unadjusted findings of public health variables in women from slums versus non-slums can be misleading due to confounding factors.
Our findings suggest that an association of childhood place of residence, mass media access and public health education should be considered before making any inference based on slum versus non-slum comparisons.