BMC public health · 2024
Assessment of socioeconomic and health vulnerability among urban slum dwellers in Bangladesh: a cross-sectional study.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Hasan Md Zahid, Rabbani Md Golam, Ahmed Mohammad Wahid, Mehdi Gazi Golam, Tisha Khadija Islam, Reidpath Daniel D, Hanifi Syed Manzoor Ahmed, Mahmood Shehrin Shaila
Аннотация
BACKGROUND: Bangladesh is rapidly urbanizing and approximately half of its urban population resides in deprived slums with limited access to basic needs. However, there is a dearth of information on vulnerability levels among slum dwellers. We aimed to assess the level of vulnerability within and between slums via various socioeconomic and health indicators.
METHODS: A cross-sectional survey of 810 randomly selected households was conducted in two purposively selected slums, Korail and Shyampur, in Dhaka from November to December 2021. Data was collected on various indicators, including demographics, education, employment, access to utility, and healthcare services. Principal component factor analysis was employed to identify the key indicators to construct the socioeconomic and health vulnerability index for the urban slums of Bangladesh (SEHVI-BD). Kaiser-Meyer-Olkin, Bartlett's test, and Cronbach's alpha coefficient test were used to assess indicators' suitability. The selected indicators were used to generate an index on a scale of 100, with a higher index value indicating a higher level of vulnerability. The estimated scores were used to categorize the vulnerability status into three levels: mild, moderate, and severe vulnerability. The Mann-Whitney-U test and Kruskal-Wallis test were applied between the generated index and other socioeconomic variables to validate the relationship.
RESULTS: A total of 27 socioeconomic and health indicators were identified that explained 60% of the variance. The indicators were then grouped into six domains on the basis of their relevance. The prevalence of severe vulnerability in the Korail slum was approximately 9% and moderate vulnerability was 30% whereas these values were approximately 58% and 37%, respectively, in the Shyampur slum. The difference in the vulnerability level between the two slums was also evident across the domains. Households in the poorest wealth quintile, with lower education levels of household heads, and having irregular income experienced higher levels of vulnerability.
CONCLUSIONS: The SEHVI-BD offers a critical tool for policymakers to identify and address vulnerabilities, facilitating more targeted public health interventions in urban Bangladesh and similar low-income settings. This study further emphasizes the importance of integrating comprehensive vulnerability assessments into public health policies to reduce inequalities and improve well-being, especially for the urban marginalized slum population.
METHODS: A cross-sectional survey of 810 randomly selected households was conducted in two purposively selected slums, Korail and Shyampur, in Dhaka from November to December 2021. Data was collected on various indicators, including demographics, education, employment, access to utility, and healthcare services. Principal component factor analysis was employed to identify the key indicators to construct the socioeconomic and health vulnerability index for the urban slums of Bangladesh (SEHVI-BD). Kaiser-Meyer-Olkin, Bartlett's test, and Cronbach's alpha coefficient test were used to assess indicators' suitability. The selected indicators were used to generate an index on a scale of 100, with a higher index value indicating a higher level of vulnerability. The estimated scores were used to categorize the vulnerability status into three levels: mild, moderate, and severe vulnerability. The Mann-Whitney-U test and Kruskal-Wallis test were applied between the generated index and other socioeconomic variables to validate the relationship.
RESULTS: A total of 27 socioeconomic and health indicators were identified that explained 60% of the variance. The indicators were then grouped into six domains on the basis of their relevance. The prevalence of severe vulnerability in the Korail slum was approximately 9% and moderate vulnerability was 30% whereas these values were approximately 58% and 37%, respectively, in the Shyampur slum. The difference in the vulnerability level between the two slums was also evident across the domains. Households in the poorest wealth quintile, with lower education levels of household heads, and having irregular income experienced higher levels of vulnerability.
CONCLUSIONS: The SEHVI-BD offers a critical tool for policymakers to identify and address vulnerabilities, facilitating more targeted public health interventions in urban Bangladesh and similar low-income settings. This study further emphasizes the importance of integrating comprehensive vulnerability assessments into public health policies to reduce inequalities and improve well-being, especially for the urban marginalized slum population.