Population health metrics · 2026
Patterns and determinants of COVID-19 mortality in Bangladesh: insights from three health and demographic surveillance systems across diverse socio-environmental settings.
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Hasan Md Mehedi, Das Susmita, Kabudula Chodziwadziwa Whiteson, Chowdhury Srizan, Akter Konok, Bashingwa Jean Juste Harrisson, Alam Sayed Saidul, Haq M Zahirul, Bhuiyan Munirul Alam, Haider M Moinuddin, Razzaque Abdur, Barr Beth A Tippett, Tollman Stephen, Hanifi Syed Manzoor Ahmed
Аннотация
BACKGROUND: The COVID-19 pandemic has severely affected health and well-being worldwide. While most countries have reported excess mortality associated with COVID-19, little is known about individual and social factors associated with COVID-19 and non-COVID-19 mortality in Bangladesh. This study addresses that gap by investigating the mortality rates from COVID-19 and other causes during the pandemic years (2020-2021), as well as their associated socio-demographic determinants using longitudinal population data.
METHODS: From 2020 to 2021, data were collected on 573,433 individuals residing in three HDSS areas in Bangladesh: Matlab (rural), Chakaria (coastal), and the slums of Dhaka (urban). Probable causes of death were determined by medical personnel using the WHO 2016 verbal autopsy (VA) tool supplemented with a COVID-19 module. Deaths were classified as COVID-19 or non-COVID-19 using the International Classification of Diseases and Related Health Problems, tenth revision (ICD-10). Factors associated with COVID-19 and non-COVID-19 mortality were examined using Cox proportional hazards models.
RESULTS: Between January 1, 2020, and December 31, 2021, a total of 6,616 deaths were recorded across the three HDSS sites, of which 5.2% were attributed to COVID-19. The COVID-19 mortality rate was highest in Matlab (58 deaths per 100,000 person-years), followed by Chakaria (15 deaths per 100,000 person-years) and the urban slums in Dhaka (11 deaths per 100,000 person-years). Household socio-economic status was significantly associated with COVID-19 mortality in the Matlab HDSS. Individuals from the lowest wealth tertile had 40% lower mortality compared to individuals from the highest wealth tertile (adjusted mortality rate ratio (aMRR): 0.60; 95% CI: 0.43-0.83). In contrast, no significant differences were observed for non-COVID-19 mortality across wealth tertiles. Age, sex, and marital status were significantly associated with both COVID-19 and non-COVID-19 deaths.
CONCLUSION: Our data revealed that COVID-19 mortality was highest in the Matlab HDSS. Age, sex, and marital status were key determinants of both COVID-19 and non-COVID-19 mortality in Matlab. Notably, individuals from households in the lowest wealth tertile in Matlab had significantly lower COVID-19 mortality compared to those from households in the highest wealth tertile, while no wealth-related differences were observed for non-COVID-19 mortality.
METHODS: From 2020 to 2021, data were collected on 573,433 individuals residing in three HDSS areas in Bangladesh: Matlab (rural), Chakaria (coastal), and the slums of Dhaka (urban). Probable causes of death were determined by medical personnel using the WHO 2016 verbal autopsy (VA) tool supplemented with a COVID-19 module. Deaths were classified as COVID-19 or non-COVID-19 using the International Classification of Diseases and Related Health Problems, tenth revision (ICD-10). Factors associated with COVID-19 and non-COVID-19 mortality were examined using Cox proportional hazards models.
RESULTS: Between January 1, 2020, and December 31, 2021, a total of 6,616 deaths were recorded across the three HDSS sites, of which 5.2% were attributed to COVID-19. The COVID-19 mortality rate was highest in Matlab (58 deaths per 100,000 person-years), followed by Chakaria (15 deaths per 100,000 person-years) and the urban slums in Dhaka (11 deaths per 100,000 person-years). Household socio-economic status was significantly associated with COVID-19 mortality in the Matlab HDSS. Individuals from the lowest wealth tertile had 40% lower mortality compared to individuals from the highest wealth tertile (adjusted mortality rate ratio (aMRR): 0.60; 95% CI: 0.43-0.83). In contrast, no significant differences were observed for non-COVID-19 mortality across wealth tertiles. Age, sex, and marital status were significantly associated with both COVID-19 and non-COVID-19 deaths.
CONCLUSION: Our data revealed that COVID-19 mortality was highest in the Matlab HDSS. Age, sex, and marital status were key determinants of both COVID-19 and non-COVID-19 mortality in Matlab. Notably, individuals from households in the lowest wealth tertile in Matlab had significantly lower COVID-19 mortality compared to those from households in the highest wealth tertile, while no wealth-related differences were observed for non-COVID-19 mortality.