Association between household air pollution and neonatal mortality in Bangladesh: A cross- sectional study based on Bangladesh Demographic and Health Survey 2017-2018
2022 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Student thesis
Abstract [en]
Background: Globally, about 3.8 million deaths, including new-borns result from household air pollution every year. Majority of these deaths occur in low-middle income countries, including Bangladesh. Moreover, Bangladesh has a high neonatal mortality rate which accounts for 30 per 1000 live births. Thus, this study aims to determine the association between household air pollution and neonatal mortality in Bangladesh.
Method: A cross-sectional study was conducted based on the data from the Bangladesh Demographic and Health Survey (BDHS), 2017-18. Out of 20,160 women (aged 15-49 years) interviewed, 8759 live births were recorded. Variables such as type of cooking fuel and place of cooking were considered to measure household air pollution exposure. The relation between household air pollution and neonatal mortality was then investigated using multivariate logistic regression analysis.
Result: Household air pollution information was available for all participants (n=8759). Among them 2.9% (n = 257) died within neonatal period (1st 28 days). The study did not observe any significant association between the usage of clean or unclean fuel and place of cooking area and neonatal death. Working mothers had 24 % less risk [OR: 0.76, CI: 0.59-0.99] and found a statistically significant association with neonatal mortality based on multivariate analysis.
Conclusion: The current study did not find any association between household air pollution and neonatal mortality in Bangladesh, although other factors might contribute to neonatal mortality
Place, publisher, year, edition, pages
2022. , p. 46
Keywords [en]
household air pollution, neonatal mortality
National Category
Medical and Health Sciences Public Health, Global Health and Social Medicine
Identifiers
URN: urn:nbn:se:uu:diva-485681OAI: oai:DiVA.org:uu-485681DiVA, id: diva2:1699083
Educational program
Master Programme in Global health
Supervisors
Examiners
2022-10-042022-09-262025-02-20Bibliographically approved