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Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional study

Shakor ASA 1 , Samsudin EZ 2 , Chen XW 3 , Ghazali MH 4

Affiliations 

  • 1 Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia; Surveillance and Crisis Preparedness Unit, Public Health Division, Selangor State Health Department, Ministry of Health Malaysia, Shah Alam, Selangor, Malaysia. Electronic address: ameerahsuad@moh.gov.my
  • 2 Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia. Electronic address: elyzarina07@yahoo.com
  • 3 Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia. Electronic address: drchenxw@uitm.edu.my
  • 4 Communicable Disease Control Unit, Public Health Division, Selangor State Health Department, Ministry of Health Malaysia, Shah Alam, Selangor, Malaysia. Electronic address: drhaikalghazali@moh.gov.my
J Infect Public Health, 2023 Dec;16(12):2068-2078.
PMID: 37950972 DOI: 10.1016/j.jiph.2023.10.016

Abstract

BACKGROUND: The phenomenon of Coronavirus disease 2019 (COVID-19) brought-in-dead (BID), i.e., COVID-19 deaths occurring outside hospital settings, suggests missed opportunities for life-saving care. However, much is still unknown with regards to its potential determinants. The present study aimed to examine the factors associated with COVID-19 BID by integrating new variables from multiple databases.

METHODS: This multi-database comparative cross-sectional study examined COVID-19 in-patient deaths (IPD) and COVID-19 BID (n = 244 in each group) in Selangor, Malaysia. BID cases, IPD cases, and their sociodemographic, clinical, and health behaviour factors were identified from the COVID-19 mortality investigation reports submitted to the Selangor State Health Department between 14 February 2022 and 31 March 2023. Data linkage was used to connect three open-source databases-GitHub-MOH, Socioeconomic Data and Applications Center, and OpenStreetMap-and identify health infrastructure and geospatial factors. The groups were compared using chi-square tests, independent t-tests, and logistic regression analyses to identify factors associated with COVID-19 BID.

RESULTS: The COVID-19 IPD and BID cases were comparable. After adjusting for confounders, non-Malaysian nationality (AOR: 3.765, 95% CI: 1.163, 12.190), obesity (AOR: 5.272, 95% CI: 1.131, 24.567), not seeking treatment while unwell (AOR: 5.385, 95% CI: 3.157, 9.186), and a higher percentage of COVID-19-dedicated beds occupied on the date of death (AOR: 1.165, 95% CI: 1.078, 1.259) were associated with increased odds of COVID-19 BID. On the other hand, being married (AOR: 0.396, 95% CI: 0.158, 0.997) and the interaction between the percentage of COVID-19-dedicated beds occupied and the percentage of ventilators in use (AOR: 0.996, 95% CI: 0.994, 0.999) emerged as protective factors.

CONCLUSION: These findings indicated that certain groups have higher odds of COVID-19 BID and thus, require closer monitoring. Considering that COVID-19 BID is influenced by various elements beyond clinical factors, intensifying public health initiatives and multi-organisational collaboration is necessary to address this issue.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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