Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional study
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 t...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Journal of Infection and Public Health |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S187603412300343X |
| _version_ | 1797615961016107008 |
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| author | Ameerah Su’ad Abdul Shakor Ely Zarina Samsudin Xin Wee Chen Muhammad Haikal Ghazali |
| author_facet | Ameerah Su’ad Abdul Shakor Ely Zarina Samsudin Xin Wee Chen Muhammad Haikal Ghazali |
| author_sort | Ameerah Su’ad Abdul Shakor |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T07:34:27Z |
| format | Article |
| id | doaj.art-ed0b35895cef48269e68ea0acbaf2556 |
| institution | Directory Open Access Journal |
| issn | 1876-0341 |
| language | English |
| last_indexed | 2024-03-11T07:34:27Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Infection and Public Health |
| spelling | doaj.art-ed0b35895cef48269e68ea0acbaf25562023-11-17T05:25:48ZengElsevierJournal of Infection and Public Health1876-03412023-12-01161220682078Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional studyAmeerah Su’ad Abdul Shakor0Ely Zarina Samsudin1Xin Wee Chen2Muhammad Haikal Ghazali3Department 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, MalaysiaDepartment of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia; Corresponding author.Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA Sungai Buloh Campus, Sungai Buloh, Selangor, MalaysiaCommunicable Disease Control Unit, Public Health Division, Selangor State Health Department, Ministry of Health Malaysia, Shah Alam, Selangor, MalaysiaBackground: 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.http://www.sciencedirect.com/science/article/pii/S187603412300343XPandemicMortalityBrought-in-deadData linkageGeographic information system |
| spellingShingle | Ameerah Su’ad Abdul Shakor Ely Zarina Samsudin Xin Wee Chen Muhammad Haikal Ghazali Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional study Journal of Infection and Public Health Pandemic Mortality Brought-in-dead Data linkage Geographic information system |
| title | Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional study |
| title_full | Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional study |
| title_fullStr | Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional study |
| title_full_unstemmed | Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional study |
| title_short | Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional study |
| title_sort | factors associated with covid 19 brought in deaths a data linkage comparative cross sectional study |
| topic | Pandemic Mortality Brought-in-dead Data linkage Geographic information system |
| url | http://www.sciencedirect.com/science/article/pii/S187603412300343X |
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