Competing risks modeling of length of hospital stay enhances risk-stratification of patient care: application to under-five children hospitalized in Malawi
IntroductionLength of hospital stay (LOS), defined as the time from inpatient admission to discharge, death, referral, or abscondment, is one of the key indicators of quality in patient care. Reduced LOS lowers health care expenditure and minimizes the chance of in-hospital acquired infections. Conv...
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Epidemiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fepid.2023.1274776/full |
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| author | Christopher C. Stanley Christopher C. Stanley Madalitso Zulu Harrison Msuku Vincent S. Phiri Lawrence N. Kazembe Jobiba Chinkhumba Jobiba Chinkhumba Tisungane Mvalo Tisungane Mvalo Don P. Mathanga Don P. Mathanga |
| author_facet | Christopher C. Stanley Christopher C. Stanley Madalitso Zulu Harrison Msuku Vincent S. Phiri Lawrence N. Kazembe Jobiba Chinkhumba Jobiba Chinkhumba Tisungane Mvalo Tisungane Mvalo Don P. Mathanga Don P. Mathanga |
| author_sort | Christopher C. Stanley |
| collection | DOAJ |
| description | IntroductionLength of hospital stay (LOS), defined as the time from inpatient admission to discharge, death, referral, or abscondment, is one of the key indicators of quality in patient care. Reduced LOS lowers health care expenditure and minimizes the chance of in-hospital acquired infections. Conventional methods for estimating LOS such as the Kaplan-Meier survival curve and the Cox proportional hazards regression for time to discharge cannot account for competing risks such as death, referral, and abscondment. This study applied competing risk methods to investigate factors important for risk-stratifying patients based on LOS in order to enhance patient care.MethodsThis study analyzed data from ongoing safety surveillance of the malaria vaccine implementation program in Malawi's four district hospitals of Balaka, Machinga, Mchinji, and Ntchisi. Children aged 1–59 months who were hospitalized (spending at least one night in hospital) with a medical illness were consecutively enrolled between 1 November 2019 and 31 July 2021. Sub-distribution-hazard (SDH) ratios for the cumulative incidence of discharge were estimated using the Fine-Gray competing risk model.ResultsAmong the 15,463 children hospitalized, 8,607 (55.7%) were male and 6,856 (44.3%) were female. The median age was 22 months [interquartile range (IQR): 12–33 months]. The cumulative incidence of discharge was 40% lower among HIV-positive children compared to HIV-negative (sub-distribution-hazard ratio [SDHR]: 0.60; [95% CI: 0.46–0.76]; P < 0.001); lower among children with severe and cerebral malaria [SDHR: 0.94; (95% CI: 0.86–0.97); P = 0.04], sepsis or septicemia [SDHR: 0.90; (95% CI: 0.82–0.98); P = 0.027], severe anemia related to malaria [SDHR: 0.54; (95% CI: 0.48–0.61); P < 0.001], and meningitis [SDHR: 0.18; (95% CI: 0.09–0.37); P < 0.001] when compared to non-severe malaria; and also 39% lower among malnourished children compared to those that were well-nourished [SDHR: 0.61; (95% CI: 0.55–0.68); P < 0.001].ConclusionsThis study applied the Fine-Gray competing risk approach to more accurately model LOS as the time to discharge when there were significant rates of in-hospital mortality, referrals, and abscondment. Patient care can be enhanced by risk-stratifying by LOS based on children's age, HIV status, diagnosis, and nutritional status. |
| first_indexed | 2024-03-11T10:48:17Z |
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| institution | Directory Open Access Journal |
| issn | 2674-1199 |
| language | English |
| last_indexed | 2025-03-21T00:25:18Z |
| publishDate | 2023-11-01 |
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| series | Frontiers in Epidemiology |
| spelling | doaj.art-b1e7151256b04e2e8b1eef3db462def12024-08-03T07:32:26ZengFrontiers Media S.A.Frontiers in Epidemiology2674-11992023-11-01310.3389/fepid.2023.12747761274776Competing risks modeling of length of hospital stay enhances risk-stratification of patient care: application to under-five children hospitalized in MalawiChristopher C. Stanley0Christopher C. Stanley1Madalitso Zulu2Harrison Msuku3Vincent S. Phiri4Lawrence N. Kazembe5Jobiba Chinkhumba6Jobiba Chinkhumba7Tisungane Mvalo8Tisungane Mvalo9Don P. Mathanga10Don P. Mathanga11MAC-Communicable Diseases Action Centre, Kamuzu University of Health Sciences, Blantyre, MalawiSchool of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, MalawiUniversity of North Carolina Project Malawi, Lilongwe, MalawiMAC-Communicable Diseases Action Centre, Kamuzu University of Health Sciences, Blantyre, MalawiSchool of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, MalawiDepartment of Computing, Mathematical and Statistical Sciences, University of Namibia, Windhoek, NamibiaMAC-Communicable Diseases Action Centre, Kamuzu University of Health Sciences, Blantyre, MalawiSchool of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, MalawiUniversity of North Carolina Project Malawi, Lilongwe, MalawiDepartment of Pediatrics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesMAC-Communicable Diseases Action Centre, Kamuzu University of Health Sciences, Blantyre, MalawiSchool of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, MalawiIntroductionLength of hospital stay (LOS), defined as the time from inpatient admission to discharge, death, referral, or abscondment, is one of the key indicators of quality in patient care. Reduced LOS lowers health care expenditure and minimizes the chance of in-hospital acquired infections. Conventional methods for estimating LOS such as the Kaplan-Meier survival curve and the Cox proportional hazards regression for time to discharge cannot account for competing risks such as death, referral, and abscondment. This study applied competing risk methods to investigate factors important for risk-stratifying patients based on LOS in order to enhance patient care.MethodsThis study analyzed data from ongoing safety surveillance of the malaria vaccine implementation program in Malawi's four district hospitals of Balaka, Machinga, Mchinji, and Ntchisi. Children aged 1–59 months who were hospitalized (spending at least one night in hospital) with a medical illness were consecutively enrolled between 1 November 2019 and 31 July 2021. Sub-distribution-hazard (SDH) ratios for the cumulative incidence of discharge were estimated using the Fine-Gray competing risk model.ResultsAmong the 15,463 children hospitalized, 8,607 (55.7%) were male and 6,856 (44.3%) were female. The median age was 22 months [interquartile range (IQR): 12–33 months]. The cumulative incidence of discharge was 40% lower among HIV-positive children compared to HIV-negative (sub-distribution-hazard ratio [SDHR]: 0.60; [95% CI: 0.46–0.76]; P < 0.001); lower among children with severe and cerebral malaria [SDHR: 0.94; (95% CI: 0.86–0.97); P = 0.04], sepsis or septicemia [SDHR: 0.90; (95% CI: 0.82–0.98); P = 0.027], severe anemia related to malaria [SDHR: 0.54; (95% CI: 0.48–0.61); P < 0.001], and meningitis [SDHR: 0.18; (95% CI: 0.09–0.37); P < 0.001] when compared to non-severe malaria; and also 39% lower among malnourished children compared to those that were well-nourished [SDHR: 0.61; (95% CI: 0.55–0.68); P < 0.001].ConclusionsThis study applied the Fine-Gray competing risk approach to more accurately model LOS as the time to discharge when there were significant rates of in-hospital mortality, referrals, and abscondment. Patient care can be enhanced by risk-stratifying by LOS based on children's age, HIV status, diagnosis, and nutritional status.https://www.frontiersin.org/articles/10.3389/fepid.2023.1274776/fullcompeting risksKaplan-Meier curveCox proportional hazardsmodelinghospital stayMalawi |
| spellingShingle | Christopher C. Stanley Christopher C. Stanley Madalitso Zulu Harrison Msuku Vincent S. Phiri Lawrence N. Kazembe Jobiba Chinkhumba Jobiba Chinkhumba Tisungane Mvalo Tisungane Mvalo Don P. Mathanga Don P. Mathanga Competing risks modeling of length of hospital stay enhances risk-stratification of patient care: application to under-five children hospitalized in Malawi Frontiers in Epidemiology competing risks Kaplan-Meier curve Cox proportional hazards modeling hospital stay Malawi |
| title | Competing risks modeling of length of hospital stay enhances risk-stratification of patient care: application to under-five children hospitalized in Malawi |
| title_full | Competing risks modeling of length of hospital stay enhances risk-stratification of patient care: application to under-five children hospitalized in Malawi |
| title_fullStr | Competing risks modeling of length of hospital stay enhances risk-stratification of patient care: application to under-five children hospitalized in Malawi |
| title_full_unstemmed | Competing risks modeling of length of hospital stay enhances risk-stratification of patient care: application to under-five children hospitalized in Malawi |
| title_short | Competing risks modeling of length of hospital stay enhances risk-stratification of patient care: application to under-five children hospitalized in Malawi |
| title_sort | competing risks modeling of length of hospital stay enhances risk stratification of patient care application to under five children hospitalized in malawi |
| topic | competing risks Kaplan-Meier curve Cox proportional hazards modeling hospital stay Malawi |
| url | https://www.frontiersin.org/articles/10.3389/fepid.2023.1274776/full |
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