Dataset Information

The impact of multimorbidity on severe COVID-19 outcomes in community and congregate settings.

ABSTRACT:

Purpose

This study examined the impact of multimorbidity on severe COVID-19 outcomes in community and long-term care (LTC) settings, alone and in interaction with age and sex.

Methods

We conducted a retrospective cohort study of all Ontarians who tested positive for COVID-19 between January-2020 and May-2021 with follow-up until June 2021. We used cox regression to evaluate the adjusted impact of multimorbidity, individual characteristics, and interactions on time to hospitalization and death (any cause).

Results

24.5% of the cohort had 2 or more pre-existing conditions. Multimorbidity was associated with 28% to 170% shorter time to hospitalization and death, respectively. However, predictors of hospitalization and death differed for people living in community and LTC. In community, increasing multimorbidity and age predicted shortened time to hospitalization and death. In LTC, we found none of the predictors examined were associated with time to hospitalization, except for increasing age that predicted reduced time to death up to 40.6 times. Sex was a predictor across all settings and outcomes: among male the risk of hospitalization or death was higher shortly after infection (e.g. HR for males at 14 days = 30.3) while among female risk was higher for both outcome in the longer term (e.g. HR for males at 150 days = 0.16). Age and sex modified the impact of multimorbidity in the community.

Conclusion

Community-focused public health measures should be targeted and consider sociodemographic and clinical characteristics such as multimorbidity. In LTC settings, further research is needed to identify factors that may contribute to improved outcomes.

SUBMITTER: Kone AP

PROVIDER: S-EPMC10043958 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The impact of multimorbidity on severe COVID-19 outcomes in community and congregate settings.

Kone Anna Pefoyo AP Martin Lynn L Scharf Deborah D Gabriel Helen H Dean Tamara T Costa Idevania I Saskin Refik R Palma Luis L Wodchis Walter P WP

Dialogues in health 20230328

<h4>Purpose</h4>This study examined the impact of multimorbidity on severe COVID-19 outcomes in community and long-term care (LTC) settings, alone and in interaction with age and sex.<h4>Methods</h4>We conducted a retrospective cohort study of all Ontarians who tested positive for COVID-19 between January-2020 and May-2021 with follow-up until June 2021. We used cox regression to evaluate the adjusted impact of multimorbidity, individual characteristics, and interactions on time to hospitalizati ...[more]

PMID: 37006909

Similar Datasets

Project description:The explosive outbreaks of COVID-19 seen in congregate settings such as prisons and nursing homes, has highlighted a critical need for effective outbreak prevention and mitigation strategies for these settings. Here we consider how different types of control interventions impact the expected number of symptomatic infections due to outbreaks. Introduction of disease into the resident population from the community is modeled as a stochastic point process coupled to a branching process, while spread between residents is modeled via a deterministic compartmental model that accounts for depletion of susceptible individuals. Control is modeled as a proportional decrease in the number of susceptible residents, the reproduction number, and/or the proportion of symptomatic infections. This permits a range of assumptions about the density dependence of transmission and modes of protection by vaccination, depopulation and other types of control. We find that vaccination or depopulation can have a greater than linear effect on the expected number of cases. For example, assuming a reproduction number of 3.0 with density-dependent transmission, we find that preemptively reducing the size of the susceptible population by 20% reduced overall disease burden by 47%. In some circumstances, it may be possible to reduce the risk and burden of disease outbreaks by optimizing the way a group of residents are apportioned into distinct residential units. The optimal apportionment may be different depending on whether the goal is to reduce the probability of an outbreak occurring, or the expected number of cases from outbreak dynamics. In other circumstances there may be an opportunity to implement reactive disease control measures in which the number of susceptible individuals is rapidly reduced once an outbreak has been detected to occur. Reactive control is most effective when the reproduction number is not too high, and there is minimal delay in implementing control. We highlight the California state prison system as an example for how these findings provide a quantitative framework for understanding disease transmission in congregate settings. Our approach and accompanying interactive website (https://phoebelu.shinyapps.io/DepopulationModels/) provides a quantitative framework to evaluate the potential impact of policy decisions governing infection control in outbreak settings.

Dataset Information

The impact of multimorbidity on severe COVID-19 outcomes in community and congregate settings.

Purpose

Methods

Results

Conclusion

Publications

The impact of multimorbidity on severe COVID-19 outcomes in community and congregate settings.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets