Determinants of COVID-19 outcomes: A systematic review

Abstract

Background: The current pandemic, COVID-19, caused by a novel coronavirus SARS-CoV-2 has claimed over a million lives worldwide in a year, warranting the need for more research into the wider determinants of COVID-19 outcomes to support evidence-based policies. Objective: This study aimed to investigate what factors determined the mortality and length of hospitalisation in individuals with COVID-19. Data Source: This is a systematic review with data from four electronic databases: Scopus, Google Scholar, CINAHL and Web of Science. Eligibility Criteria: Studies were included in this review if they explored determinants of COVID-19 mortality or length of hospitalisation, were written in the English Language, and had available full-text. Study appraisal and data synthesis: The authors assessed the quality of the included studies with the Newcastle Ottawa Scale and the Agency for Healthcare Research and Quality checklist, depending on their study design. Risk of bias in the included studies was assessed with risk of bias assessment tool for non-randomised studies. A narrative synthesis of the evidence was carried out. Results: The review included 22 studies from nine countries, with participants totalling 239,830. The included studies quality was moderate to high. The identified determinants were categorised into demographic, biological, socioeconomic and lifestyle risk factors, based on the Dahlgren and Whitehead determinant of health model. Increasing age (ORs 1.04-20.6, 95%CIs 1.01-22.68) was the common demographic determinant of COVID-19 mortality while living with diabetes (ORs 0.50-3.2, 95%CIs -0.2-0.74) was one of the most common biological determinants of COVID-19 length of hospitalisation. Review limitation: Meta-analysis was not conducted because of included studies heterogeneity. Conclusion: COVID-19 outcomes are predicted by multiple determinants, with increasing age and living with diabetes being the most common risk factors. Population-level policies that prioritise interventions for the elderly population and the people living with diabetes may help mitigate the outbreak's impact.

The second wave of SARS-CoV-2 infections and COVID-19 deaths in Germany – driven by values, social status and migration background? A county-scale explainable machine learning approach

“…Incidence and death rates were found to be higher in regions with a high proportion of diagnosed endocrine, nutritional, and metabolic diseases. This includes diabetes mellitus which has been repeatedly identified as a major risk factor for severe COVID-19 outcomes and mortality (Crankson et al, 2021).…”