ABSTRACT
BackgroundThe risk of severe COVID-19 disease is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 illness, and how this varies between countries may inform the design of possible strategies to shield those at highest risk. MethodsWe estimated the number of individuals at increased risk of severe COVID-19 disease by age (5-year age groups), sex and country (n=188) based on prevalence data from the Global Burden of Disease (GBD) study for 2017 and United Nations population estimates for 2020. We also calculated the number of individuals without an underlying condition that could be considered at-risk because of their age, using thresholds from 50-70 years. The list of underlying conditions relevant to COVID-19 disease was determined by mapping conditions listed in GBD to the guidelines published by WHO and public health agencies in the UK and US. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. ResultsWe estimate that 1.7 (1.0 - 2.4) billion individuals (22% [15-28%] of the global population) are at increased risk of severe COVID-19 disease. The share of the population at increased risk ranges from 16% in Africa to 31% in Europe. Chronic kidney disease (CKD), cardiovascular disease (CVD), diabetes and chronic respiratory disease (CRD) were the most prevalent conditions in males and females aged 50+ years. African countries with a high prevalence of HIV/AIDS and Island countries with a high prevalence of diabetes, also had a high share of the population at increased risk. The prevalence of multimorbidity (>1 underlying conditions) was three times higher in Europe than in Africa (10% vs 3%). ConclusionBased on current guidelines and prevalence data from GBD, we estimate that one in five individuals worldwide has a condition that is on the list of those at increased risk of severe COVID-19 disease. However, for many of these individuals the underlying condition will be undiagnosed or not severe enough to be captured in health systems, and in some cases the increase in risk may be quite modest. There is an urgent need for robust analyses of the risks associated with different underlying conditions so that countries can identify the highest risk groups and develop targeted shielding policies to mitigate the effects of the COVID-19 pandemic. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAs the COVID-19 pandemic evolves, countries are considering policies of shielding the most vulnerable, but there is currently very limited evidence on the number of individuals that might need to be shielded. Guidelines on who is currently believed to be at increased risk of severe COVID-19 illness have been published online by the WHO and public health agencies in the UK and US. We searched PubMed ("Risk factors" AND "COVID-19") without language restrictions, from database inception until April 5, 2020, and identified 62 studies published between Feb 15, 2020 and March 20, 2020. Evidence from China, Italy and the USA indicates that older individuals, males and those with underlying conditions, such as CVD, diabetes and CRD, are at greater risk of severe COVID-19 illness and death. Added value of this studyThis study combines evidence from large international databases and new analysis of large multimorbidity studies to inform policymakers about the number of individuals that may be at increased risk of severe COVID-19 illness in different countries. We developed a tool for rapid assessments of the number and percentage of country populations that would need to be targeted under different shielding policies. Implications of all the available evidenceQuantifying how many and who is at increased risk of severe COVID-19 illness is critical to help countries design more effective interventions to protect vulnerable individuals and reduce pressure on health systems. This information can also inform a broader assessment of the health, social and economic implications of shielding various groups.
ABSTRACT
OBJECTIVE: To develop and validate a prediction model for incident locomotor disability after 7 years in older adults. SETTING: Prospective British cohort studies: British Women's Heart and Health Study (BWHHS) for development and the English Longitudinal Study of Ageing (ELSA) for validation. SUBJECTS: Community-dwelling older adults. METHODS: Multivariable logistic regression models after selection of predictors with backward elimination. Model performance was assessed using metrics of discrimination and calibration. Models were internally and externally validated. RESULTS: Locomotor disability was reported in BWHHS by 861 of 1,786 (48%) women after 7 years. Age, a history of arthritis and low physical activity levels were the most important predictors of locomotor disability. Models using routine measures as predictors had satisfactory calibration and discrimination (c-index 0.73). Addition of 31 blood markers did not increase the predictive performance. External validation in ELSA showed reduced discrimination (c-index 0.65) and an underestimation of disability risks. A web-based calculator for locomotor disability is available (http://www.sealedenvelope.com/trials/bwhhsmodel/). CONCLUSIONS: We developed and externally validated a prediction model for incident locomotor disability in older adults based on routine measures available to general practitioners, patients and public health workers, and showed an adequate discrimination. Addition of blood markers from major biological pathways did not improve the performance of the model. Further replication in additional data sets may lead to further enhancement of the current model.
