The Lancet Global Health: Estimates Suggest One In Five People Worldwide Have An Underlying Health Condition That Could Increase Their Risk Of Severe COVID-19 If Infected

The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA.

An estimated 1.7 billion people, 22 per cent of the world’s population, have at least one underlying health condition that could increase their risk of severe Covid-19 if infected, according to a modelling study that uses data from 188 countries, published in The Lancet Global Health journal. According to the study, in India, at least 21.5 per cent of the country’s population is estimated to have underlying health conditions that may increase their risk of severe Covid-19, if infected.

Key Findings of The Lancet Study

  • The share of the population at increased risk (with at least one underlying health condition relevant to COVID-19) is highest in countries with ageing populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence.
  • The share of the population with an underlying health condition varies by age, from less than 5% of those under 20, to over 66% of those aged 70 and above.
  • As lockdown restrictions are eased, governments could use the new estimates to understand how many people should be prioritised for enhanced physical distancing measures and vaccination (if available)
  • The authors estimate that fewer individuals worldwide would actually require hospitalisation if infected – around 4% of the world population – suggesting that for many with underlying conditions, the increase in risk may be modest.

“As countries move out of lockdown, governments are looking for ways to protect the most vulnerable from a virus that is still circulating. We hope our estimates will provide useful starting points for designing measures to protect those at increased risk of severe disease. This might involve advising people with underlying conditions to adopt social distancing measures appropriate to their level of risk, or prioritising them for vaccination in the future,” says Associate Professor Andrew Clark from the London School of Hygiene & Tropical Medicine (LSHTM), UK.

Although the estimates provide an idea of the number of people governments should prioritise for protective measures, not all individuals with these conditions would go on to develop severe symptoms if infected. The authors estimate that 4% of the world’s population (349 million of 7.8 billion people) would require hospitalisation if infected, suggesting that the increased risk of severe COVID-19 could be quite modest for many with underlying conditions.

The Lancet Methodology explained

The Lancet estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020.

The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA.

It analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity.

To help interpretation of the degree of risk among those at increased risk, it also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty.

It assumed males were twice at likely as females to be at high risk. It also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years.

It generated uncertainty intervals (UIs) for its estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies.

 

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