Obesity and COVID-19 Mortality: A Cross-Country Analysis
DeGiorgi, G.; Michalik, F.
Show abstract
We highlight a robust correlation between COVID-19 mortality and obesity prevalence using available country level data on COVID-19 mortality as of August 10, 2020. Such association is robust to controlling for other potential comorbidity factors: diabetes, cardio-vascular, and respiratory diseases, further to a set of demographics, urban, and economic, and containment policies controls. We estimate that .6 log point increase in obesity prevalence, or 1 standard deviation, is associated with about an extra .9 log point per 100,000 deaths (or 50% of a standard deviation, .5{sigma}).
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Genetic and environmental influences on educational disparities in adult weight change: an individual-based pooled analysis of 11 twin cohorts 90%
- Impact of bariatric surgery on monthly earnings and employment: a national linked data study in England, 2014-2022 90%
- Obesity, walking pace and risk of severe COVID-19: Analysis of UK Biobank 89%
Similar papers in this journal
Similar papers in this journal
- Where has democracy helped the poor? Democratic transitions and early-life mortality at the country level. 89%
- Association of COVID-19 employment disruption with mental and social wellbeing: evidence from nine UK longitudinal studies 89%
- Excess Mortality during the Covid-19 pandemic: Early evidence from England and Wales 88%
Similar papers in this journal
Similar papers in this journal
- Socioeconomic inequalities and diabetes complications: An analysis of administrative data from Hungary 90%
- The COVID-19 Pandemic Predominantly Hits Poor Neighborhoods, or does it? Evidence from Germany 89%
- Reciprocal association between participation to a national election and the epidemic spread of COVID-19 in France: nationwide observational and dynamic modeling study. 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.