Background National rates of COVID-19 infection and fatality have varied dramatically since the onset of the pandemic. Understanding the conditions associated with this cross-country variation is essential to guiding investment in more effective preparedness and response for future pandemics. MethodsDaily SARS-CoV-2 infections and COVID-19 deaths for 177 countries and territories and 181 subnational locations were extracted from the Institute for Health Metrics and Evaluation's modelling database. Cumulative infection rate and infection-fatality ratio (IFR) were estimated and standardised for environmental, demographic, biological, and economic factors. For infections, we included factors associated with environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. For IFR, factors were age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardised prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita. These were standardised using indirect age standardisation and multivariate linear models. Standardised national cumulative infection rates and IFRs were tested for associations with 12 pandemic preparedness indices, seven health-care capacity indicators, and ten other demographic, social, and political conditions using linear regression. To investigate pathways by which important factors might affect infections with SARS-CoV-2, we also assessed the relationship between interpersonal and governmental trust and corruption and changes in mobility patterns and COVID-19 vaccination rates. Findings The factors that explained the most variation in cumulative rates of SARS-CoV-2 infection between Jan 1, 2020, and Sept 30, 2021, included the proportion of the population living below 100 m (5•4% [4•0-7•9] of variation), GDP per capita (4•2% [1•8-6•6] of variation), and the proportion of infections attributable to seasonality (2•1% [95% uncertainty interval 1•7-2•7] of variation). Most cross-country variation in cumulative infection rates could not be explained. The factors that explained the most variation in COVID-19 IFR over the same period were the age profile of the country (46•7% [18•4-67•6] of variation), GDP per capita (3•1% [0•3-8•6] of variation), and national mean BMI (1•1% [0•2-2•6] of variation). 44•4% (29•2-61•7) of cross-national variation in IFR could not be explained. Pandemic-preparedness indices, which aim to measure health security capacity, were not meaningfully associated with standardised infection rates or IFRs. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates. High levels of government and interpersonal trust, as wel...

Summary Background Previous analyses of democracy and population health have focused on broad measures, such as life expectancy at birth and child and infant mortality, and have shown some contradictory results. We used a panel of data spanning 170 countries to assess the association between democracy and cause-specific mortality and explore the pathways connecting democratic rule to health gains. Methods We extracted cause-specific mortality and HIV-free life expectancy estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 and information on regime type from the Varieties of Democracy project. These data cover 170 countries and 46 years. From the Financing Global Health database, we extracted gross domestic product (GDP) per capita, also covering 46 years, and Development Assistance for Health estimates starting from 1990 and domestic health spending estimates starting from 1995. We used a diverse set of empirical methods—synthetic control, within-country variance decomposition, structural equation models, and fixed-effects regression—which together provide a robust analysis of the association between democratisation and population health. Findings HIV-free life expectancy at age 15 years improved significantly during the study period (1970–2015) in countries after they transitioned to democracy, on average by 3% after 10 years. Democratic experience explains 22·27% of the variance in mortality within a country from cardiovascular diseases, 16·53% for tuberculosis, and 17·78% for transport injuries, and a smaller percentage for other diseases included in the study. For cardiovascular diseases, transport injuries, cancers, cirrhosis, and other non-communicable diseases, democratic experience explains more of the variation in mortality than GDP. Over the past 20 years, the average country's increase in democratic experience had direct and indirect effects on reducing mortality from cardiovascular disease (−9·64%, 95% CI −6·38 to −12·90), other non-communicable diseases (−9·14%, −4·26 to −14·02), and tuberculosis (−8·93%, −2·08 to −15·77). Increases in a country's democratic experience were not correlated with GDP per capita between 1995 and 2015 (ρ=–0·1036; p=0·1826), but were correlated with declines in mortality from cardiovascular disease (ρ=–0·3873; p<0·0001) and increases in government health spending (ρ=0·4002; p<0·0001). Removal of free and fair elections from the democratic experience variable resulted in loss of association with age-standardised mortality from non-communicable diseases and injuries. Interpretation When enforced by free and fair elections, democracies are more likely than autocracies to lead to health gains for causes of mortality (eg, cardiovascular diseases and transport injuries) that have not been heavily targeted by foreign aid and require health-care delivery infrastructure. International health agencies and donors might increasingly need t...

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Many scholars claim that democracy improves population health. The prevailing explanation for this is that democratic regimes distribute health-promoting resources more widely than autocratic regimes. The central contention of this article is that democracies also have a significant pro-health effect regardless of public redistributive policies. After establishing the theoretical plausibility of the nondistributive effect, a panel of 153 countries for the years 1972 to 2000 is used to examine the relationship between extent of democratic experience and life expectancy. The authors find that democratic governance continues to have a salutary effect on population health even when controls are introduced for the distribution of health-enhancing resources. Data for fifty autocratic countries for the years 1994 to 2007 are then used to examine whether media freedom—independent of government responsiveness—has a positive impact on life expectancy.

Do democracies produce better health outcomes for children than autocracies? We argue that (1) democratic governments have an incentive to reduce child mortality among low-income families and (2) that media freedom enhances their ability to deliver mortality-reducing resources to the poorest. A panel of 167 countries for the years 1961-2011 is used to test those two theoretical claims. We find that level of democracy is negatively associated with under-5 mortality, and that that negative association is greater in the presence of media freedom. These results are robust to the inclusion of country and year fixed effects, time-varying control variables, and the multiple imputation of missing values.