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2.
Sci Data ; 9(1): 319, 2022 06 16.
Article in English | MEDLINE | ID: covidwho-1960417

ABSTRACT

We have developed and made accessible for multidisciplinary audience a unique global dataset of the behavior of political actors during the COVID-19 pandemic as measured by their policy-making efforts to protect their publics. The dataset presents consistently coded cross-national data at subnational and national levels on the daily level of stringency of public health policies by level of government overall and within specific policy categories, and reports branches of government that adopted these policies. The data on these public mandates of protective behaviors is collected from media announcements and government publications. The dataset allows comparisons of governments' policy efforts and timing across the world and can serve as a source of information on policy determinants of pandemic outcomes-both societal and possibly medical.


Subject(s)
COVID-19 , Health Policy , COVID-19/prevention & control , COVID-19/therapy , Humans , Pandemics/prevention & control
3.
Am J Prev Med ; 62(3): 433-437, 2022 03.
Article in English | MEDLINE | ID: covidwho-1458812

ABSTRACT

INTRODUCTION: This study connects the aggregate strength of public health policies taken in response to the COVID-19 pandemic in the U.S. states to the governors' party affiliations and to state-level outcomes. Understanding the relationship between politics and public health measures can better prepare American communities for what to expect from their governments in a future crisis and encourage advocacy for delegating public health decisions to medical professionals. METHODS: The public health Protective Policy Index captures the strength of policy response to COVID-19 at the state level. The authors estimated a Bayesian model that links the rate of disease spread to Protective Policy Index. The model also accounted for the possible state-specific undercounting of cases and controls for state population density, poverty, number of physicians, cardiovascular disease, asthma, smoking, obesity, age, racial composition, and urbanization. A Bayesian linear model with natural splines of time was employed to link the dynamics of Protective Policy Index to governors' party affiliations. RESULTS: A 10-percentage point decrease in Protective Policy Index was associated with an 8% increase in the expected number of new cases. Between late March and November 2020 and at the state-specific peaks of the pandemic, the Protective Policy Index in the states with Democratic governors was about 10‒percentage points higher than in the states with Republican governors. CONCLUSIONS: Public health measures were stricter in the Democrat-led states, and stricter public health measures were associated with a slower growth of COVID-19 cases. The apparent politicization of public health measures suggests that public health decision making by health professionals rather than by political incumbents could be beneficial.


Subject(s)
COVID-19 , Bayes Theorem , Humans , Pandemics , Politics , Public Policy , SARS-CoV-2 , United States/epidemiology
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