Covid-19 Social Distancing Interventions by State Mandate and Their Correlation to Mortality

Abstract

Purpose Evaluate the correlation between state mandated social interventions and Covid-19 mortality Design Prospective design and retrospective analysis of Institute for Health Metrics and Evaluation (IHME) state data. Methods Twelve European Union countries were selected on April 12, 2020 from IHME data which had clearly defined and dated establishment of statewide mandates for social distancing measures to include: School closures, stay at home orders, severe travel restrictions, and closure of non-essential businesses. The state Covid-19 mortality prevalence was defined as total normalized deaths to the peak daily mortality rate. The state mortality prevalence was correlated to the total number of mandates-days from their date of establishment to the peak daily mortality date. The slope of the maximum daily mortality rate was also correlated to mandate-days. Results The slope of standardized mortality per country did have a slight negative correlation to the total mandate days (R2 = 0.083, p= 0.36), though the negative correlation was not statistically significant. The standardized mortality prevalence to the peak mortality rate per country exhibited no discernable statistical correlation to the total mandate days (R2 = 0.004,p=0.85). Discussion The analysis appears to suggest a mandate effective reduction in the slope of the mortality rate, but no effective reduction in Covid-19 mortality to its defined initial peak when interpreting the mean-effect of the mandates as present in the data. The study is presented as a potential methodology to evaluate the effectiveness of state mandated social distancing policy.

Covid-19 Social Distancing Interventions by State Mandate and their Correlation to Mortality in the United States

“…The results corroborate the findings of the previous study conducted using the same methodology with the IHME data for 12 European countries. 8 One of the advantages in this analytical approach is in the design of the social-mandate input variables to be more robust to possible biases caused by different states being in different epidemiologic infectious stages of Covid-19. It is worth noting that quite a few of the states involved in this study have since experience a second wave of infections not included in this analysis.…”

“…The methods are discussed extensively in the available preprint manuscript: "Covid-19 Social Distancing Interventions by State Mandate and Their Correlation to Mortality". 8 The purpose was to quantify the decrease in the infection/mortality or decrease in the maximum infection/mortality rate as a result of defined state mandated social distancing measures. Mortality was chosen to define endpoint peaks and rates of change over registered infections within a state.…”

“…Similar to the previous study on European countries, the study conducted two high-level bivariate analyses on this data: one for each output of interest -peak-mortality-rate (PMR) and mortality-at-peak (MAP) -against the total mandate days. 8 The total mandate days is defined the same way as it was in the previous study which is the summation from each form of social-mandate of the total number of days prior to the initial peak that form of social-mandate was implemented. After the bivariate analyses against total mandate days, we then conducted bivariate analyses with the same response variables against population density instead.…”

“…The methods are discussed extensively in the available preprint manuscript:”Covid-19 Social Distancing Interventions by State Mandate and Their Correlation to Mortality”. 8…”

“…8 The total mandate days is defined the same way as it was in the previous study which is the summation from each form of social-mandate of the total number of days prior to the initial peak that form of social-mandate was implemented. 8…”