Factors Associated with the Implementation of Non-Pharmaceutical Mitigation Interventions

Background and Purpose: Social distancing has been viewed as an essential component of a public health response to infectious-disease outbreak. Therefore, the timing of enacting mitigation interventions plays a vital role in public health. Compared to influenza and severe acute respiratory syndrome (SARS), Covid-19 has a longer incubation period, though its doubling time (i.e., the speed of the initial spread of the epidemic) or the related serial interval (i.e., the mean time it takes for an infected person to pass on the infection to others) is similar to SARS. Given that patients during the incubation period have no symptoms, responding to confirmed cases quickly has the potential to make a large difference in subsequent disease levels. This study aims to examine the determinant factors associated with 9 types of mitigation interventions implemented by States’ Governors during the study window from 12 PM March 13, U.S. Eastern Time to the ending hour is 6 PM April 7. And under the guidance of the five key “social determinants of health” activities and based on the available census data and other published statistics, we identified the following five blocks of state-level variables for inclusion as potential determinants in modeling social distance orders: demographic characteristics, economic well-being information, public health infrastructure, information related to politics, and international connectivity. In addition, to measure state governmental awareness of the Covid-19, we included in the study a time-varying covariate of cumulative Covid-19 cases per 10,000 population one-day prior to the time when a mitigation strategy was enacted. This measure is analogous to the prevalence rate of Covid-19 disease.

Methods: A proportional hazards model was employed to analyze the timing of issuing each mitigation order. The multiple-event survival model was employed to analyze the determinants of an overall model that ignores the type of mitigation, while controlling for clustering effects of the multivariate time-to-event data.

Results: Regardless of the type of mitigation, overall, there are three statistically significant determinants of the timing: population in 2019— every 100,000-population increase increases the hazard of enactment by 4% (p<.05), unemployment rate in Feb 2020 – every one-percentage-point increase increases the hazard by 50.7% (p<.001), and % of uninsured in 2018 – every one-percentage-point increase reduces the hazard by 11.3% (p<.01). And surprisingly, we found that states with a lower prevalence of Covid-19 cases per 10,000 population reacted more quickly to the outbreak, which was statistically significant in all 8 models.

Conclusions and Implications: In summary, Covid-19 prevalence rate, population size, unemployment, proportion of uninsured, poverty, level of economic development, proportion of minority people, and public health infrastructure are the key determinants affecting the timing to enact nonpharmaceutical mitigation interventions.