Jails and prisons are major sites of novel coronavirus (SARS-CoV-2) infection. Many jurisdictions in the United States have therefore accelerated the release of low-risk offenders. Early release, however, does not address how arrest and pretrial detention practices may be contributing to disease spread. Using data from Cook County Jail-one of the largest known nodes of SARS-CoV-2 spread in the United States-in Chicago, Illinois, we analyzed the relationship between jailing practices and community infections at the ZIP code level. We found that jailcommunity cycling was a significant predictor of cases of coronavirus disease 2019 (COVID-19), accounting for 55 percent of the variance in case rates across ZIP codes in Chicago and 37 percent of the variance in all of Illinois. Jail-community cycling far exceeds race, poverty, public transit use, and population density as a predictor of variance. The data suggest that cycling people through Cook County Jail alone is associated with 15.7 percent of all documented COVID-19 cases in Illinois and 15.9 percent of all documented cases in Chicago as of April 19, 2020. Our findings support arguments for reduced reliance on incarceration and for related justice reforms both as emergency measures during the present pandemic and as sustained structural changes vital for future pandemic preparedness and public health.
Black and Hispanic communities are disproportionately affected by both incarceration and COVID-19. The epidemiological relationship between carceral facilities and community health during the COVID-19 pandemic, however, remains largely unexamined. Using data from Cook County Jail, we examine temporal patterns in the relationship between jail cycling (i.e., arrest and processing of individuals through jails before release) and community cases of COVID-19 in Chicago ZIP codes. We use multivariate regression analyses and a machine-learning tool, elastic regression, with 1,706 demographic control variables. We find that for each arrested individual cycled through Cook County Jail in March 2020, five additional cases of COVID-19 in their ZIP code of residence are independently attributable to the jail as of August. A total 86% of this additional disease burden is borne by majority-Black and/or -Hispanic ZIPs, accounting for 17% of cumulative COVID-19 cases in these ZIPs, 6% in majority-White ZIPs, and 13% across all ZIPs. Jail cycling in March alone can independently account for 21% of racial COVID-19 disparities in Chicago as of August 2020. Relative to all demographic variables in our analysis, jail cycling is the strongest predictor of COVID-19 rates, considerably exceeding poverty, race, and population density, for example. Arrest and incarceration policies appear to be increasing COVID-19 incidence in communities. Our data suggest that jails function as infectious disease multipliers and epidemiological pumps that are especially affecting marginalized communities. Given disproportionate policing and incarceration of racialized residents nationally, the criminal punishment system may explain a large proportion of racial COVID-19 disparities noted across the United States.
IMPORTANCE Mass incarceration is known to foster infectious disease outbreaks, amplification of infectious diseases in surrounding communities, and exacerbation of health disparities in disproportionately policed communities. To date, however, policy interventions intended to achieve epidemic mitigation in US communities have neglected to account for decarceration as a possible means of protecting public health and safety.OBJECTIVE To evaluate the association of jail decarceration and government anticontagion policies with reductions in the spread of SARS-CoV-2. DESIGN, SETTING, AND PARTICIPANTS This cohort study used county-level data from January toNovember 2020 to analyze COVID-19 cases, jail populations, and anticontagion policies in a panel regression model to estimate the association of jail decarceration and anticontagion policies with COVID-19 growth rates. A total of 1605 counties with data available on both jail population and COVID-19 cases were included in the analysis. This sample represents approximately 51% of US counties, 72% of the US population, and 60% of the US jail population.EXPOSURES Changes to jail populations and implementation of 10 anticontagion policies: nursing home visitation bans, school closures, mask mandates, prison visitation bans, stay-at-home orders, and closure of nonessential businesses, gyms, bars, movie theaters, and restaurants. MAIN OUTCOMES AND MEASURESDaily COVID-19 case growth rates. RESULTSIn the 1605 counties included in this study, the mean (SD) prison population was 283.38 (657.78) individuals, and the mean (SD) population was 315.24 (2151.01) persons per square mile. An estimated 80% reduction in US jail populations, achievable through noncarceral management of nonviolent alleged offenses and in line with average international incarceration rates, would have been associated with a 2.0% (95% CI, 0.8%-3.1%) reduction in daily COVID-19 case growth rates. Jail decarceration was associated with 8 times larger reductions in COVID-19 growth rates in counties with above-median population density (4.6%; 95% CI, 2.2%-7.1%) relative to those below this median (0.5%; 95% CI, 0.1%-0.9%). Nursing home visitation bans were associated with a 7.3% (95% CI, 5.8%-8.9%) reduction in COVID-19 case growth rates, followed by school closures (4.3%; 95% CI, 2.0%-6.6%), mask mandates (2.5%; 95% CI, 1.7%-3.3%), prison visitation bans (1.2%; 95% CI, 0.2%-2.2%), and stay-at-home orders (0.8%; 95% CI, 0.1%-1.6%). CONCLUSIONS AND RELEVANCEAlthough many studies have documented that high incarceration rates are associated with communitywide health harms, this study is, to date, the first to show that decarceration is associated with population-level public health benefits. Its findings suggest that, among other anticontagion interventions, large-scale decarceration and changes to pretrial (continued) Key Points Question Were jail decarceration and government implementation of anticontagion policies associated with the spread of SARS-CoV-2 in US counties? Findings In this cohort study of 1605 cou...
We present and test a model of mandatory disclosure. The effects of disclosure laws on what is being disclosed are typically unknown since data on disclosed activity rarely exist in the absence of disclosure laws. We exploit data from legal settlements disclosing $316 million in pharmaceutical company payments to 316,622 physicians across the U.S. from 2009-2011. States were classified as having strong, weak, or no disclosure based on whether the data was reported only to state authorities (weak) or were publicly available (strong). Strong disclosure law was associated with reduced payments among doctors accepting less than $100 and increased payments among doctors accepting greater than $100. Weak disclosure states, despite imposing administrative compliance costs to industry, were indistinguishable from no disclosure states. This result suggests that the mechanism for fewer small payments in strong disclosure states was physicians' reduced willingness to accept payments rather than the imposition of significant administrative costs on industry. We conduct additional analysis holding fixed the cost for pharmaceutical companies of disclosing data, which was possible because Massachusetts began releasing payment data online during our sample period. Differences-indifferences analyses and multiple regression yield similar estimates for each payment category: Mandatory disclosure reduced payments for speaking and for meals but increased payments for consulting activities. Significant disclosure aversion reducing conflicts of interest is consistent with the policy goals of mandatory disclosure, though the increased payments among those receiving large payments may have been unintended.
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