Prisons and COVID-19 Spread in the United States

Kaitlyn M. Sims, MSc; Jeremy Foltz, PhD; Marin Elisabeth Skidmore, PhD

Disclosures

Am J Public Health. 2021;111(8):1534-1541. 

In This Article

Results

After controlling for covariates, we found that COVID-19 cases were 9% higher in counties with a prison (Figure 2) and that they were increasing in proportion to incarcerated population and total capacity (measured in 1000-person increments). An additional 1000-person capacity is correlated with a 4.96% increase in cases.

Figure 2.

Relationship Between State or Federal Prison Presence and Inverse Hyperbolic Sine (IHS)-Transformed Cases or Deaths by Prison Presence and Capacity: United States, 2020
Note. CI = confidence interval. We use ordinary least squares regression to estimate the relationship between state or federal prison presence (a binary indicator equal to 1 if the county does have a state or federal prison) and the number of IHS-transformed COVID-19 cases or deaths.22 Column 1 describes the treatment variable of interest (prison presence or county-level prison capacity in 1000-person increments). Column 2 describes the outcome variable of interest. The points and spikes represent the estimated effect size and 95% confidence interval, whereas the last column states these effect sizes and confidence intervals in numbers. We include state-level fixed effects to account for state policy and economic factors that may be associated with COVID-19 spread. We control for presence of a meat processor within the county, days since cases exceeded 1 per 100 000 population, logged population, population density, urban–rural classification dummies, population share commuting by public transit, population share older than 75 years, population share living in a nursing home, average temperature February to April, logged median household income, the social capital index value, and 2018 midterm Republican vote share.

We found no evidence that prisons were correlated with COVID-19 deaths. Medical researchers and epidemiologists have shown that the causal chain from cases to deaths is complex and can be affected by individual access to health care, preexisting conditions, and hospital capacity, including ventilator access.[33,34] Health care access may be higher in the vicinity of prisons, as prison employees are typically state or federal employees with health care benefits for themselves and their families. (We are unaware of prison employees being required to report for duty despite being sick, unlike in the meatpacking industry.) Our results highlight the need for a nuanced investigation of the link between prisons and fatal or nonfatal COVID-19 cases.

Figure 3 shows that the relationship between prison presence and COVID-19 cases was robust to different outbreak duration choices. Prison presence corresponded to an 11% increase in cases after 30 days and a 16% increase after 60 days, both of which were larger effects than in the pooled sample. The result plateaued after 120 days, supporting our choice of the July 1 cutoff.

Figure 3.

Relationship Between State or Federal Prison Presence and Inverse Hyperbolic Sine (IHS)-Transformed Cases or Deaths by Days Since Outbreak: United States, 2020

Note. CI = confidence interval. We use ordinary least squares regression to estimate the relationship between state or federal prison presence (using binary indicator equal to 1 if the county does have a state or federal prison) and IHS-transformed cases or deaths using a duration-equalized sample of counties a certain number of days since outbreak onset.35 Column 1 indicates the number of days since outbreak onset in that county. Column 2 indicates the outcome variable of interest. The points and spikes represent the estimated effect size and 95% confidence interval, whereas the last column states these effect sizes and confidence intervals in numbers. We include state-level fixed effects to account for state policy and economic factors that may be associated with COVID-19 spread. We control for presence of a meat processor within the county, logged population, population density, urban–rural classification dummies, population share commuting by public transit, population share older than 75 years, population share living in a nursing home, average temperature February to April, logged median household income, the social capital index value, and 2018 midterm Republican vote share.

We investigated the relationship of federal prisons, state prisons, and jails with COVID-19 outcomes in Figure 4 and Table G (available as a supplement to the online version of this article at https://www.ajph.org). When we included jails, we found no evidence that counties with a prison or jail had larger outbreaks than counties with neither. Considering each type of facility separately, we found that cases were 11% higher in counties with a state prison, whereas cases were no higher in counties with a federal prison or a jail. The weak relationship between jails and cases was likely due to attenuation bias, since 61.2% of all counties had at least 1 jail or prison, whereas only 31% of counties had a state or federal prison. The null result for federal prisons suggests that the federal prison lockdown, which went into effect on April 1, 2020, may have been successful in slowing COVID-19 spread in and around prisons.[36,37]

Figure 4.

Relationship Between Prisons or Jail Presence by Governance Type and Either COVID-19 Outbreak Delay, Inverse Hyperbolic Sine (IHS)-Transformed Cases, or IHS-Transformed Deaths: United States, 2020
Note. CI = confidence interval. We use ordinary least squares regression to estimate the relationship between prisons or jail presence by governance type (a binary indicator equal to 1 if the county has a jail, state prison, or federal prison) and either COVID-19 outbreak delay (days since cases exceeded 1 per 100 000 population in the county), IHS-transformed cases, or IHS-transformed deaths.22 Column 1 describes the treatment variable of interest (a binary indicator for whether the county has a jail, a state prison, or a federal prison). Column 2 describes the outcome variable of interest. The points and spikes represent the estimated effect size and 95% confidence interval, whereas the last column states these effect sizes and confidence intervals in numbers. We include state-level fixed effects to account for state policy and economic factors that may be associated with COVID-19 spread. We control for presence of a meat processor within the county, days since cases exceeded 1 per 100 000 population, logged population, population density, urban–rural classification dummies, population share commuting by public transit, population share older than 75 years, population share living in a nursing home, average temperature February to April, logged median household income, the social capital index value, and 2018 midterm Republican vote share.

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