Tuesday, August 10, 2021

USA PRISON NATION

Crowding in prisons increases inmates’ risk for COVID-19 infections

COVID MAKES US POLITICAL PRISONERS

Policy changes could help minimize this threat

MASSACHUSETTS GENERAL HOSPITAL

BOSTON – Crowding in prisons dramatically increases the risk for COVID-19 infections among inmates, according to a new study by researchers at Massachusetts General Hospital (MGH). The authors of the study, published in JAMA Internal Medicine, argue that policy changes are necessary to protect the vulnerable population of incarcerated men and women.

Earlier studies found that the incidence of COVID-19 infection is significantly higher in prisons than in the general population, but the degree to which crowding contributed to the problem was unknown. The senior author of the JAMA Internal Medicine study, MGH infectious diseases physician Amir Mohareb, MD, has worked on a pro bono basis with several advocacy groups working to ensure that infection control measures are implemented in prisons during the coronavirus pandemic. One group he advised, Prisoners’ Legal Services of Massachusetts, was suing the state’s Department of Correction over its practices. Mohareb and his colleagues analyzed a trove of data in Massachusetts that allowed them to examine more closely what’s driving the high incidence of COVID-19 in prisons.

One element the data lacked was detailed information about each individual inmate who became ill, which would have allowed Mohareb and his team to study the characteristics of who got COVID-19 and who didn’t. However, they had other critical data, including weekly reports on the number of positive COVID-19 tests at 14 Massachusetts state prisons, the population of each prison, and the number of inmates the facility was designed to hold (known as design capacity). “So we asked, What are characteristics of these facilities that might lead to more COVID-19 transmission?” says Mohareb, who is also a researcher at MGH’s Medical Practice Evaluation Center.

Their analysis found that crowding at the facilities varied greatly during the observation period, with the population at some dropping as low as 25 percent of design capacity, while others were extremely crowded, reaching up to 155 percent of design capacity. Mohareb and his colleagues found that as facilities became more crowded, the threat to inmates rose: Every increase of 10 percentage points in a prison population relative to the facility’s design capacity raised the risk of getting infected with COVID-19 by 14 percent. As Mohareb notes, that means a facility doesn’t have to be exceeding its design capacity to increase the danger for inmates, since a prison that’s operating at 80 percent capacity is riskier than one at 70 percent capacity. “We may need to have stricter thresholds for where we draw the line on how crowded a facility can be,” he says.

To study the effect of crowding another way, Mohareb’s team calculated the percentage of inmates in each prison who were housed in single cells during each week of the observation period. They found that every 10-percentage-point increase in the proportion of inmates living in single cells reduced the risk of COVID-19 infection in that prison by 18 percent.

Similar to other studies, this investigation found that inmates in prisons have a significantly greater risk—more than sixfold—for becoming infected with COVID-19 compared to the general public. But in a novel finding, Mohareb and colleagues showed that infection rates in prisons tended to reflect those of their surrounding communities. “We found a very close association,” says Mohareb. When numbers of COVID-19 cases were low in Massachusetts during the summer of 2020, they tended to be low in prisons, too. And as numbers spiked in many communities late last year, they also soared in local prisons. “Prisons are intricately linked to their surrounding communities,” says Mohareb, noting that greater attention to infection control (through vaccination and routine testing) among guards, support staff, vendors, and others who come and go from these facilities is essential.

While COVID-19 vaccination became available to inmates in Massachusetts state prisons earlier this year, it is optional; what’s more, news reports indicate that a significant portion of prison workers remain unvaccinated. Mohareb and his coauthors argue that policymakers should strongly consider decarceration—releasing prisoners deemed to be at low risk for reoffending—as a way to lower the risk for COVID-19 in prisons. “It was the almost universal opinion of experts in public health, infectious disease and epidemiology from the start of the pandemic that prisons were going to be places of immense suffering unless inmates were released in a coordinated manner,” says Mohareb. “And that really didn’t happen.”

###

Mohareb is also an instructor in Medicine at Harvard Medical School (HMS). Abigail I. Leibowitz, the first author of the paper, completed this work while earning a master’s degree in public health at the Harvard T.H. Chan School of Public Health and is currently a medical student at the University of Colorado. Other authors include MGH infectious disease physician Mark Siedner, MD, MPH, an associate professor of Medicine at HMS, and MGH psychiatrist Alexander C. Tsai, MD, PhD, an associate professor of Psychiatry at HMS.

Support for this work was provided by the National Institutes of Health and the Sullivan Family Foundation.

About the Massachusetts General Hospital
Massachusetts General Hospital, founded in 1811, is the original and largest teaching hospital of Harvard Medical School. The Mass General Research Institute conducts the largest hospital-based research program in the nation, with annual research operations of more than $1 billion and comprises more than 9,500 researchers working across more than 30 institutes, centers and departments. In August 2021, Mass General was named #5 in the U.S. News & World Report list of "America’s Best Hospitals."

JOURNAL

JAMA Internal Medicine

DOI

10.1001/jamainternmed.2021.4392

METHOD OF RESEARCH

Observational study

SUBJECT OF RESEARCH

People

ARTICLE TITLE

Association Between Prison Crowding and COVID-19 Incidence Rates in Massachusetts Prisons, April 2020-January 2021

ARTICLE PUBLICATION DATE

9-Aug-2021

COI STATEMENT

Ms Leibowitz reported provision of pro bono advising in support of Prisoners’ Legal Services of Massachusetts in litigation against the Massachusetts Department of Correction regarding the agency’s response to COVID-19. Dr Tsai reported receiving grants from the Sullivan Family Foundation during the conduct of the study; other from Public Library of Science (stipend for work as specialty consulting editor for PLOS Medicine) and other from Elsevier (stipend for work as editor in chief of SSM Mental Health) outside the submitted work. Dr Mohareb reported receiving grants from the National Institutes of Health (T32AI007433) outside the submitted work; and provision of pro bono advising and written expert declarations in the following litigation regarding COVID-19 and incarcerated persons: Mays v Dart (Ill 2020), Foster v Mici (Mass 2020, 2021), and Savino v Hodgson (Mass 2020). No other disclosures were reported.

Key Points

Question Is prison crowding associated with the risk of COVID-19 among incarcerated persons?

Findings In this longitudinal ecological study including all incarcerated persons in 14 Massachusetts state prisons from April 2020 to January 2021, on average 6876 persons, COVID-19 incidence was significantly higher in prisons operating at a higher percentage of their design capacity and was significantly lower in prisons where a higher proportion of incarcerated people were housed in single-cell units.

Meaning Prison crowding was associated with increased COVID-19 incidence rates; strategies that reduce crowding and increase single-cell occupancy should be explored to mitigate COVID-19 risk in prisons.

Abstract

Importance COVID-19 incidence and mortality are higher among incarcerated persons than in the general US population, but the extent to which prison crowding contributes to their COVID-19 risk is unknown.

Objective To estimate the associations between prison crowding, community COVID-19 transmission, and prison incidence rates of COVID-19.

Design, Setting, and Participants This was a longitudinal ecological study among all incarcerated persons in 14 Massachusetts state prisons between April 21, 2020, and January 11, 2021.

Exposures The primary exposure of interest was prison crowding, measured by (1) the size of the incarcerated population as a percentage of the prison’s design capacity and (2) the percentage of incarcerated persons housed in single-cell units. The analysis included the weekly COVID-19 incidence in the county where each prison is located as a covariate.

Main Outcomes and Measures The primary outcome was the weekly COVID-19 incidence rate as determined by positive SARS-CoV-2 tests among incarcerated persons at each prison over discrete 1-week increments.

Results There was on average 6876 people incarcerated in 14 prisons during the study period. The median level of crowding during the observation period ranged from 25% to 155% of design capacity. COVID-19 incidence was significantly higher in prisons where the incarcerated population was a larger percentage of the prison’s design capacity (incidence rate ratio [IRR] per 10-percentage-point difference, 1.14; 95% CI, 1.03-1.27). COVID-19 incidence was lower in prisons where a higher proportion of incarcerated people were housed in single-cell units (IRR for each 10-percentage-point increase in single-cell units, 0.82; 95% CI, 0.73-0.93). COVID-19 transmission in the surrounding county was consistently associated with COVID-19 incidence in prisons (IRR [for each increase of 10 cases per 100 000 person-weeks in the community], 1.06; 95% CI, 1.05-1.08).

Conclusions and Relevance This longitudinal ecological study found that within 14 Massachusetts state prisons, increased crowding was associated with increased incidence rates of COVID-19. Researchers and policy makers should explore strategies that reduce prison crowding, such as decarceration, as potential ways to mitigate COVID-19 morbidity and mortality among incarcerated persons.

Introduction

COVID-19 has presented unique clinical and public health challenges for carceral settings, including jails, prisons, and immigration detention centers. The US has the largest carceral system in the world in terms of both total number of incarcerated persons and per capita incarceration.¹ COVID-19 incidence in state and federal prisons is estimated to be more than 5 times higher than that in the general population.² Similarly, age- and sex-adjusted COVID-19 mortality rates have been reported to be 2 to 3 times higher among incarcerated persons than in the general US population.³

There are many possible reasons for higher COVID-19 incidence in carceral settings, such as crowded living conditions; reduced access to testing and medical care; inconsistent implementation of risk mitigation techniques, such as hand hygiene and mask wearing; frequent passage of correctional staff, vendors, and visitors between the community and correctional facilities and frequent transfer of incarcerated individuals between housing units and from local jails to state prisons; and environmental factors, such as poor ventilation and cell structure.⁴^,5 In the US, correctional facilities also regularly exceed their design capacity, and several physicians and public health experts have suggested that there is an association between the level of crowding in prisons and the COVID-19 risk among incarcerated persons.⁶^,7 However, both state policy makers and state court decisions have noted a lack of epidemiologic evidence supporting this proposed association.⁸

Our study aims to address this lack of empirical evidence and determine the extent to which prison crowding is associated with COVID-19 risk. Our primary objective was to estimate the association between prison crowding and the incidence of COVID-19 among incarcerated persons in Massachusetts state prisons.

Methods

Study Design

We conducted a longitudinal ecological study of COVID-19 incidence in Massachusetts state prisons between April 21, 2020, and January 11, 2021. Prisons are distinct from jails, houses of correction, and juvenile detention facilities because they have a lower rate of turnover within their incarcerated populations. Our analysis included 14 of the 16 state prisons operated by Massachusetts Department of Correction with a total of 38 weeks of data. We excluded 2 facilities that are unique in their level of turnover and availability of on-site medical personnel: the Lemuel Shattuck Hospital Correctional Unit, which provides medical care to incarcerated persons within a public hospital, and the Massachusetts Alcohol and Substance Abuse Center, which houses individuals in short-term substance use rehabilitation. All 14 prisons uniformly implemented the same COVID-19 mitigation policies: mask wearing, hand hygiene, cessation of in-person visitation during periods of high community transmission, quarantine for newly incarcerated persons, symptom screening by correctional staff, and isolation and testing for anyone with symptoms suggestive of COVID-19.⁸ This analysis was conducted using deidentified, publicly available data sets and thus was exempt from human subjects review and informed consent processes based on the Common Rule (45 CFR § 46.104(d)(4)).

Data Sources

Data were derived from 5 publicly available sources. First, we used COVID-19 reports from the Massachusetts Department of Correction, which state the number of positive SARS-CoV-2 tests in each prison and the number of people incarcerated in each prison. These reports have been published on a weekly or daily basis since April 13, 2020, pursuant to Committee for Public Counsel Services v Chief Justice of the Trial Court (SJC-12926), and the data can be downloaded from ACLU of Massachusetts.⁹ When there was daily reporting, we calculated an average population for each prison across each 1-week increment. Second, we used the Massachusetts Department of Correction weekly Institution Cell Housing Reports, which state the number of people within each prison housed in single cells and the total number of people incarcerated in each prison. These data have been available since June 15, 2020.¹⁰ Third, we used the Massachusetts Department of Correction Quarterly Report on the Status of Prison Capacity to determine the design capacity, security level, and sex of individuals in each prison.¹¹ Fourth, we used the data repository from the 2019 Novel Coronavirus Visual Dashboard, operated by the Johns Hopkins University Center for Systems Science and Engineering, to obtain the number of COVID-19 cases in each Massachusetts county.¹² Fifth, we used 2019 US Census Bureau estimates for the population of Massachusetts state and counties.

Outcome and Exposure Measures

Our primary outcome measure was the weekly COVID-19 incidence rate as determined by positive SARS-CoV-2 tests among incarcerated persons at each prison per week. Our primary exposure of interest was the weekly level of crowding in each prison, which we estimated using 2 definitions. First, we calculated each prison’s average population in a given week as a percentage of its design capacity. The design capacity of a prison refers to “the number of inmates that planners intended for a facility”¹³ and is available from the Massachusetts Department of Correction Quarterly Report on the Status of Prison Capacity.¹¹ Second, we calculated the percentage of incarcerated persons in each prison housed in single cells during each week. In this second definition, we used prison population estimates from the Institution Cell Housing Reports to match the source of single-cell housing data.¹⁰

Covariates

We included the weekly COVID-19 incidence in the county where each prison is located as a covariate. These estimates were calculated by dividing the number of new cases per week in relevant counties by Census Bureau population data and then scaling to generate cases per 10 000 person-weeks. We did not have access to individual-level demographic data for incarcerated persons, such as age, comorbidities, socioeconomic status, prior zip code, or race/experiences of discrimination.

Primary Analysis

We used generalized estimating equations Poisson regression with robust SEs to account for the repeated-measures structure of our data. Our unit of observation was each 1-week increment for each prison. The outcome of interest for the model was the case count in each prison per week, and we included an offset term with each prison’s weekly average population size. We evaluated 3 models of COVID-19 incidence, all of which adjusted for the county-level COVID-19 incidence covariate. In model 1, we considered prison crowding as a continuous variable: incarcerated population as a percentage of the prison’s design capacity, which was determined by the Massachusetts Department of Correction Quarterly Report on the Status of Prison Capacity.¹¹ In model 2, to consider a nonlinear association between crowding and incidence, we categorized prison crowding into 3 levels: those with incarcerated populations below 70%, between 70% and 100%, and greater than 100% of the prison’s design capacity. We chose these thresholds because they have been considered feasible policy goals for moderate and aggressive decarceration strategies.⁸ In model 3, we defined prison crowding as a continuous percentage of incarcerated persons housed in single-cell units. We used 2-tailed P values and considered P < .05 to be statistically significant.

Sensitivity Analyses

We conducted a number of sensitivity analyses to assess the robustness of the findings. To assess for potential lagged associations between crowding and SARS-CoV-2 incidence owing to the estimated incubation period of infection,¹⁴^-16 we evaluated the same 3 models but specified lags of crowding 1 week and 2 weeks prior to the outcome. We also evaluated whether the sex of the incarcerated population in each prison could be a confounder by including sex in our multivariable models. Although prison security level was considered as a possible covariate in sensitivity analyses, we did not analyze this variable owing to its collinearity with the prison crowding variables.

Secondary Analysis

As a secondary analysis, we compared the overall incidence rate of COVID-19 among incarcerated persons in our study with the concurrent rate of the general population of Massachusetts. The COVID-19 incidence rate among incarcerated persons was calculated as the total number of positive tests during the study period divided by the observed person-time. The COVID-19 incidence rate in the Massachusetts general population was calculated as the number of new cases during the study period divided by the observed person-time. All analyses were conducted using Stata, version 16.1 (StataCorp LLC.).

Results

Prison Characteristics

The 14 prisons included in the analysis have a total design capacity of 7287 persons (range, 100-1084 persons per prison). Twelve prisons were designated male and 2 were designated female. During the study period, there was an average of 6876 persons housed within the 14 facilities. Prisons ranged in their median crowding level during the study period from 25% to 155% of design capacity. Prisons ranged from a median of 7% of people housed in single-cell units during the study period to 100% of people housed in single-cell units (Table 1).

Primary Analysis

In model 1, COVID-19 incidence was significantly greater among prisons operating at a higher percentage of their design capacity (incidence rate ratio [IRR] [per 10-percentage-point difference], 1.14; 95% CI, 1.03-1.27) (Table 2). In model 2, COVID-19 incidence was significantly greater among facilities operating above 100% of design capacity (IRR, 4.86; 95% CI, 1.37-17.27), compared with less than 70% of design capacity. Prisons operating at 70% to 100% of design capacity also had a higher estimated COVID-19 incidence (IRR, 3.39; 95% CI, 0.83-13.84), but this was not statistically significant in the primary analysis. In model 3, COVID-19 incidence was significantly lower in prisons in which a higher percentage of people were housed in single-cell units (IRR [per 10-percentage-point difference in single-cell occupancy]. 0.82; 95% CI, 0.73-0.93).

Across all regression models, community COVID-19 transmission in the county where each prison is located was associated with prison-level COVID-19 incidence (IRR [per 10 cases per 100 000 person-weeks], 1.06; 95% CI, 1.05-1.08 for model 1). Graphical depiction of the incidence of COVID-19 in prisons during the study period across strata of crowding and COVID-19 incidence in surrounding counties is presented in the Figure.

Sensitivity Analyses

When using 1- and 2-week lags, the crowding metric for all 3 models retained statistical significance and had point estimates within the CI of the primary analysis. Furthermore, prisons operating at 70% to 100% of design capacity also had a statistically significant association with higher COVID-19 incidence when specified with a 1- or 2-week lag (Table 3) compared with those operating at less than 70% design capacity (1-week lag IRR, 5.08; 95% CI, 1.51-17.02; 2-week lag IRR, 4.39; 95% CI, 1.09-17.73). All crowding metrics were robust to inclusion of sex as a covariate. COVID-19 incidence in the county surrounding each prison remained significantly associated with prison cases in all sensitivity analyses.

Secondary Analysis

During the study period, there were 2497 reported cases of COVID-19 among incarcerated persons. The mean COVID-19 incidence rate in prisons was 956 cases per 100 000 person-weeks. In comparison, COVID-19 incidence in the state of Massachusetts was 150 cases per 100 000 person-weeks during the study period (IRR, 6.38; 95% CI, 6.14-6.64).

Discussion

In this study, we found that COVID-19 incidence among incarcerated persons was associated with prison crowding and with transmission in the surrounding community. We used 2 different variables to estimate the association between prison crowding and the rate of COVID-19 in Massachusetts state prisons. These 2 metrics—incarcerated population as a percentage of design capacity and the percentage of incarcerated persons in single-cell units— fluctuated during the study period, and both were associated with a higher incidence of COVID-19.

There are 3 important findings in this study. First, for every 10% increase in prison population (as a percentage of prison design capacity), there was a 14% increased risk of COVID-19. Similarly, compared with weeks when prisons maintained an incarcerated population below 70% of design capacity, prisons operating between 70% and 100% and prisons operating at more than 100% of their design capacity had approximately 3- and 5-fold higher incidence rates of COVID-19, respectively. Our study demonstrates an association between prison crowding and increased COVID-19 incidence and suggests that COVID-19 risk can remain high even in prisons that operate below their design capacity when community transmission is high. Our results are consistent with a recently published stochastic compartmental model, which showed that a strategy of decreased prison crowding was associated with reduced transmission, hospitalizations, and deaths.¹⁷

Second, we found that for each 10% increase in the percentage of people housed in single-cell units, facility COVID-19 incidence rates were reduced by 18%. This observation is consistent with the stochastic compartmental model described previously.¹⁷ These observations are also consistent with prior studies of other communicable diseases in prisons.¹⁸ A recent study evaluating the association between COVID-19 incidence and nursing home crowding in Ontario, Canada, found that nursing home facilities with more single-occupancy rooms had a lower incidence of COVID-19 compared with nursing home facilities that had more multiperson rooms.¹⁹ In prisons where cells are shared between 2 or more people, our study suggests that decreasing the prison population and maximizing single-cell occupancy may reduce the incidence of COVID-19. Single-celling strategies that involve solitary confinement are not a recommended COVID-19 risk mitigation strategy, given their harmful effects on the health of incarcerated persons.²⁰^-23 Rather, single cells should only be used to increase physical distancing between individuals and should not prevent other prison activities, such as outdoor recreation or communication with loved ones.⁴

Our third important finding was that there was an association between COVID-19 incidence in prisons and community transmission. This association was observed despite the implementation of systemwide hygienic precautions, mask wearing, and quarantine and isolation. This suggests that COVID-19 risk in incarcerated populations is associated with the force of infection in the surrounding community and that correctional staff and contractors are important vehicles of COVID-19 transmission into prison facilities and vice versa. The transfer of incarcerated persons from jails to prison or between prisons may have introduced COVID-19 in some cases, although this was likely mitigated by the Massachusetts Department of Correction’s 14-day quarantine policies for all incoming individuals. Introduction of cases into facilities by visitors is an unlikely explanation for this finding because visitation was suspended during periods of high community-level transmission.⁸ This association between COVID-19 in the prison and the surrounding community is consistent with a recently published study observing that county-level incarceration rates in jails are associated with a 6.5% increase in mortality from infectious diseases.²⁴

However, the burden of COVID-19 does not appear to be shared equally by incarcerated persons and those in the broader community.²⁵^,26 During the study period, COVID-19 incidence in prisons was 956 per 100 000 person-weeks, which was more than 6 times higher than among the general Massachusetts population. Although the degree to which detected cases reflects true prevalence in these 2 populations is uncertain, our study’s finding that incarcerated persons had a higher incidence of COVID-19 than the general public is consistent with similar research at both state and federal levels.³ Our study suggests that interventions that mitigate COVID-19 risk in surrounding communities, and, by extension, among prison staff members, could serve multiple public health priorities for the incarcerated population and the population at large.

Limitations

This study should be interpreted within its design limitations. First, population density was not randomly assigned, and we did not have access to individual-level data. Unmeasured variables that are associated with both prison crowding and COVID-19 incidence could confound our estimates of the association between these 2 variables. In particular, prison architecture was not considered in this study and could have biased our estimates of the association between prison crowding and COVID-19 incidence away from the null. Second, our definition of COVID-19 incidence relied on public reporting of SARS-CoV-2 testing results, so asymptomatic and untested COVID-19 cases were not observed. The Massachusetts state prison system did not conduct regular asymptomatic surveillance testing, so our analysis might underestimate the extent of asymptomatic infection. The stated testing policy was uniform across all facilities involved in our analysis, but we cannot be certain about the extent to which underreporting was uniform across facilities. Third, we also did not include data on testing among correctional staff. Our data were limited to results captured by the Massachusetts Department of Correction and excluded health care and other external testing sources. Fourth, these analyses do not allow us to conclude the extent to which prison security level is associated with COVID-19 incidence independent of prison crowding owing to its collinearity with the crowding metrics. Security level may be related to the extent to which incarcerated persons are allowed to congregate, the amount of movement between different areas of a prison, or enforcement of COVID-19 policies. Consequently, further research is needed to delineate the exact nature of this association. Fifth, our findings are best generalizable to similar correctional facilities and may not generalize to juvenile detention centers.

Despite these limitations, our findings indicate that prison crowding may be an important driver of COVID-19 incidence in state prisons. Public health experts have proposed a number of potential ways to reduce COVID-19 among incarcerated persons, including decarceration and expanded applications of medical parole. Importantly, we found a gradient of increasing COVID-19 incidence even among prisons that operated well below their design capacities, suggesting that crowding reduction policies in settings of high community transmission may be most effective in reducing the risk of COVID-19 if they are based on conservative thresholds of prison crowding. Given that carceral populations experience a higher COVID-19 mortality rate compared with the general population,²⁷^,28 policies such as decarceration could be explored as public health interventions to reduce the risk of COVID-19 infections and mortality in this vulnerable population. Although public safety must be considered when enacting decarceration policies, prior research has shown that significant reductions in prison populations can be accomplished without endangering public safety.²⁹ Similar risks exist in immigration detention facilities, which are also subject to crowded conditions.³⁰ In each of these settings, other policies should also be pursued to reduce COVID-19 transmission risk and related morbidity and mortality, such as vaccination for both correctional staff and incarcerated persons, routine symptom screening, asymptomatic testing, and mask wearing/hand hygiene.¹⁷^,31 Our study has broad implications for public health policy given that COVID-19 vaccine coverage in carceral settings continues to be suboptimal, both in the US and abroad. Future studies should also examine the associations between prison crowding (and decarceration) with other transmissible infections.

Conclusions

In this longitudinal ecological study, COVID-19 incidence within Massachusetts state prisons during April 2020 to January 2021 was associated with prison crowding and community transmission. Interventions that promote prison depopulation and reduce staff exposure should be pursued to mitigate COVID-19 risk among incarcerated persons.

Article Information

Accepted for Publication: May 28, 2021.

Published Online: August 9, 2021. doi:10.1001/jamainternmed.2021.4392

Corresponding Author: Amir M. Mohareb, MD, Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St, 16th Floor, Boston, MA 02114 (amohareb@mgh.harvard.edu).

Author Contributions: Ms Leibowitz and Dr Mohareb had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Leibowitz, Mohareb.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: All authors.

Administrative, technical, or material support: Mohareb.

Supervision: Tsai, Mohareb.

Conflict of Interest Disclosures: Ms Leibowitz reported provision of pro bono advising in support of Prisoners’ Legal Services of Massachusetts in litigation against the Massachusetts Department of Correction regarding the agency’s response to COVID-19. Dr Tsai reported receiving grants from the Sullivan Family Foundation during the conduct of the study; other from Public Library of Science (stipend for work as specialty consulting editor for PLOS Medicine) and other from Elsevier (stipend for work as editor in chief of SSM-Mental Health) outside the submitted work. Dr Mohareb reported receiving grants from the National Institutes of Health (T32AI007433) outside the submitted work; and provision of pro bono advising and written expert declarations in the following litigation regarding COVID-19 and incarcerated persons: Mays v Dart (Ill 2020), Foster v Mici (Mass 2020, 2021), and Savino v Hodgson (Mass 2020). No other disclosures were reported.

Funding/Support: This work was supported by the National Institutes of Health (grant No. T32 AI007433 to Dr Mohareb). Dr Tsai acknowledges funding support from the Sullivan Family Foundation.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health or of the Sullivan Family Foundation.

References

Association Between Prison Crowding and COVID-19 Incidence Rates in Massachusetts Prisons, April 2020-January 2021 | Public Health | JAMA Internal Medicine | JAMA Network

How society’s inequalities showed up in COVID outcomes

Peer-Reviewed Publication

UNIVERSITY OF UTAH

COVID-19 rates by per capita income levels — IMAGE: COVID-19 CASES FOR EACH PER CAPITA INCOME, ZIP CODE GROUP OF SALT LAKE COUNTY, UTAH (USA) BETWEEN 17 FEBRUARY AND 12 JUNE 2020. THE DASHED VERTICAL LINES SHOW THE START (BLUE) AND END (RED) OF LOCKDOWN DIRECTIVES. THE COLOR SCALE RANGES FROM RED (LOWEST INCOME TO HIGHEST INCOME). view more
CREDIT: DANIEL MENDOZA

Racial minorities comprise around a quarter of Utah’s population but represent a third of COVID-19 cases in the state. A similar story has played out across the country. Why have racial minorities been unequally affected by the COVID-19 pandemic?

Researchers are still working out the answer to this question, but a new study from University of Utah researchers including Daniel Mendoza and Tabitha Benney explores the hypothesis that variation in income and occupational status, on a neighborhood-by-neighborhood scale, may be the reason. During the 2020 lockdowns, residents of affluent areas in Salt Lake County, Utah were able to stay at home more than residents of the least affluent zip codes, suggesting that the “essential worker” occupations of the least-affluent areas, which are also the highest minority populations, placed them at greater risk for contracting COVID-19. Subsequently, the least-affluent zip codes experienced nearly ten times the COVID incidence rate of affluent areas.

“We were shocked at the nearly tenfold difference in contagion rate increase when comparing the groups we had defined,” Mendoza says. “I think it was a very sobering moment when we realized how deep the disparities truly were in our own backyard.”

The study is published in the journal COVID.

Salt Lake County’s disparities

Two factors make Salt Lake County an ideal site for exploring the link between inequality and COVID-19 infection. First, says Benney, an associate professor of political science, a dense network of traffic sensors produces extraordinarily detailed traffic and mobility data, organized by zip code. Pair that with a similarly detailed level of COVID-19 incidence rates and demographic, occupational and income data, and a high-resolution picture emerges.

Second, says Mendoza, a research assistant professor in the Department of Atmospheric Sciences and visiting assistant professor in the Department of City & Metropolitan Planning, Salt Lake County exhibits “strongly marked socioeconomic disparities. The substantial differences in race, income and occupation are very clear and provide a strong basis for inequality analysis.”

The divide in Salt Lake County roughly follows the I-15 freeway, which separates the county into east and west sides. The east side has a higher per capita income and percentage of white-collar workers. The divide isn’t strictly racial, however, with a more diverse northeast and less diverse southwest quadrant of the valley.

But with COVID-19 overlain onto this socioeconomic landscape, a pattern emerged.

“The first time our team crunched the numbers,” Benney says, “we were all dismayed to see how well income and occupation related to COVID incidence rates.”

What is structural inequality?

How does income and occupation relate to race? The researchers explored that question through the lens of structural inequality, which is a system of privilege in institutions and policies that place people on an unequal starting footing in society. This inequality, the researchers write, “create[s] relational patterns that effectively socialize and dictate how individuals see the world and their place in it. Inequality is considered structural when policies produced by the system keep some groups from getting ahead, regardless of their actions.”

In the first few months of the COVID-19 pandemic, as white-collar office workers and others stayed home, those deemed ‘essential’ workers still journeyed out to keep hospitals running, grocery store shelves stocked and packages moving around the country. In this case, the structural inequalities at work would be those that placed racial minorities disproportionately into lower-income occupations, and thus disproportionately into the category of blue-collar worker least likely to be able to stay home during the initial lockdown.

“The true front-line workers were far more varied than expected,” Benney says. “Medical workers are the heroes for sure, but janitors, repair people and folks that kept our homes and our families healthy throughout the pandemic were, and may again, be facing greater risks due to their starting point in life and the occupation they have today.”

The evidence for the unequal effect of lockdowns on different occupations and incomes comes from traffic data collected between February and June 2020 – before, during and after the main lockdown phase of the pandemic. Traffic decreased in zip codes with high percentages of high-income, white-collar and white residents by up to 50%. But in the least affluent zip codes, traffic decreased by only around 15%.

Statistical correlations linked those traffic patterns to income, occupation and, eventually, to COVID-19 outcomes.

“Income and occupation go hand in hand much more so than race and either of the variables,” says Mendoza, who also holds appointments as an adjunct assistant professor in the Pulmonary Division at the School of Medicine and as a senior scientist at the NEXUS Institute. In a place like Salt Lake County, structural inequalities can lead to income and occupational divides falling along racial lines.

Benney says that policies such as lockdowns, which expose some populations to higher disease risk, need to be better designed and implemented in future waves of the current pandemic and beyond. “In this case, because more affluent communities were more likely to stay home under the Stay-Home-Stay-Safe Directive in Utah, this behavior appears to have shifted the disease risk away from the wealthiest, most white, and white-collar workers, who were already more likely to rebound from a crisis,” she says. While Utahns benefitted overall from the directive, she adds, designing this policy with low income, essential workers in mind may help prevent the spread of disease, improve outcomes for vulnerable populations, and create a more resilient society overall.

Facing successive waves

Since the end of the study period in June 2020, the COVID-19 pandemic has continued with a surge in winter 2020-21, the rollout of vaccines and the growing impact of the Delta variant. Both Mendoza and Benney emphasize the need for policymakers to consider vulnerable populations, including those from low-income zip codes, in crafting a pandemic response.

“Frankly, we should be showing our support for these people by masking up in public, getting vaccinated, and looking out for our community in any way we can,” Benney says.

“Our hope is that our research provides insight into the most vulnerable and affected groups and we can pay attention to their specific needs and take care of them as they take care of the rest of us,” Mendoza adds.

Find the full study here.