Brief Report: Dynamics of racial disparities in all-cause mortality during the COVID-19 pandemic

As research documenting disparate impacts of COVID-19 by race and ethnicity grows, little attention has been given to dynamics in mortality disparities during the pandemic and whether changes in disparities persist. We estimate age-standardized monthly all-cause mortality in the United States from January 2018 through February 2022 for seven racial/ethnic populations. Using joinpoint regression, we quantify trends in race-specific rate ratios relative to non-Hispanic White mortality to examine the magnitude of pandemic-related shifts in mortality disparities. Prepandemic disparities were stable from January 2018 through February 2020. With the start of the pandemic, relative mortality disadvantages increased for American Indian or Alaska Native (AIAN), Native Hawaiian or other Pacific Islander (NHOPI), and Black individuals, and relative mortality advantages decreased for Asian and Hispanic groups. Rate ratios generally increased during COVID-19 surges, with different patterns in the summer 2021 and winter 2021/2022 surges, when disparities approached prepandemic levels for Asian and Black individuals. However, two populations below age 65 fared worse than White individuals during these surges. For AIAN people, the observed rate ratio reached 2.25 (95% CI = 2.14, 2.37) in October 2021 vs. a prepandemic mean of 1.74 (95% CI = 1.62, 1.86), and for NHOPI people, the observed rate ratio reached 2.12 (95% CI = 1.92, 2.33) in August 2021 vs. a prepandemic mean of 1.31 (95% CI = 1.13, 1.49). Our results highlight the dynamic nature of racial/ethnic disparities in mortality and raise alarm about the exacerbation of mortality inequities for Indigenous groups due to the pandemic.     

Racial disparities in the content of primary care office visits

BACKGROUND: Little is known about racial disparities in primary care at the level of the office visit. OBJECTIVE: To assess racial disparities in the receipt of commonly performed/recommended procedures during routine primary care office visits and examine trends in disparities over time. DESIGN, SETTING, AND PATIENTS: The sample included 88,303 visits by adults to 3,260 primary care physicians in office-based practices in the National Ambulatory Medical Care Surveys, 1985, 1989, 1990, 1991, 1992, and 1995 to 2001. MEASUREMENTS: Adjusted odds for receipt or recommendation of commonly performed office procedures. RESULTS: During the years 1985 to 2001, African Americans, compared with whites, had lower odds of receiving a Pap test (odds ratio (OR) 0.81; 95% confidence interval (CI) 0.70 to 0.93), rectal exam (OR 0.67; 95% CI 0.56 to 0.80), smoking cessation counseling (OR 0.80; 95% CI 0.66 to 0.96), and mental health advice (OR 0.51; 95% CI 0.38 to 0.69), but had higher odds for visual screening (OR 1.38; 95% CI 1.08 to 1.77), weight advice counseling (OR 1.27; 95% CI 1.13 to 1.44) and receiving a follow-up appointment (OR 1.45; 95% CI 1.29 to 1.64). These findings were not appreciably altered by adjustment for physician practice characteristics including percent African American or Medicaid patients. Disparities disfavoring African Americans in cholesterol testing and smoking cessation observed during 1985 to 1992 were not observed in 1995 to 2001. CONCLUSIONS: The findings suggest that race is associated with the type of primary care received by patients, at least for selected procedures, with evidence that some disparities have diminished over time. The authors have no conflicts of interest to report. Grant Support: Agency for Health care Research and Quality R01 HS 10910-01A2.     

Depression in older adults during the COVID-19 pandemic: A systematic review

Background

Depression affected 5.7% of people aged 60 years and over prior to the pandemic and has increased by approximately 28%. The aim of this study is to identify and describe factors associated with depressive symptoms, the diagnostic assessment instruments and interventions used to evaluate and treat depression in adults aged 60 years and older since the onset of the COVID-19 pandemic.

Methods

Four electronic databases were systematically searched to identify eligible studies published since the beginning of the COVID-19 pandemic. A total of 832 articles were screened, of which 53 met the inclusion criteria.

Results

Factors contributing to depressive symptoms in older adults prior to the pandemic were grouped into the following categories: sociodemographic characteristics (i.e., being female); loneliness and weak social support; limitations in daily functioning, physical activity and neurocognitive impairment; and clinical factors. The following groups of factors directly related to the pandemic were found: stress-related factors and feelings or worries related to the pandemic; information access (e.g., receiving news about COVID-19 through the media); factors directly related to COVID-19 (e.g., having infected acquaintances); and factors related to the measures that were taken to reduce the spread of COVID-19 (e.g., confinement measures). The most frequently used instrument to assess depressive symptoms was the Geriatric Depression Scale Short Form (GDS-SF). Four studies implemented interventions during the pandemic that led to significant reductions in depressive symptoms and feelings of loneliness.

Conclusions

Improved understanding of pandemic-associated risk factors for depression can inform person-cantered care. It is important continued mental healthcare for depression for older adults throughout crises, such as the COVID-19 pandemic. Remote delivery of mental healthcare represents an important alternative during such times. It is crucial to address depression in older adults (which often causes disability), since the pandemic situation has increased depressive symptoms in this population.     

Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic

Staying home and avoiding unnecessary contact is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States enacted policies to encourage distancing and some mandated staying home. Understanding how these policies interact with individuals’ voluntary responses to the COVID-19 epidemic is a critical initial step in understanding the role of these nonpharmaceutical interventions in transmission dynamics and assessing policy impacts. We use variation in policy responses along with smart device data that measures the amount of time Americans stayed home to disentangle the extent that observed shifts in staying home behavior are induced by policy. We find evidence that stay-at-home orders and voluntary response to locally reported COVID-19 cases and deaths led to behavioral change. For the median county, which implemented a stay-at-home order with about two cases, we find that the response to stay-at-home orders increased time at home as if the county had experienced 29 additional local cases. However, the relative effect of stay-at-home orders was much greater in select counties. On the one hand, the mandate can be viewed as displacing a voluntary response to this rise in cases. On the other hand, policy accelerated the response, which likely helped reduce spread in the early phase of the pandemic. It is important to be able to attribute the relative role of self-interested behavior or policy mandates to understand the limits and opportunities for relying on voluntary behavior as opposed to imposing stay-at-home orders.

Worldwide, people stayed home to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing the COVID-19 pandemic. This behavioral shift helped prevent the COVID-19 pandemic from being worse. How much of the staying home response was driven by individuals acting in their own interests in response to health risks, and how much was the result of policy mandates or orders? The need for evidence to resolve this question is characterized by two statements from US governors’ offices. Governor Burgum of North Dakota stated (1), “We believe in the power of individual responsibility. And we need individual responsibility now more than ever to slow the spread of COVID-19,” whereas, following new stay-at-home orders, the office of Governor Brown of Oregon said (2), “If people aren’t going to take this virus seriously, we are prepared to offer consequences…[and] hold people accountable in making smart choices that can save another’s life.” Mandates can accelerate or strengthen the public response. This is a necessary, but not sufficient, condition for mandated nonpharmaceutical interventions to reduce SARS-CoV-2 transmission. For such transmission reductions to reduce deaths, the mandates must not compromise health services (3). However, mandates are also politically costly, difficult to sustain, and difficult to enforce. There have been over 1,000 lawsuits filed in the United States over COVID-19 public measures (4), and public health resources may need to be diverted to defend against these lawsuits. COVID-19 vaccines do not nullify the importance of relying on voluntary behavior or mandates. Vaccines could still take a substantial amount of time to distribute, and there are a large number of pathogens similar to SARS-CoV-2 that could cause another pandemic (5).Theory suggests that people alter behavior voluntarily to avoid becoming sick, including staying home (6–10). Evidence from the 2009 H1N1 swine flu pandemic (11–13), Lyme disease (14), the 2003 SARS epidemic (15, 16), and HIV (17, 18) support the theory. The emerging evidence from COVID-19 (19–21) also supports the theory that people alter behavior in response to infectious disease risk. Localized shutdowns in Mexico City during the H1N1 pandemic encouraged people to stay in, but the response was short lived (22).The 2020 COVID-19 pandemic is one of the first opportunities to investigate how public health mandates in the form of nonpharmaceutical interventions interact with voluntary behavioral shifts during epidemics. All 50 US states and the District of Columbia issued emergency orders. A total of 39 states and the District of Columbia issued stay-at-home or shelter-in-place orders, which are two names for the same thing (hereafter stay-at-home). However, there is substantial heterogeneity in the timing of local cases and stay-at-home orders (Fig. 1). Most counties were under emergency orders (99.4%) and closed schools (98%) prior to experiencing a single COVID-19 case (SI Appendix, Fig. S1). Recent studies (23–25) have attempted to evaluate the effectiveness of restrictions that ordered people to stay in on slowing the spread of SARS-CoV-2. Still, these studies assume no voluntary change in behavior as the alternative scenario to restrictions. Yet voluntary behavior is a fundamental part of the transmission process. If the true data generating process for an epidemic involves the theoretically predicted voluntary behavioral avoidance response, then an empirical model that assumes away that behavioral response can fit the epidemiological data as well as a model that specifies the true data generating process (6). The problem is that the misspecified model introduces many opportunities for confounding processes in epidemiology (26), and a misspecified model cannot provide information about behavioral response, whether voluntary or because of public health mandates. Therefore, we focus on the first step in a potential causal chain.Open in a separate windowFig. 1.Date that counties enacted stay-at-home order (A) and date of the first case reported within a county (B).It is important to understand how self-interest and policy mandates interact. The interactions between voluntarily staying home and policy-induced staying home can be viewed through two lenses. On the one hand, the policy can overlap or displace voluntary behavior that would have happened anyway. If the policy mandate overlaps or displaces voluntary contributions of behavioral change and leads to a similar outcome, then mandates achieve the outcome at a greater cost. Ostrom (27) expands on the costs of displacing or crowding out voluntary behavior, writing, “external interventions crowd out intrinsic motivation if the individuals affected perceive them [the policies] to be controlling.” She argues that many policies adopted in modern democracies presume authorities must solve all collective action problems, thereby crowding out citizenship, wasting resources, and undermining democracy. Stay-at-home mandates likely fit this characterization. Empirical evidence suggests that as mandatory involuntary contributions increase, voluntary contributions are increasingly crowded out, even when there is a private benefit to the contribution (28).Conversely, a mandate could achieve a stronger or faster response, arriving at the “full” response faster. Speed is valuable during an epidemic, even if the final response is similar in magnitude. Moreover, a stronger or faster response may be necessary to avoid exceeding healthcare capacity, which could lead to higher morbidity and mortality rates for the same number of cases. The response induced by a mandate may be faster or stronger because people may act to protect themselves voluntarily but fail to internalize the fact that if they become infected, then they can infect others (29).The metric that provides a common fact to weigh these two perspectives is the case equivalent response—how many more cases would have been needed to elicit the same behavioral response as the mandate. This simultaneously evaluates whether the mandate displaces or overlaps voluntary behavior and measures the speeding-up effect. If the case equivalent response is of similar size as reported cases up to likely measurement error at the time the stay-at-home mandate begins, then we consider the effect epidemiologically small. Conversely, if the case equivalent response is many orders of magnitude greater than current cases and measurement error, then the stay-at-home mandate likely has a large behavioral effect, which may translate into reduced cases and reduced deaths, all else equal.To measure the case equivalent response, we focus on the number of minutes per day that people spend at home, measured using smart device location data. For well over a decade, epidemiologists have used surveys of contact behavior to parameterize epidemiological models (30–34). Bayham et al. (13) refined earlier work (35), inferring likely contact patterns from the American Time Use Survey (ATUS). They showed that time-use data, based on the ATUS, produced similar contact patterns to those based on the detailed surveys epidemiologist relied on. Bayham et al. also showed that in the case of H1N1, conditioning cases on time spent at home gave a similar reduction in cases as an epidemiological model using the fully specified contact structure. Hence, we use the smart device data to measure the time spent at home as a measure of avoidance behavior. We confirm the primary results with other measures of time use.We define voluntary response as an increase in minutes at home as a function of reported cases within the county, after controlling for mandates, which provides a relatively local measure of risk. We also consider reports of national and state cases. We focus on three policy mandates that led to “involuntary” behavioral responses. First, we consider stay-at-home orders, which we combine with shelter-in-place orders, colloquially called “lockdowns.” While people were ordered to stay home, exceptions were made for vaguely defined essential activities, and these orders were seldom enforced by police. Therefore, we put “involuntary” in quotes—people could ignore the orders. Nevertheless, the intent of the orders was to keep people home involuntarily. Second, we consider school closures that induced parents to stay home from work to care for children. Third, we consider emergency orders that raised awareness and may have led businesses to close or encourage working from home but did not provide direct public mandates. In SI Appendix, we repeat the analysis using reported deaths instead of, and along with, cases. We acknowledge that the voluntary aspect of behavior is hard to define. Closures likely increased the salience of concern for COVID-19, making “voluntary” difficult to define in the COVID-19 upheaval, which is why we focus on the early phase of the epidemic in the United States. However, the salience of other indirect policy impacts may have been achievable through more targeted policies.Here, we use the variation in policy responses along with smart device data to measure the amount of time Americans stayed home and adjusted other behaviors in response to pathogen risk and stay-at-home orders. We contribute to the body of evidence that finds strong voluntary avoidance behavior. We also contribute to the body of evidence that finds a strong effect of mandates. We fill a gap in the literature by connecting voluntary response and policy-induced responses within a single empirical framework. We then compute the case equivalent response of the stay-at-home mandate. Imposing a stay-at-home mandate fast forwards or displaces the voluntary staying home. We find that most counties imposed stay-at-home orders with few cases; these stay-at-home orders induced a time at home equivalent to tens of additional cases, but that most counties would have achieved a similar amount of time allocated to home if cases rose as they did in areas without the stay-at-home orders. However, for some counties stay-at-home orders altered behavior in a manner equivalent to thousands of cases, suggesting the need for policy to adapt to local conditions.     

Enabling social care services for older adults during periods of long-term social isolation: Service provider perspectives

Objective

To investigate how small, local organisations were impacted by and responded to COVID-19 in their delivery of social care services to older adults (70 years and older). Lessons learnt and future implications are discussed.

Methods

Six representatives from four social care services (five females and one male) participated in individual semistructured interviews. Responses were analysed thematically.

Results

The key themes identified were service providers' experience, perceived needs of older adults and adapting services. Service providers positioned themselves as front-line essential workers for their older adult clients, resulting in some emotional toll and distress for the service providers. They provided information, wellness checks and at-home assistance to keep their older adult clients connected.

Conclusions

Service providers feel more prepared for future restrictions but flag the potential of training and supporting older adults to use technology to stay connected, as well as the need for more readily available funding to allow services to adapt quickly during times of crisis.     

Prevalence and risk factors of sleep disturbance in adults with underlying health conditions during the ongoing COVID-19 pandemic

To determine the prevalence of sleep disturbance during the coronavirus disease 2019 (COVID-19) pandemic among US adults who are more vulnerable to complications because of age and co-morbid conditions, and to identify associated sociodemographic and psychosocial factors. Cross-sectional survey linked to 3 active clinical trials and 2 cohort studies, conducted between 11/30/2020 and 3/3/2021. Five academic internal medicine practices and 2 federally qualified health centers. A total of 715 adults ages 23 to 91 years living with one or more chronic conditions. A fifth (20%) of participants reported poor sleep. Black adults were twice as likely to report poor sleep compared to Whites. Self-reported poor physical function (51%), stress (42%), depression (28%), and anxiety (36%) were also common and all significantly associated with poor sleep. Age ≥70 years and having been vaccinated for COVID-19 were protective against poor sleep. Sex, education, income, alcohol use, and employment status were not significantly associated with sleep quality. In this diverse sample of adults with chronic conditions, by race, ethnicity, and socioeconomic status, disparities in sleep health amid the ongoing pandemic were apparent. Worse physical function and mental health were associated with poor sleep and should be considered targets for health system interventions to prevent the many subsequent consequences of disturbed sleep on health outcomes. Measurements: self-reported sleep quality, physical function, stress, depression, and anxiety.     

Perceived deterioration in health status among older adults in Europe and Israel following the first wave of the COVID-19 pandemic

The COVID-19 pandemic has created challenges in providing medical care for people with health conditions other than COVID-19. The study aims to assess the prevalence of older adults’ reportage of decline in health relative to pre-pandemic and to identify its determinants.The study is based on the Survey of Health, Ageing and Retirement in Europe (SHARE) data collected during the pandemic. It comprised 51,778 people in twenty-seven European countries and Israel. Participants were asked about changes in their health status relative to pre-pandemic. Bivariate analysis and logistic regression were used to identify factors associated with worsening of health.Nine percent of people (average age 70 years) reported a worsening of health relative to pre-pandemic. A logistic regression revealed a significant relation of the probability of a downturn in health to forgoing, postponing, or being denied an appointment for medical care. Multiple chronic illnesses, developing COVID-19, having at least one form of psychosocial distress, higher age, and lower economic capacity were also found significantly related to the probability of a decline in health.Older adults’ comprehensive health needs must be addressed even when healthcare services are under strain due to pandemic outbreaks. Policymakers should attend to the healthcare needs of people whose vulnerability to the pandemic is amplified by chronic health conditions and low socioeconomic status. Public healthcare systems may experience a massive rebound of demand for health care, a challenge that should be mitigated by delivery of healthcare services and the provision of the financial resources that they need.     

Sociodemographic determinants of telehealth utilisation in general practice during the COVID-19 pandemic in Australia

This analysis assessed the sociodemographic characteristics of telehealth utilisation during the coronavirus disease 2019 (COVID-19) pandemic from March 2020 to August 2021 in Australia. Drawing on 860 general practice providers among 3 161 868 patients, 24 527 274 consultations were recorded. Telehealth accounted for 37.6% of the consultations, with 2.4% through videoconferencing and 35.2% through phone consultations. Our multivariate regression analyses indicated low utilisation of videoconferencing compared with phone consultations among older adults, those living in rural communities and migrants from non-English speaking countries.     

Challenges and opportunities in conducting research with older adults with dementia during COVID-19 and beyond

The coronavirus disease 19 (COVID-19) pandemic has created significant and new challenges for the conduct of clinical research involving older adults with Alzheimer's disease and related dementias (ADRD). It has also stimulated positive adaptations in methods for engaging older adults with ADRD in research, particularly through the increased availability of virtual platforms. In this paper, we describe how we adapted standard in-person participant recruitment and qualitative data collection methods for virtual use in a study of decision-making experiences in older adults with ADRD. We describe key considerations for the use of technology and virtual platforms and discuss our experience with using recommended strategies to recruit a diverse sample of older adults. We highlight the need for research funding that supports the community-based organizations on which improving equity in ADRD research participation often depends.     

Carceral-community epidemiology,structural racism,and COVID-19 disparities

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.

High rates of incarceration in crowded detention facilities have been documented as a significant population-level risk factor for the transmission of infectious diseases such as HIV, influenza, tuberculosis, viral hepatitis, and yet other diseases (1–11). Such facilities function as disease incubators, providing sites for easy viral and bacterial replication with a ready supply of tightly packed bodies that are rendered even more vulnerable by inadequate healthcare, poor living conditions, and associated comorbidities (12, 13). As a result, notably overcrowded prisons, jails, and immigrant detention facilities under a system of mass incarceration in the United States effectively constitute infectious disease multipliers (14–21). Given the daily inflow/outflow of staff and detainees, these disease reservoirs—cultivated through disregard for the welfare of incarcerated people — also function as epidemiological pumps that fuel continued disease penetrance in surrounding communities (22–26). We refer to this dynamic as “carceral-community epidemiology” to emphasize that health in carceral facilities is in continuous biosocial interrelation with community health, national public health, and global biosecurity.During the COVID-19 pandemic, American jails and prisons have predictably emerged as the world’s leading sites of COVID-19 outbreaks. Prior to the resumption of the school year, carceral facilities constituted 90 of the top 100 clusters in the United States as of September 1, 2020 (27). As of March 2021, they featured more than 626,000 publicly documented cases––almost certainly a substantial undercount due to the absence of oversight to ensure adequate testing protocols, data accuracy, and public reporting (27, 28). This crisis was not unanticipated (19, 20, 29, 30). Amid long-standing political acceptance of mass incarceration in the United States, which houses nearly 25% of the world’s incarcerated people despite only representing 4.2% of the global population (31), early warnings from public health experts were followed by delayed and inadequate policy action to alter arrest and incarceration practices in response to pandemic conditions (21, 32) Furthermore, while US jail populations initially declined in late spring and summer months, they have since rebounded toward prepandemic levels, increasing by 10% in the final months of 2020 (33). In this context, it is notable that while a considerable amount of appropriate attention has focused on the risks to which incarcerated individuals are being subjected during COVID-19, comparatively little scientific, media, and policy attention has highlighted the risks that carceral epidemics pose not only to incarcerated people but also to the health of the public at large (34).It is clear that COVID-19 spreads quickly within US prisons and jails (35), but ascertaining the degree to which cases manifesting in carceral institutions spread to surrounding communities requires more investigation. An early modeling study, which necessarily relied on various assumptions and estimated an eventual total toll of 200,000 deaths from COVID-19, suggested that up to 76,000 deaths in US communities could result from spillover of COVID-19 epidemics in prisons and jails (36, 37). As the number of COVID-19 deaths in the United States now approaches 600,000, it appears likely that a large proportion of total COVID-19 deaths may ultimately be attributable to jail- and prison-linked spread of the novel coronavirus.As of yet, only one peer-reviewed study has addressed carceral-community epidemiological ties during the COVID-19 pandemic with empirical data analysis based on real-world, rather than projected, dynamics. Controlling for race, poverty, public transit use, and population density, the study’s cross-sectional analysis showed a strong independent association between the arrest and cycling of individuals through Cook County Jail in Chicago before release and COVID-19 case rates in these individuals’ home ZIP codes in Illinois (38).In addition to this preliminary finding, parallel racial disparities in the American criminal punishment system and COVID-19 cases also suggest a likely epidemiological link between COVID-19 outbreaks in carceral institutions and high case rates in highly policed Black and Hispanic communities (39). American policing and carceral practices disproportionately affect communities of color, who make up only 37% of the general population but 67% of the prison population (40). Communities subjected to high rates of poverty, which often overlap with racialized communities in the United States but also include poor White communities, are also disproportionately affected by policing and incarceration (41, 42) As has been widely noted, COVID-19 cases and deaths in the United States are, like arrest and incarceration, disproportionately affecting communities of color and communities in poverty (43–46). Despite these demographic overlaps and the questions they provoke, little research exists on the relationship between policing and/or incarceration policies and community rates of COVID-19.The lack of research in this area owes in large part to inadequate data access, low data quality, and obstructive noncooperation from authorities overseeing jails, prisons, and immigration detention facilities (28). The collection and distribution of such data are often controlled, with little to no regulatory oversight, by county sheriffs and related officials whose positions depend upon electoral politics, both directly and indirectly. This may foster a prioritization of anticipated ramifications of negative media coverage rather than a prioritization of effective public health action and facilitation of necessary research (47).The hazards of this system were confirmed, for example, in an August 2020 Supreme Court case, in which documents revealed that the Orange County Jail deliberately misled a lower court. The dissenting opinion, written by Justices Sotomayor and Ginsberg, affirmed a lower court’s assessment that “the Jail was deliberately indifferent to the health and safety of its inmates.” Furthermore, they noted that “despite knowing the severe threat posed by COVID–19 and contrary to its own apparent policies, the Jail exposed its inmates to significant risks from a highly contagious and potentially deadly disease [… and] has misrepresented its actions to the District Court and failed to safeguard the health of the inmates in its care” (48). Amid widespread legal failures to protect public health, including the Supreme Court’s majority opinion in this case, such abuses and misrepresentations by jail and prison administrators remain difficult to detect, document, and prevent. In this context, recent bicameral legislative efforts in Congress that attempt to force greater data transparency are important for collecting vital public health data and facilitating evidenced-based policymaking (49, 50). Currently proposed legislation does not, however, include adequate provisions to address the problem of data quality, possible data manipulation and misrepresentation, and the associated need for independent oversight to ensure proper COVID-19 testing protocols, accurate data collection, and publicly accessible data infrastructure.In this setting of minimal high-quality data access and correspondingly few peer-reviewed studies, researchers have suspected that the constant circulation of staff and detainees between jails and communities—a weekly flow of 200,000 jail detainees (51) alongside daily movement of over 420,000 jail and prison guards (52)—poses considerable risk for the broader transmission of COVID-19 in communities. Although prisons, which house those who have been convicted of crimes and are serving sentences typically longer than one year, are also porous institutions in constant biosocial interrelation with surrounding communities, the degree of daily inflow/outflow of jails is notably higher. While prisons release ∼600,000 people annually, jails cycle through nearly 11 million admissions each year (41, 53). It is also important to note that jails primarily house pretrial detainees who have not been convicted of a crime and most of whom remain in jails for only a matter of hours, days, or weeks before being released to their communities. Pretrial detainees make up 74% of the typical daily population in US jails (and 43% of this daily population is comprised of Black individuals, who constitute only 13% of the overall national population) alongside a minority of jail detainees who have been convicted of low-level offenses and are serving sentences of less than one year (41). Highly dynamic jail populations with constant flow of new immunologically naïve individuals suggests that jails are an especially important nexus for spread of COVID-19 in US communities.Against this backdrop, this study improves upon our previously published cross-sectional analysis of the relationship between incarceration and community spread of COVID-19 (38). With new access to longitudinal COVID-19 data that corresponds with jail release data at the ZIP code level, we are able to further characterize our previous study’s observed correlation between jail cycling and COVID-19 case rates in Illinois ZIP codes by including analyses over time to better inform an evaluation of possible causal relationships between jail cycling and COVID-19 spread in surrounding communities. We also more closely analyze the relationship between jail-linked coronavirus spread and racial health inequities as a mechanism of structural racism (39, 54). Our previous study’s single cross-sectional analysis added preliminary quantitative evidence to anecdotal observations, but it could not assess temporal patterns, the plausibility of reverse causality (i.e., that higher preexisting COVID-19 case rates in ZIP codes with high rates of jail cycling––not jail cycling itself––account for the observed relationship), nor the longer-term epidemiological dynamics associated with jail cycling. We now use repeated cross-sectional analyses to examine temporal dynamics in the relationship between jail cycling and community COVID-19 spread.