Influence of geospatial resolution on sociodemographic predictors of COVID-19 in Massachusetts.

PURPOSE: When studying health risks across a large geographic region such as a state or province, researchers often assume that finer-resolution data on health outcomes and risk factors will improve inferences by avoiding ecological bias and other issues associated with geographic aggregation. However, coarser-resolution data (e.g., at the town or county-level) are more commonly publicly available and packaged for easier access, allowing for rapid analyses. The advantages and limitations of using finer-resolution data, which may improve precision at the cost of time spent gaining access and processing data, have not been considered in detail to date. METHODS: We systematically examine the implications of conducting town-level mixed-effect regression analyses versus census-tract-level analyses to study sociodemographic predictors of COVID-19 in Massachusetts. In a series of negative binomial regressions, we vary the spatial resolution of the outcome, the resolution of variable selection, and the resolution of the random effect to allow for more direct comparison across models. RESULTS: We find stability in some estimates across scenarios, changes in magnitude, direction, and significance in others, and tighter confidence intervals on the census-tract level. Conclusions regarding sociodemographic predictors are robust when regions of high concentration remain consistent across town and census-tract resolutions. CONCLUSIONS: Inferences about high-risk populations may be misleading if derived from town- or county-resolution data, especially for covariates that capture small subgroups (e.g., small racial minority populations) or are geographically concentrated or skewed (e.g., % college students). Our analysis can help inform more rapid and efficient use of public health data by identifying when finer-resolution data are truly most informative, or when coarser-resolution data may be misleading.

Inequities in COVID-19 vaccine and booster coverage across Massachusetts ZIP codes after the emergence of Omicron: A population-based cross-sectional study.

BACKGROUND: Inequities in Coronavirus Disease 2019 (COVID-19) vaccine and booster coverage may contribute to future disparities in morbidity and mortality within and between Massachusetts (MA) communities. METHODS AND FINDINGS: We conducted a population-based cross-sectional study of primary series vaccination and booster coverage 18 months into the general population vaccine rollout. We obtained public-use data on residents vaccinated and boosted by ZIP code (and by age group: 5 to 19, 20 to 39, 40 to 64, 65+) from MA Department of Public Health, as of October 10, 2022. We constructed population denominators for postal ZIP codes by aggregating census tract population estimates from the 2015-2019 American Community Survey. We excluded nonresidential ZIP codes and the smallest ZIP codes containing 1% of the state's population. We mapped variation in ZIP code-level primary series vaccine and booster coverage and used regression models to evaluate the association of these measures with ZIP code-level socioeconomic and demographic characteristics. Because age is strongly associated with COVID-19 severity and vaccine access/uptake, we assessed whether observed socioeconomic and racial/ethnic inequities persisted after adjusting for age composition and plotted age-specific vaccine and booster coverage by deciles of ZIP code characteristics. We analyzed data on 418 ZIP codes. We observed wide geographic variation in primary series vaccination and booster rates, with marked inequities by ZIP code-level education, median household income, essential worker share, and racial/ethnic composition. In age-stratified analyses, primary series vaccine coverage was very high among the elderly. However, we found large inequities in vaccination rates among younger adults and children, and very large inequities in booster rates for all age groups. In multivariable regression models, each 10 percentage point increase in "percent college educated" was associated with a 5.1 (95% confidence interval (CI) 3.9 to 6.3, p < 0.001) percentage point increase in primary series vaccine coverage and a 5.4 (95% CI 4.5 to 6.4, p < 0.001) percentage point increase in booster coverage. Although ZIP codes with higher "percent Black/Latino/Indigenous" and higher "percent essential workers" had lower vaccine coverage (-0.8, 95% CI -1.3 to -0.3, p < 0.01; -5.5, 95% CI -7.3 to -3.8, p < 0.001), these associations became strongly positive after adjusting for age and education (1.9, 95% CI 1.0 to 2.8, p < 0.001; 4.8, 95% CI 2.6 to 7.1, p < 0.001), consistent with high demand for vaccines among Black/Latino/Indigenous and essential worker populations within age and education groups. Strong positive associations between "median household income" and vaccination were attenuated after adjusting for age. Limitations of the study include imprecision of the estimated population denominators, lack of individual-level sociodemographic data, and potential for residential ZIP code misreporting in vaccination data. CONCLUSIONS: Eighteen months into MA's general population vaccine rollout, there remained large inequities in COVID-19 primary series vaccine and booster coverage across MA ZIP codes, particularly among younger age groups. Disparities in vaccination coverage by racial/ethnic composition were statistically explained by differences in age and education levels, which may mediate the effects of structural racism on vaccine uptake. Efforts to increase booster coverage are needed to limit future socioeconomic and racial/ethnic disparities in COVID-19 morbidity and mortality.

Examining implicit neural bias against vaccine hesitancy.

COVID-19 pandemic has changed the world in many ways. At the societal level, disparities in attitudes toward the COVID-19 vaccines have led to polarization and intense animosity. In this study, we use a novel paradoxical thinking intervention that was found to be effective in difficult and violent intergroup contexts, and measure its effectiveness in a novel unobtrusive way in an important and timely context, namely prejudice against vaccine hesitancy. In the midst of a vaccination campaign, 36 young Finnish adults either went through the intervention or through a control condition. Magnetoencephalography then measured a neural response that is thought to reflect intergroup bias and possibly implicit prejudice. This neural response was reduced among the participants receiving the intervention, compared to the control group, thereby suggesting a potential mechanism of intergroup bias that is affected by a psychological intervention even during a campaign that castigates aggressively vaccine-hesitant individuals. The findings reported here contribute to the recent accumulating evidence of the potential of neuroimaging to reveal covert mental effects by psychological interventions. They may also have societal implications for moderating the polarized attitudes in a new era of pandemics.

Missing Race and Ethnicity Data among COVID-19 Cases in Massachusetts.

Infectious disease surveillance frequently lacks complete information on race and ethnicity, making it difficult to identify health inequities. Greater awareness of this issue has occurred due to the COVID-19 pandemic, during which inequities in cases, hospitalizations, and deaths were reported but with evidence of substantial missing demographic details. Although the problem of missing race and ethnicity data in COVID-19 cases has been well documented, neither its spatiotemporal variation nor its particular drivers have been characterized. Using individual-level data on confirmed COVID-19 cases in Massachusetts from March 2020 to February 2021, we show how missing race and ethnicity data: (1) varied over time, appearing to increase sharply during two different periods of rapid case growth; (2) differed substantially between towns, indicating a nonrandom distribution; and (3) was associated significantly with several individual- and town-level characteristics in a mixed-effects regression model, suggesting a combination of personal and infrastructural drivers of missing data that persisted despite state and federal data-collection mandates. We discuss how a variety of factors may contribute to persistent missing data but could potentially be mitigated in future contexts.

Changes in Ultrafine Particle Concentrations near a Major Airport Following Reduced Transportation Activity during the COVID-19 Pandemic.

Mobility reductions following the COVID-19 pandemic in the United States were higher, and sustained longer, for aviation than ground transportation activity. We evaluate changes in ultrafine particle (UFP, Dp < 100 nm, a marker of fuel-combustion emissions) concentrations at a site near Logan Airport (Boston, Massachusetts) in relation to mobility reductions. Several years of particle number concentration (PNC) data prepandemic [1/2017-9/2018] and during the state-of-emergency (SOE) phase of the pandemic [4/2020-6/2021] were analyzed to assess the emissions reduction impact on PNC, controlling for season and wind direction. Mean PNC was 48% lower during the first three months of the SOE than prepandemic, consistent with 74% lower flight activity and 39% (local)-51% (highway) lower traffic volume. Traffic volume and mean PNC for all wind directions returned to prepandemic levels by 6/2021; however, when the site was downwind from Logan Airport, PNC remained lower than prepandemic levels (by 23%), consistent with lower-than-normal flight activity (44% below prepandemic levels). Our study shows the effect of pandemic-related mobility changes on PNC in a near-airport community, and it distinguishes aviation-related and ground transportation source contributions.

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

A Restructured Hospital Into a One-Building Organization for COVID-19 Patients: A Resilient and Effective Response to the Pandemic.

The COVID-19 pandemic is a unique crisis challenging healthcare institutions as it rapidly overwhelmed hospitals due to a large influx of patients. This major event forced all the components of the healthcare systems to adapt and invent new workflows. Thus, our tertiary care hospital was reorganized entirely. During the cruising phase, additional staff was allocated to a one-building organization comprising an intensive care unit (ICU), an acute care unit, a physical medicine and rehabilitation unit, and a COVID-19 screening area. The transfer of patients from a ward to another was more efficient due to these organizations and pavilion structure. The observed mortality was low in the acute care ward, except in the palliative unit. No nosocomial infection with SARS-CoV-2 was reported in any other building of the hospital since this organization was set up. This type of one-building organization, integrating all the components for comprehensive patient care, seems to be the most appropriate response to pandemics.

A Restructured Hospital Into a One-Building Organization for COVID-19 Patients: A Resilient and Effective Response to the Pandemic

The COVID-19 pandemic is a unique crisis challenging healthcare institutions as it rapidly overwhelmed hospitals due to a large influx of patients. This major event forced all the components of the healthcare systems to adapt and invent new workflows. Thus, our tertiary care hospital was reorganized entirely. During the cruising phase, additional staff was allocated to a one-building organization comprising an intensive care unit (ICU), an acute care unit, a physical medicine and rehabilitation unit, and a COVID-19 screening area. The transfer of patients from a ward to another was more efficient due to these organizations and pavilion structure. The observed mortality was low in the acute care ward, except in the palliative unit. No nosocomial infection with SARS-CoV-2 was reported in any other building of the hospital since this organization was set up. This type of one-building organization, integrating all the components for comprehensive patient care, seems to be the most appropriate response to pandemics.

A comparison of patient same-day discharge selection after shoulder arthroplasty before and after the COVID-19 pandemic.

Background: Early discharge has been a target of cost control efforts, given the growing demand for joint replacement surgery. Select patients are given the choice for same-day discharge (SDD) or overnight stay after shoulder arthroplasty. The COVID-19 pandemic changed patient perspectives regarding hospital visitation and admission. The purpose of this study was to determine if the COVID-19 pandemic impacted the utilization of SDD after shoulder arthroplasty. We hypothesize that patients undergoing shoulder arthroplasty after the start of the COVID-19 pandemic will have higher rates of SDD. Methods: A retrospective continuous review was performed on 370 patients who underwent a primary anatomic (total shoulder arthroplasty) or reverse shoulder arthroplasty between August 2019 and December 2020 by a single surgeon. This group of patients represent the 185 arthroplasty cases completed before the COVID-19 pandemic and the first 185 patients after the start of the pandemic. April 1, 2020, was chosen as the cutoff for pre-COVID patients, as this represents the date a statewide ban on elective surgery was declared. All patients were counseled preoperatively regarding SDD and given the choice to stay overnight, unless medically contraindicated. Demographics, medical history, length of stay, 30- and 90-day readmissions, and 90-day emergency room (ER) and urgent care visits were obtained from medical records and compared. Two-tailed student t-tests, chi-square tests, and Fischer's exact were performed where appropriate. Results: The 2 groups were similar in age, body mass index, gender distribution, and Outpatient Arthroplasty Risk Assessment score. During the collection period, there were more anatomic shoulder arthroplasties performed after (54%) than before (44%) the COVID-19 pandemic (P = .029). Patients treated after the start of the COVID-19 pandemic were almost 3 times more likely to have an SDD (P < .001), with 85.4% (158/185) of patients being discharged the same day after COVID-19, compared with 34.6% (64/185) before COVID-19. Discharge disposition (location of discharge) was significantly different, as 99% (183/185) of patients undergoing surgery after the start of the COVID-19 pandemic were discharged home, compared with 94% (174/185) of patients before COVID-19. There was no difference in 30-day readmissions, 90-day readmissions, and 90-day (ER) and urgent care visits between the 2 groups. Conclusion: Our study suggests that the COVID-19 pandemic has dramatically impacted patient choices for SDD within a single surgeon's practice, with nearly 3 times as many patients electing for SDD. Readmissions and ER visits were similar, indicating that SDD remains a safe alternative for patients after total shoulder arthroplasty and reverse shoulder arthroplasty. Level of evidence: Level III; Retrospective Comparative Study.

Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts.

BACKGROUND: Associations between community-level risk factors and COVID-19 incidence have been used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020. METHODS: Using publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods from March to October 2020. We examined town-level demographic variables, including population proportions by race, ethnicity, and age, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM2.5), and institutional facilities. We calculated incidence rate ratios (IRR) associated with these predictors and compared these values across the multiple time periods to assess variability in the observed associations over time. RESULTS: Associations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage of Black residents (IRR = 1.12 [95%CI: 1.12-1.13]) in early spring, IRR = 1.01 [95%CI: 1.00-1.01] in early fall) and COVID-19 incidence. The association with number of long-term care facility beds per capita also decreased over time (IRR = 1.28 [95%CI: 1.26-1.31] in spring, IRR = 1.07 [95%CI: 1.05-1.09] in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidences of COVID-19 throughout the pandemic (e.g., IRR = 1.30 [95%CI: 1.27-1.33] in spring, IRR = 1.20 [95%CI: 1.17-1.22] in fall). Towns with higher proportions of Latinx residents also had sustained elevated incidence over time (IRR = 1.19 [95%CI: 1.18-1.21] in spring, IRR = 1.14 [95%CI: 1.13-1.15] in fall). CONCLUSIONS: Town-level COVID-19 risk factors varied with time in this study. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence may have decreased across the first 8 months of the pandemic, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level.

A social virus: Intergroup dehumanization and unwillingness to aid amidst COVID-19 − Who are the main targets?☆

Previous studies have shown that external threats, such as financial crises and natural disasters, might fuel negative attitudes, emotions, and behaviors towards outgroup members. However, it is unclear what types of outgroups are likely to be targeted when an external threat is taking its toll. In this study, we examine two types of outgroups that might be at risk of becoming victims of intergroup hostility. The first is the “ultimate scapegoat” outgroup which has a long history of negative relations with the ingroup. The second is the "context-dependent" outgroup which is viewed as an outgroup only in certain contexts. We utilized the COVID-19 crisis and the highly diverse social makeup of Israeli society to explore the extent to which each type of outgroup would be targeted. Results from our study (N = 664), conducted during the first peak of COVID-19 in Israel, show that higher levels of exposure to COVID-19 predicted lower willingness to aid outgroups and that outgroup dehumanization mediated this association. However, this held true only when the target outgroup was a context-dependent outgroup. When the target group was the ultimate scapegoat, exposure to COVID-19 did not predict ingroup willingness to aid them. Our findings contribute to our theoretical and practical knowledge on how intergroup hostility proliferates during external threats and, as such, are valuable to scholars, practitioners, and policymakers working to reduce intergroup tensions during large-scale crises.

A social virus: Intergroup dehumanization and unwillingness to aid amidst COVID-19 - Who are the main targets?

Previous studies have shown that external threats, such as financial crises and natural disasters, might fuel negative attitudes, emotions, and behaviors towards outgroup members. However, it is unclear what types of outgroups are likely to be targeted when an external threat is taking its toll. In this study, we examine two types of outgroups that might be at risk of becoming victims of intergroup hostility. The first is the "ultimate scapegoat" outgroup which has a long history of negative relations with the ingroup. The second is the "context-dependent" outgroup which is viewed as an outgroup only in certain contexts. We utilized the COVID-19 crisis and the highly diverse social makeup of Israeli society to explore the extent to which each type of outgroup would be targeted. Results from our study (N = 664), conducted during the first peak of COVID-19 in Israel, show that higher levels of exposure to COVID-19 predicted lower willingness to aid outgroups and that outgroup dehumanization mediated this association. However, this held true only when the target outgroup was a context-dependent outgroup. When the target group was the ultimate scapegoat, exposure to COVID-19 did not predict ingroup willingness to aid them. Our findings contribute to our theoretical and practical knowledge on how intergroup hostility proliferates during external threats and, as such, are valuable to scholars, practitioners, and policymakers working to reduce intergroup tensions during large-scale crises.

Community predictors of COVID-19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data.

BACKGROUND: The COVID-19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community-level risk factors that can change over time. METHODS: Individual COVID-19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: "Phase 1" (March-June 2020) and "Phase 2" (September 2020 to February 2021). Institutional cases associated with long-term care facilities, prisons, or homeless shelters were identified using address data and modeled separately. Census tract sociodemographic and occupational predictors were drawn from the 2015-2019 American Community Survey. We used mixed-effects negative binomial regression to estimate incidence rate ratios (IRRs), accounting for town-level spatial autocorrelation. RESULTS: Case incidence was elevated in census tracts with higher proportions of Black and Latinx residents, with larger associations in Phase 1 than Phase 2. Case incidence associated with proportion of essential workers was similarly elevated in both Phases. Mortality IRRs had differing patterns from case IRRs, decreasing less substantially between Phases for Black and Latinx populations and increasing between Phases for proportion of essential workers. Mortality models excluding institutional cases yielded stronger associations for age, race/ethnicity, and essential worker status. CONCLUSIONS: Geocoded home address data can allow for nuanced analyses of community disease patterns, identification of high-risk subgroups, and exclusion of institutional cases to comprehensively reflect community risk.

Descriptive characterization of sound levels in an environmental justice city before and during a global pandemic.

Many environmental justice communities face elevated exposures to multiple stressors, given biases in urban and environmental policy and planning. This paper aims to evaluate sound level exposure in a densely populated environmental justice city in close proximity to major roadways, a nearby airport and high levels of industrial activity. In this study we collected various sound level metrics to evaluate the loudness and frequency composition of the acoustical environment in Chelsea, Massachusetts, USA. A total of 29 week-long sites were collected from October 2019 to June 2020, a time period that also included the influence of the COVID-19 pandemic, which drastically altered activity patterns and corresponding sound level exposures. We found that Chelsea is exposed to high levels of sound, both day and night (65 dB (A), and 80 dB and 90 dB for low frequency, and infrasound sound levels). A spectral analysis shows that 63 Hz was the dominant frequency. Distance to major roads and flight activity (both arrivals and departures) were most strongly correlated with all metrics, most notably with metrics describing contributing from lower frequencies. Overall, we found similar patterns during the COVID-19 pandemic but at levels up to 10 dB lower. Our results demonstrate the importance of noise exposure assessments in environmental justice communities and the importance of using additional metrics to describe communities inundated with significant air, road, and industrial sound levels. It also provides a snapshot of how much quieter communities can be with careful and intentional urban and environmental policy and planning.

Climate Change and Health Justice: New Perspectives on Pressing Challenges

In 2017, AJPH launched a new section on "Climate Change and Health Justice," reflecting growing evidence of the near-term effects of climate change on public health and the substantial disparities in those effects. AJPH invites research articles as well as other contributions that provide empirical evidence of and relevant commentary on the effects of climate change or climate action on health before, during, and after a global pandemic. Racial and health justice clearly extend well beyond COVID-19, and we are interested in numerous public health topics with a similar lens on informing or evaluating interventions that address root causes of disparities. Because many of these root causes are related to longstanding racist policies such as redlining, the Journal is interested in both contemporary empirical evidence and history essays that are relevant to understanding current conditions and developing an antiracist public health agenda.