Social Determinants of Health Mediate COVID-19 Disparities in South Florida

Identifying social determinants of health in populations exposed to structural inequities: a qualitative study of the COVID-19 pandemic experiences of Black and Latinx people living with HIV and cardiovascular risks

Introduction

Black and Latinx communities experience inequities in the social determinants of health (SDOH) and high rates of chronic illnesses [e.g., cardiovascular disease (CVD), HIV]. The COVID-19 pandemic amplified these long-standing SDOH disparities. However, scant attention has been paid to the pandemic-related experiences of populations exposed to structural inequities.

Methods

Using a semi-structured interview guide, 60 in-depth telephone interviews were conducted with Black and Latinx people living with HIV (PLWH) and CVD risks to assess: (1) perceived personal and community risk for COVID-19; (2) knowledge of and access to COVID-19 public health information; (3) barriers to COVID-19 public health recommendations and vaccine uptake; and (4) perceptions of HIV, CVD, and COVID-19. Interviews were professionally transcribed into either English or Spanish. Spanish transcripts were translated into English. Rapid qualitative analysis was used to summarize each transcript into a structured templaicte corresponding to interview guide domains. Summaries were combined into matrices for identification and comparison of themes across domains.

Results

Participants reported risks for COVID-19 due to being immunocompromised and SDOH, including transportation, exposure to risks conferred by others, living in under-resourced neighborhoods, and housing insecurity. Participants engaged in protective countermeasures by adhering to public health mandates. Relationships with providers, participating in community support groups, and digital inclusion and literacy were salient with respect to dissemination of COVID-19 information and vaccine uptake. Experiences with managing a chronic illness facilitated vaccine acceptance. Participants described language barriers, experiences of discrimination, and a historical lack of trust in medical systems and vaccines.

Discussion

This study provides a real-time narrative from PLWH and CVD risks who were vulnerable during the height of the COVID-19 pandemic. Implications include the need for continuity with providers and established community networks, increasing internet access and digital health literacy, and addressing historical trauma incurred in medical settings. It is critical to understand the impact of traditional SDOH on those living with chronic illness as well as other social determinants that shed light on access to public health information, adherence to public health recommendations, and vaccine uptake among populations exposed to structural inequities.

Lessons Learned in Implementing a Multidisciplinary Long COVID Clinic

The diagnosis and treatment of long COVID patients is challenging. Our aim is to share lessons learned using a multidisciplinary approach within the Veterans Affairs system. Our long COVID clinic is based in primary care but has imbedded rehabilitation specialists, nutrition, whole health, and different specialists within internal medicine. We conducted an extensive work-up to evaluate the presence of end-organ damage, ongoing inflammation, and dysautonomia. Our treatments are based on the prior experience that the Veterans Affairs system has on chronic fatigue syndrome and gulf war illness.

Low-dose Naltrexone Improves post-COVID-19 condition Symptoms

PURPOSE

Treatments for myalgic encephalomyelitis and chronic fatigue syndrome can be adapted for post-COVID-19 condition. Our aim was to compare treatments in patients from our post-COVID-19 clinic.

METHODS

We conducted a retrospective cohort study and included consecutive patients enrolled in our post-COVID-19 clinic. We included patients who received low-dose naltrexone, amitriptyline, duloxetine, and physical therapy, and evaluated improvements in fatigue, pain, dyspnea, and brain fog recorded in the electronic health record. We calculated the adjusted relative hazard of improvement using Cox proportional models. We adjusted for demographic characteristics, comorbidities, and prior COVID-19 hospitalization.

FINDINGS

We included the first 108 patients with post-COVID-19 enrolled in the clinic. Most of the patients received amitriptyline. The relative hazard of improvement for those taking low-dose naltrexone was 5.04 (95% CI, 1.22-20.77; P = 0.02) compared with physical therapy alone. Both fatigue and pain were improved in patients taking low-dose naltrexone; only fatigue was improved in patients taking amitriptyline.

IMPLICATIONS

Post-COVID-19 condition symptoms may improve in patients taking medications adapted from myalgic encephalomyelitis and chronic fatigue syndrome. Randomized controlled trials should evaluate these medications and translational studies should further evaluate their mechanisms of action.

Vitamin D Supplementation and Prior Oral Poliovirus Vaccination Decrease Odds of COVID-19 Outcomes among Adults Recently Inoculated with Inactivated Poliovirus Vaccine

BACKGROUND

Structural and functional commonalities between poliovirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suggest that poliovirus inoculation may induce antibodies that mitigate the coronavirus disease (COVID-19). No known studies have evaluated COVID-19 risk factors in adults recently vaccinated against poliovirus.

STUDY OBJECTIVE

Among adults with no history of COVID-19 infection or vaccination, who recently received an inactivated poliovirus vaccine (IPV), we sought to determine which biological factors and social determinants of health (SDOH) may be associated with (1) testing positive for SARS-CoV-2, (2) experiencing COVID-19 symptoms, and (3) a longer duration of COVID-19 symptoms.

METHODS

The influence of biological factors and SDOH on SARS-CoV-2 infection and COVID-19 symptoms were evaluated among 282 adults recently inoculated with IPV. Participant-reported surveys were analyzed over 12 months post-enrollment. Bivariate and multivariate linear and logistic regression models identified associations between variables and COVID-19 outcomes.

RESULTS

Adjusting for COVID-19 vaccinations, variants, and other SDOH, secondary analyses revealed that underlying conditions, employment, vitamin D, education, and the oral poliovirus vaccination (OPV) were associated with COVID-19 outcomes. The odds of testing positive for SARS-CoV-2 and experiencing symptoms were significantly reduced among participants who took vitamin D (OR 0.12 and OR 0.09, respectively). Unemployed or part-time working participants were 72% less likely to test positive compared with full-time workers. No prior dose of OPV was one of the strongest predictors of SARS-CoV-2 infection (OR 4.36) and COVID-19 symptoms (OR 6.95).

CONCLUSIONS

Findings suggest that prophylactic measures and mucosal immunity may mitigate the risk and severity of COVID-19 outcomes. Larger-scale studies may inform future policies.

COVID-19 Vaccine Acceptance Among Latino/a Immigrants: The Role of Collective Responsibility and Confidence

Research on COVID-19 vaccine hesitancy has been sparse among Latino/a immigrants, a population at high risk for infection. This exploratory study examines rates of vaccine acceptance and its association with psychological antecedents of vaccination among Latino/a immigrants. A cross-sectional telephone survey on perceptions of COVID-19 was administered between October 2020 to February 2021 in South Florida to 200 adult Latino/a immigrants. Descriptive statistics, bivariate analysis, and logistic regression were employed to determine the influence of independent variables on vaccine acceptance. Most participants indicated a willingness to get vaccinated. Participants with higher confidence (aOR = 10.2, 95% CI: 4.8-21.8) and collective responsibility scores were (aOR = 3.1, 95%CI:1.3-6.9) more likely to report vaccine acceptance than those with lower scores. No other psychological antecedents or demographic variables were significantly associated with vaccine acceptance. Study results provide insights into motivating factors for vaccination that can inform culturally tailored education campaigns to increase vaccine acceptability in this population.

Access to Health Care Improves COVID-19 Vaccination and Mitigates Health Disparities Among Medicare Beneficiaries

BACKGROUND

COVID-19 disproportionately impacts the elderly, particularly racial/ethnic minorities and those with low socioeconomic status (SES). These latter groups may also have higher vaccine hesitancy. We aim to evaluate if access to care improves COVID-19 vaccination rates and improves health disparities.

METHODS

We conducted a retrospective cohort study of Medicare patients receiving care in a high-touch capitated network across ten states. We collected type and date of COVID-19 vaccine and demographic and clinical data from the inpatient and outpatient electronic health records and socioeconomic status from the US census. Our primary outcome was completing vaccination using logistic regression.

RESULTS

Our cohort included 93,224 patients enrolled in the network during the study period. Sixty nine percent of all enrolled patients completed full vaccination. Those who completed vaccination did it with Pfizer (46%), Moderna (49%), and Jannsen (4.6%) vaccines. In adjusted models, we found that the following characteristics increased the odds of being vaccinated: being male, increasing age, BMI, and comorbidities, being Black or Hispanic, having had the flu vaccine in 2020, and increasing number of office primary care visits. Living in a neighborhood with higher social deprivation and having dual Medicaid/Medicare enrollment decreased the odds of completing full vaccination.

CONCLUSIONS

Increasing office visit in a high-touch primary care model is associated with higher vaccination rates among elderly populations who belong to racial/ethnic minorities or have low socioeconomic status. However, lower SES and Medicaid populations continue to have difficulty in completing vaccination.

KEY POINTS

• High COVID-19 vaccination rates of minorities enrolled in Medicare can be achieved. • Lower socioeconomic status is associated with completing vaccination. • Increasing office visits can lead to higher vaccination rates.

Evaluation of Abbott ID NOW COVID-19 POC test performance characteristics and integration in the regional health network workflows to improve health care delivery

With the recent global surge of SARS-CoV-2 Delta variant, there continues to be high demand for COVID-19 diagnostic testing. Abbott ID NOW is a rapid, CLIA-waived, COVID-19 diagnostic test ideally suited for use in urgent care settings or where access to diagnostic testing is limited. In this study we describe the results of rigorous validation of ID NOW and post-implementation study of POC test utilization patterns within community hospitals and clinics. Performance of ID NOW was validated by comparison of the results from 207 consecutive, paired, specimens tested on the ID NOW and on the m2000/Alinity m platforms. Once validated, ID NOW devices were placed for clinical use at four regional hospitals and clinics. We found that the ID NOW and m2000/Alinity m positive and negative percent agreement were 94.5% (95% CI, 85.1% to 98.1%) and 99.3% (95% CI, 96.4% to 99.9%), respectively. As of August 2021, a total of 2,301 tests were performed by ID NOW at individual regional network sites. The population tested consisted of 55.5% White and 42.9% Black patients, with Black patients presenting predominantly in the hospitals, while White patients were more evenly distributed between hospital and clinic sites. Disease prevalence observed among patients tested by ID NOW (12.3%) was aligned with overall prevalence seen at regional sites (11.3%). In summary, the ID NOW test can provide rapid and accurate results in a variety of near-to-patient and POC settings. If used correctly, it could serve as a valuable diagnostic tool to enable equal access to care and improve healthcare delivery within large health network systems.

A data-driven approach to quantify disparities in power outages

This research proposes a data-driven approach to identify possible disparities in a utility's outage management practices. The approach has been illustrated for an Investor-Owned Utility located in the Midwest region in the U.S. Power outage data for approximately 5 years between March 2017 and January 2022 was collected for 36 ZIP/postal codes located within the utility's service territory. The collected data was used to calculate the total number of outages, customers affected, and the duration of outages during those 5 years for each ZIP code. Next, each variable was normalized with respect to the population density of the ZIP code. After normalizing, a K-means clustering algorithm was implemented that created five clusters out of those 36 ZIP codes. The difference in the outage parameters was found to be statistically significant. This indicated differential experience with power outages in different ZIP codes. Next, three Generalized Linear Models were developed to test if the presence of critical facilities such as hospitals, 911 centers, and fire stations, as socioeconomic and demographic characteristics of the ZIP codes, can explain their differential experience with the power outage. It was found that the annual duration of outages is lower in the ZIP codes where critical facilities are located. On the other hand, ZIP codes with lower median household income have experienced more power outages, i.e., higher outage counts in those 5 years. Lastly, the ZIP codes with a higher percentage of the White population have experienced more severe outages that have affected more customers.

Household income is associated with functional outcomes in a multi-institutional cohort of patients with ischemic stroke and COVID-19

OBJECTIVES

The COVID-19 pandemic has heightened awareness of health disparities associated with socioeconomic status (SES) across the United States. We examined whether household income is associated with functional outcomes after stroke and COVID-19.

MATERIALS AND METHODS

This was a multi-institutional, retrospective cohort study of consecutively hospitalized patients with SARS-CoV-2 and radiographically confirmed stroke presenting from March through November 2020 to any of five comprehensive stroke centers in metropolitan Chicago, Illinois, USA. Zip-code-derived household income was dichotomized at the Chicago median. Logistic regression was used to examine the relationship between household income and good functional outcome (modified Rankin Scale 0-3 at discharge, after ischemic stroke).

RESULTS

Across five hospitals, 159 patients were included. Black patients comprised 48.1%, White patients 38.6%, and Hispanic patients 27.7%. Median household income was $46,938 [IQR: $32,460-63,219]. Ischemic stroke occurred in 115 (72.3%) patients (median NIHSS 7, IQR: 0.5-18.5) and hemorrhagic stroke in 37 (23.7%). When controlling for age, sex, severe COVID-19, and NIHSS, patients with ischemic stroke and household income above the Chicago median were more likely to have a good functional outcome at discharge (OR 7.53, 95% CI 1.61 - 45.73; P=0.016). Race/ethnicity were not included in final adjusted models given collinearity with income.

CONCLUSIONS

In this multi-institutional study of hospitalized patients with stroke, those residing in higher SES zip codes were more likely to have better functional outcomes, despite controlling for stroke severity and COVID-19 severity. This suggests that area-based SES factors may play a role in outcomes from stroke and COVID-19.

Social Determinants of Health and Racial/Ethnic Disparities in COVID-19 Mortality at the County Level in the Commonwealth of Virginia

BACKGROUND

Mortality due to coronavirus disease-2019 (COVID-19) among Black and Hispanic populations is disproportionately high compared to white populations. This study aimed to explore the association between COVID-19 mortality and social determinants of health (SDOH) among Black and Hispanic populations in Virginia.

METHOD

County-level publicly available COVID-19 mortality data from Virginia, covariates, and SDOH indicators were used. An independent t-test and hierarchical multiple regression analysis were performed to assess the association between SDOH and COVID-19 death rates, with a focus on racial/ethnic disparities.

RESULTS

Counties in the lowest quartile had a mean death rate of 44.72 (SD = 13.8), while those in the highest quartile had a mean death rate of 239.02 (SD = 123.9) per 100, 000 people ( P < .001). Counties with the highest death rates had significantly lower mean socioeconomic status. The regression analysis revealed that 32% of the variance in the COVID-19 mortality rate was associated with SDOH after controlling for the covariates ( P < .01). Identifying as Hispanic ethnicity accounted for 8.5% of the variance, while median household income, being uninsured, and education accounted for 32.7%, 12.9%, and 7.1%, respectively.

CONCLUSIONS

The findings provide evidence that disparities in SDOH experienced by Hispanic populations play a significant role in increased COVID-19 mortality, thus highlighting the social needs of low-income, low-education, and Hispanic populations to advance equity in health outcomes.

Unanticipated consequences of COVID-19 pandemic policies on pediatric acute appendicitis surgery

BACKGROUND

Global pandemics may limit access to specialized care, delaying diagnosis and treatment of common acute surgical diseases. We analyzed the impact of the novel coronavirus disease 2019 (COVID-19) pandemic on acute appendicitis at an urban tertiary care center. We hypothesize that pandemics are associated with delayed presentation and worsened clinical sequelae, specifically, higher incidences of perforation in children.

METHODS

We retrospectively assessed patients admitted to our institution with acute appendicitis in pre-pandemic control (February 2018-June 2019) and COVID-19 (February 2020-June 2021) cohorts. Primary outcomes included complicated appendicitis rates (perforation/abscess/bowel obstruction), COVID-19 status, complications and travel distance to our institution. 1107 patients met inclusion criteria: 491 (44.4%) during the control period and 616 (55.6%) in the COVID-19 cohort. Statistical analysis involved t-tests, contingency tables and logistic regression modelling for key variables.

RESULTS

A larger proportion of complicated appendicitis occurred during COVID-19 compared to controls (28.3% vs 38.8%, p < 0.001). Symptom duration at presentation and length of stay were not significantly different. Duration of antibiotic treatment, surgery length, readmission rate and travel distances were significantly higher during COVID-19. The pre-pandemic cohort had a significantly younger age distribution.

CONCLUSION

Pediatric appendicitis was significantly impacted during COVID-19, demonstrated by increased rates of complicated appendicitis, surgery duration and antibiotic duration. This may be an unintended secondary consequence of patients avoiding healthcare facilities for non-pandemic related illnesses or lockdown policies. Government policies directing all provincial pediatric appendicitis cases to pediatric institutions increased travel distances for our patients and had unanticipated consequences and resource requirements on tertiary healthcare.

LEVEL OF EVIDENCE

Level III for "Treatment Studies".

A Qualitative Exploration of COVID-19 Vaccine Hesitancy Among Hispanic/Latine and African American and Black Middle-Aged and Older Adults in South Florida

Racial and ethnic health disparities are more conspicuous in the United States since the start of the coronavirus disease 2019 (COVID-19) pandemic. While the urgency of these disparities was already alarming, the pandemic has exacerbated longstanding issues in health equity, disproportionate impacts, and social determinants of health. Vaccine hesitancy was a crucial factor during the U.S. COVID-19 vaccination campaign. We conducted a qualitative exploration of vaccine hesitancy through thematic analysis of four focus groups with Hispanic/Latine and African American/Black adults and senior citizens (N=23). The focus groups were conducted between February and April of 2021, in both English and Spanish. All participants (mean age=66.2, female 78.3%) were recruited by community-based organizations in the South Florida area. We explored six categories: (1) barriers to prevention and preventive behaviors, (2) barriers to vaccination against COVID-19, (3) facilitators of prevention and preventative behaviors, (4) facilitators of vaccination against COVID-19, (5) trusted sources of information, and (6) suggested macrolevel measures. These categories are discussed vis-à-vis COVID-19 disparities among racial and ethnic minorities. Implications for public health policy and future vaccination campaigns are outlined and discussed.

Consensus elements for observational research on COVID-19-related long-term outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and its long-term outcomes may be jointly caused by a wide range of clinical, social, and economic characteristics. Studies aiming to identify mechanisms for SARS-CoV-2 morbidity and mortality must measure and account for these characteristics to arrive at unbiased, accurate conclusions. We sought to inform the design, measurement, and analysis of longitudinal studies of long-term outcomes among people infected with SARS-CoV-2. We fielded a survey to an interprofessional group of clinicians and scientists to identify factors associated with SARS-CoV-2 infection and subsequent outcomes. Using an iterative process, we refined the resulting list of factors into a consensus causal diagram relating infection and 12-month mortality. Finally, we operationalized concepts from the causal diagram into minimally sufficient adjustment sets using common medical record data elements. Total 31 investigators identified 49 potential risk factors for and 72 potential consequences of SARS-CoV-2 infection. Risk factors for infection with SARS-CoV-2 were grouped into five domains: demographics, physical health, mental health, personal social, and economic factors, and external social and economic factors. Consequences of coronavirus disease 2019 (COVID-19) were grouped into clinical consequences, social consequences, and economic consequences. Risk factors for SARS-CoV-2 infection were developed into a consensus directed acyclic graph for mortality that included two minimally sufficient adjustment sets. We present a collectively developed and iteratively refined list of data elements for observational research in SARS-CoV-2 infection and disease. By accounting for these elements, studies aimed at identifying causal pathways for long-term outcomes of SARS-CoV-2 infection can be made more informative.

COVID-19 Infections and Mortality in Florida Counties: Roles of Race, Ethnicity, Segregation, and 2020 Election Results

PURPOSE

This study investigates the association of racial and ethnic composition, segregation, and 2020 presidential election voting results with COVID-19 infections and deaths in Florida counties.

METHODS

Florida county COVID-19 infection and death counts reported through March 2021 were supplemented with socioeconomic characteristics and 2020 presidential results to form the dataset employed in this ecological study. Poisson regression analysis measured the association of infection and mortality rates with county demographic and economic characteristics, then assessed the moderating role of county political preferences.

RESULTS

Counties with higher proportions of Black residents experience disproportionately higher COVID-19 infection and mortality rates. Disparities are further inflated in counties with larger Republican vote shares. That voting effect extends to Hispanic population proportions and segregation, both of which are associated with higher COVID-19 infection and mortality rates in more Republican-leaning counties.

CONCLUSIONS

Communities challenged by pre-existing health disparities, segregation, and economic hardship before the pandemic bear disproportionate risk of COVID-19 infection and mortality. Factors associated with voter preference for the 2020 Republican presidential candidate compound those problems, worsening consequences for all county residents, suggesting deeper structural health challenges.

Association of Neighborhood Income with Clinical Outcomes Among Pregnant Patients with Cardiac Disease

Cardiovascular disease is the leading cause of pregnancy mortality. Socioeconomic and racial disparities in pregnancy are well established. Despite this, little is known about the impact of social determinants of health in pregnant patients with heart disease. This study aims to determine whether pregnant patients with heart disease living in lower income neighborhoods and managed at cardio-obstetrics programs have higher rates of cardiac events or preterm deliveries compared with those living in higher income neighborhoods. This is a retrospective cohort study of 206 patients between 2010 and 2020 at a quaternary care hospital in Northern California. The exposure was household income level based on neighborhood defined by the US Census data. Patients in lower income neighborhoods (N = 103) were 45% Hispanic, 34% White, and 14% Asian versus upper income neighborhoods (N = 103), which were 48% White, 31% Asian, and 12% Hispanic (p < 0.001). There was no significant difference in the rates of intrapartum cardiac events (10% vs. 4%; p = 0.16), postpartum cardiac events (14% vs. 17%; p = 0.7), and preterm delivery (24% vs. 17%; p = 0.23). The rates of antepartum hospitalization were higher for lower income neighborhoods (42% vs 22%; p = 0.004). While there is no significant difference in cardiac events and preterm delivery rates between patients from low versus high income neighborhoods, patients from lower income neighborhoods have higher antepartum hospitalization rates. Earlier identification of clinical deterioration provided by a cardio-obstetrics team may contribute to increased hospitalizations, which might mitigate socioeconomic disparities in outcomes for these pregnant patients with heart disease.

Evaluating the impact of mobility in COVID-19 incidence and mortality: A case study from four states of Mexico

Introduction

The COVID-19 pandemic in Mexico began at the end of February 2020. An essential component of control strategies was to reduce mobility. We aimed to evaluate the impact of mobility on COVID- incidence and mortality rates during the initial months of the pandemic in selected states.

Methods

COVID-19 incidence data were obtained from the Open Data Epidemiology Resource provided by the Mexican government. Mobility data was obtained from the Observatory for COVID-19 in the Americas of the University of Miami. We selected four states according to their compliance with non-pharmaceutical interventions and mobility index. We constructed time series and analyzed change-points for mobility, incidence, and mortality rates. We correlated mobility with incidence and mortality rates for each time interval. Using mixed-effects Poisson models, we evaluated the impact of reductions in mobility on incidence and mortality rates, adjusting all models for medical services and the percentage of the population living in poverty.

Results

After the initial decline in mobility experienced in early April, a sustained increase in mobility followed during the rest of the country-wide suspension of non-essential activities and the return to other activities throughout mid-April and May. We identified that a 1% increase in mobility yielded a 5.2 and a 2.9% increase in the risk of COVID-19 incidence and mortality, respectively. Mobility was estimated to contribute 8.5 and 3.8% to the variability in incidence and mortality, respectively. In fully adjusted models, the contribution of mobility to positive COVID-19 incidence and mortality was sustained. When assessing the impact of mobility in each state compared to the state of Baja California, increased mobility conferred an increased risk of incident positive COVID-19 cases in Mexico City, Jalisco, and Nuevo León. However, for COVID-19 mortality, a differential impact of mobility was only observed with Jalisco and Nuevo León compared to Baja California.

Conclusion

Mobility had heterogeneous impacts on COVID-19 rates in different regions of Mexico, indicating that sociodemographic characteristics and regional-level pandemic dynamics modified the impact of reductions in mobility during the COVID-19 pandemic. The implementation of non-pharmaceutical interventions should be regionalized based on local epidemiology for timely response against future pandemics.

Associations between Florida counties' COVID-19 case and death rates and meaningful use among Medicaid providers: Cross-sectional ecologic study

Although the Health Information Technology for Economic and Clinical Health (HITECH) Act has accelerated adoption of Electronic Health Records (EHRs) among Medicaid providers, only half achieved Meaningful Use. Furthermore, Meaningful Use' impact on reporting and/or clinical outcomes remains unknown. To address this deficit, we assessed the difference between Medicaid providers who did and did not achieve Meaningful Use regarding Florida county-level cumulative COVID-19 death, case and case fatality rates (CFR), accounting for county-level demographics, socioeconomic and clinical markers, and healthcare environment. We found that cumulative incidence rates of COVID-19 deaths and CFRs were significantly different between the 5025 Medicaid providers not achieving Meaningful Use and the 3723 achieving Meaningful Use (mean 0.8334/1000 population; SD = 0.3489 vs. mean = 0.8216/1000; SD = 0.3227, respectively) (P = .01). CFRs were .01797 and .01781, respectively, P = .04. County-level characteristics independently associated with increased COVID-19 death rates and CFRs include greater concentrations of persons of African American or Black race, lower median household income, higher unemployment, and higher concentrations of those living in poverty and without health insurance (all P < .001). In accordance with other studies, social determinants of health were independently associated with clinical outcomes. Our findings also suggest that the association between Florida counties' public health outcomes and Meaningful Use achievement may have had less to do with using EHRs for reporting of clinical outcomes and more to do with using EHRs for coordination of care-a key measure of quality. The Florida Medicaid Promoting Interoperability Program which incentivized Medicaid providers towards achieving Meaningful Use, has demonstrated success regarding both rates of adoption and clinical outcomes. Because the Program ends in 2021, we support programs such as HealthyPeople 2030 Health IT which address the remaining half of Florida Medicaid providers who have not yet achieved Meaningful Use.

COVID-19 double jeopardy: the overwhelming impact of the social determinants of health

Background

The COVID-19 pandemic has strained healthcare systems by creating a tragic imbalance between needs and resources. Governments and healthcare organizations have adapted to this pronounced scarcity by applying allocation guidelines to facilitate life-or-death decision-making, reduce bias, and save as many lives as possible. However, we argue that in societies beset by longstanding inequities, these approaches fall short as mortality patterns for historically discriminated against communities have been disturbingly higher than in the general population.

Methods

We review attack and fatality rates; survey allocation protocols designed to deal with the extreme scarcity characteristic of the earliest phases of the pandemic; and highlight the larger ethical perspectives (Utilitarianism, non-Utilitarian Rawlsian justice) that might justify such allocation practices.

Results

The COVID-19 pandemic has dramatically amplified the dire effects of disparities with respect to the social determinants of health. Patients in historically marginalized groups not only have significantly poorer health prospects but also lower prospects of accessing high quality medical care and benefitting from it even when available. Thus, mortality among minority groups has ranged from 1.9 to 2.4 times greater than the rest of the population. Standard allocation schemas, that prioritize those most likely to benefit, perpetuate and may even exacerbate preexisting systemic injustices.

Conclusions

To be better prepared for the inevitable next pandemic, we must urgently begin the monumental project of addressing and reforming the structural inequities in US society that account for the strikingly disparate mortality rates we have witnessed over the course of the current pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-022-01629-0.

Social Determinants and Indicators of COVID-19 Among Marginalized Communities: A Scientific Review and Call to Action for Pandemic Response and Recovery

Coronavirus disease 2019 (COVID-19) has placed massive socio-psychological, health, and economic burdens including deaths on countless lives; however, it has disproportionally impacted certain populations. Co-occurring Social Determinants of Health (SDoH) disparities and other underlying determinants have exacerbated the COVID-19 pandemic. This literature review sought to (1) examine literature focused on SDoH and COVID-19 outcomes ie, infectivity, hospitalization, and death rates among marginalized communities; and (2) identify SDoH disparities associated with COVID-19 outcomes. We searched electronic databases for studies published from October 2019 to October 2021. Studies that were selected were those intersecting SDoH indicators and COVID-19 outcomes and were conducted in the United States. Our review underscored the disproportionate vulnerabilities and adverse outcomes from COVID-19 that have impacted racial/ethnic minority communities and other disadvantaged groups (ie, senior citizens, and displaced/homeless individuals). COVID-19 outcomes were associated with SDoH indicators, ie, race/ethnicity, poverty, median income level, housing density, housing insecurity, health-care access, occupation, transportation/commuting patterns, education, air quality, food insecurity, old age, etc. Our review concluded with recommendations and a call to action to integrate SDoH indicators along with relevant health data when implementing intelligent solutions and intervention strategies to pandemic response/recovery among vulnerable populations.

Socio-Economic, Demographic and Health Determinants of the COVID-19 Outbreak

OBJECTIVE

In this study, the effects of social and health indicators affecting the number of cases and deaths of the COVID-19 pandemic were examined. For the determinants of the number of cases and deaths, four models consisting of social and health indicators were created.

METHODS

In this quantitative research, 93 countries in the model were used to obtain determinants of the confirmed cases and determinants of the COVID-19 fatalities.

RESULTS

The results obtained from Model I, in which the number of cases was examined with social indicators, showed that the number of tourists, the population between the ages of 15 and 64, and institutionalization had a positive effect on the number of cases. The results obtained from the health indicators of the number of cases show that cigarette consumption affects the number of cases positively in the 50th quantile, the death rate under the age of five affects the number of cases negatively in all quantiles, and vaccination positively affects the number of cases in 25th and 75th quantile values. Findings from social indicators of the number of COVID-19 deaths show that life expectancy negatively affects the number of deaths in the 25th and 50th quantiles. The population over the age of 65 and CO2 positively affect the number of deaths at the 25th, 50th, and 75th quantiles. There is a non-linear relationship between the number of cases and the number of deaths at the 50th and 75th quantile values. An increase in the number of cases increases the number of deaths to the turning point; after the turning point, an increase in the number of cases decreases the death rate. Herd immunity has an important role in obtaining this finding. As a health indicator, it was seen that the number of cases positively affected the number of deaths in the 50th and 75th quantile values and the vaccination rate in the 25th and 75th quantile values. Diabetes affects the number of deaths positively in the 75th quantile.

CONCLUSION

The population aged 15-64 has a strong impact on COVID-19 cases, but in COVID-19 deaths, life expectancy is a strong variable. On the other hand, it has been found that vaccination and the number of cases interaction term has an effect on the mortality rate. The number of cases has a non-linear effect on the number of deaths.

Socioeconomic Determinants of COVID-19 Incidence and Mortality in Florida

A wide variety of social determinants of health have been associated with various risks and impacts on quality of life. Specifically, poverty, lack of insurance coverage, large household sizes, and social vulnerability are all factors implicated in incidence and mortality rates of infectious disease. However, no studies have examined the relationship of these factors to the COVID-19 pandemic on a state-wide level in Florida. Thereby, the objective of this study is to examine the relationship between average household size, poverty, uninsured populations, social vulnerability index (SVI), and rates of COVID-19 cases and deaths in Florida counties. The objective was accomplished by analyzing the cumulative case and death reports from state and local health departments in Florida. The data was compiled into a single dataset by the CDC COVID-19 Task Force. Using US Census Bureau data, all Florida counties were classified into tertiles of the separate categories of poverty rate, average household size, uninsured rates, and SVI (Social Vulnerability Index). The poverty level was classified as low (0-12.3%), moderate (12.3-17.3%), and high (>17.3% below the federal poverty line). The uninsured population proportion was classified as low (0-7.1%), moderate (7.1-11.4%), and high (>11.4% uninsured residents). Average county household size was classified as low (0-2.4), moderate (2.4-2.6), and high (>2.6). The Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index (SVI) used US census data on 15 social determinants of vulnerability to evaluate and assist disadvantaged communities. SVI tertiles were low (0-0.333), moderate (0.334-0.666), and high (0.667-1) on a range of 0-1, with higher numbers signifying communities with many factors of social vulnerability. The mean cumulative cases and deaths per 100,000 inhabitants were calculated in each tertile for each category. Analysis of the data revealed that case and mortality rates due to COVID-19 in the high poverty counties were markedly higher in Florida than the national average. In contrast, moderate and low poverty rates were below average. Similarly, counties with a high SVI had case and mortality rates greatly above state and national averages. Counties with a high proportion of uninsured displayed the highest case rates. However, mortality rates were the highest in counties with a low proportion of uninsured individuals. No clear correlation was observed between COVID-19 rates and household size. It was concluded that compiled CDC and US census data suggests a significant correlation between poverty, social vulnerability, lack of insurance coverage, and increased incidence and mortality from COVID-19. Future research should statistically analyze the correlations and examine the individual factors of SVI as potential COVID-19 predictors.

Geographic disparities in COVID-19 case rates are not reflected in seropositivity rates using a neighborhood survey in Chicago

To date, COVID-19 case rates are disproportionately higher in Black and Latinx communities across the US, leading to more hospitalizations, and deaths in those communities. These differences in case rates are evident in comparisons of Chicago neighborhoods with differing race and/or ethnicities of their residents. Disparities could be due to neighborhoods with more adverse health outcomes associated with poverty and other social determinants of health experiencing higher prevalence of SARS-CoV-2 infection or due to greater morbidity and mortality resulting from equivalent SARS-CoV-2 infection prevalence. We surveyed five pairs of adjacent ZIP codes in Chicago with disparate COVID-19 case rates for highly specific and quantitative serologic evidence of any prior infection by SARS-CoV-2 to compare with their disparate COVID-19 case rates. Dried blood spot samples were self-collected at home by internet-recruited participants in summer 2020, shortly after Chicago's first wave of the COVID-19 pandemic. Pairs of neighboring ZIP codes with very different COVID-19 case rates had similar seropositivity rates for anti-SARS-CoV-2 receptor binding domain IgG antibodies. Overall, these findings of comparable exposure to SARS-CoV-2 across neighborhoods with very disparate COVID-19 case rates are consistent with social determinants of health, and the co-morbidities related to them, driving differences in COVID-19 rates across neighborhoods.

A Multi-Period Curve Fitting Model for Short-Term Prediction of the COVID-19 Spread in the U.S. Metropolitans

The COVID-19 has wreaked havoc upon the world with over 248 million confirmed cases and a death toll of over 5 million. It is alarming that the United States contributes over 18% of these confirmed cases and 14% of the deaths. Researchers have proposed many forecasting models to predict the spread of COVID-19 at the national, state, and county levels. However, due to the large variety in the mitigation policies adopted by various state and local governments; and unpredictable social events during the pandemic, it is incredibly challenging to develop models that can provide accurate long-term forecasting for disease spread. In this paper, to address such a challenge, we introduce a new multi-period curve fitting model to give a short-term prediction of the COVID-19 spread in Metropolitan Statistical Areas (MSA) within the United States. Since most counties/cities within a single MSA usually adopt similar mitigation strategies, this allows us to substantially diminish the variety in adopted mitigation strategies within an MSA. At the same time, the multi-period framework enables us to incorporate the impact of significant social events and mitigation strategies in the model. We also propose a simple heuristic to estimate the COVID-19 fatality based on our spread prediction. Numerical experiments show that the proposed multi-period curve model achieves reasonably high accuracy in the prediction of the confirmed cases and fatality.

Health care experiences during the COVID-19 pandemic by race and social determinants of health among adults age ≥ 58 years in the REGARDS study

BACKGROUND

Understanding health care experiences during the COVID-19 pandemic may provide insights into patient needs and inform policy. The objective of this study was to describe health care experiences by race and social determinants of health.

METHODS

We conducted a telephone survey (July 6, 2020-September 4, 2021) among 9492 Black and White participants in the longitudinal REasons for Geographic And Racial Differences in Stroke cohort study, age 58-105 years, from the continental United States. Among participants with symptoms of COVID-19, outcomes were: 1. Sought care or advice for the illness; 2. Received a SARS-CoV-2 test for the illness; and 3. Tested positive. Among participants without symptoms of COVID-19, outcomes were: 1. Wanted a test; 2. Wanted and received a test; 3. Did not want but received a test; and 4. Tested positive. We examined these outcomes overall and in subgroups defined by race, household income, marital status, education, area-level poverty, rural residence, Medicaid expansion, public health infrastructure ranking, and residential segregation.

RESULTS

The average age of participants was 76.8 years, 36% were Black, and 57% were female. Among participants with COVID-19 symptoms (n = 697), 74% sought care or advice for the illness, 50% received a SARS-CoV-2 test, and 25% had a positive test (50% of those tested). Among participants without potential COVID-19 symptoms (n = 8795), 29% wanted a SARS-CoV-2 test, 22% wanted and received a test, 8% did not want but received a test, and 1% tested positive; a greater percentage of participants who were Black compared to White wanted (38% vs 23%, p < 0.001) and received tests (30% vs 18%, p < 0.001) and tested positive (1.4% vs 0.8%, p = 0.005).

CONCLUSIONS

In this national study of older US adults, many participants with potential COVID-19 symptoms and asymptomatic participants who desired testing did not receive COVID-19 testing.

Contrasting the experiences for high- and low-income Asian Americans during COVID-19

There is a lack of quantitative research examining how the pandemic has affected individuals at different income levels. The Asian American population has the highest level of income inequality and serves as an excellent case study for examining differences in experience between income groups. A non-probability sample of 3084 Asian American adults living in the US was surveyed in June 2020, examining health-related behaviors and outcomes. Descriptive analyses and chi-squared statistics were conducted to identify differences in income groups (low, medium, high) among Asian Americans across regional subgroups (East, South, Southeast, Multiethnic) and disaggregated ethnicities (Chinese, Asian Indian, Japanese, and Filipino). In bivariable analyses, a significantly (p < 0.05) greater percentage of high-income individuals during the pandemic reported having enough money to buy the food they needed, a away to get to the store for food, and reported stores where they get food had everything they needed. High-income Chinese, Japanese, and Filipino individual also noted that, since the COVID-19 crisis, they are now working partially or fully from home. In the total sample, multivariable adjusted logistic regressions revealed medium- and low-income individuals to have low odds of working partially or fully from home (AOR:0.55, 95%CI:0.42-0.72), higher odds of not having enough money to buy the food they needed (AOR:3.54, 95%CI:1.43-11.81), and higher odds of eating less (AOR:1.58, 95%CI:1.14-2.22). These results highlight the importance of considering income distribution when characterizing disparities in health behaviors within racial/ethnic minority groups and underscore the need to bolster the infrastructure supporting low-income Asian Americans.

User-centered design of a scalable, electronic health record-integrated remote symptom monitoring intervention for patients with asthma and providers in primary care

OBJECTIVE

To determine user and electronic health records (EHR) integration requirements for a scalable remote symptom monitoring intervention for asthma patients and their providers.

METHODS

Guided by the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a user-centered design process involving English- and Spanish-speaking patients and providers affiliated with an academic medical center. We conducted a secondary analysis of interview transcripts from our prior study, new design sessions with patients and primary care providers (PCPs), and a survey of PCPs. We determined EHR integration requirements as part of the asthma app design and development process.

RESULTS

Analysis of 26 transcripts (21 patients, 5 providers) from the prior study, 21 new design sessions (15 patients, 6 providers), and survey responses from 55 PCPs (71% of 78) identified requirements. Patient-facing requirements included: 1- or 5-item symptom questionnaires each week, depending on asthma control; option to request a callback; ability to enter notes, triggers, and peak flows; and tips pushed via the app prior to a clinic visit. PCP-facing requirements included a clinician-facing dashboard accessible from the EHR and an EHR inbox message preceding the visit. PCP preferences diverged regarding graphical presentations of patient-reported outcomes (PROs). Nurse-facing requirements included callback requests sent as an EHR inbox message. Requirements were consistent for English- and Spanish-speaking patients. EHR integration required use of custom application programming interfaces (APIs).

CONCLUSION

Using the NASSS framework to guide our user-centered design process, we identified patient and provider requirements for scaling an EHR-integrated remote symptom monitoring intervention in primary care. These requirements met the needs of patients and providers. Additional standards for PRO displays and EHR inbox APIs are needed to facilitate spread.

Path Analysis to Assess Socio-Economic and Mitigation Measure Determinants for Daily Coronavirus Infections

(1) Background: With the rapid global spread of the coronavirus disease 2019 (COVID-19) and the relatively high daily cases recorded in a short time compared to other types of seasonal flu, the world remains under continuous threat unless we identify the key factors that contribute to these unexpected records. This identification is important for developing effective criteria and plans to reduce the spread of the COVID-19 pandemic and can guide national authorities to tighten or reduce mitigation measures, in addition to spreading awareness of the important factors that contribute to the propagation of the disease. (2) Methods: The data represents the daily infections (210 days) in four different countries (China, Italy, Iran, and Lebanon) taken approximately in the same duration, between January and March 2020. Path analysis was implemented on the data to detect the significant factors that affect the daily COVID-19 infections. (3) Results: The path coefficients show that quarantine commitment (β = −0.823) and full lockdown measures (β = −0.775) have the largest direct effect on COVID-19 daily infections. The results also show that more experience (β = −0.35), density in society (β = −0.288), medical resources (β = 0.136), and economic resources (β = 0.142) have indirect effects on daily COVID-19 infections. (4) Conclusions: The COVID-19 daily infections directly decrease with complete lockdown measures, quarantine commitment, wearing masks, and social distancing. COVID-19 daily cases are indirectly associated with population density, special events, previous experience, technology used, economic resources, and medical resources.

Sociodemographic characteristics determine download and use of a Corona contact tracing app in Germany-Results of the COSMO surveys

During the SARS-CoV-2 pandemic mobile health applications indicating risks emerging from close contacts to infected persons have a large potential to interrupt transmission chains by automating contact tracing. Since its dispatch in Germany in June 2020 the Corona Warn App has been downloaded on 25.7 Mio smartphones by February 2021. To understand barriers to download and user fidelity in different sociodemographic groups we analysed data from five consecutive cross-sectional waves of the COVID-19 Snapshot Monitoring survey from June to August 2020. Questions on the Corona Warn App included information on download, use, functionality, usability, and consequences of the app. Of the 4,960 participants (mean age 45.9 years, standard deviation 16.0, 50.4% female), 36.5% had downloaded the Corona Warn App. Adjusted analysis found that those who had downloaded the app were less likely to be female (Adjusted Odds Ratio for men 1.16 95% Confidence Interval [1.02;1.33]), less likely to be younger (Adjusted Odds Ratio for age 18 to 39 0.47 [0.32;0.59] Adjusted Odds Ratio for age 40 to 64 0.57 [0.46;0.69]), less likely to have a lower household income (AOR 0.55 [0.43;0.69]), and more likely to live in one of the Western federal states including Berlin (AOR 2.31 [1.90;2.82]). Willingness to disclose a positive test result and trust in data protection compliance of the Corona Warn App was significantly higher in older adults. Willingness to disclose also increased with higher educational degrees and income. This study supports the hypothesis of a digital divide that separates users and non-users of the Corona Warn App along a well-known health gap of education, income, and region.

A Call for Better, Not Faster, Research Ethics Committee Reviews in the Covid-19 Era

With the rapid spread of SARS-CoV2 has come a rapid proliferation of clinical research studies, resulting in considerable strain on research ethics committees (RECS), which need to review study proposals. RECs are pressured to move through the review process quickly so that studies can get underway to address the pandemic. These committees are also asked to increase efficiency without relaxing the standards for ethical review. RECs are accustomed to external pressure for approval from investigators; however, in the Covid-19 era, this pressure is coming from not only the sponsors and investigators but also many other stakeholders, including world leaders, the community, the media, and professional organizations. Drawing on the authors' experiences on a central REC reviewing complex multicenter Covid-19 studies, this commentary describes challenges that are inherent to Covid-19 research studies, such as the difficulty of obtaining informed consent from patients ill with the highly infectious virus. The commentary recommends several steps that RECs can take to ensure ethical review of research studies during the Covid-19 pandemic and future infectious disease outbreaks.

The Lines That Held Us: Assessing Racial and Socioeconomic Disparities in SARS-CoV-2 Testing

BACKGROUND

Racial disparities in SARS-CoV-2 prevalence are apparent. Race is a sociocultural construct, necessitating investigation into how sociocultural factors contribute.

METHODS

This cross-sectional study linked laboratory data of adult patients between February 29 and May 15, 2020 with socio-demographics variables from the 2018 American Community Survey (ACS). Medical sites included healthcare organizations in Michigan, New York, North Carolina, California, Florida, Pennsylvania, and Washington. Race was treated as a proxy for racism and not biological essentialism. Laboratory data included patient age, sex, race, ethnicity, test result, test location, and residential ZIP code. ACS data included economic and educational variables contributing to an SES Index, population density, proportion Medicaid, and racial composition for corresponding ZIP code. Associations between race/socioeconomic variables and test results were examined using odds ratios (OR).

RESULTS

Of 126 452 patients [mean (SD) age 51.9 (18.4) years; 52 747 (41.7%) men; 68 856 (54.5%) White and 27 805 (22.0%) Black], 18 905 (15.0%) tested positive. Of positive tests, 5238 (SD 27.7%) were White and 7223 (SD 38.2%) were Black. Black race increased the odds of a positive test; this finding was consistent across sites [OR 2.11 (95% CI 1.95-2.29)]. When subset by race, higher SES increased the odds of a positive test for White patients [OR 1.10 (95% CI 1.05-1.16)] but decreased the odds for Black patients [OR 0.92 (95% CI 0.86-0.99)]. Black patients, but not White patients, who tested positive overwhelmingly resided in more densely populated areas.

CONCLUSIONS

Black race was associated with SARS-CoV-2 positivity and the relationship between SES and test positivity differed by race, suggesting the impact of socioeconomic status on test positivity is race-specific.

Association of Race and Ethnicity with COVID-19 Test Positivity and Hospitalization Is Mediated by Socioeconomic Factors

Rationale: Black race and Hispanic ethnicity are associated with increased risks for coronavirus disease (COVID-19) infection and severity. It is purported that socioeconomic factors may drive this association, but data supporting this assertion are sparse. Objectives: To evaluate whether socioeconomic factors mediate the association of race/ethnicity with COVID-19 incidence and outcomes. Methods: We conducted a retrospective cohort study of adults tested for (cohort 1) or hospitalized with (cohort 2) COVID-19 between March 1, 2020, and July 23, 2020, at the University of Miami Hospital and Clinics. Our primary exposure was race/ethnicity. We considered socioeconomic factors as potential mediators of our exposure's association with outcomes. We used standard statistics to describe our cohorts and multivariable regression modeling to identify associations of race/ethnicity with our primary outcomes, one for each cohort, of test positivity (cohort 1) and hospital mortality (cohort 2). We performed a mediation analysis to see whether household income, population density, and household size mediated the association of race/ethnicity with outcomes. Results: Our cohorts included 15,473 patients tested (29.0% non-Hispanic White, 48.1% Hispanic White, 15.0% non-Hispanic Black, 1.7% Hispanic Black, and 1.6% other) and 295 patients hospitalized (9.2% non-Hispanic White, 56.9% Hispanic White, 21.4% non-Hispanic Black, 2.4% Hispanic Black, and 10.2% other). Among those tested, 1,256 patients (8.1%) tested positive, and, of the hospitalized patients, 47 (15.9%) died. After adjustment for demographics, race/ethnicity was associated with test positivity-odds-ratio (95% confidence interval [CI]) versus non-Hispanic White for Non-Hispanic Black: 3.21 (2.60-3.96), Hispanic White: 2.72 (2.28-3.26), and Hispanic Black: 3.55 (2.33-5.28). Population density mediated this association (percentage mediated, 17%; 95% CI, 11-31%), as did median income (27%; 95% CI, 18-52%) and household size (20%; 95% CI, 12-45%). There was no association between race/ethnicity and mortality, although this analysis was underpowered. Conclusions: Black race and Hispanic ethnicity are associated with an increased odds of COVID-19 positivity. This association is substantially mediated by socioeconomic factors.

[Social inequalities in the regional spread of SARS-CoV-2 infections]

Hintergrund und Ziel

Ob sozioökonomische Faktoren die Ausbreitung von SARS-CoV‑2 beeinflussen, ist nicht ausreichend beantwortet, da frühere Studien in der Regel kumulative Inzidenzen betrachtet und die zeitliche Entwicklung der Ausbreitung außer Acht gelassen haben. Dieser Beitrag konzentriert sich daher auf die Entwicklung von regionalen Neuinfektionen in Zusammenhang mit sozioökonomischen Faktoren. Ausgehend vom internationalen Forschungsstand präsentieren wir eigene Analysen von Meldedaten aus Deutschland.

Methoden

Diese Studie untersucht regionale Daten gemeldeter COVID-19-Fälle für die 401 Landkreise und kreisfreien Städte (Kreisebene) in Deutschland und vergleicht den zeitlichen Verlauf entlang sozioökonomischer Merkmale der Kreise. Betrachtet werden altersstandardisierte wöchentliche Inzidenzen für den Zeitraum 03.02.2020–28.03.2021. Sozial- und Wirtschaftsindikatoren auf Kreisebene stammen aus der INKAR(Indikatoren und Karten zur Raum- und Stadtentwicklung)-Datenbank (z. B. Einkommen, Beschäftigtenquote, Wohnfläche).

Ergebnisse

Während in der ersten und zu Beginn der zweiten Welle der Pandemie Kreise mit höherem mittleren Haushaltseinkommen höhere Inzidenzen hatten, stiegen sie in Kreisen mit niedrigem Einkommen ab Dezember 2020 deutlich an. Kreise mit einem hohen Anteil an Beschäftigten allgemein und speziell solchen im Produktionssektor hatten gerade in der zweiten und dritten Welle hohe Inzidenzen. Kreise mit einer geringen Wohnfläche je Einwohner hatten ab November 2020 ausgeprägt höhere Inzidenzen.

Schlussfolgerung

Der regionale Verlauf der Pandemie unterscheidet sich nach Sozial- und Wirtschaftsindikatoren. Eine differenzierte Betrachtung dieser Unterschiede könnte Hinweise auf zielgruppenspezifische Schutz- und Teststrategien geben und helfen, soziale Faktoren zu identifizieren, die Infektionen begünstigen.

Zusatzmaterial online

Zusätzliche Informationen sind in der Online-Version dieses Artikels (10.1007/s00103-021-03387-w) enthalten.

Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?

Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age, comorbidities, and health outcomes is critical to assessing and managing public health risks. From a US government database of 1.4 million patient records collected in May 2020, we extracted the relationships between age and number of comorbidities at the individual level to predict the likelihood of hospitalization, admission to intensive care, and death. We then applied the relationships to each US state and a selection of different countries in order to see whether they predicted observed outcome rates. We found that age and comorbidity data within these geographical regions do not explain much of the international or within-country variation in hospitalization, ICU admission, or death. Identifying alternative explanations for the limited predictive power of comorbidities and age at the population level should be considered for future research.

COVID-19 fatalities by zip codes and socioeconomic indicators across various U.S. regions

Background

There is a paucity of literature addressing COVID-19 case-fatality ratios (CFR) by zip code (ZC). We aim to analyze trends in COVID-19 CFR, population density, and socioeconomic status (SES) indicators (unemployment, median household income) to identify ZCs heavily burdened by COVID-19.

Methods

Cross-sectional study to investigate the US prevalence of COVD-19 fatalities by ZC and SES. CFRs were calculated from state/county Departments of Health. Inclusion criteria were counties that reported cases/deaths by ZC and a CFR≥2%. This study was reported in line with the STROCSS criteria.

Results

609/1,853 ZCs, spanning 327 counties in 7 states had CFRs ≥2%. A significant positive correlation was found between the CFR and median household income (Pearson correlation:0.107; 95% CI [289.1,1937.9]; p < 0.001). No significant correlations exist between the CFR, and population/mi (Sen-Crowe et al., 2020) [2] or unemployment rate. Significant associations exist between the CFR and young males and elderly females without public insurance. CFR was inversely associated with persons aged <44 and individuals aged ≥65. The percentage of nursing homes (NHs) within cities residing within high CFR ZCs range from 8.7% to 67.6%.

Conclusion

Significant positive association was found between the CFR and median household income. Population/mi (Sen-Crowe et al., 2020) [2] and unemployment rates, did not correlate to CFR. NHs were heavily distributed in high CFR zip codes. We recommend the targeted vaccination of zip codes with a large proportion of long-term care facilities. Finally, we recommend for improved screening and safety guidelines for vulnerable populations (e.g nursing home residents) and established protocols for when there is evidence of substantial infectious spread.

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First national sampling of COVID-19 Case Fatality Ratio (CFR) at the zip code level.

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Population density and unemployment rate did not correlate to COVID-19 CFR.

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Significant positive correlation was found between CFR and median household income.

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Nursing homes were concentrated in zip codes with high COVID-19 CFR.

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States should make COVID-19 data available at the zip code level.