Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis

Final assessment of the COVID-19 pandemic impact between the different social and economic strata population of the city of Buenos Aires

Background

During the pandemic, epidemiological communications reported an estimation of excess deaths. However, the final calculation requires a detailed analysis. The study aim was to ascertain the number and distribution of COVID-19 fatalities among various socio-economic strata in a large, moderate to low-income city.

Study design

Observational time series analysis in a large city, treated as a natural experiment.

Methods

Analysis of death certificates, demographic data, and health system records of positive RT-PCR COVID-19 tests from 2015 to 2021, categorizing by age, sex, and place of residence. The study measured the pandemic's impact on mortality, including COVID and non-COVID deaths, using corrected Poisson regression models for different demographics and assessing socio-economic status impact via ecological community-level analysis.

Results

Compared to the pre-pandemic period (2015-2019, IRR = 1.00), the sex- and age-adjusted rate of all-cause death increased significantly during the pandemic (2020-2021) IRR = 1.109 [1.054-1.167], p < 0.0001. This was observed in both males (IRR = 1.158 [1.1-1.219], p < 0.0001) and females (IRR = 1.068 [1.016-1.124], p = 0.01). There was no observed effect of the pandemic on the historical trend in the progressive reduction of mortality in people under 35 years of age. The increase in deaths was at the expense of COVID (+11,175 deaths) and cardiovascular causes (IRR = 1.114 [1.020-1.217] p = 0.017). During the pandemic, there was a significant increase in deaths at home (IRR = 1.219 [1197-1.242], p < 0.0001), especially in people dying of cardiovascular causes (IRR = 1.391 [1.360-1.422], p < 0.0001). The increase in the adjusted mortality rate during the pandemic was socially conditioned.

Conclusions

The pandemic not only led to increased COVID-19 mortality but also heightened fatalities from non-COVID causes, reflecting a potential bias in healthcare resource allocation towards SARS-CoV-2 at the expense of chronic pathologies care.

The differential impacts of COVID-19 mortality on mental health by residential geographic regions: The Los Angeles Pandemic Surveillance Cohort Study

OBJECTIVE

This study examines the association between changes in mental health before and during the COVID-19 pandemic and COVID-19 mortality across geographic areas and by race/ethnicity.

METHODS

A cross-sectional survey was conducted in Los Angeles County between April and May 2021. The study used the Patient Health Questionnaire-2 to assess major depression risk. Participants' home ZIP codes were classified into low, middle, and high COVID-19 mortality impacted areas (CMIA).

RESULTS

While there were existing mental health disparities due to differences in demographics and social determinants of health across CMIA in 2018, the pandemic exacerbated the disparities, especially for residents living in high CMIA. Non-White residents in high CMIA reported the largest deterioration in mental health. Differences in mental health by CMIA persisted after controlling for resident characteristics.

CONCLUSIONS

Living in an area with higher COVID-19 mortality rates may have been associated with worse mental health, with Non-White residents reporting worse mental health outcomes in the high mortality area.

POLICY IMPLICATIONS

It is crucial to advocate for greater mental health resources in high COVID-19 mortality areas especially for racial/ethnic minorities.

Perspectives of community-based organizations on digital health equity interventions: a key informant interview study

BACKGROUND

Health and healthcare are increasingly dependent on internet and digital solutions. Medically underserved communities that experience health disparities are often those who are burdened by digital disparities. While digital equity and digital health equity are national priorities, there is limited evidence about how community-based organizations (CBOs) consider and develop interventions.

METHODS

We conducted key informant interviews in 2022 purposively recruiting from health and welfare organizations engaged in digital equity work. Nineteen individuals from 13 organizations serving rural and/or urban communities from the local to national level participated in semi-structured interviews via Zoom regarding their perspectives on digital health equity interventions. Directed content analysis of verbatim interview transcripts was conducted to identify themes.

RESULTS

Themes emerged at individual, organizational, and societal levels. Individual level themes included potential benefits from digital health equity, internet access challenges, and the need for access to devices and digital literacy. Organizational level themes included leveraging community assets, promising organizational practices and challenges. For the societal level, the shifting complexity of the digital equity ecosystem, policy issues, and data for needs assessment and evaluation were described. Several example case studies describing these themes were provided.

DISCUSSION AND CONCLUSION

Digital health equity interventions are complex, multi-level endeavors. Clear elucidation of the individual, organizational, and societal level factors that may impact digital health equity interventions are necessary to understanding if and how CBOs participate in such initiatives. This study presents unique perspectives directly from CBOs driving programs in this new arena of digital health equity.

Inequality in COVID-19 mortality in Quebec associated with neighbourhood-level vulnerability domains

OBJECTIVES

We measured disparities in COVID-19 mortality associated with increasing vulnerability to severe outcomes of infectious disease at the neighbourhood level to identify domains for prioritization of public interventions.

METHODS

In this retrospective ecological study, we calculated COVID-19 mortality rate ratios (RR) comparing neighbourhoods with the greatest vulnerability relative to lowest vulnerability using the five domains from the COVID-19 vulnerability index for Quebec using hospital data from the first year of the pandemic and vulnerability levels from 13,182 neighbourhoods. We estimated the attributable fraction to assess disparities in COVID-19 mortality associated with vulnerability. Domains covered biological susceptibility, sociocultural characteristics, socioeconomic characteristics, and indoor and outdoor risk factors for exposure to SARS-CoV-2.

RESULTS

Vulnerable neighbourhoods accounted for 60.7% of COVID-19 deaths between March 2020 and February 2021. Neighbourhoods with biological susceptibility accounted for 46.1% and indoor exposure for 44.6% of deaths. Neighbourhoods with socioeconomic vulnerability experienced 23.5%, outdoor exposure 14.6%, and sociocultural vulnerability 9.0% of deaths. Neighbourhoods with high relative vulnerability had 4.66 times greater risk of COVID-19 mortality compared with those with low vulnerability (95%CI 3.82-5.67). High vulnerability in the biological (RR 3.33; 95%CI 2.71-4.09), sociocultural (RR 1.50; 95%CI 1.27-1.77), socioeconomic (RR 2.08; 95%CI 1.75-2.48), and indoor (RR 3.21; 95%CI 2.74-3.76) exposure domains were associated with elevated risks of mortality compared with the least vulnerable neighbourhoods. Outdoor exposure was unassociated with mortality (RR 1.17; 95%CI 0.96-2.43).

CONCLUSION

Public intervention to protect vulnerable populations should be adapted to focus on domains most associated with COVID-19 mortality to ensure addressing local needs.

Who Gets Sick From COVID-19? Sociodemographic Correlates of Severe Adult Health Outcomes During Alpha- and Delta-Variant Predominant Periods: September 2020-November 2021

BACKGROUND

Because COVID-19 case data do not capture most SARS-CoV-2 infections, the actual risk of severe disease and death per infection is unknown. Integrating sociodemographic data into analysis can show consequential health disparities.

METHODS

Data were merged from September 2020 to November 2021 from 6 national surveillance systems in matched geographic areas and analyzed to estimate numbers of COVID-19-associated cases, emergency department visits, and deaths per 100 000 infections. Relative risks of outcomes per infection were compared by sociodemographic factors in a data set including 1490 counties from 50 states and the District of Columbia, covering 71% of the US population.

RESULTS

Per infection with SARS-CoV-2, COVID-19-related morbidity and mortality were higher among non-Hispanic American Indian and Alaska Native persons, non-Hispanic Black persons, and Hispanic or Latino persons vs non-Hispanic White persons; males vs females; older people vs younger; residents in more socially vulnerable counties vs less; those in large central metro areas vs rural; and people in the South vs the Northeast.

DISCUSSION

Meaningful disparities in COVID-19 morbidity and mortality per infection were associated with sociodemography and geography. Addressing these disparities could have helped prevent the loss of tens of thousands of lives.

The California Cognitive Assessment Battery (CCAB)

Introduction

We are developing the California Cognitive Assessment Battery (CCAB) to provide neuropsychological assessments to patients who lack test access due to cost, capacity, mobility, and transportation barriers.

Methods

The CCAB consists of 15 non-verbal and 17 verbal subtests normed for telemedical assessment. The CCAB runs on calibrated tablet computers over cellular or Wi-Fi connections either in a laboratory or in participants' homes. Spoken instructions and verbal stimuli are delivered through headphones using naturalistic text-to-speech voices. Verbal responses are scored in real time and recorded and transcribed offline using consensus automatic speech recognition which combines the transcripts from seven commercial ASR engines to produce timestamped transcripts more accurate than those of any single ASR engine. The CCAB is designed for supervised self-administration using a web-browser application, the Examiner. The Examiner permits examiners to record observations, view subtest performance in real time, initiate video chats, and correct potential error conditions (e.g., training and performance failures, etc.,) for multiple participants concurrently.

Results

Here we describe (1) CCAB usability with older (ages 50 to 89) participants; (2) CCAB psychometric properties based on normative data from 415 older participants; (3) Comparisons of the results of at-home vs. in-lab CCAB testing; (4) We also present preliminary analyses of the effects of COVID-19 infection on performance. Mean z-scores averaged over CCAB subtests showed impaired performance of COVID+ compared to COVID- participants after factoring out the contributions of Age, Education, and Gender (AEG). However, inter-cohort differences were no longer significant when performance was analyzed with a comprehensive model that factored out the influences of additional pre-existing demographic factors that distinguished COVID+ and COVID- cohorts (e.g., vocabulary, depression, race, etc.,). In contrast, unlike AEG scores, comprehensive scores correlated significantly with the severity of COVID infection. (5) Finally, we found that scoring models influenced the classification of individual participants with Mild Cognitive Impairment (MCI, z-scores < -1.50) where the comprehensive model accounted for more than twice as much variance as the AEG model and reduced racial bias in MCI classification.

Discussion

The CCAB holds the promise of providing scalable laboratory-quality neurodiagnostic assessments to underserved urban, exurban, and rural populations.

Is there a relationship between internet access and COVID-19 mortality? Evidence from Nigeria based on a spatial analysis

With over 6.5 million deaths due to COVID-19, it has become an issue of global health concern. Early findings have identified several social determinants of deaths from COVID-19. However, very few studies have been done on the relationship between internet access and COVID-19 mortality in the context of developing countries. Using geospatial methods, this study examines the relationship between internet access and COVID-19 mortality disparity in Nigeria. In contrast to the widely reported relationship in the literature that internet access lowers the risk of COVID-19 mortality, the current study finds that geographical locations with the highest internet access are the hotspots of COVID-19 mortality in Nigeria, especially some parts of southwest Nigeria. In addition, findings show that population density and unemployment are risk factors of COVID-19 mortality. The study recommends educating the population on the use of online health information and the need to adhere strictly to non-pharmaceutical and vaccination interventions to reduce the number of deaths caused by the virus.

Real-world data to evaluate effects of a multi-level dissemination strategy on access, outcomes, and equity of monoclonal antibodies for COVID-19

Introduction

Multi-level dissemination strategies are needed to increase equitable access to effective treatment for high-risk outpatients with COVID-19, particularly among patients from disproportionately affected communities. Yet assessing population-level impact of such strategies can be challenging.

Methods

In collaboration with key contributors in Colorado, we conducted a retrospective cohort study to evaluate a multi-level dissemination strategy for neutralizing monoclonal antibody (mAb) treatment. Real-world data included county-level, de-identified output from a statewide mAb referral registry linked with publicly available epidemiological data. Outcomes included weekly number of mAb referrals, unique referring clinicians, and COVID-19 hospitalization rates. We assessed weekly changes in outcomes after dissemination strategies launched in July 2021.

Results

Overall, mAb referrals increased from a weekly average of 3.0 to 15.5, with an increase of 1.3 to 42.1 additional referrals per county in each post-period week (p < .05). Number of referring clinicians increased from a weekly average of 2.2 to 9.7, with an additional 1.5 to 22.2 unique referring clinicians observed per county per week beginning 5 weeks post-launch (p < .001). Larger effects were observed in communities specifically prioritized by the dissemination strategies. There were no observed differences in COVID-19 hospitalization rates between counties with and without mAb treatment sites.

Conclusion

Real-world data can be used to estimate population impact of multi-level dissemination strategies. The launch of these strategies corresponded with increases in mAb referrals, but no apparent population-level effects on hospitalization outcomes. Strengths of this analytic approach include pragmatism and efficiency, whereas limitations include inability to control for other contemporaneous trends.

Years of life lost to drug overdose in the Latinx community during the most recent wave of overdose deaths

BACKGROUND AND OBJECTIVES

This study estimated years of life lost (YLL) among US Latinx individuals during the most recent wave of drug overdose deaths.

METHODS

A serial cross-sectional study of YLL (life expectancy minus age at death) from death certificate records of Latinx individuals who died from drug overdoses from 2015 to mid-2022.

RESULTS

Over the study period, 58,209 Latinx individuals aged 15-64 years died from drug overdoses resulting in 2,266,784 YLL. Age-group YLL differences remained stable, but gender YLL trajectories diverged.

CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE

This study extends our understanding of the immense loss to Latinx communities from preventable drug deaths.

Immunogenicity and safety of heterologous mRNA-1273/MVC-COV1901 vaccination versus homologous mRNA1273 vaccination: A randomized, double-blind controlled study

BACKGROUND/PURPOSE

MVC-COV1901 is a protein vaccine based on the same SARS-CoV-2 strain used in mRNA vaccine mRNA-1273. Data are lacking on immunogenicity and safety of MVC-COV1901 as heterologous boost for people already received one dose of mRNA-1273.

METHODS

This is a randomized, double-blind trial that recruited adults aged 20-70 years who previously received a single dose of mRNA-1273 vaccine and were randomly assigned in a 1:1 ratio to receive a second dose with the homologous vaccine or protein-based MVC-COV1901 8-12 weeks after the first dose. The primary outcome was neutralizing antibody titers in terms of the geometric mean titer (GMT) 14 days after the second dose. Safety was assessed in all participants who received a dose of the study vaccine. The study is registered with ClinicalTrials.gov (NCT05079633).

RESULTS

From September 30 to November 5, 2021, 144 participants were enrolled and randomly assigned to the MVC-COV1901 boost group (n = 72) or the mRNA-1273 boost group (n = 72). The neutralizing antibodies on Day 15 and the anti-SARS-CoV-2 IgG titers on Day 15 and 29 of homologous mRNA-1273 were significantly higher than those of heterologous mRNA-1273/MVC-COV1901. Cellular immune responses were comparable in both groups. However, adverse events were much more frequent after the mRNA-1273 boost than after the MVC-COV1901 boost.

CONCLUSION

Our results show that heterologous boost with MVC-COV1901 yielded an inferior immunogenicity but significantly fewer adverse events, compared with homologous boost with mRNA-1273. In people experienced severe adverse events after prime dose of mRNA-1273, as well as in periods when the supply of mRNA-1273 is limited, MVC-COV1901 could serve as an acceptable alternative heterologous boost.

Does clinical research account for diversity in deploying digital health technologies?

Digital health technologies (DHTs) should expand access to clinical research to represent the social determinants of health (SDoH) across the population. The frequency of reporting participant SDoH data in clinical publications is low and is not known for studies that utilize DHTs. We evaluated representation of 11 SDoH domains in 126 DHT-enabled clinical research publications and proposed a framework under which these domains could be captured and subsequently reported in future studies. Sex, Race, and Education were most frequently reported (in 94.4%, 27.8%, and 20.6% of publications, respectively). The remaining 8 domains were reported in fewer than 10% of publications. Medical codes were identified that map to each of the proposed SDoH domains and the resulting resource is suggested to highlight that existing infrastructure could be used to capture SDoH data. An opportunity exists to increase reporting on the representation of SDoH among participants to encourage equitable and inclusive research progress through DHT-enabled clinical studies.

SARS-CoV-2 infections in migrants and the role of household overcrowding: a causal mediation analysis of Virus Watch data

BACKGROUND

Migrants are over-represented in SARS-CoV-2 infections globally; however, evidence is limited for migrants in England and Wales. Household overcrowding is a risk factor for SARS-CoV-2 infection, with migrants more likely to live in overcrowded households than UK-born individuals. We aimed to estimate the total effect of migration status on SARS-CoV-2 infection and to what extent household overcrowding mediated this effect.

METHODS

We included a subcohort of individuals from the Virus Watch prospective cohort study during the second SARS-CoV-2 wave (1 September 2020-30 April 2021) who were aged ≥18 years, self-reported the number of rooms in their household and had no evidence of SARS-CoV-2 infection pre-September 2020. We estimated total, indirect and direct effects using Buis' logistic decomposition regression controlling for age, sex, ethnicity, clinical vulnerability, occupation, income and whether they lived with children.

RESULTS

In total, 23 478 individuals were included. 9.07% (187/2062) of migrants had evidence of infection during the study period vs 6.27% (1342/21 416) of UK-born individuals. Migrants had 22% higher odds of infection during the second wave (total effect; OR 1.22, 95% CI 1.01 to 1.47). Household overcrowding accounted for approximately 36% (95% CI -4% to 77%) of these increased odds (indirect effect, OR 1.07, 95% CI 1.03 to 1.12; proportion accounted for: indirect effect on log odds scale/total effect on log odds scale=0.36).

CONCLUSION

Migrants had higher odds of SARS-CoV-2 infection during the second wave compared with UK-born individuals and household overcrowding explained 36% of these increased odds. Policy interventions to reduce household overcrowding for migrants are needed as part of efforts to tackle health inequalities during the pandemic and beyond.

Population based cohort to examine association between geospatial antibiotic factors and urinary tract infection outcomes

INTRODUCTION

Urinary tract infections (UTIs) pose a significant health care burden. Outpatient antibiotic geospatial factors (eg, geographic prescribing and geographic resistance) may be associated with inpatient outcomes. This study examined the relationship between these factors, severe UTI, and hospitalization for severe UTI.

METHODS

The first cohort included hospitalized, female, Medicare beneficiaries, aged >50 years. The primary outcome was severe UTI (defined as CSS diagnosis code of 159 with an APR-DRG severity of illness code of 3 or 4). The association between geospatial first-line prescribing (FLP) and severe UTI was assessed. The second cohort examined the association between these geospatial FLP and risk of hospitalization with severe UTI. Multivariable regression was used to produce adjusted odds ratios and adjusted risk ratios.

RESULTS

In the first cohort (n = 14,474), low FLP was not associated with severe UTI (P = .87) in univariable analysis. In multivariable analysis, low FLP was associated with severe UTI was (aOR: 1.08 [95% CI 1.00, 1.16]). In the second cohort (n = 2,972,174), the admission rate was 47.0 and 49.8 per 10,000 (low FLP vs high FLP, respectively [P < .001]). The aRR for admission was 1.26 (95% CI 1.14, 1.39) in areas with low FLP.

CONCLUSIONS

This study suggests that geospatial antibiotic factors may influence inpatient outcomes in women aged >50 with UTI. Further research is needed to corroborate our findings.

Patient-facing job role is associated with SARS-CoV-2 positivity among healthcare workers in long term care facilities in Minnesota, August-December, 2020

OBJECTIVE

Healthcare workers (HCWs) in long-term care facilities (LTCFs) are disproportionately affected by severe acute respiratory coronavirus virus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19). To characterize factors associated with SARS-CoV-2 positivity among LTCF HCWs, we performed a retrospective cohort study among HCWs in 32 LTCFs in the Minneapolis-St Paul region.

METHODS

We analyzed the outcome of SARS-CoV-2 polymerase chain reaction (PCR) positivity among LTCF HCWs during weeks 34-52 of 2020. LTCF and HCW-level characteristics, including facility size, facility risk score for resident-HCW contact, and resident-facing job role, were modeled in univariable and multivariable generalized linear regressions to determine their association with SARS-CoV-2 positivity.

RESULTS

Between weeks 34 and 52, 440 (20.7%) of 2,130 unique HCWs tested positive for SARS-CoV-2 at least once. In the univariable model, non-resident-facing HCWs had lower odds of infection (odds ratio [OR], 0.50; 95% confidence interval [CI], 0.36-0.70). In the multivariable model, the odds remained lower for non-resident-facing HCW (OR, 0.50; 95% CI, 0.36-0.71), and those in medium- versus low-risk facilities experienced higher odds of testing positive for SARS-CoV-2 (OR, 1.47; 95% CI, 1.08-2.02).

CONCLUSIONS

Our findings suggest that COVID-19 cases are related to contact between HCW and residents in LTCFs. This association should be considered when formulating infection prevention and control policies to mitigate the spread of SARS-CoV-2 in LTCFs.

Retrospective analysis of equity-based optimization for COVID-19 vaccine allocation

Marginalized racial and ethnic groups in the United States were disproportionally affected by the COVID-19 pandemic. To study these disparities, we construct an age-and-race-stratified mathematical model of SARS-CoV-2 transmission fitted to age-and-race-stratified data from 2020 in Oregon and analyze counterfactual vaccination strategies in early 2021. We consider two racial groups: non-Hispanic White persons and persons belonging to BIPOC groups (including non-Hispanic Black persons, non-Hispanic Asian persons, non-Hispanic American-Indian or Alaska-Native persons, and Hispanic or Latino persons). We allocate a limited amount of vaccine to minimize overall disease burden (deaths or years of life lost), inequity in disease outcomes between racial groups (measured with five different metrics), or both. We find that, when allocating small amounts of vaccine (10% coverage), there is a trade-off between minimizing disease burden and minimizing inequity. Older age groups, who are at a greater risk of severe disease and death, are prioritized when minimizing measures of disease burden, and younger BIPOC groups, who face the most inequities, are prioritized when minimizing measures of inequity. The allocation strategies that minimize combinations of measures can produce middle-ground solutions that similarly improve both disease burden and inequity, but the trade-off can only be mitigated by increasing the vaccine supply. With enough resources to vaccinate 20% of the population the trade-off lessens, and with 30% coverage, we can optimize both equity and mortality. Our goal is to provide a race-conscious framework to quantify and minimize inequity that can be used for future pandemics and other public health interventions.

Sociodemographic Differences in COVID-19 Pandemic Experiences Among Families in the United States

Importance

Few population-based studies in the US collected individual-level data from families during the COVID-19 pandemic.

Objective

To examine differences in COVID-19 pandemic-related experiences in a large sociodemographically diverse sample of children and caregivers.

Design, Setting, and Participants

The Environmental influences on Child Health Outcomes (ECHO) multi-cohort consortium is an ongoing study that brings together 64 individual cohorts with participants (24 757 children and 31 700 caregivers in this study) in all 50 US states and Puerto Rico. Participants who completed the ECHO COVID-19 survey between April 2020 and March 2022 were included in this cross-sectional analysis. Data were analyzed from July 2021 to September 2022.

Main Outcomes and Measures

Exposures of interest were caregiver education level, child life stage (infant, preschool, middle childhood, and adolescent), and urban or rural (population <50 000) residence. Dependent variables included COVID-19 infection status and testing; disruptions to school, child care, and health care; financial hardships; and remote work. Outcomes were examined separately in logistic regression models mutually adjusted for exposures of interest and race, ethnicity, US Census division, sex, and survey administration date.

Results

Analyses included 14 646 children (mean [SD] age, 7.1 [4.4] years; 7120 [49%] female) and 13 644 caregivers (mean [SD] age, 37.6 [7.2] years; 13 381 [98%] female). Caregivers were racially (3% Asian; 16% Black; 12% multiple race; 63% White) and ethnically (19% Hispanic) diverse and comparable with the US population. Less than high school education (vs master's degree or more) was associated with more challenges accessing COVID-19 tests (adjusted odds ratio [aOR], 1.88; 95% CI, 1.06-1.58), lower odds of working remotely (aOR, 0.04; 95% CI, 0.03-0.07), and more food access concerns (aOR, 4.14; 95% CI, 3.20-5.36). Compared with other age groups, young children (age 1 to 5 years) were least likely to receive support from schools during school closures, and their caregivers were most likely to have challenges arranging childcare and concerns about work impacts. Rural caregivers were less likely to rank health concerns (aOR, 0.77; 95% CI, 0.69-0.86) and social distancing (aOR, 0.82; 95% CI, 0.73-0.91) as top stressors compared with urban caregivers.

Conclusions

Findings in this cohort study of US families highlighted pandemic-related burdens faced by families with lower socioeconomic status and young children. Populations more vulnerable to public health crises should be prioritized in recovery efforts and future planning.

Retrospective Analysis of Equity-Based Optimization for COVID-19 Vaccine Allocation

Marginalized racial and ethnic groups in the United States were disproportionally affected by the COVID-19 pandemic. To study these disparities, we construct an age-and-race-stratified mathematical model of SARS-CoV-2 transmission fitted to age-and-race-stratified data from 2020 in Oregon and analyze counter-factual vaccination strategies in early 2021. We consider two racial groups: non-Hispanic White persons and persons belonging to BIPOC groups (including non-Hispanic Black persons, non-Hispanic Asian persons, non-Hispanic American Indian or Alaska Native persons, and Hispanic or Latino persons). We allocate a limited amount of vaccine to minimize overall disease burden (deaths or years of life lost), inequity in disease outcomes between racial groups (measured with five different metrics), or both. We find that, when allocating small amounts of vaccine (10% coverage), there is a trade-off between minimizing disease burden and minimizing inequity. Older age groups, who are at a greater risk of severe disease and death, are prioritized when minimizing measures of disease burden, and younger BIPOC groups, who face the most inequities, are prioritized when minimizing measures of inequity. The allocation strategies that minimize combinations of measures can produce middle-ground solutions that similarly improve both disease burden and inequity, but the trade-off can only be mitigated by increasing the vaccine supply. With enough resources to vaccinate 20% of the population the trade-off lessens, and with 30% coverage, we can optimize both equity and mortality. Our goal is to provide a race-conscious framework to quantify and minimize inequity that can be used for future pandemics and other public health interventions.

Multilevel Determinants of Digital Health Equity: A Literature Synthesis to Advance the Field

Current digital health approaches have not engaged diverse end users or reduced health or health care inequities, despite their promise to deliver more tailored and personalized support to individuals at the right time and the right place. To achieve digital health equity, we must refocus our attention on the current state of digital health uptake and use across the policy, system, community, individual, and intervention levels. We focus here on (a) outlining a multilevel framework underlying digital health equity; (b) summarizingfive types of interventions/programs (with example studies) that hold promise for advancing digital health equity; and (c) recommending future steps for improving policy, practice, and research in this space.

The Workforce Needed to Address Population Health

Policy Points Although a single definition of the population health workforce does not yet exist, this workforce needs to have the skills and competencies to address the social determinants of health, to understand intersectionality, and to coordinate and work in concert with an array of skilled providers in social and health care to address multiple health drivers. On-the-job training programs and employer support are needed for the current health workforce to gain skills and competencies to address population health. Funding and leadership combined are critical for developing the population health workforce with the goal of supporting a broad set of workers beyond health and social care to include, for example, those in urban planning, law enforcement, or transportation professions to address population health.

Mindful mindfulness reporting: Media portrayals of scientific evidence for meditation mobile apps

Promoting mental health is a major global challenge. As mindfulness meditation apps can help maintain and restore good mental health, it is important to understand how their efficacy and safety are portrayed in the media. This study systematically evaluates whether evidence from academic research is used to communicate the health effects of two popular mindfulness apps, Calm and Smiling Mind. A scoping review mapped research findings from 16 relevant articles, and a media analysis examined the types of evidence used in news reporting. Analysing 105 news articles revealed that 98% did not use evidence from academic research on app-based meditation to support health claims. Only 28.5% of articles included advice from a health expert, and 9.5% mentioned potential risks and alternative treatments. Stronger evidence-based reporting on the health effects of mindfulness apps is needed to enable people to make more informed decisions for their health and wellbeing.

Impacts of Individual Patient Language and Neighborhood Ethnic Enclave on COVID-19 Test Positivity Among Hispanic/Latinx Patients in San Francisco

BACKGROUND

Given the known disparities in COVID-19 within the Hispanic/Latinx community, we sought to examine the interaction between individual-level and neighborhood-level social determinants of health using linked electronic health record data.

METHODS

We examined electronic health record data linked to neighborhood data among Hispanic/Latinx patients tested for COVID-19 between March 1, 2020, and February 28, 2021, from 2 large health care systems in San Francisco. Hispanic/Latinx ethnic enclave is measured using an index of census-tract level indicators of ethnicity, nativity, and language. Multilevel logistic regression models examined associations between ethnic enclave and COVID-19 positivity (COVID-19+), adjusting for patient-level sociodemographic and clinical characteristics and health system. Cross-level interactions were used to test whether associations between ethnic enclave and COVID-19+ differed by patient language preference.

RESULTS

Among 26,871 patients, mean age was 37 years, 56% had Spanish-language preference, and 21% were COVID-19+. In unadjusted models, patients living in the highest versus lowest Hispanic/Latinx enclave had 3.2 higher odds of COVID-19+ (95% CI, 2.45-4.24). Adjusted, the relationship between ethnic enclave and COVID-19+ was attenuated, but not eliminated (odds ratio: 1.4; 95% CI, 1.13-1.17). Our results demonstrated a significant cross-level interaction, such that the influence of ethnic enclave was modified by patient language preference. For individuals with Spanish-language preference, risk of COVID-19+ was high regardless of neighborhood context, whereas for those with English preference, neighborhood ethnic enclave more than doubled the odds of infection.

CONCLUSIONS

Findings suggest that a multilevel and intersectional approach to the study of COVID-19 inequities may illuminate dimensions of health inequity that affect marginalized communities and offer insights for targeted clinical and community-based interventions.

The impact of the COVID-19 pandemic on cardiovascular health behaviors and risk factors: A new troubling normal that may be here to stay

In March 2020, the Coronavirus disease 2019 (COVID-19) outbreak was officially declared a global pandemic, leading to closure of public facilities, enforced social distancing and stay-at-home mandates to limit exposures and reduce transmission rates. While the severity of this "lockdown" period varied by country, the disruptions of the pandemic on multiple facets of life (e.g., daily activities, education, the workplace) as well as the social, economic, and healthcare systems impacts were unprecedented. These disruptions and impacts are having a profound negative effect on multiple facets of behavioral health and psychosocial wellbeing that are inextricably linked to cardiometabolic health and associated with adverse outcomes of COVID-19. For example, adoption of various cardiometabolic risk behavior behaviors observed during the pandemic contributed to irretractable trends in weight gain and poor mental health, raising concerns on the possible long-term consequences of the pandemic on cardiometabolic disease risk, and vulnerabilities to future viral pandemics. The purpose of this review is to summarize the direct and indirect effects of the pandemic on cardiometabolic health risk behaviors, particularly related to poor diet quality, physical inactivity and sedentary behaviors, smoking, sleep patterns and mental health. Additional insights into how the pandemic has amplified cardiovascular risk behaviors, particularly in our most vulnerable populations, and the potential implications for the future if these modifiable risk behaviors do not become better controlled, are described.

Sentinel Surveillance of SARS-CoV-2 Rates and Equity Impacts using Labor and Delivery Patients in Phoenix, Arizona

Proactive management of SARS-CoV-2 requires timely and complete population data to track the evolution of the virus and identify at risk populations. However, many cases are asymptomatic and are not easily discovered through traditional testing efforts. Sentinel surveillance can be used to estimate the prevalence of infections for geographical areas but requires identification of sentinels who are representative of the larger population. Our goal is to evaluate applicability of a population of labor and delivery patients for sentinel surveillance system for monitoring the prevalence of SARS-CoV-2 infection. We tested 5307 labor and delivery patients from two hospitals in Phoenix, Arizona, finding 195 SARS-CoV-2 positive. Most positive cases were associated with people who were asymptomatic (79.44%), similar to statewide rates. Our results add to the growing body of evidence that SARS-CoV-2 disproportionately impacts people of color, with Black people having the highest positive rates (5.92%). People with private medical insurance had the lowest positive rates (2.53%), while Medicaid patients had a positive rate of 5.54% and people without insurance had the highest positive rates (6.12%). With diverse people reporting for care and being tested regardless of symptoms, labor and delivery patients may serve as ideal sentinels for asymptomatic detection of SARS-CoV-2 and monitoring impacts across a wide range of social and economic classes. A more robust system for infectious disease management requires the expanded participation of additional hospitals so that the sentinels are more representative of the population at large, reflecting geographic and neighborhood level patterns of infection and risk.

Race, ethnicity and mortality in the United States during the first year of the COVID-19 pandemic: an assessment

The fact that a disproportionate share of the disease burden from COVID-19, including mortality, has been borne by racial and ethnic minority communities is well documented. The purpose of this paper is to reassess the "race/ethnicity effect" in COVID-19 mortality in the United States. Using an ecological regression framework and county-level data, the study aims to make two contributions. First, it estimates the race/ethnicity effect for all major racial/ethnic groups at four important junctures during the first year of the pandemic. In doing so, it seeks to provide the fullest possible picture of the nature and evolution of the race/ethnicity effect. Second, it estimates the race/ethnicity effect net of basic socioeconomic factors (SEF). This helps to identify the likely mechanisms through which the race/ethnicity effect operates. Racial/ethnic composition is flexibly measured in two ways-by percentage contributions to county population and by indicators of group plurality. The ecological regressions revealed a positive association between the size of three racial/ethnic groups-non-Hispanic Blacks, non-Hispanic American Indian and Alaskan Natives (AIAN) and Hispanics-and county mortality, although the association was stronger and more consistent for Blacks and AIANs. Furthermore, accounting for basic SEF had different impacts on the race/ethnicity-mortality association for the three groups. For Hispanics, it was almost fully mediated. For Blacks, it decreased but remained statistically significant [62-6% higher mortality associated with a 1-standard deviation increase in Black share of county population; 2.3-1.1 times higher mortality in the average Black plurality county]. For AIANs, it was largely unaffected or even increased [44-10% higher mortality associated with a 1-standard deviation increase in AIAN share; 6.2-1.8 times higher mortality in AIAN plurality county). For all groups, the race/ethnicity effect generally decreased as the pandemic wore on during the first year.

Supplementary Information

The online version contains supplementary material available at 10.1007/s44155-022-00019-9.

Stronger association of perceived health with socio-economic inequality during COVID-19 pandemic than pre-pandemic era

Objective

The COVID-19 pandemic has changed peoples’ routine of daily living and posed major risks to global health and economy. Few studies have examined differential impacts of economic factors on health during pandemic compared to pre-pandemic. We aimed to compare the strength of associations between perceived health and socioeconomic position (household income, educational attainment, and employment) estimated before and during the pandemic.

Methods

Two waves of nationwide survey [on 2018(T1;n = 1200) and 2021(T2;n = 1000)] were done for 2200 community adults. A balanced distribution of confounders (demographics and socioeconomic position) were achieved across the T2 and T1 by use of the inverse probability of treatment weighting. Distributions of perceived health [= (excellent or very good)/(bad, fair, or good)] for physical-mental-social-spiritual subdomains were compared between T1 and T2. Odds of bad/fair/good health for demographics and socioeconomic position were obtained by univariate logistic regression. Adjusted odds (aOR) of bad/fair/good health in lower household income(< 3000 U.S. dollars/month) were retrieved using the multiple hierarchical logistic regression models of T1 and T2.

Results

Perceived health of excellent/very good at T2 was higher than T1 for physical(T1 = 36.05%, T2 = 39.13%; P = 0.04), but were lower for mental(T1 = 38.71%, T2 = 35.17%; P = 0.01) and social(T1 = 42.48%, T2 = 35.17%; P < 0.001) subdomains. Odds of bad/fair/good health were significantly increased at T2 than T1 for household income (physical-mental-social; all Ps < 0.001) and educational attainment (social; P = 0.04) but not for employment (all Ps > 0.05). AORs of bad/fair/good health in lower household income were stronger in T2 than T1, for mental [aOR (95% CI) = 2.15(1.68–2.77) in T2, 1.33(1.06–1.68) in T1; aOR difference = 0.82(P < 0.001)], physical [aOR (95% CI) = 2.64(2.05–3.41) in T2, 1.50(1.18–1.90) in T1; aOR difference = 1.14(P < 0.001)] and social [aOR (95% CI) = 2.15(1.68–2.77) in T2, 1.33(1.06–1.68) in T1; aOR difference = 0.35(P = 0.049)] subdomains.

Conclusions

Risks of perceived health worsening for mental and social subdomains in people with lower monthly household income or lower educational attainment became stronger during the COVID-19 pandemic compared to pre-pandemic era. In consideration of the prolonged pandemic as of mid-2022, policies aiming not only to sustain the monthly household income and compulsory education but also to actively enhance the perceived mental-social health status have to be executed and maintained.

Spatial-temporal differences of COVID-19 vaccinations in the U.S

Although the disparities in COVID-19 outcomes have been proved, they have not been explicitly associated with COVID-19 full vaccinations. This paper examines the spatial and temporal patterns of the county-level COVID-19 case rates, fatality rates, and full vaccination rates in the United States from December 24, 2020 through September 30, 2021. Statistical and geospatial analyses show clear temporal and spatial patterns of the progression of COVID-19 outcomes and vaccinations. In the relationship between two time series, the fatality rates series was positively related to past lags of the case rates series. At the same time, case rates series and fatality rates series were negatively related to past lags of the full vaccination rates series. The lag level varies across urban and rural areas. The results of partial correlation, ordinary least squares (OLS) and Geographically Weighted Regression (GWR) also confirmed that the existing COVID-19 infections and different sets of socioeconomic, healthcare access, health conditions, and environmental characteristics were independently associated with COVID-19 vaccinations over time and space. These results empirically identify the geographic health disparities with COVID-19 vaccinations and outcomes and provide the evidentiary basis for targeting pandemic recovery and public health mitigation actions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s44212-022-00019-9.