The impact of COVID-19 on healthcare coverage and access in racial and ethnic minority populations in the United States.

This study described spatiotemporal changes in health insurance coverage, healthcare access, and reasons for non-insurance among racial/ethnic minority populations in the United States during the COVID-19 pandemic using four national survey datasets. Getis-Ord Gi* statistic and scan statistics were used to analyze geospatial clusters of health insurance coverage by race/ethnicity. Logistic regression was used to estimate odds of reporting inability to access healthcare across two pandemic time periods by race/ethnicity. Racial/ethnic differences in insurance were observed from 2010 through 2019, with the lowest rates being among Hispanic/Latino, African American, American Indian/Alaska Native, and Native Hawaiian/Pacific Islander populations. Pre-pandemic insurance coverage rates were geographically clustered. The percentage of adults citing change in employment status as the reason for non-insurance increased by about 7% after the start of the pandemic, with a small decrease observed among African American adults. Almost half of adults reported reduced healthcare access in June 2020, with 38.7% attributing reduced access to the pandemic; however, by May 2021, the percent of respondents reporting reduced access for any reason and due to the pandemic fell to 26.9% and 12.7%, respectively. In general, racial/ethnic disparities in health insurance coverage and healthcare access worsened during the pandemic. Although coverage and access improved over time, pre-COVID disparities persisted with African American and Hispanic/Latino populations being the most affected by insurance loss and reduced healthcare access. Cost, unemployment, and eligibility drove non-insurance before and during the pandemic.

Linking Local-Level Chronic Disease and Social Vulnerability Measures to Inform Planning Efforts: A COPD Example.

INTRODUCTION: Data are publicly available to identify geographic differences in health outcomes, including chronic obstructive pulmonary disease (COPD), and social vulnerability; however, examples of combining data across sources to understand disease burden in the context of community vulnerability are lacking. METHODS: We merged county and census tract model-based estimates of COPD prevalence from PLACES (www.cdc.gov/PLACES) with social vulnerability measures from the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry Social Vulnerability Index (https://www.atsdr.cdc.gov/placeandhealth/svi), including 4 themes (socioeconomic, household composition and disability, minority status and language, and housing type and transportation), and the overall Social Vulnerability Index (SVI). We used the merged data set to create vulnerability profiles by COPD prevalence, explore joint geographic patterns, and calculate COPD population estimates by vulnerability levels. RESULTS: Counties and census tracts with high COPD prevalence (quartile 4) had high median vulnerability rankings (range: 0-1) for 2 themes: socioeconomic (county, 0.81; tract, 0.77) and household composition and disability (county, 0.75; tract, 0.81). Concordant high COPD prevalence and vulnerability for these themes were clustered along the Ohio and lower Mississippi rivers. The estimated number of adults with COPD residing in counties with high vulnerability was 2.5 million (tract: 4.7 million) for the socioeconomic theme and 2.3 million (tract: 5.0 million) for the household composition and disability theme (high overall SVI: county, 4.5 million; tract, 4.7 million). CONCLUSION: Data from 2 publicly available tools can be combined, analyzed, and visualized to jointly examine local COPD estimates and social vulnerability. These analyses can be replicated with other measures to expand the use of these cross-cutting tools for public health planning.

Factors associated with naloxone availability and dispensing through Michigan's pharmacy standing order.

Background: Pharmacy standing order policies allow pharmacists to dispense naloxone, thereby increasing access to naloxone. Objectives: To describe pharmacy standing order participation and associations of pharmacy and community characteristics that predict naloxone availability and dispensing across eight counties in Michigan. Methods: We conducted a telephone survey of 662 standing order pharmacies with a response rate of 81% (n = 539). Pharmacies were linked with census tract-level demographics, overdose fatality rates, and dispensing data. County maps were created to visualize pharmacy locations relative to fatality rates. Regression models analyzed associations between pharmacy type, neighborhood characteristics, fatality rates, and these outcomes: naloxone availability, having ever dispensed naloxone, and counts of naloxone dispensed. Results: The prevalence of standing order pharmacies was 54% (n = 662/1231). Maps revealed areas with higher fatality rates had fewer pharmacies participating in the standing order or lacked any pharmacy access. Among standing order pharmacies surveyed, 85% (n = 458/539) had naloxone available and 82% had ever dispensed (n = 333/406). The mean out-of-pocket cost of Narcan® was $127.77 (SD: 23.93). National chains were more likely than regional chains to stock naloxone (AOR = 3.75, 95%CI = 1.77, 7.93) and to have ever dispensed naloxone (AOR 3.02, 95%CI = 1.21,7.57). Higher volume of naloxone dispensed was associated in neighborhoods with greater proportions of public health insurance (IRR = 1.38, 95%CI = 1.21, 1.58) and populations under 44 years old (IRR = 1.24, 95%CI = 1.04, 1.48). There was no association with neighborhood overdose fatality rates or race in regression models. Conclusion: As deaths from the opioid epidemic continue to escalate, efforts to expand naloxone access through greater standing order pharmacy participation are warranted.

Stimulant storm - state health department psychostimulant age-adjusted mortality rate correlates with psychostimulant-based Michigan Poison Center case exposures over time.

BACKGROUND: Deaths attributable to psychostimulants with abuse potential have increased in the United States (US) in recent years. Methamphetamine use, in particular, has risen sharply. We evaluated the correlation between amphetamine- and methamphetamine-related case exposures reported to the Michigan Poison Center (MiPC) coinciding with psychostimulant age-adjusted mortality rates from the Michigan Department of Health and Human Services (MDHHS). METHODS: We compared amphetamine and methamphetamine exposures reported to the MiPC from 2012 to 2018, queried from ToxSentry® database, to MDHHS reports on resident death certificates with attributed death due to "overdose, regardless of intent" and related cause of death attributed to psychostimulants with abuse potential. Linear regression assessed goodness-of-fit. Slope with standard error and adjusted R2 were reported. Psychostimulants included methamphetamine, 3,4-methylenedioxy-methamphetamine (MDMA), dextroamphetamine, levoamphetamine, and methylphenidate. RESULTS: Psychostimulant deaths reported by MDHHS increased from 17 to 165 between 2012 and 2018. The average age-adjusted rate of psychostimulant-involved overdose deaths per 100,000 state residents rose from 0.2 to 1.8. Linear regression of MiPC amphetamine exposure rates with state health department-reported age-adjusted psychostimulant mortality rates yielded a slope of 1.93, SE 0.5, p value 0.035, and adjusted R2 0.55. Linear regression of MiPC methamphetamine exposure rates with state health department-reported age-adjusted psychostimulant mortality rates yielded a slope of 0.78, SE 0.27, p value 0.012, and adjusted R2 0.70 suggesting a strong correlation. CONCLUSION: Psychostimulant use and associated deaths in the US are increasing, representing an evolving public health threat. Michigan demonstrates consistency with national trends and data from the MiPC correlates strongly with state-reported age-adjusted psychostimulant mortality rates. Strengthening collaboration between poison centers and state health departments is critical for detection and mitigation efforts and can thereby inform resource allocation.