Optimizing Disaster Preparedness Planning for Minority Older Adults: One Size Does Not Fit All

By 2050, one in five Americans will be 65 years and older. The growing proportion of older adults in the U.S. population has implications for many aspects of health including disaster preparedness. This study assessed correlates of disaster preparedness among community-dwelling minority older adults and explored unique differences for African American and Hispanic older adults. An electronic survey was disseminated to older minority adults 55+, between November 2020 and January 2021 (n = 522). An empirical framework was used to contextualize 12 disaster-related activities into survival and planning actions. Multivariate logistic regression models were stratified by race/ethnicity to examine the correlates of survival and planning actions in African American and Hispanic older adults, separately. We found that approximately 6 in 10 older minority adults did not perceive themselves to be disaster prepared. Medicare coverage was positively associated with survival and planning actions. Income level and prior experience with disaster were related to survival actions in the African American population. In conclusion, recognizing the gaps in disaster-preparedness in elderly minority communities can inform culturally sensitive interventions to improve disaster preparedness and recovery

Access to technology, internet usage, and online health information-seeking behaviors in a racially diverse, lower-income population

BackgroundThis study examined access to technology, internet usage, and online health information-seeking behaviors, in a racially diverse, lower-income population.MethodsData were obtained via a cross-sectional survey of low-income communities in Houston, Los Angeles, and New York between April and August 2023. Binary responses to the following online health information-seeking behaviors, internet and technology access, were examined: using the internet to (i) understand a medical diagnosis, (ii) fill a prescription, (iii) schedule a healthcare appointment, (iv) email communication with a healthcare provider, and (v) access electronic health records and medical notes.Results41% of survey respondents identified as non-Hispanic Black individuals, 33% as non-Hispanic White individuals, and 22% as Hispanic individuals. 69% reported a pre-tax annual household income of less than $35,000. 97% reported ownership/access to a smart device; 97% reported access to reliable internet. In the past year, only 59% reported using the internet to better understand their medical diagnosis, 36% reported filling a prescription online, 47% scheduled a medical appointment online, 47% viewed electronic health records online, and 56% emailed healthcare providers. Female sex, higher incomes, and having at least a bachelor’s degree were significantly associated with all five online health information-seeking attributes.ConclusionDespite high technology adoption rates, we observed suboptimal online health information-seeking behaviors. This underutilization has potential adverse implications for healthcare access and use given the documented advantage of HIT. Efforts to increase health information-seeking behaviors should explore the identification of HIT barriers, and patient education to increase familiarity and usage in this population

Correlates of Social Isolation Among Community-Dwelling Older Adults During the COVID-19 Pandemic

The past year has severely curtailed social interactions among older adults given their high rates of COVID-19 morbidity and mortality. This study examined social, behavioral, and medical correlates of social isolation among community-dwelling older adults during the COVID-19 pandemic and stratified findings to explore unique differences in two typically neglected populations, African American and Hispanic older adults

Leveraging Public-Private Partnerships During COVID-19: Providing Virtual Field Opportunities for Student Learners and Addressing Social Isolation in Older Adults

While preventive and management measures are important to mitigate the spread of COVID-19, strategies like social distancing can have devastating effects on older adults who are already at risk for social isolation and loneliness. In response, two Colleges of Health Professions (Social Work and Nursing) at a large public University leveraged a partnership with a national health and wellbeing company to address social isolation and loneliness in Houston area older adults during the COVID-19 pandemic. This intergenerational linkage initiative involved 707 older adults and 177 graduate social work and nursing students. This study describes the process of developing a virtual educational opportunity for students while also meeting the needs of vulnerable older adults in Houston, the third largest, and one of the most diverse cities in the U.S. Findings include student/learner outcomes, as well as self-reported improvements in loneliness scores, and unhealthy physical and mental health days among enrolled older adults

Hospital Length of Stay in Patients with and without Serious and Persistent Mental Illness: Evidence of Racial and Ethnic Differences

Background: Prior studies have documented racial and ethnic differences in mental healthcare utilization, and extensively in outpatient treatment and prescription medication usage for mental health disorders. However, limited studies have investigated racial and ethnic differences in length of inpatient stay (LOS) in patients with and without Serious and Persistent Mental Illness. Understanding racial and ethnic differences in LOS is necessary given that longer stays in hospital are associated with adverse health outcomes, which in turn contribute to health inequities. Objective: To examine racial and ethnic differences in length of stay among patients with and without serious and persistent mental illness (SPMI) and how these differences vary in two age cohorts: patients aged 18 to 64 and patients aged 65+. Methods: This study employed a retrospective cohort design to address the research objective, using the 2018 Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample. After merging the 2018 National Inpatient Sample’s Core and Hospital files, Generalized Linear Model (GLM), adjusting for covariates, was applied to examine associations between race and ethnicity, and length of stay for patients with and without SPMI. Results: Overall, patients from racialized groups were likely to stay longer than White patients regardless of severe mental health status. Of all races and ethnicities examined, Asian patients had the most extended stays in both age cohorts: 8.69 days for patients with SPMI and 5.73 days for patients without SPMI in patients aged 18 to 64 years and 8.89 days for patients with SPMI and 6.05 days for patients without SPMI in the 65+ cohort. For individuals aged 18 to 64, differences in length of stay were significantly pronounced in Asian patients (1.6 days), Black patients (0.27 days), and Native American patients/patients from other races (0.76 days) if they had SPMI. For individuals aged 65 and older, Asian patients (1.09 days) and Native American patients/patients from other races (0.45 days) had longer inpatient stays if they had SPMI. Conclusion: Racial and ethnic differences in inpatient length of stay were most pronounced in Asian patients with and without SPMI. Further studies are needed to understand the mechanism(s) for these differences

Hospital Length of Stay in Patients with and without Serious and Persistent Mental Illness: Evidence of Racial and Ethnic Differences

Background: Prior studies have documented racial and ethnic differences in mental healthcare utilization, and extensively in outpatient treatment and prescription medication usage for mental health disorders. However, limited studies have investigated racial and ethnic differences in length of inpatient stay (LOS) in patients with and without Serious and Persistent Mental Illness. Understanding racial and ethnic differences in LOS is necessary given that longer stays in hospital are associated with adverse health outcomes, which in turn contribute to health inequities. Objective: To examine racial and ethnic differences in length of stay among patients with and without serious and persistent mental illness (SPMI) and how these differences vary in two age cohorts: patients aged 18 to 64 and patients aged 65+. Methods: This study employed a retrospective cohort design to address the research objective, using the 2018 Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample. After merging the 2018 National Inpatient Sample’s Core and Hospital files, Generalized Linear Model (GLM), adjusting for covariates, was applied to examine associations between race and ethnicity, and length of stay for patients with and without SPMI. Results: Overall, patients from racialized groups were likely to stay longer than White patients regardless of severe mental health status. Of all races and ethnicities examined, Asian patients had the most extended stays in both age cohorts: 8.69 days for patients with SPMI and 5.73 days for patients without SPMI in patients aged 18 to 64 years and 8.89 days for patients with SPMI and 6.05 days for patients without SPMI in the 65+ cohort. For individuals aged 18 to 64, differences in length of stay were significantly pronounced in Asian patients (1.6 days), Black patients (0.27 days), and Native American patients/patients from other races (0.76 days) if they had SPMI. For individuals aged 65 and older, Asian patients (1.09 days) and Native American patients/patients from other races (0.45 days) had longer inpatient stays if they had SPMI. Conclusion: Racial and ethnic differences in inpatient length of stay were most pronounced in Asian patients with and without SPMI. Further studies are needed to understand the mechanism(s) for these differences

ChatGPT’s Performance in Cardiac Arrest and Bradycardia Simulations Using the American Heart Association's Advanced Cardiovascular Life Support Guidelines: Exploratory Study

BackgroundChatGPT is the most advanced large language model to date, with prior iterations having passed medical licensing examinations, providing clinical decision support, and improved diagnostics. Although limited, past studies of ChatGPT’s performance found that artificial intelligence could pass the American Heart Association’s advanced cardiovascular life support (ACLS) examinations with modifications. ChatGPT’s accuracy has not been studied in more complex clinical scenarios. As heart disease and cardiac arrest remain leading causes of morbidity and mortality in the United States, finding technologies that help increase adherence to ACLS algorithms, which improves survival outcomes, is critical. ObjectiveThis study aims to examine the accuracy of ChatGPT in following ACLS guidelines for bradycardia and cardiac arrest. MethodsWe evaluated the accuracy of ChatGPT’s responses to 2 simulations based on the 2020 American Heart Association ACLS guidelines with 3 primary outcomes of interest: the mean individual step accuracy, the accuracy score per simulation attempt, and the accuracy score for each algorithm. For each simulation step, ChatGPT was scored for correctness (1 point) or incorrectness (0 points). Each simulation was conducted 20 times. ResultsChatGPT’s median accuracy for each step was 85% (IQR 40%-100%) for cardiac arrest and 30% (IQR 13%-81%) for bradycardia. ChatGPT’s median accuracy over 20 simulation attempts for cardiac arrest was 69% (IQR 67%-74%) and for bradycardia was 42% (IQR 33%-50%). We found that ChatGPT’s outputs varied despite consistent input, the same actions were persistently missed, repetitive overemphasis hindered guidance, and erroneous medication information was presented. ConclusionsThis study highlights the need for consistent and reliable guidance to prevent potential medical errors and optimize the application of ChatGPT to enhance its reliability and effectiveness in clinical practice

Rethinking access to care: A spatial-economic analysis of the potential impact of pharmacy closures in the United States.

Data chronicling the geo-locations of all 61,589 pharmacies in the U.S. (from the Homeland Infrastructure Foundation-Level Data (HIFLD) Open Data interface, updated on April 2018) across 215,836 census block groups were combined with Medically Underserved Areas (MUAs) information, and the Centers for Disease Control and Prevention's Social Vulnerability Index (CDC-SVI). Geospatial techniques were applied to calculate the distance between the center of each census block and the nearest pharmacy. We then modeled the expected additional travel distance if the nearest pharmacy to the center of a census block closed and estimated additional travel costs, CO2 emissions, and lost labor productivity costs associated with the additional travel. Our findings revealed that MUA residents have almost two times greater travel distances to pharmacies than non-MUAs (4,269 m (2.65 mi) vs. 2,388 m (1.48 mi)), and this disparity is exaggerated with pharmacy closures (107% increase in travel distance in MUAs vs. 75% increase in travel distance in non-MUAs). Similarly, individuals living in MUAs experience significantly greater average annual economic costs than non-MUAs ($34,834 ±$668 vs. $22,720 ±$326). Our findings suggest the need for additional regulations to ensure populations are not disproportionately affected by these closures and that there is a significant throughput with community stakeholders before any pharmacy decides to close

Population-based assessment of the burden of COVID-19 infection in African countries: a first-year report card and public health implications

Background: The COVID-19 pandemic constitutes a global health threat and poses a major burden on the African continent. We assessed the real-world burden of COVID-19 infection in African Union (AU) member states to determine the distributional patterns of epidemiological measures during the first 1 year of the pandemic. Methods: This retrospective cross-sectional study utilized COVID-19 data from publicly available data repositories of the African Center for Disease Control and Prevention and Our World in Data for the period February 2020 to January 2021. AU member states were classified into low, medium, and high burdens based on COVID-19 morbidity. We conducted descriptive and inferential analyses of COVID-19-reported cases, deaths, recoveries, active cases, COVID-19 tests, and epidemiological measures that included morbidity and mortality rates, case fatality rate (CFR), and case ratios. Results: A total of 3.21 million cases were reported during the 1-year period, with 2.6 million recoveries, 536,784 cases remaining active, and 77,486 deaths. Most countries (49.1%, n  = 26) in AU experienced a low burden of COVID-19 infection compared to 28.3% ( n  = 15) with medium burden and 22.6% ( n  = 12) with high burden. AU nations with a high burden of the disease were mainly in the northern and southern regions. South Africa recorded the highest number of cases (1.31 million), followed by Morocco with 457,625 and Tunisia with 175,065 cases. Correspondently, death tolls for these countries were 36,467, 7888, and 5528 deaths, respectively. Of the total COVID-19 tests performed (83.8 million) during the first 1 year, 62.43% were from high-burden countries. The least testing occurred in the medium-burden (18.42%) countries. The overall CFR of AU was 2.21%. A morbidity rate of 327.52/10 5 population and mortality rate of 5.96/10 5 population were recorded during the first 1-year period with significant variations ( p  < 0.0001) across burden levels. Continental morbidity and mortality rates of 17,359/10 5 and 315.933/10 5 populations were recorded with significant correlation ( r  = 0.863, p  < 0.0001) between them and variations across selected epidemiological measures by COVID-19 burden levels. Conclusion: Understanding the true burden of the disease in AU countries is important for establishing the impact of the pandemic in the African continent and for intervention planning, preparedness, and deployment of resources during COVID-19 surges and future pandemics