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The coronavirus disease 2019 (COVID-19) pandemic presented unique challenges to the U.S. healthcare system, particularly for nonprofit U.S. hospitals that are obligated to provide community benefits in exchange for federal tax exemptions. We sought to examine how hospitals initiated, modified, or disbanded community benefits programming in response to the COVID-19 pandemic. We used the free-response text in Part IV of Internal Revenue Service (IRS) Form 990 Schedule H (F990H) to assess health equity and disparities. We combined traditional key term frequency and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering approaches with a novel Generative Pre-trained Transformer (GPT) 3.5 summarization approach. Our research reveals shifts in community benefits programming. We observed an increase in COVID-related terms starting in the 2019 tax year, indicating a pivot in community focus and efforts toward pandemic-related activities such as telehealth services and COVID-19 testing and prevention. The clustering analysis identified themes related to COVID-19 and community benefits. Generative Artificial Intelligence (GenAI) summarization with GPT3.5 contextualized these changes, revealing examples of healthcare system adaptations and program cancellations. However, GPT3.5 also encountered some accuracy and validation challenges. This multifaceted text analysis underscores the adaptability of hospitals in maintaining community health support during crises and suggests the potential of advanced AI tools in evaluating large-scale qualitative data for policy and public health research. [ABSTRACT FROM AUTHOR]

We sought to examine how the impact of revocable behavioral interventions, e.g., shelterin-place, varies throughout an epidemic, as well as the role that the proportion of susceptible individuals had on an intervention's impact. We estimated the theoretical impacts of start day, length, and intensity of interventions on disease transmission and illustrated them on COVID-19 dynamics inWake County, North Carolina, to inform how interventions can be most effective. We used a Susceptible, Exposed, Infectious, and Recovered (SEIR) model to estimate epidemic curves with modifications to the disease transmission parameter (b). We designed modifications to simulate events likely to increase transmission (e.g., long weekends, holiday seasons) or behavioral interventions likely to decrease it (e.g., shelter-in-place, masking). We compared the resultant curves' shape, timing, and cumulative case count to baseline and across other modified curves. Interventions led to changes in COVID-19 dynamics, including moving the peak's location, height, and width. The proportion susceptible, at the start day, strongly influenced their impact. Early interventions shifted the curve, while interventions near the peak modified shape and case count. For some scenarios, in which the transmission parameter was decreased, the final cumulative count increased over baseline. We showed that the timing of revocable interventions has a strong impact on their effect. The same intervention applied at different time points, corresponding to different proportions of susceptibility, resulted in qualitatively differential effects. Accurate estimation of the proportion susceptible is critical for understanding an intervention's impact. The findings presented here provide evidence of the importance of estimating the proportion of the population that is susceptible when predicting the impact of behavioral infection control interventions. Greater emphasis should be placed on the estimation of this epidemic component in intervention design and decision-making. Our results are generic and are applicable to other infectious disease epidemics, as well as to future waves of the current COVID-19 epidemic. Developed into a publicly available tool that allows users to modify the parameters to estimate impacts of different interventions, these models could aid in evaluating behavioral intervention options prior to their use and in predicting case increases from specific events. [ABSTRACT FROM AUTHOR]

Aims We tested the hypothesis that high novelty seeking (NS—an externalizing trait), sweet-liking (SL—a phenotype that may reflect processing of hedonic stimuli) and initial insensitivity to the impairing effects of alcohol (SRE-A) act independently and synergistically to increase the likelihood of having alcohol-related problems in young adults. Methods A sample of 145 young adults, ages 18–26, balanced for gender and alcohol use disorders identification test (AUDIT) scores <8 or ≥8 were selected from a prior sample. NS, SL and SRE-A were assessed along with AUDIT score and family history of alcoholism (FH). The effect of phenotypes and their interaction on the likelihood of alcohol problems was assessed. Results All three phenotypes contribute to the total AUDIT score. The best-fitting model explaining 35.8% of AUDIT variance includes all three phenotypes and an interaction between NS and SL/sweet-disliking (SDL) status. The addition of FH to the model explains an additional 4% of variance in both models. Classification and regression tree analysis showed that the main phenotype influencing AUDIT score is NS. The SL/SDL phenotype is a strong modifying factor for high NS. SRE-A was shown to be a weak modifier for individuals with low NS. Conclusion The evidence supports the hypothesis that the presence of multiple alcohol use disorders (AUD) risk phenotypes with different underlying neurobiological mechanisms within an individual (SL, NS and SRE-A) represents a higher likelihood for developing alcohol-related problems and may allow for a graded assessment of risk for AUD and offer the possibility for early intervention strategies. [ABSTRACT FROM AUTHOR]

Purpose: Rates of alcohol-related mortality (including deaths attributed to chronic alcohol use as well as acute causes involving alcohol) have been increasing in the United States, particularly for certain population subgroups, such as women. This review summarizes associations of area-level social determinants of health with alcohol-related mortality. These determinants, measured at the community, county, or state level, include alcohol control policies, health care availability, and a community's socioeconomic environment. Examining multiple geographic levels illuminates how macro-level social determinants and local contexts contribute to alcohol-related mortality to inform intervention. Attention to the broad variety of social determinants of alcohol-related mortality could ultimately improve community health., Search Methods: A literature search of three databases-PubMed, Web of Science, and Cumulative Index to Nursing and Allied Health Literature (CINAHL)-conducted between March 13 and May 16, 2023, identified peer-reviewed studies published from 1990 to May 2023 that modeled at least one area-level social determinant of health as a predictor or correlate of area-level rates of alcohol-related mortality in the United States. Unpublished dissertations, commentaries, editorials, review papers, and articles published in languages other than English were excluded. Two team members reviewed each abstract to verify that the article addressed alcohol-related mortality and included at least one area-level social determinant of health., Search Results: The authors screened 313 abstracts and excluded 210 that did not meet inclusion criteria. The full texts of 103 articles were retrieved. Upon further screening, 30 articles were excluded (two were not obtained), leaving 71 studies for detailed review., Discussion and Conclusions: Many studies analyzed fatal alcohol-related motor vehicle crashes or cirrhosis/liver disease mortality. Fewer analyzed other mortality causes related to chronic alcohol consumption. No studies focused on racism and discrimination, community-level prevention activities, or community social services in relation to alcohol-related mortality. Few studies examined major health policy changes or addressed health care system factors. Although the variation across studies complicates systematic comparison of the results, some key themes did emerge from the reviewed studies, such as the beneficial effects of stronger alcohol policies and the importance of socioeconomic conditions as determinants of alcohol-related mortality. Research using a more diverse set of theoretically informed social determinants may help examine whether, how, and for whom racism and discrimination as well as health policies and social services impact alcohol-related mortality. Finally, there is a gap in research linking local community contexts with alcohol-related mortality. Better understanding of subgroup differences, interactions between different contextual factors, and specific mechanisms of action may help identify promising new strategies to improve population health and reduce alcohol-related mortality., Competing Interests: Disclosures: The authors declare no competing financial or nonfinancial interests.