Category: Statistics

Am J Public Health. 2026 May;116(5):619-624. doi: 10.2105/AJPH.2025.308390.

ABSTRACT

Evidence-based interventions for Ending the HIV Epidemic inadequately reach Latino men who have sex with men, particularly in Florida and Puerto Rico, stressing the need for implementation strategies. SOMOS Alianza (San Juan, Orlando, Miami Organizational Strategic Alliance) connects implementers, community members, and researchers pursuing Ending the HIV Epidemic goals among Latino men who have sex with men. We describe SOMOS Alianza, present its post-one-year outcomes, and explore how strategic alliances serve as implementation strategies that reduce HIV disparities. (Am J Public Health. 2026;116(5):619-624. https://doi.org/10.2105/AJPH.2025.308390).

PMID:41950451 | DOI:10.2105/AJPH.2025.308390

Am J Public Health. 2026 May;116(5):657-664. doi: 10.2105/AJPH.2025.308362.

ABSTRACT

The COVID-19 pandemic has renewed attention to farmworker health disparities, prompting scholars to examine the structural determinants of agricultural worker health. Through analysis of archival materials, government documents, and period newspapers, we trace the evolution and institutionalization of migrant farm labor on Maryland’s Eastern Shore from 1900 to 1950. We identify four key periods of transformation: the postemancipation agricultural adjustment (1900-1915), the rise of seasonal commodity agriculture (1915-1930), the response to mass displacement (1930-1940), and wartime labor management (1940-1950). At each stage, we demonstrate how agricultural industry interests deliberately cultivated conditions of racial stratification, worker precarity, and social isolation to establish and maintain the migrant labor system. Although contemporary public health frameworks often treat these conditions as independent social determinants of health, this history reveals them as essential, deliberately produced features of the migrant labor system itself. Understanding this historical context is crucial for public health practitioners working to address persistent health disparities in agricultural work, particularly as the COVID-19 pandemic has heightened attention to farmworker vulnerability. (Am J Public Health. 2026;116(5):657-664. https://doi.org/10.2105/AJPH.2025.308362).

PMID:41950448 | DOI:10.2105/AJPH.2025.308362

Am J Public Health. 2026 May;116(5):711-721. doi: 10.2105/AJPH.2025.308407.

ABSTRACT

Objectives. To determine whether optimism and anger modify the association between neighborhood disadvantage and incident cardiovascular disease (CVD) and whether these relationships vary by ethnoracial group. Methods. We drew data from 4326 participants in the Multi-Ethnic Study of Atherosclerosis (MESA: 2000-2019), a cohort of US adults aged 45 to 84 years without baseline CVD. We measured neighborhood disadvantage using the Area Deprivation Index. We assessed optimism and anger (reaction and temperament) by self-report. We used multilevel Cox proportional hazards models to estimate hazard ratios for incident CVD over 19 years of follow-up, adjusting for demographic, behavioral, and clinical factors. Results. A total of 879 incident CVD events occurred. Greater neighborhood disadvantage was associated with higher CVD risk. Tract-level optimism attenuated this association, whereas tract-level anger amplified it. Effects of optimism were stronger among Black participants, whereas anger more strongly exacerbated risk among Hispanic participants. Conclusions. Psychosocial resilience and risk factors modify the impact of neighborhood disadvantage on CVD, with important ethnoracial differences. Public Health Implications. Structural and community-partnered strategies are needed to address ethnoracial differences in how psychosocial factors modify the cardiovascular effects of neighborhood disadvantage. (Am J Public Health. 2026;116(5):711-721. https://doi.org/10.2105/AJPH.2025.308407).

PMID:41950447 | DOI:10.2105/AJPH.2025.308407

Prehosp Emerg Care. 2026 Apr 8:1-9. doi: 10.1080/10903127.2026.2655289. Online ahead of print.

ABSTRACT

OBJECTIVES: Trauma remains the leading cause of death among Canadians under 45, with over 70% of these deaths occurring in the prehospital setting. In Ontario, Canada, paramedics’ decision to initiate or withhold resuscitation in traumatic cardiac arrest (TCA) is governed by basic life support (BLS) and advanced life support (ALS) patient care standards. This study explores paramedics’ decisions to withhold cardiopulmonary resuscitation (CPR) in cases of prehospital TCA.

METHODS: We conducted a retrospective review of case files relating to coroner investigations of prehospital TCA across two emergency medical services (EMS) covering a mixed urban/suburban region in Ontario, Canada, with a population of approximately 4.3 million people, from January 2018 to July 2022. We reviewed all deaths where EMS records were available in the death investigation files and where paramedics did not provide CPR. Paramedics’ documentation of reasons to withhold CPR was reviewed and compared to post-mortem findings. Descriptive statistics were used to describe the findings.

RESULTS: We identified 90 cases of prehospital TCA where no CPR was provided by paramedics. Of these, 55 cases (61%) had documented, injuries incompatible with life (decapitation, open head or torso wounds with visible outpouring of brain or abdominal contents) or signs of irreversible death (rigor mortis, lividity, decomposition). Post-mortem examination confirmed paramedics’ findings of injuries incompatible with life in 29 cases (89%). For the remaining 35 cases (39%), CPR was withheld due to a combination of prolonged time from TCA to EMS contact, severity of injuries deemed non-survivable, significant external blood loss, and following remote physician agreement in 31 (89%) cases. Of these, 29 (83%) had post-mortem findings demonstrating anatomical injuries that made the TCA irreversible.

CONCLUSIONS: The majority of decisions to withhold CPR in prehospital TCA cases are based on signs that are clearly incompatible with life, identified by paramedics with high specificity. In the absence of such findings, paramedics consider factors like prolonged time intervals, overall injury severity, and seek guidance through remote physician supervision before deciding whether to withhold resuscitation efforts.

PMID:41950410 | DOI:10.1080/10903127.2026.2655289

JMIR Aging. 2026 Apr 8;9:e84616. doi: 10.2196/84616.

ABSTRACT

BACKGROUND: Older adults frequently experience cognitive, physical, sensory, motivational, and environmental barriers that affect medication management. Medication adherence technologies (MATs) can support adherence, but their usability varies widely depending on individual abilities and device features. Prior research has largely focused on overall adherence or user experience, providing limited insight into feature-level usability challenges.

OBJECTIVE: The aim of the study is to develop and internally validate a personalized predictive model to predict the success of MAT subtasks for older adults with diverse cognitive, physical, sensory, motivational, and environmental capabilities.

METHODS: A mixed methods approach was used, incorporating the assessment of impairments using various standardized questionnaires, measurement of usability metrics through cognitive walkthroughs, and one-on-one semistructured interviews. For this study, we used “subtasks” as the representative of features of the devices. A subtask is a discrete, individual action that forms part of a larger task, specifically designed to achieve a step in the overall process. Participants tested between 1 and 7 devices from a selection of 13 devices. The proportion of subtask success was taken as the outcome measure. Predictors included demographic, clinical, cognitive, physical, sensory, motivational, and environmental characteristics. Personalized predictive modeling using cosine similarity and generalized linear models were compared with nonpersonalized and naive models. Model performance was evaluated using mean square error (MSE) through cross-validation and held-out validation.

RESULTS: A total of 117 participants (mean age 74.6, SD 7.9 years) were recruited, including 96 participants for usability testing and 21 for the validation, all varying in cognitive, physical, sensory, motivational, and environmental abilities. Both personalized (m=0.25) and nonpersonalized models (m=1.0) outperformed naive predictions (m=1.21), demonstrating that subtask-level success can be predicted using routinely measurable demographic and functional characteristics. During cross-validation, personalized models achieved optimal performance at a matching proportion of m=0.25, with MSEs lower than those observed at higher matching levels, although differences compared with nonpersonalized models were not statistically significant (Self-Medication Assessment Tool [SMAT]: P=.50; Daily Living Tasks Dependent on Vision [DLTV]: P=.43). In the held-out validation cohort, personalized models achieved MSEs of 0.89 (SMAT-based) and 1.16 (DLTV-based) at m=0.20, whereas nonpersonalized models demonstrated better performance with MSEs of 0.726 (SMAT-based) and 0.815 (DLTV-based). Models incorporating performance-based vision measures (SMAT-based) consistently outperformed those using self-reported vision scores (DLTV-based) across both personalized and nonpersonalized settings.

CONCLUSIONS: This study demonstrates the feasibility of predicting subtask success of MATs in older adults. While personalization showed limited added benefit in this dataset, the subtask-focused model provides clinically meaningful insights to support evidence-informed selection of medication technologies, reduce usability-related medication errors, and improve adherence outcomes.

PMID:41950360 | DOI:10.2196/84616

JMIR Hum Factors. 2026 Apr 8;13:e86076. doi: 10.2196/86076.

ABSTRACT

BACKGROUND: Electronic health records (EHRs) play an essential role in modern health care, enabling data sharing and improving patient safety; however, even though vendors must adhere to International Organization for Standardization-related usability standards for EHR certification, persistent usability issues continue to undermine efficiency, contribute to clinician burden, and increase the risk of preventable errors.

OBJECTIVE: This scoping review synthesizes existing research on EHR adoption and usability, emphasizing theoretical models, measurement approaches, factors, and analytic methods used to assess or predict usability. We identify gaps and opportunities for integrating predictive analytics and artificial intelligence (AI) to advance research and improve the usability of EHRs.

METHODS: Following Joanna Briggs Institute and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we systematically searched MEDLINE, Web of Science, IEEE Xplore, and Scopus library databases for studies published between January 1, 2009, and April 9, 2025. Inclusion criteria focused on empirical research using predictive methods or models related to EHR usability. Data were charted and synthesized thematically.

RESULTS: Of the 2323 screened papers, 47 studies met inclusion criteria. Most research examined or predicted EHR adoption (not usability) using dominant frameworks, such as the technology acceptance model, unified theory of acceptance and use of technology, and the information system success model, which comprised usability. Factors related to usability-particularly perceived usefulness, perceived ease of use, effort expectancy, and facilitating conditions via the EHR adoption models-appeared frequently. Regression-based methods and structural equation modeling were the most common analytic techniques. No studies applied predictive modeling or AI to predict EHR usability.

CONCLUSIONS: The focus of this study on the prediction of EHR usability and adoption for the past 15 years is distinctive in the literature. It extends prior usability reviews (mostly focusing on adoption, not prediction of usability). Predictive modeling for EHR usability remains underdeveloped throughout 2009 to 2025. Dominant frameworks in the EHR literature continue to prioritize predicting adoption over operational usability. These models rely heavily on self-reported, cross-sectional measures captured at a single postimplementation time point, embedding systematic bias and obscuring longitudinal usability dynamics. Despite the application of increasingly sophisticated predictive techniques-primarily variants of regression and structural equation modeling-usability has remained analytically subordinate to adoption and acceptance constructs for more than 15 years. As a result, widely used models, such as the technology acceptance model and unified theory of acceptance and use of technology, position usability merely as an antecedent to intention or use, rather than as an independent, system-level property that can be empirically measured, modeled, and predicted. Therefore, there is substantial opportunity to integrate predictive analytics, AI, and longitudinal usability measures to build dynamic models.

PMID:41950355 | DOI:10.2196/86076

JMIR Hum Factors. 2026 Apr 8;13:e78236. doi: 10.2196/78236.

ABSTRACT

BACKGROUND: Social media is one of the most accessible and extensive sources of data for tracking and understanding public reactions to COVID-19 policies. Cultural differences between the United States and Japan have resulted in highly distinctive policies and public reactions in each country.

OBJECTIVE: This study aims to analyze the public opinions surrounding COVID-19 mask mandate through 1,102,876 and 560,873 geo-tagged tweets from the United States and Japan during the period from 2020 to 2022. We conducted 3 stages of analysis-relevance to COVID-19 masks, stance for or against masking, and whether the tweets indicate users wearing masks-to understand individuals’ stance towards the mask mandate and their actual mask-wearing behavior.

METHODS: We adopted a semisupervised approach to enhance BERT (Bidirectional Encoder Representations from Transformers) classification results due to data imbalance, which were then visualized through time series and map representations.

RESULTS: In the United States, our data showed that individuals with a bachelor’s degree or higher, as well as those living in states with higher household incomes, are positively correlated with positive attitudes toward mask-wearing. In contrast, in Japan, those with higher education levels or individuals aged 65 years and older were positively correlated with tweets categorized as having a stance against the mask mandate. Key events in Japan, such as the announcement of the state of emergency and the Olympics, served as major triggers for the number boost in public opinion.

CONCLUSIONS: Our analysis of over 1.6 million tweets from the United States and Japan revealed that public opinion shifted notably in response to major events and policy changes during the COVID-19 pandemic. While some trends align with previous research, correlations with education, age, and income suggest that social media data may reflect underlying societal divisions and algorithm-driven biases.

PMID:41950353 | DOI:10.2196/78236

PLoS One. 2026 Apr 8;21(4):e0331831. doi: 10.1371/journal.pone.0331831. eCollection 2026.

ABSTRACT

Wildfires pose a major challenge for many nations. Rapid mapping of their extent is key to evaluating their impacts. We present the first operational monthly burned-area processing chain for Indonesia, based on largely automated processing of Sentinel-2 imagery in Google Earth Engine. Our approach uses a Random Forest applied to Sentinel-2 imagery and integrates FIRMS fire hotspots to reduce false positives. The resulting 20-m monthly burned-area maps cover the entire country. From January 2019 to December 2024, fires burned a cumulative 5.62 million hectares (Mha), including 2.92 Mha that burned once and 1.12 Mha that burned multiple times. This represents a total burned extent of 4.04 Mha. Compared to the MCD64A1 product, our dataset detects more burns with higher spatial detail and accuracy. In total, 122,164 hectares of primary humid forest burned, representing 2.2% of the burned area. In 2019 and 2023, fire activity accelerated around July and peaked in September-October, coinciding with Oceanic Niño Index (ONI) values ≥ +0.5 °C and Indian Ocean Dipole (IOD) values ≥ +1.5°C. In contrast, neutral or negative phases from 2020 to 2022 corresponded with minimal burning. The year 2024 recorded intermediate fire activity without strong climatic anomalies. These findings confirm that climatic anomalies are associated with fire activity in Indonesia, reaffirming the importance of ONI and IOD for early warning. Our results suggest that prevention efforts are limiting forest fires, as burns in 2019 and 2023 remained lower than during earlier events. Monthly burn-scar updates are available on Nusantara Atlas (www.nusantara-atlas.org), an open-access platform for monitoring deforestation in Southeast Asia.

PMID:41950337 | DOI:10.1371/journal.pone.0331831

Sci Adv. 2026 Apr 10;12(15):eaec2739. doi: 10.1126/sciadv.aec2739. Epub 2026 Apr 8.

ABSTRACT

Despite the common perception, most fatal landslides occur in human-transformed environments. Even on steep terrain, anthropogenic disturbances may fundamentally modulate landslides. Most of our knowledge regarding landslide-human interaction is restricted to local models or regional heuristic assessments based on empirical evidence. In this study, we used land-use-land-cover change as a metric to explain human pressure as a preconditioning factor for fatal landslide occurrences to provide a global overview. We addressed countries’ income levels, populations, exposure, and a dataset of ≈60 years of land-use-land-cover changes with mountainous landmasses to compare landslides and fatalities across 46 countries. Our statistical analyses show that land-use-land-cover changes have a substantially greater influence on the density of fatal landslides and landslide fatalities than physical factors such as topography and precipitation, especially in lower-income countries. We observed a marginal landslide impact when the land-use-land-cover change was low, regardless of the income class. Our results emphasize that effective land-use-land-cover planning is critical to decreasing landslide fatalities, especially in low- and lower-middle-income countries.

PMID:41950326 | DOI:10.1126/sciadv.aec2739

Sci Adv. 2026 Apr 10;12(15):eaec3183. doi: 10.1126/sciadv.aec3183. Epub 2026 Apr 8.

ABSTRACT

Influenza viruses encountered in childhood can leave a lasting immunological imprint. To disentangle the mortality effects of age, the circulating seasonal strain, and immune history, we fit statistical mortality models to 54 years of influenza mortality data from the United States. We find strong signatures of subtype-level imprinting in H1N1-dominated seasons following the 2009 pandemic-cohorts imprinted with more similar H1N1 strains experience greater protection. Furthermore, we find large differences in age-specific mortality risk across cohorts based on their imprinted strain and the seasonal strains that were dominant throughout their lifetime. In contrast to older H1N1- and H2N2-imprinted cohorts, our results show that more recent cohorts imprinted with H3N2 have experienced substantially higher mortality through most of their lifetime. Our results highlight the long-term consequences of immune imprinting and its impact on period and cohort influenza mortality. Overall, our findings have important implications for vaccination efforts and future influenza mortality burden.

PMID:41950318 | DOI:10.1126/sciadv.aec3183