Disaster Medicine and Public Health Preparedness

Introduction Louisiana ranks last in the United States of America in terms of maternal health outcomes. Previous works have highlighted the impact of some social determinants of health on the incidence of adverse birth outcomes. These works have subjectively selected specific social determinants of health from larger datasets. Here, we attempt to replicate their results with objective variable selection techniques. Methods By deriving principal components from the Agency of Healthcare Research and Quality's parish-level social determinants of health dataset, we were able to objectively find social determinants of health associations instead of the conventional subjective variable selection approach. Then, we applied Bayesian linear mixed-effects models to calculate more conservative parameter estimates about the effects of social determinants of health on adverse birth outcome incidence. Then, we used local Moran's I to identify clusters of spatially autocorrelated parishes. Finally, we combined the results of these two methods and inspected the relationship between important predictors and clusters of spatial autocorrelation. Results We identified several significant effects on the incidence of adverse birth outcomes, including populational composition and economic attainment, and several clusters of high and low incidences of adverse birth outcomes in Louisiana. There was also a concordant relationship between important predictors from our predictive models and the cluster assignments of Local Moran's I. Conclusion Our results validate previous works in the subject area and hold implications for precision development of maternal health interventions in Louisiana.

PurposeTo characterize maternal transport patterns in Iowa, a state with levels of maternal care and without formal perinatal regions, and assess whether transport decisions reflect efficient, risk-appropriate coordination. MethodsWe analyzed 2010-2023 Iowa birth records, which included 2,251 maternal transports between obstetric facilities across 106 unique routes. We characterized transport patterns and applied a community detection algorithm to identify "communities" of obstetric facilities that disproportionately transport among themselves. FindingsSuburban and rural counties have elevated transport rates compared to urban counties. 2,189 transports (97%) were from lower-to higher-level facilities. Among these, 2,037 (93%) were to Level III tertiary care centers. 567 transports (25.2%) bypassed a closer facility offering an equivalent or higher level of care than its destination facility. Health system affiliation was associated with bypassing transport, indicating potential organizational rather than purely geographic drivers of transport decisions. Three "communities" of obstetric facilities largely shaped by geographic proximity were identified. ConclusionsAlthough Iowa does not have formal perinatal regions, patterns of maternal transport are mostly in line with three de facto regions. Some potential inefficiencies were identified, such as obstetric facilities transporting to a farther facility when a closer facility offered the same level of care or higher. These findings may help identify opportunities to enhance care coordination among obstetric facilities, optimize maternal transport networks, and improve regionalization of maternal care.

Scorpionism is a public health concern in warm regions, particularly affecting children under 10 years old. Timely treatment with antivenom, provided free by the Brazilian Unified Health System, at strategic care points (PEs) is crucial to prevent avoidable deaths. Our study focused on the Sao Paulo state (SP), which has the largest population in Brazil. The objectives were to adapt a network analysis method suited to SPs context; to assess the efficiency of the SP PE network coverage, considering the 90-minute response time; and to determine the ideal number of vials to be stored at each PE. After adapting the healthcare network analysis, we applied spatial coverage models to evaluate the adequacy of PE response times. We also estimated the demand for antivenom vials at each PE based on Notifiable Diseases Information System data from 2021 to 2023, which is currently limited to the state level. We identified 12 areas lacking coverage, of which only one was suitable for a new PE. The estimated serum requirements aligned with SP's current distributions. However, the estimation carried out according to the PEs has the advantage of reducing the risk of antivenom shortages, especially in emergencies, thus ensuring timely care to prevent avoidable deaths. Our adapted method and PE serum estimates can enhance the scorpion sting care system by supporting geographic planning and optimizing resource allocation. Moreover, these findings and methodologies have potential applicability to other Brazilian regions and warm countries facing similar challenges, contributing to improved access and outcomes for scorpionism victims.

Misinformation circulating through digital platforms and community networks increasingly challenges public health communication, particularly in low- and middle-income countries. Frontline health educators play a critical role in addressing misinformation and promoting accurate health information within primary health care systems; however, empirical evidence on their preparedness to manage infodemics remains limited. This study assessed the training needs and response capacity of primary health care health educators in Lagos State, Nigeria. A convergent mixed-methods design was employed across three districts. Quantitative data were collected from 95 health educators using the 30-item Health Educators Infodemic Management Training Needs Assessment Questionnaire (HEIM-TNAQ). Qualitative data were obtained through six focus group discussions involving 56 educators and 25 key informant interviews with supervisors and programme managers. Quantitative data were analysed using descriptive statistics and t-tests, while qualitative data were analysed thematically. Participants demonstrated relatively strong knowledge of health misinformation (mean = 71.5), but only moderate decision-response skills (48.6) and low confidence in addressing misinformation (42.5). Integration of misinformation response into routine practice was also limited (46.3), and no significant differences were observed between respondents with or without prior training. Qualitative findings revealed frequent exposure to vaccine rumours, spiritual explanations for illness, and misinformation circulating through social media and community networks. Strengthening infodemic management within primary health care requires practical training, behavioural communication skills, and institutional mechanisms for systematic rumour monitoring and response.

Background/Objectives: Women+ (e.g., women and individuals assigned female at birth) experience disproportionate health risks and persistent gaps in access to care, despite regionally coordinated health systems. Women+ health research remains significantly underfunded and understudied, contributing to inequities in diagnosis, treatment, and outcomes. This study aims to collaboratively identify and prioritize the most pressing unanswered research questions related to women+ health in the Maritime provinces of Canada. Methods: This study will use a modified Priority Setting Partnership (PSP) methodology based on the James Lind Alliance framework. A mixed-methods participatory approach will be used, including bilingual online surveys (French, English) and a one-day consensus workshop. Participants will include women+, healthcare professionals, researchers, policymakers, and the public residing in the Maritime provinces (Nova Scotia, New Brunswick, and Prince Edward Island). An initial survey will collect research uncertainties through open-ended questions. A second interim survey will rank verified uncertainties, followed by a facilitated workshop to achieve consensus on the Top 10 research priorities. Qualitative data will be analyzed using content analysis, and descriptive statistics will summarize participant demographics. Anticipated Results: This project is expected to generate a collaboratively developed, evidence-informed Top 10 list of research priorities for women+ health in the Maritimes. The process will also identify thematic gaps in existing research and assess feasibility considerations to inform future study design and implementation. Conclusions: By centering women+ voices and engaging diverse interest holders, this study will establish a shared regional research agenda to guide future research, funding, and policy initiatives for women+ health research.

This study evaluates the feasibility of implementing artificial intelligence (AI)-driven disease surveillance systems at Julius Nyerere International Airport (JNIA) in Tanzania, a key hub for regional and international travel. Through a mixed-methods approach combining qualitative interviews and quantitative surveys, the research assesses the infrastructure, human resource capacity, and regulatory frameworks necessary for AI integration. Findings indicate that while Port Health Officers are strongly optimistic about AIs potential to enhance disease detection, the airport faces significant barriers, including outdated infrastructure, insufficient technical resources, and a lack of trained personnel. Ethical and privacy concerns, particularly surrounding data security, also emerged as key challenges, compounded by limited public awareness and the socio-cultural acceptability of AI systems. Furthermore, the study identifies gaps in national policies and inter-agency coordination that hinder the effective implementation of AI technologies. The research concludes that while current conditions render AI adoption infeasible, strategic investments in infrastructure, workforce training, and policy development could pave the way for future integration, enhancing public health surveillance at JNIA and potentially other airports in low- and middle-income countries. This study contributes critical insights into the barriers and opportunities for AI-driven disease surveillance in low-resource settings, specifically focusing on a high-priority transit point, international airports. It emphasizes the importance of region-specific solutions to enhance health security in East Africa and supports the broader global health agenda by advocating for international collaboration and the development of scalable disease surveillance systems. Future research should explore pilot AI implementations at other airports to evaluate real-world challenges and refine AI systems for broader applicability, including cost-effectiveness analyses and integration of public perspectives on AI.

BACKGROUND: Integrated WASH interventions are essential for improving public health by increasing access to safe water, sanitation, and hygiene services. This study evaluates their impact on water access and household knowledge, attitudes, and practices (KAP) in rural communities by comparing intervention and non-intervention areas. METHODS: A cross-sectional survey was conducted in May 2025 across six kebele administrations (three intervention and three control). Data were collected from 396 households with children under five using structured questionnaires, with equal representation from both groups. Descriptive analysis was applied to compare outcomes. RESULTS: Children in intervention areas experienced significantly lower diarrhea rates (2.5% vs. 34.9%). Households also showed improved health behaviors, including higher rates of facility births (88.9% vs. 63.6%), breastfeeding (98% vs. 89.9%), and vaccination (78.8% vs. 59.1%). Access to safe water improved markedly: all intervention households used protected sources, spent less time collecting water (13.9 vs. 55.8 minutes), and consumed more water daily (20.6 vs. 10.5 liters). Safely managed water services reached 59.6% compared to just 1% in control areas. Sanitation and hygiene practices were also better, with higher latrine access (95% vs. 78.3%), reduced open defecation (23.2% vs. 52%), and increased handwashing with soap (48.5% vs. 12.1%). Knowledge, attitudes, and practices were significantly stronger in intervention communities. CONCLUSION: Integrated WASH interventions significantly improve water access, hygiene practices, and child health outcomes. Sustaining these benefits requires continued investment in infrastructure, community awareness, and behavior change programs. KEY WORDS: Water, sanitation and hygiene, KAP, rural Ethiopia

Background: Palliative care services improve quality of life and health outcomes for individuals living with chronic and life-limiting illnesses. Although these services have expanded considerably in urban areas, their availability remains limited in many rural communities. This study aimed to identify key components of integrated palliative care services and examine how these elements are implemented within rural healthcare systems in southern Minnesota. Methods: A qualitative case study using deductive content analysis was conducted. Semi-structured interviews were carried out with healthcare professionals involved in palliative and hospice care serving rural communities in southern Minnesota. Results: Participants identified several essential components of integrated palliative care, including multidisciplinary care teams, continuity of care across healthcare settings, interprofessional collaboration, and early identification of patients who may benefit from palliative care. Existing services in southern Minnesota incorporate several integrated elements, such as coordinated care teams, individualized care plans, nurse-led case management, professional training, and the use of virtual visits for geographically distant patients. However, participants also identified important gaps, including limited availability of palliative care services in rural areas, fragmented continuity of care, challenges in early patient identification, funding and insurance barriers, and the absence of a unified palliative care network. Conclusions: While palliative care services in southern Minnesota demonstrate important strengths, further efforts are required to improve service integration, coordination, and access for rural populations. Strengthening integrated PCSs may help reduce disparities in access to care and improve service delivery for rural patients and their families. These findings may inform the development of integrated palliative care models in rural healthcare systems beyond the study setting.

BackgroundDigital health plays an indispensable role in facilitating data analysis and use for enhancing healthcare delivery across health settings. However, there is scant information on the extent to which digital health influences the improvement of primary health services delivery through data use. This study examined the determinants that influence the use of digital health to improve health service delivery in council hospitals in Tanzania. MethodsA cross-sectional design was employed in six regions, involving 12 council hospitals. We used a self-administered questionnaire to collect data from 203 members of hospital quality improvement teams. Descriptive analysis was used to determine the frequency, proportion, and mean of responses, while bootstrapping analysis was conducted to test the statistically significant influence of digital health factors on data use for improving health service delivery. ResultsResults show moderate agreement on data compatibility for planning and decision-making, with 40.4% of respondents agreeing it supports ordering commodities, 43.8% for staff allocation, and 38.4% for planning. However, dissatisfaction was higher for user-friendliness (47.8%), reliability (up to 65.5%), and usefulness (up to 63.5%). Overall, 50.2% (M=2.74{+/-}0.87) disagreed that digital systems effectively support data use. Structural model analysis confirmed significant positive influence of usefulness ({beta}=0.199, p<0.001) and access to quality data ({beta}=0.729, p<0.001) on data use, which strongly impacted service delivery ({beta}=0.593, p<0.001), despite some factors showing no direct influence. ConclusionThe study finds that current digital health initiatives only modestly improve the user-friendliness, reliability, and usefulness of data systems, partly due to fragmented, non-interoperable platforms that burden data management. However, compatibility, usability, reliability, and usefulness of digital tools significantly enhance access to quality data and data-driven decisions. The study recommends strengthening and integrating existing systems and providing continuous digital health training to institutionalize data-informed decision-making.

ABSTRACT Background: Student nurses satisfaction with their academic programme is crucial for measuring the success of nursing training institutions. However, in Ghana, studies on student nurses' satisfaction have mainly focused on clinical learning, neglecting their satisfaction with the academic programme as a whole. This study therefore, assessed the predictors of student nurses satisfaction with their academic programme. Methods: A quantitative cross-sectional study design was used in the study. A systematic random sampling technique was employed to recruit 241 student nurses from two Nursing Training Institutions in the Eastern Region of Ghana. The Nursing Student Satisfaction Scale (NSSS) was used for data collection and data was analyzed using Statistical Package for Social Sciences (SPSS) version 27 software. Results: Correlation analysis revealed significant positive associations between satisfaction with curriculum (r = 0.583, p<0.001), faculty role (r = 0.650, p<0.001), social interaction (r=0.680, p<0.001), and overall satisfaction with the academic programme. After adjusting for the school of the student nurses, the school environment (B=0.354, p =0.000) and social interaction (B=0.291, p=0.001) emerged as significant predictors of student nurses' satisfaction with their academic programme. Conclusion: The study highlights the need for interventions to enhance the school environment and foster positive social interactions to improve student nurses satisfaction with their academic programme.

Objective: To investigate in the National School Feeding Program (PNAE) the local level drivers and barriers to the implementation of four guidelines: the banning of sugary drinks; restrictions on the procurement of processed and ultra-processed foods; the mandatory increase in weekly servings of fruits and vegetables offered to students; and mandatory direct procurement from family farmers. Design: Qualitative study that used semi-structured interviews. Street level bureaucracy theory informed the theoretical framework and thematic analysis. Setting: Brazilian municipalities, across the country five geographic regions (North, Northeast, Southeast, South, and Midwest). Participants: Stakeholders (e.g. nutritionists, school cooks, and food procurement managers) involved in the local implementation of the PNAE program across the country. Results: Ninety stakeholders were interviewed. Stakeholders reported having autonomy to perform their activities, collaboration and support from other members within the local government and food providers, adequate infrastructure such as a well-equipped kitchens, the availability of trained personnel, and political commitment as drivers for optimum program implementation. Reported barriers included lack of support and resistance to change among cooks, teachers and parents; insufficient physical and human resources; and limited political commitment. When barriers outweighed drivers, interviewees reported adapting their practices, often in restrictive ways that could compromise the implementation of the program. Conclusions: Drivers and barriers to local PNAE implementation were generally similar across studied municipalities, although their magnitude varied. In contexts of greater economic vulnerability and fiscal constraint, additional support and targeted actions from the federal government may be required to strengthen local implementation

With Japans rapidly aging population, demand for home healthcare is projected to increase by 62% by 2040. This study quantitatively evaluated accessibility to 24-hour home healthcare and regional disparities across all 335 secondary medical areas (SMAs) in Japan using the Enhanced Two-Step Floating Catchment Area (E2SFCA) method. We conducted a nationwide cross-sectional study analyzing approximately 430,000 population points at 500-meter mesh resolution. The E2SFCA integrated demand (age-adjusted population), supply (24-hour home care support clinics and hospitals), and transportation (road networks). Accessibility scores (ASs) and Gini coefficients were calculated for each SMA. Wards hierarchical cluster analysis classified regional types, and multiple regression based on the Penchansky and Thomas five-dimensional access framework identified factors associated with the median AS (ASM) and Gini coefficient. The median ASM was 45.71 (0.00-153.49), and the median Gini coefficient was 0.33 (0.06-0.93). Cluster analysis identified six types ranked by descending ASM, from C1 (high access, equitable; n = 48) to C6 (access desert; n = 23). C6 had a median ASM of 0.00 and Gini coefficient of 0.74, indicating virtually no access within a 30-minute catchment. Home-visit standardized claim ratios, used as external validation, declined monotonically from C1 (125.6) to C6 (17.6). For ASM, 24-hour visiting nursing stations ({beta} = +0.369) and clinic physicians ({beta} = +0.342) showed the strongest positive associations, with non-residential area negatively associated ({beta} = -0.273). For the Gini coefficient, non-residential area showed the strongest positive association ({beta} = +0.523). Taxable income per taxpayer was not significantly associated with either outcome. Non-residential area was associated with both lower accessibility and greater intra-regional inequality, suggesting that geographic constraints may limit the effectiveness of resource investment alone. Uniform nationwide implementation of policies shifting care from long-term care beds to home healthcare may not be feasible; region-specific approaches considering geographic characteristics are necessary.

BackgroundThe rapid proliferation of digital platforms has transformed health information access but has also led to increased exposure to misinformation. Existing research lacks standardized tools to quantify individual-level exposure to health misinformation in a comprehensive manner. ObjectiveTo develop a novel composite index--the Misinformation Exposure Index (MEI)--to measure multidimensional exposure to health misinformation among social media users. MethodsA questionnaire-based pilot study was conducted among a young adult population to assess patterns of health information exposure, source utilization, trust, and behavioural responses. The MEI was developed using a multi-domain framework comprising Exposure Frequency, Source Diversity and Risk, Trust in Information, and Behavioural Response. Responses were scored using Likert scales and weighted domain contributions to generate a composite score ranging from 0 to 100. ResultsParticipants demonstrated moderate to high engagement with digital platforms for health information, with reliance on both formal and informal sources. Variability in trust and verification behaviours was observed, with a proportion of participants reporting adoption of health-related practices without professional consultation. Composite MEI scores indicated that most individuals fell within the moderate exposure category, with a subset exhibiting high exposure characterized by frequent engagement with high-risk sources and behavioural influence. ConclusionThe MEI provides a novel and comprehensive framework for quantifying health misinformation exposure by integrating exposure patterns, source characteristics, trust, and behavioural outcomes. The index has potential applications in public health surveillance and intervention design. Further validation through large-scale studies is warranted to establish its reliability and generalizability.

BackgroundIn long-term care facilities (LTCFs), close-contact identification often relies on staff recall and monitoring records because residents may be unable to self-report reliably. How these different record-generation processes relate to proximity-based sensor measurements in routine LTCF workflow remain unclear, and how such differences may influence contact-based decision-making in outbreak response is not well understood. MethodsWe conducted a five-day observational study in a Japanese LTCF using ultra-wideband (UWB) indoor positioning. Twenty-seven participants wore UWB tags, including 16 residents and 11 staff members; 10 staff members completed questionnaires. We compared UWB-derived proximity with questionnaire-derived contacts from staff self-report and monitoring-based proxy records, and assessed directional discrepancies under multiple distance-time thresholds. ResultsQuestionnaire-based records and UWB-derived proximity showed different patterns of discrepancy across contact types. Within this facility, resident-related monitoring-based proxy records showed relatively small directional discrepancies, whereas staff self-reports tended to identify additional resident-staff contacts under the baseline threshold ([&le;]1.0 m for [&ge;]15 min). Several alternative thresholds were associated with discrepancies closer to zero than the baseline, although the apparent ranking varied by summary metric. ConclusionsIn this single-facility observational study, different contact-list generation processes were associated with different patterns of discrepancy relative to a proximity-based operational measure. These findings support interpretation in terms of workflow-specific contact-list generation rather than a single universally optimal threshold and may help inform facility-level review of contact identification practices in LTCFs. These findings support aligning contact identification strategies with facility-specific workflows to improve the feasibility and effectiveness of IPC practices in LTCFs.

Background: Oral health remains inadequately integrated within the Ayushman Bharat Digital Mission (ABDM), particularly in terms of structured risk assessment and its linkage to insurance-based decision-making. There is a growing need for scalable models that can connect clinical oral health data with digital health systems and support future artificial intelligence (AI)-driven applications. Aim: To develop and pilot test the ABHA-O-SHINE framework for oral health risk prediction and insurance prioritization, with a future scope for AI integration within the Ayushman Bharat Health Account (ABHA) ecosystem. Materials and Methods: A cross-sectional pilot study was conducted among 126 participants attending the outpatient department of Swargiya Dadasaheb Kalmegh Smruti Dental College and Hospital, Nagpur. Participants were selected based on predefined inclusion and exclusion criteria. Data collection included a structured questionnaire and clinical examination using the WHO Oral Health Assessment Form (2013). A composite risk score (0 to 14) was developed incorporating behavioral and clinical parameters. Participants were categorized into low, moderate, and high-risk groups, and corresponding insurance priority levels were assigned. Statistical analysis included descriptive statistics, Chi-square test, Spearman correlation, and binary logistic regression. Results: The majority of participants were categorized under moderate to high-risk groups. Tobacco use showed a statistically significant association with higher risk levels (p less than 0.05). Positive correlations were observed between total risk score and clinical indicators such as DMFT and CPI. Logistic regression analysis identified tobacco use and clinical scores as significant predictors of high-risk categorization. Conclusion: The ABHA-O-SHINE framework demonstrates feasibility in integrating oral health risk assessment with an insurance prioritization model. The framework is designed to be AI-compatible, enabling future automation through machine learning and image-based analysis within the ABDM ecosystem. Keywords: ABHA, ABDM, Oral Health, Risk Assessment, Insurance, Artificial Intelligence.

Protracted conflict in Sudan since April 2023 has severely disrupted routine immunization services, particularly in the Darfur region, resulting in widespread vaccine stockouts, declining coverage, and increased risk of vaccine-preventable disease outbreaks. Traditional national supply routes became largely inaccessible, exacerbating inequities in immunization access for conflict-affected and displaced populations. This paper examines the design, implementation, and outcomes of a cross-border vaccine deployment strategy implemented in 2025 through Chad to restore vaccine availability in Darfur. Using programmatic data, shipment records, coverage reports, and partner monitoring outputs, the study assessed the operational feasibility, partnership arrangements, and public health impact of the intervention on routine immunization and outbreak response. In 2025, nearly 20 million doses of vaccines were successfully delivered to the five Darfur states through cross-border operations, supporting routine immunization services and outbreak response campaigns. Average coverage for the first dose of a DPT-containing vaccine (DPT1) increased from 22.6% in 2024 to 83.2% in 2025, while DPT3 and MCV1 coverage rose to 55.4% and 50.4%, respectively. Oral cholera vaccine campaigns achieved 90.4% coverage among targeted populations, and polio outbreak response campaigns exceeded 100% administrative coverage, reflecting both successful reach and uncertainties in target population estimates due to population displacement. Investments in cold chain infrastructure and strengthened coordination among government, UNICEF, Gavi, and implementing partners were critical to these outcomes. The findings demonstrate that cross-border vaccine deployment can serve as a viable and effective mechanism for restoring immunization availability and support recovery of immunization service delivery in a highly constrained conflict setting. While not a substitute for functional national systems, such approaches are essential life-saving interventions during acute crises and should be integrated into preparedness planning for fragile and conflict-affected contexts.

Background Non communicable diseases (NCDs) are significant public health concerns in Bangladesh, placing a heavy burden on the healthcare system. While the situation before COVID-19 was well-documented, it is unclear how the pandemic has impacted the prevalence and risk factors of these diseases. This study provides the first comparative assessment of the prevalence and determinants of diabetes mellitus (DM) and hypertension (HTN) before and after the pandemic, utilizing comprehensive multilevel data source and mixed-effects modeling to capture the shifting epidemiological burden. Methods We analyzed biomarker data from two nationally representative Bangladesh Demographic and Health Surveys (BDHS) 2017-18 and 2022. Diagnosis followed WHO guidelines for fasting blood glucose and blood pressure. Mixed-effect logistic regression models were employed to identify risk factors while accounting for the hierarchical survey design. The Intra-class Correlation Coefficient (ICC) was calculated to quantify the proportion of variance attributable to unobserved community-level heterogeneity. Results The study indicates a profound shift in the national burden of NCDs. Diabetes prevalence more than doubled, from 23% in 2017-18 to 49% in 2022, while hypertension prevalence declined from 22% to 15%, a pattern that may reflect survival bias among individuals with severe comorbidities. The previously strong bidirectional association between DM and HTN weakened in the post pandemic period, hypertension continued to predict diabetes (AOR = 1.17), but diabetes was no longer a significant predictor of hypertension. Community-level determinants became substantially more influential, with local environmental factors playing a much larger role in shaping diabetes prevalence compared to the pre-pandemic period. Urban residence emerged as a significant new risk factor for diabetes in 2022 (AOR = 1.62; 95% CI: 1.34-1.96). Furthermore, the socioeconomic gap in diabetes risk narrowed as the disease affected more wealth groups, while higher educational attainment continued to serve as a protective factor against hypertension (AOR = 0.64; 95% CI: 0.54-0.75). Conclusion The post pandemic landscape of NCDs in Bangladesh shows a clear divergence, marked by a rapid increase in diabetes contrasted with a stabilization in hypertension prevalence. Through comparative mixed effects modeling, this study advances beyond simple prevalence comparisons to demonstrate the growing impact of urban environments and community level factors on metabolic health. These evolving patterns underscore the need for integrated public health strategies that address emerging environmental risks and geographically specific vulnerabilities to support progress toward Sustainable Development Goal Target3.4. Keywords: Bangladesh, BDHS, Community-level variability, COVID 19, Diabetes mellitus, Hypertension, Mixed-effects modeling, Non-communicable diseases, Public health

Introduction Nontyphoidal Salmonella enterica (NTS) is a major zoonotic enteric pathogen. Animal contact-related NTS outbreaks have increased in the United States of America (U.S.) over the last decade. Geospatial analysis can identify locations with elevated risk of NTS outbreaks where public health authorities can focus their NTS prevention and intervention efforts. Methods We analyzed NTS outbreak data reported from individual states to the Centers for Disease Control via the National Outbreak Reporting System between 2009 and 2022 across the continental contiguous U.S. A geospatial analytical framework that included disease mapping, spatial interpolation, and global and local clustering methods was applied to identify regions with high NTS outbreak rates. Results A total of 104 NTS single-state outbreaks were reported to the National Outbreak Reporting System (NORS) during the study period. The mean annual incidence rate was 0.02 NTS outbreaks per million person-years. The primary animal contact categories associated with these outbreaks were mammals (cattle, pigs, sheep, and horses), birds (backyard chickens, ducklings, and turkeys), and reptiles (turtles and lizards). Exposure settings included farms, fairgrounds, agricultural feed stores, veterinary clinics, dairy/agricultural settings, and residential settings. The local cluster detection methods consistently identified areas with significantly high NTS animal contact-related outbreak rates in the Mountain West, Midwest, and Northeast of the US. Conclusion NTS animal contact-related single-state outbreaks revealed distinct spatial clustering across the United States, with potentially higher risks in the Mountain West, Midwest, and Northeast. Diversity of animal-contact sources and exposure settings depicted complex transmission dynamics of NTS. Focused prevention and control programs in these areas are needed to mitigate the burden of NTS outbreaks.

Background. People with lived/living experience of health conditions, as well as caregivers, are increasingly engaged in research. This study aimed to develop and pilot test a new tool measuring the impact of lived/living experience engagement on the research. The measure is called the Measure of Engagement Tool for Research and lived Experience (METRE). Method. We conducted a qualitative descriptive study among 28 people with lived/living experience and caregivers and 12 academic researchers to understand the impacts of engagement. Using the findings, we drafted the METRE. We pilot tested the METRE among 13 people with lived/living experience and caregivers and 10 academic researchers. Insights were used to refine the scale. Results. Qualitatively, participants identified multiple domains of impact of engagement on research, which guided scale development. Pilot testing of the draft METRE revealed it being straightforward to complete, providing a thorough evaluation of the impact of engagement. However, some areas of improvement were recommended. The draft items showed acceptable preliminary performance. Conclusions. An assessment tool is now available to assess the impact of lived/living experience engagement on the research. Additional research is required to evaluate its psychometric properties. Tools to evaluate the impact of engagement on research will help advance the science of engagement and support engaged research teams in their work.

Background and ObjectivesPatient portals have become essential infrastructure for healthcare delivery following the 21st Century Cures Act, yet adoption remains inequitable. Understanding demographic and geographic determinants of portal activation is critical for addressing digital health disparities, particularly among neurology patients who face unique access barriers. We examined the demographic, geographic, and neighborhood-level factors associated with patient portal activation among neurology patients at multiple geographic scales in the Washington, DC metropolitan area. MethodsWe conducted a retrospective cohort study of 72,417 adult neurology patients seen at two academic medical centers sharing an electronic health record in Washington, DC (February 2021-February 2026). We examined portal activation using multivariable logistic regression and geographic analysis at four nested scales: the metropolitan catchment area, DCs eight wards, individual census tracts (via geocoded patient addresses), and individual DC residents. ResultsPortal activation was 64.7% overall. Activation varied by race/ethnicity (Non-Hispanic White 76.1%, Non-Hispanic Black 57.0%, Non-Hispanic Asian 57.6%, Hispanic 55.0%) and geography (DC Ward 2: 82.0% vs. Ward 7: 48.0%). Ward-level educational attainment (r = 0.948), broadband access (r = 0.889), and income (r = 0.811) were strongly correlated with activation. Within individual wards, Non-Hispanic White patients activated at 84-91% while Non-Hispanic Black patients activated at 48-64%, demonstrating that neighborhood resources alone do not explain disparities. DiscussionPatient portal activation is shaped by demographic, socioeconomic, and geographic factors operating at multiple levels. Persistent within-ward racial disparities indicate that geographically targeted interventions must be paired with culturally tailored approaches to achieve digital health equity.