Journal of Public Health Management and Practice最新文献

Context: The burden of vastly different diseases has been used to predict National Institutes of Health (NIH) research funding, typically including only 1 year of data. A multi-year analysis of related diseases would provide a more accurate assessment of which diseases are funded appropriately compared to their burden, enabling targeted lobbying efforts and policy changes to ensure equitable funding.

Objectives: Determining under- or over-funded psychiatric and neurological diseases over a 13-year period.

Design: This ecological study utilized disability-adjusted life years (DALYs) to quantify disease burden. Quantile regression residuals and 95% CIs at several quantiles determined under- or over-funding. Joinpoint regression delineated DALY trends using average annual percent change (AAPC).

Setting: The United States.

Participants: Persons contributing DALYs from attention-deficit/hyperactivity disorder, autism, depressive disorders, eating disorders, schizophrenia, epilepsy, headache disorders, multiple sclerosis, Parkinson's disease, or stroke.

Intervention: None.

Main outcome measure: The difference between actual and predicted funding.

Results: Changes in DALYs had a greater impact at higher funding levels. Eating and headache disorders were significantly underfunded at all quantiles. The severity of underfunding at the 50 th quantile decreased for eating (363% [CI: 329%, 386%] to 190% [CI: 170%, 204%]) and headache (1497% [CI: 1164%, 1665%] to 676% [CI: 514%, 759%]) disorders from 2011 to 2023. Disability-adjusted life years for eating disorders decreased (AAPC = -1.0% [CI: -1.1%, -0.9%]), whereas DALYs for headache disorders increased marginally (AAPC = 0.1% [CI: 0.1%, 0.1%]).

Conclusions: Changes in disease burden were correlated with more significant funding changes in diseases with greater baseline funding. The 2 lowest-funded diseases, eating and headache disorders, are being drastically underfunded compared to their disease burden. While this was not correlated with significant aberrations in disease burden trends in the study period, further lobbying efforts and policy changes must be considered to ensure these diseases receive equitable funding.

Context: Health information exchanges (HIEs) are generally underutilized as data sources for public health surveillance, potentially decreasing the ability of public health practitioners to leverage the rich, real-time, clinical, and public health data therewith contained.

Objectives: To systematize the process of implementing an academic-nonprofit partnership (ANPP) designed to leverage multijurisdictional HIE data for public health surveillance of Long COVID by Systematizing the ANPP's. Implementation, with a focus on its operational strengths, opportunities, challenges, and strategies for its sustained growth.

Design: Document review and analysis informed by the Centers for Disease Control and Prevention's Surveillance System Evaluation framework. We employed a systematic approach to the collection and interpretation of 5 types of documents to describe the experience of implementing a multistakeholder, multijurisdictional, HIE-based ANPP for public health surveillance from 2022 to 2025.

Setting: Multijurisdictional settings in the US.

Participants: The ANPP and its constituent organizations.

Intervention: Implementation of the ANPP.

Main outcome measure: Identification of critical lessons learned including key partnership elements, encountered challenges, surveillance framework application, and strategies for implementing a multistakeholder, multijurisdictional ANPP for public health surveillance.

Results: A participatory, iterative approach was used to engage stakeholders on ANPP implementation. Foundational strengths included complementary partner expertise and robust data environments, which created unique opportunities for comprehensive Long COVID surveillance. Challenges involved navigating varied institutional, legal, and regulatory requirements, complex data permission structures, and coordinating teams across different time zones. Key lessons learned highlighted that extensive initial investment in legal frameworks, data environments, and communication protocols, though time-consuming, significantly improves surveillance capabilities. Strategies to ensure success included replacing large meetings with focused working groups, fostering frequent communication, and implementing rigorous inter-team data quality control.

Conclusion: Implementing multistakeholder, multijurisdictional, HIE-based surveillance necessitates substantial upfront investment. Continuous refinement and strategic efforts are vital for overcoming operational complexities and maximizing HIE potential for robust public health surveillance.

Context: Public health organizations are increasingly recognizing the value and potential of data science. However, a gap remains in understanding how data science is being applied in public health.

Objective: This article provides a comprehensive overview of data science applications in real-world public health settings. By describing the characteristics of projects supported by the Centers for Disease Control and Prevention's Data Science Upskilling (DSU) program during 2019-2023, we seek to guide future efforts in public health data science workforce development and data modernization.

Methods: We manually reviewed DSU applications and final presentations about the projects compiled during 2019-2023. We analyzed projects based on 7 characteristics, including public health domain and task, data science topic and method, data modality, tools, and programming languages used.

Results: DSU supported 112 data science projects across 5 annual cohorts (2019-2023). Many projects addressed the COVID-19 pandemic (13%), infectious diseases (13%), and vaccines (11%). Approximately half the projects used data visualization (54%) and statistics (51%), with 42% employing artificial intelligence (AI) and machine learning (ML). Furthermore, 52% of projects were designed to support decision making, and 22% sought to improve processes and programs. Learners primarily used RStudio (50%), Jupyter Notebooks (41%), and Power BI (26%), along with Python (56%) and R (55%). AI and ML use increased from 33% of projects in 2019 to 56% in 2023, demonstrating an evolving focus on advanced methodologies.

Conclusions: Many teams prioritized data visualization, such as dashboards and visualization tools to support decision making, indicating opportunities for additional infrastructure and training in this area. We observed increasing use of AI and ML, suggesting a need for staff upskilling in these domains. Optimally leveraging data science technologies will require workforce development strategies and data modernization efforts to keep pace with the rapidly evolving field.

Context: Human papillomavirus (HPV) is the most common sexually transmitted infection in the US. Despite the availability of safe and effective vaccination to prevent these infections, vaccination rates have remained suboptimal. To design effective evidence-based strategies, a robust understanding of the facilitators of HPV vaccination is needed. Despite the important role of public health departments in rural immunization services delivery, HPV vaccination practices in such settings have not been well described.

Objective: This study aimed to examine multilevel factors that influence HPV vaccination practices within rural public health departments, and to identify strategies used by public health departments in promoting vaccination for adolescent patients.

Design: We conducted in-depth, semistructured exploratory interviews with 21 immunization nurses between June and August 2022. We used a positive deviance design to sample nurses working in county public health departments that were in the top and bottom quartiles for the proportion of missed opportunities to vaccinate against HPV. Transcripts were reviewed independently by 2 interviewers and analyzed to identify key themes.

Participants: Immunization nurses working in rural public health departments in Montana.

Results: Interviews with 21 nurses revealed that increased parental hesitancy toward the HPV vaccine and the communication styles used during vaccine discussions were key factors influencing HPV vaccine uptake among adolescents receiving care at rural public health departments. Participants highlighted the need to engage adolescents through tailored vaccine messaging, create training opportunities for nurses in vaccine conversations, invest in social media campaigns, encourage collaborations with schools and community organizations, and promote HPV vaccination at every patient encounter to improve vaccination rates.

Conclusion: Public health nurses play a crucial role in adolescent immunization services delivery in rural areas. Identifying vaccination strategies implemented by higher-performing public health departments is critical for informing future initiatives.

Context: Populations with complex chronic conditions (CCCs), especially those reliant on medical technology, face disproportionate risks during disasters. Medicaid enrollees make up a large portion of these vulnerable populations, yet states often lack rapid identification systems to inform disaster planning.

Program: Florida Medicaid developed a claims-based method to identify enrollees with CCCs, including those dependent on medical technology, to support emergency preparedness and response activities.

Implementation: Using the Florida Medicaid Management Information System, the team applied a validated diagnostic and procedural code framework to classify enrollees into 12 CCC categories. Data were analyzed by age, geography, and technology dependence. Health plans received real-time reports before and after hurricanes in 2022 to 2024.

Evaluation: Among 4.4 million enrollees, 7.2% had CCCs, and 18.2% of those were technology dependent. Geographic mapping showed higher concentrations in rural and coastal areas prone to disasters. Health plans reported using the data to contact members and coordinate services during hurricane recovery.

Discussion: This effort demonstrates the feasibility and utility of applying claims data to support disaster management. Other states may adapt this approach to improve emergency response and continuity of care for Medicaid's most medically vulnerable populations.

Context: Flooding poses ongoing risks to Thai communities, particularly in low-lying areas such as Tha Song Khon Subdistrict in Northeastern Thailand. While government agencies provide some support, gaps persist in early response, health infrastructure, and community preparedness. Climate change has exacerbated these vulnerabilities, highlighting the need for scalable, community-based disaster preparedness models.

Objectives: To develop and evaluate the effectiveness of a participatory, community-based flood disaster preparedness model as a climate change adaptation strategy.

Design: Participatory action research guided by the Plan-Do-Check-Act framework is implemented in 4 phases: assessment, planning, implementation, and evaluation.

Setting: Fourteen flood-prone villages in Tha Song Khon Subdistrict, Maha Sarakham Province, Northeastern Thailand.

Participants: A total of 298 individuals, including 200 flood-affected residents, 48 village health volunteers, 28 community representatives, 14 local leaders, 6 rescue officers, and 2 public health staff.

Intervention: Implementation of a community-based flood preparedness model, including training of Community Trainers and Community Emergency Response Volunteers (CERVs), establishment of 2 pilot learning centers, and provision of educational materials and first aid equipment.

Main outcome measure: Changes in disaster preparedness knowledge and skills, community engagement in flood response, and sustainability of preparedness activities 6 months post-intervention.

Results: Participants demonstrated improved emergency response and first aid skills. Six months post-intervention, 2 of 14 villages continued model activities and maintained preparedness resources. However, gaps remained in sanitation awareness and access to emergency medical equipment.

Conclusions: Participatory, community-based models can enhance flood preparedness and resilience. The approach shows promise for adaptation and scaling in other climate-vulnerable regions.

Background: Hypertensive retinopathy reflects systemic microvascular injury and serves as a prognostic marker for cardiovascular risk. Racial disparities in hypertension-related outcomes are well documented, but limited data exist on whether the presence of hypertensive retinopathy confers differential cardiovascular risk across Black and White populations.

Methods: In this retrospective cohort study using the TriNetX Research Network (2010-2021), we analyzed 49 14 propensity score-matched adults (24 557 Black and 24 557 White) with diagnoses of hypertension and hypertensive retinopathy. Baseline characteristics were balanced across 34 variables. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for major cardiovascular outcomes.

Results: Black patients with hypertensive retinopathy had significantly higher rates of all-cause hospitalization (46.4% vs. 43.9%; HR 1.076, 95% CI 1.048-1.105), acute heart failure (10.9% vs. 9.7%; HR 1.125, 95% CI 1.064-1.189), myocardial infarction (5.7% vs. 5.0%; HR 1.139, 95% CI 1.055-1.230), and ischemic stroke (9.7% vs. 7.9%; HR 1.228, 95% CI 1.157-1.304). Atrial fibrillation/flutter was less common in Black patients (8.9% vs. 10.4%; HR 0.848, 95% CI 0.801-0.898).

Conclusions: Among patients with hypertensive retinopathy, Black individuals experienced worse cardiovascular outcomes compared to White individuals. These findings highlight the importance of hypertensive retinopathy as a clinical risk marker and emphasize the need for equitable, targeted interventions to mitigate racial disparities in hypertension-related complications.

Objectives: Countries implemented COVID-19 containment measures based on their decentralization level, which influenced public trust in both the government and the vaccine. This study aimed to evaluate how decentralization influenced the effectiveness of COVID-19 containment measures, public trust in government, and trust in the vaccine across 18 countries.

Design: A cross-sectional design was employed, drawing on a multi-country dataset from an international survey conducted in 2022. Data were collected through stratified random sampling, using an online survey administered via country-specific panels.

Setting: Multiple states with 18 countries.

Participants: 18 000 adult participants.

Main outcome measures: Decentralization was assessed using the Regional Authority Index, and government containment responses were measured with the COVID-19 Government Response Stringency Index. Trust in government and trust in COVID-19 vaccines were evaluated through survey items on confidence in equitable vaccine delivery and perceived vaccine safety, respectively, both measured on a 5-point Likert scale. Sociodemographic characteristics (age, gender, education, and income) were included as individual-level covariates, while country-level indicators (GDP per capita, Socio-Demographic Index, Healthcare Access and Quality Index, Global Health Security Index, Sustainable Development Goals Index, health expenditure, COVID-19 case rates, and inflation) were incorporated as contextual variables.

Results: Decentralization significantly increased public trust in government but was negatively associated with the stringency of containment measures. Vaccine trust and higher education were positively linked to trust in government and vaccine acceptance. However, higher COVID-19 case rates correlated with reduced public trust. Government stringency was driven more by outbreak severity than decentralization alone.

Conclusions: Decentralization can bolster trust in government, but its impact on containment effectiveness depends on local capacity and coordination. These findings highlight the importance of institutional trust and governance structures in pandemic response outcomes.