Public Health & Policy

Global genomic surveillance of β-lactam resistance in Escherichia coli across human, animal, and environmental reservoirs.

BACKGROUND: Escherichia coli poses a global health threat from increasing β-lactam resistance. This study uses genomic and One-Health data to map resistance patterns and enhance antimicrobial resistance (AMR) prediction and management. METHODS: This study performed a One-Health whole-genome analysis of 30,554 E. coli isolates from human, animal, and environmental sources, spanning from 2000 to 202...

Treatment-Effect-Based Versus Risk-Based Targeting of Care Management Outreach in Medicaid: A Retrospective Cohort Study with Machine Learning.

Medicaid care-management programs typically allocate scarce outreach capacity to beneficiaries with the highest predicted risk of an acute event, assuming that risk and responsiveness are aligned and stable across short intervals. The authors tested whether targeting outreach by predicted individualized treatment effect-the conditional average treatment effect (CATE) recomputed each calendar month...

Applications of generative artificial intelligence in long-term care services: A systematic review of the evidence on efficacy for improving resident health related quality of life.

BACKGROUND: Generative artificial intelligence (GAI) has rapidly expanded into health and social care, offering new opportunities for communication, c...

The Best of Both Worlds: How Combining a Large Language Model and a Rule-Based Algorithm Makes Catheter-Associated Urinary Tract Infection Surveillance More Efficient.

BACKGROUND: Catheter-associated urinary tract infection (CAUTI) surveillance is critical for patient safety. Many healthcare systems use electronic al...

Digital doppelgangers in psychiatry.

Digital doppelgangers are individualized, continuously updated digital representations of a person constructed from behavioral, physiological, and con...

Deep technologies for responsible gambling: A narrative review of policies and implementation strategies.

BACKGROUND AND AIMS: Deep technologies, such as artificial intelligence, blockchain, and behavioural analytics, have emerged as promising tools to add...

Evaluating large language models for assessment of psychosis risk.

Psychosis prevention relies on early detection of individuals at clinical high risk for psychosis (CHR-P). The effectiveness of the CHR-P state is con...

Bridging Audience Segmentation and Message Intervention: AI-Generated HBM-Based Messages for Diabetes Prevention.

Health communication often faces a segmentation-intervention gap: psychographic segmentation identifies meaningful audience profiles, but these profil...

Machine learning-optimized discharge timing in typhoid care: Implications for clinical outcomes, cost efficiency, and health system performance.

OBJECTIVE: To develop and validate a machine learning model identifying typhoid fever patients at risk of unnecessarily prolonged hospitalization, and...

Evolution of swine influenza surveillance and one health governance in Taiwan: from zoonotic risk management to integrated preparedness.

Influenza A viruses (IAVs) remain a persistent global threat because of rapid antigenic change, frequent genetic reassortment, and cross-species trans...

Performance of artificial intelligence in the collection of patient history in general practice.

OBJECTIVE: Automating administrative tasks, such as compiling a patient's medical history, could help general practitioners in their daily work. AI pe...

Development of a high-resolution PM2.5 model in Taiwan using integrated low-cost air quality sensors and satellite observations.

BACKGROUND: Air pollution modeling generally suffers from the limited and uneven distribution of fixed air quality monitoring (AQM) stations. Low-cost...

Exploring prediction of tick and tick-borne encephalitis cases in Sweden using citizen science data.

Tick-borne encephalitis (TBE) remains a severe public health threat in affected areas with shifting geographic distribution linked to environmental ch...

Preconception, prenatal and postnatal exposure to gaseous air pollutants (NO2, O3) and neurodevelopmental disorders in children among Medicaid recipients: a national cohort study.

Although numerous etiological factors have been proposed for neurodevelopmental disorders, the contribution of environmental exposures remains insuffi...

Machine learning-based analysis of risk factors and construction of a predictive model for hyperuricemia in Chinese health examination population.

Hyperuricemia (HUA) imposes a growing public health burden, calling for better risk stratification tools. In this cross-sectional study of 4906 Chines...

Secure and explainable fraud detection in healthcare claims using blockchain-based machine learning.

Healthcare insurance fraud causes substantial financial losses, operational inefficiencies, and reduced trust among patients, providers, and insurers....

From behavioral to structural correlates: a multi-phase analysis of global reported mpox.

BACKGROUND: Mpox epidemiology has expanded from endemic zoonotic spillover to sustained human-to-human transmission. While behavioral risk factors hav...

Targeting progressive multiple sclerosis: Toward mechanism-informed precision medicine.

Multiple sclerosis has undergone a therapeutic revolution over the past three decades. Randomized clinical trials and real-world data demonstrate that...

Environmental and management drivers of gastrointestinal nematodes in shelter dogs across mainland Portugal revealed by ecological niche modelling.

Gastrointestinal nematodes (GIN) are a major health concern in dogs, particularly in high-density environments such as shelters. Toxocara canis, Ancyl...

Predicting the absence of World Health Organization fungal priority pathogens in agricultural soils using machine learning and ITS metabarcoding.

Environmental fungal pathogens relevant to human and animal health pose significant risks, particularly in regions with intensive farming and climate ...