Machine learning techniques in hepatic encephalopathy: a scoping review.

Introduction: Hepatic encephalopathy (HE) is defined as a specific type of cerebral dysfunction that encompasses a wide range of cognitive, psychomotor, and psychiatric disturbances. The burgeoning field of Artificial Intelligence (AI), particularly Machine Learning (ML), offers promising avenues for early detection and enhanced control of HE. This scoping review aims to provide a consolidated overview of AI’s role in the diagnosis and management of HE, thereby informing and guiding future research endeavors in this domain.

Geospatial Visualization of Dialysis Accessibility in Shiraz: A Nonanalytical Geographic Information System Approach.

Background And Aims: Chronic kidney disease (CKD) poses a substantial global health challenge, contributing to increased morbidity and mortality, diminished quality of life, and escalating healthcare expenditures. Despite advancements in nephrology and dialysis technologies, disparities in hemodialysis (HD) accessibility remain prevalent, leading to suboptimal patient outcomes and increased healthcare burdens. Geographic Information Systems (GIS) facilitate the spatial analysis of healthcare service distribution, identifying inequities in access.

Quantifying Spatial variation in environmental and sociodemographic drivers of leptospirosis in the Dominican Republic using a geographically weighted regression model.

Spatial variation in drivers of leptospirosis transmission in the Dominican Republic is poorly understood. To inform targeted public health interventions, we aimed to characterise risk factors and drivers of transmission on a fine spatial scale. We analysed data from 2078 participants in two provinces, Espaillat and San Pedro de Macoris (SPM), collected from a 2021 cross-sectional survey.

Urban gentrification and infectious diseases: an interdisciplinary narrative review.

Urban gentrification, the transformation of neighbourhoods by influx of new residential groups, leading to displacement of lowerincome communities, is a complex, multifaceted process with significant but generally unexplored public health implications. This study focused on the impact of this process on infectious disease dynamics investigating key factors such as sociodemographic disparities, economic conditions, housing and urban environmental changes. A systemic literature research was performed based on the search terms: gentrification and infectious disease in PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar, with additional references identified using the snowballing method.

Geospatial analysis of leptospirosis clusters and risk factors in two provinces of the Dominican Republic.

Background: Drivers of leptospirosis transmission can vary across regions, leading to spatial clustering of infections. This study aims to identify clusters of leptospirosis seroprevalence in the Dominican Republic (DR) and factors associated with high-risk areas.

Methodology/principal Findings: We analysed data from two provinces, Espaillat and San Pedro de Macoris (SPM), obtained on a national survey conducted in 2021 (n = 2,078).

The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure.

Spatial epidemiology, defined as the study of spatial patterns in disease burdens or health outcomes, aims to estimate disease risk or incidence by identifying geographical risk factors and populations at risk (Morrison et al., 2024). Research in spatial epidemiology relies on both conventional approaches and Machine- Learning (ML) algorithms to explore geographic patterns of diseases and identify influential factors (Pfeiffer & Stevens, 2015).

National spatiotemporal patterns of influenza-like illness in Iran: A pre-COVID-19 study (2015-2019).

Lower respiratory tract infections, including Influenza-Like Illness (ILI), contribute significantly to local and global mortality rates. This study aimed to identify high-risk areas for ILI incidence at the county level in Iran during the 4-year period prior to COVID-19. Data were analyzed from 109,167 ILI notifications collected between March 2015 and March 2019 through "The Iran Influenza Surveillance System (IISS)".

Spatial multilevel analysis of individual, household, and community factors associated with COVID-19 vaccine hesitancy in the Dominican Republic.

Vaccine hesitancy or refusal poses a significant public health challenge resulting in the resurgence of preventable diseases and undermining the effectiveness of national and global health initiatives. This study investigates multilevel determinants of COVID-19 vaccine hesitancy in the Dominican Republic (DR) shortly after the launch of the national COVID-19 vaccination campaign in February 2021. Participants aged 18 years and older were enrolled through a national multistage cluster survey conducted from June-October 2021.

Nationwide spatial epidemiological dataset of over 100,000 influenza-like illness notifications in Iran by county (2015-2019).

Objectives: This data note documents influenza-like illness (ILI) notifications in Iran by county from 2015 to 2019 as a pre-COVID-19 dataset, providing individual and spatial data for further comprehensive spatiotemporal analysis. Due to the high contagion rate of ILI and global health impact, precise geographic mapping serves as a critical tool for public health officials and researchers to monitor, mitigate, and predict epidemics. By utilizing advanced spatial-temporal epidemiological analysis to study disease occurrence patterns, this geodatabase can enable a better understanding and more effective management of ILIs in the future.

A study protocol for developing a spatial vulnerability index for infectious diseases of poverty in the Caribbean region.

Infectious diseases of poverty (IDoP) affect disproportionately resource-limited and marginalized populations, resulting in spatial patterns of vulnerability across various geographical areas. Currently, no spatial indices exist to quantify vulnerability to IDoP at a fine geographical level within countries, such as municipalities or provinces. Without such an index, policymakers cannot effectively allocate resources or target interventions in the most vulnerable areas.

Spatiotemporal clusters of acute respiratory infections associated with socioeconomic, meteorological, and air pollution factors in South Punjab, Pakistan.

Background: In Pakistan, acute respiratory infections (ARI) continue to be a major public health problem. However, there is still a lack of scholarly work regarding different environmental and socioeconomic influencing factors and how they interact with respiratory infections. Furthermore, we do not know much about geographic variation in this context.

Empowering access: unveiling an overall composite spatial accessibility index to healthcare services in Southeastern Iran.

Background: Access to healthcare is critical for population health; however, geographic barriers persist especially in rural and deprived regions. This study aims to develop an overall composite potential spatial accessibility index to healthcare facilities and services in Sistan and Baluchestan Province in southeast Iran.

Methods: This study employed the enhanced two-step floating catchment area (E2SFCA) method to create an overall composite spatial accessibility index for healthcare facilities and services in Sistan and Baluchestan Province, southeast Iran.

Cardiovascular health in perspective: a comprehensive five-year geodatabase of hospitalizations and environmental factors in Mashhad, Iran.

Objectives: This data note presents a comprehensive geodatabase of cardiovascular disease (CVD) hospitalizations in Mashhad, Iran, alongside key environmental factors such as air pollutants, built environment indicators, green spaces, and urban density. Using a spatiotemporal dataset of over 52,000 hospitalized CVD patients collected over five years, the study supports approaches like advanced spatiotemporal modeling, artificial intelligence, and machine learning to predict high-risk CVD areas and guide public health interventions.

Data Description: This dataset includes detailed epidemiologic and geospatial information on CVD hospitalizations in Mashhad, Iran, from January 1, 2016, to December 31, 2020.

Klebsiella pneumoniae species complex bloodstream infection in adult patients: changing epidemiology and determinants of poor outcomes.

Purpose: Klebsiella pneumoniae is a common cause of hospital- and community-acquired infection and can readily acquire multiple antimicrobial resistance determinants leading to poor health outcomes. We define the contemporary burden of disease, risk factors for antimicrobial resistance, and poor health outcomes for patients with K. pneumoniae bloodstream infection (Kp-BSI).

Fatal journeys: causes of death in international travellers in South America.

Background: Understanding mortality among travellers is essential for mitigating risks and enhancing travel safety. However, limited evidence exists on severe illnesses and injuries leading to death among travellers, particularly in low- and middle-income countries and remote regions.

Methods: We conducted a retrospective census study using country-level observational data from death certificates of travellers of seven South American countries (Argentina, Brazil, Chile, Colombia, Ecuador, Peru and Uruguay) from 2017 to 2021.

STROBE-GEMA: a STROBE extension for reporting of geographically explicit ecological momentary assessment studies.

Context: While a growing body of research has been demonstrating how exposure to social and built environments relate to various health outcomes, specific pathways generally remain poorly understood. But recent technological advancements have enabled new study designs through continuous monitoring using mobile sensors and repeated questionnaires. Such geographically explicit momentary assessments (GEMA) make it possible to link momentary subjective states, behaviors, and physiological parameters to momentary environmental conditions, and can help uncover the pathways linking place to health.

Investigating the role of the built environment, socio-economic status, and lifestyle factors in the prevalence of chronic diseases in Mashhad: PLS-SEM model.

Background: Chronic diseases remain a significant contributor to both mortality and disability in our modern world. Physical inactivity and an unhealthy diet are recognized as significant behavioral risk factors for chronic diseases, which can be influenced by the built environment and socio-economic status (SES). This study aims to investigate the relationship between the built environment, SES, and lifestyle factors with chronic diseases.

A gender-specific geodatabase of five cancer types with the highest frequency of occurrence in Iran.

Objectives: Cancer is a global health challenge with complex characteristics. Despite progress in research and treatment, a universally effective prevention strategy is lacking. Access to reliable information, especially on occurrence rates, is vital for cancer management.

Associations between gentrification, census tract-level socioeconomic status, and cycling infrastructure expansions in Montreal, Canada.

Background: Cycling infrastructure investments support active transportation, improve population health, and reduce health inequities. This study examines the relationship between changes in cycling infrastructure (2011-2016) and census tract (CT)-level measures of material deprivation, visible minorities, and gentrification in Montreal.

Methods: Our outcomes are the length of protected bike lanes, cyclist-only paths, multi-use paths, and on-street bike lanes in 2011, and change in total length of bike lanes between 2011 and 2016 at the CT level.

Cancer patterns in Iran: a gender-specific spatial modelling of cancer incidence during 2014-2017.

Background: Cancer is a significant public health concern and the second leading cause of death. This study aims to visualize spatial patterns of top common cancer types and identify high-risk and low-risk counties for these cancers in Iran from 2014 to 2017.

Methods: In this study, we analyzed 482,229 newly diagnosed cancer cases recorded by the Iranian National Population-Based Cancer Registry from 2014 to 2017.

A Mixed Model Approach for Estimating the Optimal Food Fortification of Vitamin D: Experiment Based on Mashhad Cohort Study in Iran.

Background: Vitamin D deficiency is a prevalent problem in worldwide healthcare related to several system disorders. Food fortification as a solution is associated with several challenges including insufficient coverage of the entire population, required degree of fortification, the vehicles used for fortification and potential toxicity. This study aimed to determine the optimal amount of vitamin D for fortification without surpassing the upper intake level (UL) of intake at the 95th percentile of the Iranian population and compare two methods of food fortification.

From Snow's map of cholera transmission to dynamic catchment boundary delineation: current front lines in spatial analysis.

The history of mapping infectious diseases dates back to the 19th century when Dr John Snow utilised spatial analysis to pinpoint the source of the 1854 cholera outbreak in London, a ground-breaking work that laid the foundation for modern epidemiology and disease mapping (Newsom, 2006). As technology advanced, so did mapping techniques. In the late 20th century, geographic information systems (GIS) revolutionized disease mapping by enabling researchers to overlay diverse datasets to visualise and analyse complex spatial patterns (Bergquist & Manda 2019; Hashtarkhani et al.

Where to place emergency ambulance vehicles: use of a capacitated maximum covering location model with real call data.

This study integrates geographical information systems (GIS) with a mathematical optimization technique to enhance emergency medical services (EMS) coverage in a county in the northeast of Iran. EMS demand locations were determined through one-year EMS call data analysis. We formulated a maximal covering location problem (MCLP) as a mixed-integer linear programming model with a capacity threshold for vehicles using the CPLEX optimizer, an optimization software package from IBM.