BackgroundWe describe the development of a comprehensive proof-of-concept index of environmental exposures for children based on evidence-informed connections to health behaviours– the Healthy Environments Index for Children (HEIC) - with two sub-indices relating to the food environment (HEIC-FE) and physical activity environment (HEIC-PA) in Taranaki, New Zealand. Associations between this theory-informed index and health outcomes in a cohort of children and adolescents identified with overweight or obesity and enrolled in a community-based healthy lifestyle programme and randomised controlled trial were examined.MethodsThe HIEC was developed using Geographic Information Systems (GIS) and 15 variables selected from a series of systematic literature reviews identifying environmental factors associated with childhood obesity. Activity spaces around each participant’s residential address, and the route to their nearest school were created and used to estimate environmental exposure. Health data from the Whānau Pakari randomised controlled trial (n = 179 at baseline, 121 at 12-months, 95 at 24-months) was integrated to test associations between HEIC and health outcomes. Statistical analyses included spearman rank correlations, multinomial linear regression, and geographically weighted regression.ResultsHigher HEIC scores (indicating health-promoting environments) tended to be clustered within the cities and towns, while rural areas had low HEIC scores. Strong and consistent associations were not identified between HEIC indices and health outcomes in our study population. However, higher HEIC food environments were associated with increased water intake and decreased sweet drink intake at 24-months.ConclusionsThe theory-informed HEIC and its two subindices may be useful tools for policy and practice aiming at improving child health outcomes. However, they require validation in larger studies in other areas of New Zealand.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12942-025-00409-7.

BackgroundEnvironmental factors significantly influence health behaviors and outcomes. While Google Street View (GSV) has emerged as a cost-effective tool for environmental auditing in various countries, its feasibility in Taiwan remains unexplored. This study aimed to examine the validity and reliability of GSV-based environmental audits in Taiwan.MethodsFour administrative districts in Taipei representing different population densities and socioeconomic status were selected. A total of 74 street segments within 40 streets were evaluated using both virtual and field audits. The S-VAT was modified to include 8 categories (38 items) of neighborhood characteristics. To assess criterion validity, field and virtual audits were conducted by one rater with a minimum two-week interval. Inter-rater reliability was evaluated by comparing two raters’ virtual audit results, while intra-rater reliability was assessed through repeated virtual audits by the same rater. Cohen’s Kappa and percentage agreement were used for statistical analysis.ResultsWalking-related (k = 0.768), cycling-related (k = 0.921), and public transport features demonstrated high reliability. Lower reliability was found in aesthetics and grocery stores, primarily due to GSV limitations: aesthetic features (litter, graffiti) were affected by viewing angles and temporal variations, while grocery stores were challenging to assess due to restricted storefront visibility and signage clarity.ConclusionsThe S-VAT demonstrates good validity and reliability for environmental auditing in Taiwan, particularly for transportation-related features. However, caution should be exercised when assessing grocery stores and aesthetic features. This study validates GSV as a feasible tool for conducting environmental audits in Taiwan.

BackgroundThe COVID-19 had an outstanding impact on well-being and mental health, which might have elicited geographical variations over time. This study examines the eventual impact of COVID-19 on self-reported mental distress in the mainland USA.AimsThere were two main aims. First, to evaluate the pre-pandemic (2019; n=412,597) and post-pandemic (2021; n=440,075) mental distress spatial distribution. Second, to contrast spatial data across three age groups, young (18–44 years), middle-aged (45–65 years), and old (older than 65 years).MethodWe considered a the Bayesian modified Besag–York–Molliè (BYM2) model, which is a Bayesian hierarchical model. Mental distress was the response variable function of age group, year and spatially structured and unstructured effects.ResultsThe main findings indicate a positive spatial dependence between states of general mental distress before and after the COVID-19 and across age groups with substantial unstructured component. Moreover, younger individuals reported higher levels of mental distress and suffered the major worsening due to the pandemic.ConclusionsCOVID-19 had a detrimental impact on mental health across the population, with consistent evidence of positive spatial dependence across states. Notably, young adults emerged as particularly vulnerable, exhibiting concerning levels of mental distress problems and being more sensitive to the effects of the pandemic. Henceforth, young adults might require specific tailored public health policies in eventual major pandemic events.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12942-025-00418-6.

BackgroundPneumonia remains a major cause of morbidity and mortality, particularly in low- and middle-income countries, such as Pakistan. In this study, we aimed to examine the spatial and temporal patterns of pneumonia incidence in South Punjab, Pakistan, and to analyze their association with socio-ecological factors.MethodsWe used case report data from the district health information system (DHIS) over the years 2016 to 2020 and applied global and local Moran’s I to identify spatial autocorrelation. Furthermore, we employed hot and cold spot analysis to identify significant areas with high and low pneumonia incidence. We used Emerging Hot Spot Analysis (EHSA) and time series clustering to examine shifting and temporal patterns of incidence, respectively. In addition, Generalized Linear Regression (GLR) and Multiscale Geographically Weighted Regression (MGWR) models were used to analyze geographic variation in the association of socio-ecological factors and pneumonia incidence.ResultsOur results showed no significant global clustering of pneumonia incidence. Local Moran’s I identified a low-low cluster in DG Khan, while Hot Spot Analysis detected one hot spot in Rajanpur. Multan City showed higher case counts, but this reflected population concentration rather than elevated incidence rates. The temporal analysis confirmed a significant seasonal variation, as well as a decrease in certain Tehsils and an increase in others. Our MGWR model revealed that better female literacy reduced incidence rates of pneumonia, whereas poor housing quality increased incidence rates of pneumonia, particularly in the southwestern areas of South Punjab.ConclusionsWe conclude that socio-ecological variables significantly influenced the incidence of pneumonia in South Punjab, and this association varies substantially over time and space. Our results emphasize the need for locally specific public health interventions to minimize pneumonia incidence in vulnerable populations in Pakistan. Our spatial epidemiological approach can be adapted to other regions of Pakistan and similar socio-ecological contexts in low- and middle-income countries.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12942-025-00420-y.

Most of the growing prospective analytic methods in space–time disease surveillance and intended functions of disease surveillance systems focus on earlier detection of disease outbreaks, disease clusters, or increased incidence. The spread of the virus such as SARS-CoV-2 has not been spatially and temporally uniform in an outbreak. With the identification of an infectious disease outbreak, recognizing and evaluating anomalies (excess and decline) of disease incidence spread at the time of occurrence during the course of an outbreak is a logical next step. We propose and formulate a hypergeometric probability model that investigates anomalies of infectious disease incidence spread at the time of occurrence in the timeline for many geographically described populations (e.g., hospitals, towns, counties) in an ongoing daily monitoring process. It is structured to determine whether the incidence grows or declines more rapidly in a region on the single current day or the most recent few days compared to the occurrence of the incidence during the previous few days relative to elsewhere in the surveillance period. The new method uses a time-varying baseline risk model, accounting for regularly (e.g., daily) updated information on disease incidence at the time of occurrence, and evaluates the probability of the deviation of particular frequencies to be attributed to sampling fluctuations, accounting for the unequal variances of the rates due to different population bases in geographical units. We attempt to present and illustrate a new model to advance the investigation of anomalies of infectious disease incidence spread by analyzing subsamples of spatiotemporal disease surveillance data from Taiwan on dengue and COVID-19 incidence which are mosquito-borne and contagious infectious diseases, respectively. Efficient R packages for computation are available to implement the two approximate formulae of the hypergeometric probability model for large numbers of events.

To address challenges arising from rapid urban development, China has formulated and implemented the New-Type Urbanization strategy. However, empirical research on the specific impacts between New-Type Urbanization and health expenditures remains limited. Using panel data from 31 Chinese provinces (2012-2019), this study constructed a comprehensive evaluation index system for New-Type Urbanization across four dimensions: demographic, economic, social, and ecological. Geographically and Temporally Weighted Regression was employed to examine the spatial effects, influencing factors, and spatial heterogeneity of New-Type Urbanization's impact on health expenditures. The results show that China's health expenditures primarily exhibit High-High and Low-Low clustering patterns with spatial fluctuations. Meanwhile, the impact of New-Type Urbanization on health expenditures demonstrates spatiotemporal heterogeneity and non-stationarity. As urbanization levels increase, the negative effects of health expenditure clustering expand, while the influence of economic urbanization weaken. Our findings fill the research gap regarding the impacts between New-Type Urbanization and health expenditures, while also providing direction for New-Type Urbanization development to support the implementation of health policies aimed at controlling health expenditure growth.

BackgroundMental disorders significantly burden Indigenous communities, worsened by limited culturally appropriate services. Spatial inequalities in access further disadvantage Indigenous peoples, especially in socio-economically challenged areas. This paper measures the spatial accessibility of Indigenous community-controlled mental health services in South East Queensland, Australia and examines its social inequalities across the region.MethodsWe considered both population and health service providers’ capacity to maximise service coverage in measuring potential access to the services. Using Geographical Information Systems (GIS) technologies, a Gaussian-based two-step floating catchment area (G2SFCA) method was applied to quantify accessibility under four driving time thresholds ranging from 15 to 60 minutes. Bivariate global and local Moran’s I statistics were used to analyse social inequalities in accessibility across various geographical areas.ResultsAccessibility was higher in urban areas than those towards the peri-urban and rural areas; the overall spatial coverage was relatively limited for service access within the 15- or 30-minute driving time threshold, compared with the 45- or 60-minute driving time threshold. Lower levels of accessibility were identified in areas with a concentration of Indigenous and socio-economically disadvantaged populations.ConclusionsThis study advances a socially informed spatial inequality assessment framework. Unlike previous research exploring accessibility qualitatively, our framework innovatively integrates spatial analysis, Indigenous-specific population data and culturally sensitive provider capacity metrics within an advanced G2SFCA model. This approach uniquely exposes the compounded socio-spatial barriers to mental health services for Indigenous populations across South East Queensland’s urban-rural continuum. The resulting accessibility and inequality maps, combined with a summary of focus areas and their associated socio-demographic profiles, provide a direct policy lever to prioritise intervention for Indigenous communities experiencing the greatest disadvantage. By bridging spatial analysis with Indigenous cultural contexts, this work offers a replicable model for equitable, community-driven healthcare resource allocation for Indigenous peoples globally.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12942-025-00415-9.

While road traffic noise is an emerging environmental risk for cardiovascular mortality, its age-group-specific effects on stroke mortality remain unclear. This study further explored socioeconomic disparities in this association. We conducted a retrospective cohort study (2011-2019) with 36,240 hospitalized stroke patients in Fuxin, China. Residential noise levels were estimated using street view imagery analyzed by a novel and multimodal deep learning model. Age-grouped cox proportional hazards models adjusted for NO2, NDVI (Normalized Difference Vegetation Index), and sociodemographic covariates were applied to assess mortality risks. Among elderly patients aged ≥60 years with lower medical insurance, each 5-dB increase in residential road noise was associated with a 93.6% increase in stroke mortality risk (HR = 1.936, 95% CI: 1.024-3.660; p = 0.042). The estimated exposure prevalence in this subgroup was 3%, yet the population attributable fraction reached 1.7%. In contrast, no significant associations were found among patients with higher insurance coverage. Younger Males had a 51.3% higher mortality risk than females (adjusted HR=1.513, 95% CI: 1.142-2.005), independent of environmental exposures. NO2 and NDVI were not significantly associated with mortality across subgroups. These findings highlight the need for noise mitigation strategies that prioritize vulnerable populations, particularly the elderly and those with limited healthcare access.

Gun violence is a leading cause of death in the United States. Understanding the geospatial patterns of gun violence and how the COVID-19 pandemic may have affected them is essential for developing evidence-based prevention strategies. This study investigates whether COVID-19 altered the geospatial patterns of gun violence in Syracuse, New York. To assess spatial and temporal trends, we analyzed the annual total gunshots (ATG) from 2009–2023 aggregated in census block groups and applied geospatial techniques including mean center, standard distance, Moran’s I, and Getis-Ord Gi*. The ATG number was higher before the pandemic than during the pandemic, something not observed in other studies. Its geographic centers before and during the pandemic clustered within or near one census block and the associated standard distance remained similar between the two periods. Both global patterns and local clusters of ATG in the two periods not only showed similar patterns and consistent local hotspots located in similar areas, but also logarithmically related to the ATG number with statistical significance, suggesting that gun violence rates intensified within established areas rather than spreading citywide and demonstrated a similar distance-decay effect in both periods. This effect suggests that the incidence of gunshots diminished with increasing distance from the core concentrated zone, challenging assumptions of spatial spillover or contagion models in crime studies. These findings suggest that entrenched structural conditions, such as neighborhood-level socioeconomic disparities, are the primary drivers of gun violence patterns, rather than temporary pandemic-related policies. Methodologically, the study highlights the importance of long-term, meso-scale geospatial analyses to uncover persistent violence dynamics and guide preventive interventions. We argue that future violence prevention strategies should focus on enduring geospatial patterns of gun violence and their underlying structural determinants, rather than reacting solely to short-term fluctuations in incident frequency.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12942-025-00412-y.

The optimal placement of ambulances is critical for ensuring timely emergency medical responses, especially in regions with high accident frequencies. In Rwanda, where road accidents are a leading cause of injury and death, the strategic positioning of ambulances can significantly reduce response times and improve survival rates. The national records of Rwanda reveal a rising trend in the number of road accidents and deaths. In 2020, there were 4203 road traffic crashes throughout Rwanda with 687 deaths, data from 2021 demonstrated 8639 road traffic crashes with 655 deaths. Then in 2022 national statistics indicated 10,334 crushes with 729 deaths. The study used emergency response and road accident data collected by Rwanda Biomedical Centre in two fiscal years 2021-2022 and 2022-2023 consolidated with the administrative boundary of Rwandan sectors (shapefiles). The main objective was to optimize ambulance locations based on road accident data using machine learning algorithms. The methodology of this study used the random forest model to predict emergency response time and k-means clustering combined with linear programming to identify optimal hotspots for ambulance locations in Rwanda. Random forest yields an accuracy of 94.3%, and positively classified emergency response time as 926 fast and 908 slow. K-means clustering combined with an optimization technique has grouped accident locations into two clusters and identified 58 optimal hotspots (stations) for ambulance locations in different regions of Rwanda with an average distance of 1092.773m of ambulance station to the nearest accident location. Machine learning may identify hidden information that standard statistical approaches cannot, the developed model for random forest and k-means clustering combined with linear programming reveals a strong performance for optimizing ambulance location using road accident data.