Malaria continues to be one of the most significant infectious diseases in terms of morbidity and mortality. In many parts of North America, including parts of southern Canada, competent malaria vectors Anopheles quadrimaculatus and Anopheles freeborni are present. With climate change, Canada may be increasingly suitable for transmission of the malaria parasite Plasmodium spp. The objective of this study was to identify the geographic locations in Canada where, and the frequency with which, temperature conditions may be suitable for autochthonous transmission of Plasmodium vivax and Plasmodium falciparum under current and projected climate. Temperature and duration thresholds from historic Plasmodium spp. transmission studies were applied on gridded historical and projected data to compute yearly frequencies of suitable conditions in Canada. The resulting yearly frequencies from 2000 to 2023 show rising trends for both Plasmodium species, with surges reaching 34% of the Canadian population temporarily living under suitable temperature conditions for Plasmodium falciparum, and 56% for Plasmodium vivax. Projected populations percentages vary significantly with the Plasmodium species, climate change scenario, and climate model considered. Our results underscore the increasing risk of autochthonous transmission of malaria in Canada due to climate change.

BackgroundMalaria continues to pose a significant global health challenge, affecting approximately 200 million individuals annually and resulting in an estimated 600,000 deaths each year. In Tanzania, malaria ranks among the top five most commonly reported diseases in healthcare facilities, thus contributing to a substantial burden on the healthcare system. This study analyzed aggregated monthly malaria count data for the period 2016-2023, to explore spatio-temporal trends in malaria risk and assess the effects of climatic factors and vector control interventions across Tanzania mainland regions.MethodsThe Standardized Incidence Ratio (SIR) was used to assess malaria risk distribution, while a Bayesian spatio-temporal model using integrated nested Laplace approximations (INLA) was employed to evaluate the impact of climatic factors and vector control interventions. The model accounted for spatial and temporal effects by using a Conditional Autoregressive (CAR) dependence structure and a random walk of order two (RW2). The analysis was categorized into two age groups, with a cut-off at 5 years.ResultsThe study recorded a total of 23.4 million malaria cases in individuals aged 5 years and above, and 17.3 million cases in children under 5 years. The SIR and the model results identified regions with high malaria risk, and the model indicated that from 2016 to 2023, the malaria risk decreased by 11.0% for children under 5 years and by 10.0% for individuals aged at least 5 years. The use of long-lasting insecticide nets (LLINs) reduced the risk of malaria by 1.2% in children under 5 years and by 7.0% in individuals aged 5 years and above. Factors such as minimum temperature, wind speed, and high Normalized Difference Vegetation Index (NDVI) were associated with an increased malaria risk for both age groups. Relative humidity and maximum temperature, both lagged by two months, were associated with an increased malaria risk in children under 5 years, while maximum temperature lagged by one month was associated with increased malaria risk in individuals aged 5 years and above. Similarly, minimum temperature lagged by two and three months was associated with increased malaria risk in individuals aged 5 years and above and in children under 5 years, respectively. In addition, maximum temperature and wind speed lagged by one and three months were associated with decreased malaria risk in both groups.ConclusionThe environmental factors identified in this study, alongside the spatial mapping, are critical for devising targeted malaria control strategies, especially in regions where LLINs have reduced transmission. These findings are essential for identifying high-risk areas in endemic regions and for prioritizing immediate interventions

BackgroundAir pollution exposure is a leading health risk mainly due to its detrimental respiratory and cardiovascular effects. Ambient air quality varies greatly across time and space, most anthropogenic pollutants being higher in cities than rural areas. Residents of rural areas who commute to cities for work are also exposed to the air pollution there. Therefore, exposure assessments that neglect population mobility produce biased estimates.MethodsIn this study, we quantify the effect of recurrent mobility on long-term air pollution exposure and its attributable mortality for the pollutants NO_2, O_3, PM_{2.5} and PM_{10}, for 584 districts of Catalonia (Spain) in 2022. We use anonymized phone-based mobility data to infer the dynamic distribution of the residents of each district among the different areas, considering only recurrent mobility. We also utilise finely-resolved air quality data for the four pollutants from the bias-corrected CALIOPE model, projected over the districts. We integrate dynamic population with the air quality to calculate dynamic exposure estimates, and compute the effect of mobility on long-term exposure with respect to the static estimates. We also calculate the mortality attributable to each pollutant and the effect of mobility.ResultsConsidering the four pollutants, between 75.9% and 86.3% of the districts present significant effects of mobility on exposure. Rural areas surrounding cities display increased exposures to NO_2, PM_{2.5} and PM_{10}, and decreased exposures to O_3. The magnitude of these effects stays under 1 upmug/m^3 when considering the complete populations, but they increase up to 8.3 upmug/m^3 of change when we focus on the mobile populations. However, the effects on attributable mortality are negligible.ConclusionsOur work evidences the impact of cities on the air pollution exposure of people living far away from them, made possible by recurrent mobility. Our results show that correcting exposure profiles by mobility might not have a large impact at the population level when inter-area mobility is relatively low, but can be very significant for individuals and population segments with specific mobility habits, and as such should be taken into account for the design of public health policies.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12942-025-00410-0.

BackgroundClonorchis sinensis, the liver fluke responsible for clonorchiosis, presents a persistent public health burden in Guangxi (Southern China) and Vietnam. Its transmission is influenced by a complex interplay of ecological, climatic, and socio-cultural factors.MethodsWe compiled infection occurrence data from systematic literature reviews and national surveys conducted between 2000 and 2018. Environmental and climatic predictors were obtained from long-term raster datasets. Machine learning models, including logistic regression and tree-based ensemble methods, were used to assess associations between predictor variables and C. sinensis presence. Partial dependence plots were employed to refine predictor selection and explore marginal effects.ResultsRaw freshwater fish consumption was identified as the most influential predictor. In Guangxi, 54.9% of counties reported raw fish consumption, compared to 31.7% in Vietnam. Logistic regression achieved the highest predictive accuracy (AUC = 0.941). Climatic comparisons showed that Vietnam had a higher annual mean temperature (Bio1: 23.37 °C vs. 20.86 °C), greater temperature seasonality (Bio4: 609.33 vs. 464.92), and higher annual precipitation (Bio12: 1731.64 mm vs. 1607.56 mm) than Guangxi, contributing to spatial differences in endemicity. High-risk zones were concentrated along the China–Vietnam border, suggesting the need for geographically targeted interventions.ConclusionThe findings underscore the combined influence of ecological and behavioral factors on C. sinensis transmission. The predictive modeling framework offers valuable insights for surveillance planning and cross-border disease control, reinforcing the role of ecological epidemiology in guiding parasitic disease prevention strategies.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1186/s12942-025-00404-y.

BackgroundEpidemiological studies investigating long-term health effects of air pollution typically only consider the residential locations of the participants, thereby ignoring the space-time activity patterns that likely influence total exposure. This paper, part of a study in which residential-only and mobility-integrated exposures were compared in two tracking campaigns, reflects on GPS device choice, privacy, and recruitment strategy.MethodsTracking campaigns were conducted in Switzerland and the Netherlands. Participants completed a baseline questionnaire, carried a GPS device (SODAQ) for 2 weeks, and used a smartphone app for a time activity diary. The app also tracked GPS, albeit less frequently. Tracks were combined with air pollution surfaces to quantify NO2 and PM2.5 exposure by activity.ResultsIn Switzerland, participants were recruited from the COVCO-Basel cohort (33% recruitment rate; 489 of 1,475). In the Netherlands, -random recruitment was unsuccessful (1.4% rate; 41 of 3,000). Targeted recruitment with leaflets and a financial incentive (25 Euro voucher) increased participation to 189. Comparisons between smartphone app and SODAQ device data showed moderate to high correlations (R2 > 0.57) for total NO2 exposure and NO2 exposure at home in both study areas. Activity-specific correlations ranged from 0.43 to 0.63. PM2.5 correlations in Switzerland were moderate to high, but lower in the Netherlands (R2 = 0.28–0.58), due to smaller spatial contrast in observed PM2.5 levels (RMSE < 0.68 µg/m3).ConclusionsTracking can be effectively conducted using a mobile app or GPS device. The app’s low-frequency GPS readings (every 3–4 min) were sufficient for long-term air pollution exposure assessment. For finer-scale readings, a dedicated GPS device is recommended. Tracking campaigns are crucial for studying personal exposure to air pollution but face challenges due to low recruitment rates and strict privacy regulations. Leveraging an existing cohort can improve recruitment, while targeted leaflet distribution with financial incentives can enhance participation in studies without a pre-recruited group.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12942-025-00405-x.

As urbanization accelerates, the height of urban buildings continues to rise, which may influence the provision of Emergency Medical Services (EMS). However, a current limitation is that related studies often neglect the impact of spatial variations in building height on EMS accessibility equality. Most scholars have focused primarily on EMS road travel-either the Departure Road Trip (DRT) or the Transport Trip (TT)-while overlooking the effects of building height on the in-building EMS trip, known as the Patient Access Trip (PAT). EMS accessibility was measured using a proximity-based method and a Gaussian two-step floating catchment area (G-2SFCA) model under two scenarios: Scenario 1 considered only DRT, whereas Scenario 2 incorporated both DRT and PAT influenced by building heights. DRT travel times were simulated using Baidu Map's Application Programming Interface (API), and PAT times were calculated based on building elevator/stairs characteristics. Accessibility equality was assessed using multi-ring buffer analysis, Lorenz curves, and Gini coefficients. According to the empirical study in Wuhan, China, first, the spatial variations in building height was evident across the city. The building heights in city centre and sub-centres are generally taller compared to those in suburban areas. Second, the variations in building height can obviously affect EMS accessibility. However, the impact of building height on EMS accessibility varies across different regions. The effect is particularly pronounced in sub-centres located around 14km from the city centre, whereas it is relatively limited in suburban areas. Third, the incorporation of spatial disparities in building height into EMS accessibility modeling reveals increased inequality in EMS provision across the city. Spatial disparities in building heights across a city significantly influence EMS accessibility inequality. Given the widespread differences in building heights worldwide, this study provides valuable findings for healthcare policymakers to improve EMS systems.

Green spaces provide diverse health benefits, and provision of green spaces is often linked to lower incidences of adiposity. Undergraduates, who are at a transitional stage of development, represent a critical population for obesity prevention. However, recent studies suggest that the health effects of green space may vary by type. Furthermore, inferring any causal relationship between green spaces and adiposity using a cross-sectional research design remains challenging. To address these issues, this study utilized a large, representative sample of 21,990 undergraduates from 89 universities across 29 provinces in China, and employed a quasi-experimental approach to explore the impacts of specific green space types on body mass index (BMI). Propensity score matching was used to make the students who were influenced by green spaces comparable to those who were not. A difference-in-differences model was applied to estimate the causal effects of three types of green spaces (trees, bushes, and grass) on BMI. To further explore the underlying mechanisms, we examined two potential mediators: energy expenditure (physical activity) and energy intake (unhealthy food consumption). The results revealed that trees had a negative impact on BMI, whereas bushes and grass had no significant effect. Physical activity serves as a significant mediator linking tree exposure to adiposity changes, while unhealthy food intake showed no statistically significant mediation effect. In the stratified analysis, trees had significantly negative effects only on males. These findings highlight the importance of distinguishing green space types and provide causal evidence linking tree exposure to reduced BMI among undergraduates.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12942-025-00402-0.

This study developed and validated the Spatial Methodology Appraisal of Research Tool (SMART) using group concept mapping with discipline experts. The 16-item tool comprises four domains: (1) methods preliminaries, (2) data quality, (3) spatial data problems, and (4) spatial analysis methods. Validity testing demonstrated excellent content validity and expert agreement. Future studies will assess its usability and reliability to ensure consistent results. Its application in spatial epidemiology and health geography will enable more rigorous and transparent evidence synthesis. This contribution represents a significant step forward in improving the standards of quality appraisal in spatial research.

BackgroundThe increasing availability globally of building footprint datasets has brought new opportunities to support a geographic approach to health programme planning. This is particularly acute in settings with high disease burdens but limited geospatial data available to support targeted planning. The comparability of building footprint datasets has recently started to be explored, but the impact of utilising a particular dataset in analyses to support decision making for health programme planning has not been studied. In this study, we quantify the impact of utilising four different building footprint datasets in analyses to support health programme planning, with an example of malaria vector control initiatives in Zambia.MethodsUsing the example of planning indoor residual spraying (IRS) campaigns in Zambia, we identify priority locations for deployment of this intervention based on criteria related to the area, proximity and counts of building footprints per settlement. We apply the same criteria to four different building footprint datasets and quantify the count and geographic variability in the priority settlements that are identified.ResultsWe show that nationally the count of potential priority settlements for IRS varies by over 230% with different building footprint datasets, considering a minimum threshold of 25 sprayable buildings per settlement. Differences are most pronounced for rural settlements, indicating that the choice of dataset may bias the selection to include or exclude settlements, and consequently population groups, in some areas.ConclusionsThe results of this study show that the choice of building footprint dataset can have a considerable impact on the potential settlements identified for IRS, in terms of (i) their location and count, and (ii) the count of building footprints within priority settlements. The choice of dataset potentially has substantial implications for campaign planning, implementation and coverage assessment. Given the magnitude of the differences observed, further work should more broadly assess the sensitivity of health programme planning metrics to different building footprint datasets, and across a range of geographic contexts and health campaign types.

BackgroundGeographic masking is an important but under-utilized technique for protecting and disseminating sensitive geospatial health data. Geographic masks work by displacing static point locations such that the people those locations describe cannot be identified, while at the same time preserving important spatial patterns for analysis. Unfortunately, there is a lack of available tooling surrounding geographic masks which we believe creates an unnecessary barrier towards the adoption of these techniques. As such, this article presents a set of tools for performing, evaluating, and developing geographic masks, called MaskMyPy.ResultsMaskMyPy is an open-source Python package that includes functions for performing geographic masks, including donut, street, location swapping, and Voronoi masks. It also includes a range of tools for evaluating the results of these masks, both with regard to privacy and information loss. Finally, it includes a special class called the ‘Atlas’ that aims to dramatically streamline mask execution and evaluation. We conducted a short case study to illustrate the power of MaskMyPy in geographic masking research, and in doing so showed that mask performance can range widely due solely to randomization. As such, we recommend that masking researchers test their masks repeatedly across a variety of test datasets.ConclusionMaskMyPy makes it easy to apply a variety of geographic masks to a set of sensitive points and then measure which mask provided the most privacy while suffering the least information loss. We believe this style of tooling is important to not only make geographic masks accessible to non-experts, but to enable expert users to better interrogate the masks they develop, and in doing so drive the geographic masking discipline forward.