International Journal of Health Geographics最新文献

Background: To date, few HIV-related population-based data are available for refugee populations. Household surveys typically require reliable population counts and well-defined geographic areas, which are often not available for refugee settlements. We describe the gridded population sampling approach as an option for conducting such a survey in Uganda and describe its application for a household survey in Uganda and assess its utility among refugee populations.

Methods: The Uganda Refugee Population-based HIV Impact Assessment (RUPHIA) 2021 was a cross-sectional, population-based HIV survey among refugee households in Ugandan settlements, excluding Kampala. We collected shapefiles and population counts for the refugee settlements. These shapefiles from the various geographic areas of interest represented the aggregated refugee settlement zones (including all settlements with available zone shapefiles) and served as the base for creating the sample frame. The sample frame was constructed by disaggregating United Nations High Commission for Refugees population counts from large refugee settlement zones into 100 × 100 m grid cells using WorldPop's peanutButter-Disaggregate app that uses building footprint information to distribute the population into the grid cells. We then utilized a gridded population sampling approach which redistributed the population into manageable-sized areas of contiguous grid cells based on their estimated population size, forming enumeration area-like sampling units using the publicly available GridEZ algorithm.

Results: The resulting gridded population dataset had 43,193 100 m x 100 m cells with an estimated mean of 31 people per cell and a range from 2 to 1028. The final gridded population sample frame had 2636 GridEZ units with an average population of 500 ranging from 178 to 1531. The sample frame performed well for survey activities, with few issues encountered in the field, although the size measures for number of households had some inaccuracies, due to issues such as compounds having multiple structures.

Conclusions: Gridded population sampling was successfully utilized for this refugee study, saving time and money that would have been needed if enumeration of all the refugee settlements had been required. Gridded population sampling is a useful tool when census data are outdated or unavailable or when the population is dynamic, such as with refugees or other mobile or at-risk populations for surveillance or as part of a humanitarian response.

Background: Mental-health geomatics require reliable ways to convert high-frequency GPS trajectories into meaningful place types that support indicators such as homestay, location entropy, and spatial extent of daily activities. Raw coordinates are typically noisy and carry little semantic information. We introduce H3-MOSAIC(H3-based Multimodal OSM-and-Satellite AI for Classification), a multimodal generative framework that fuses OpenStreetMap (OSM) building text and satellite imagery on H3 grids to infer place semantics from high-frequency GPS.

Methods: Raw GPS was smoothed by minute-level speed filtering, then assigned to Level 10 H3 hexagons. Cells were retained if the mean speed was ≤ 1.2 m/s and the cumulative duration was ≥ 15 min, contiguous cells were merged, and home was defined as the cell with the longest dwell from 23:45 to 06:00. We compared text-only OSM classification with image-based and fused approaches across open-source models (DeepSeek, CLIP, LLaVA, Qwen-VL) and proprietary models (GPT-4o-mini, Gemini-2.5-flash-lite). Performance was assessed by accuracy, Cohen's kappa, precision, recall, F-measure, and confusion matrices. Day level associations between H3 semantic exposures and stress were examined by a random forest model and explainable methods.

Results: Multimodal methods outperformed single-modality baselines. In the 11-class task, accuracies were: CLIP 0.179, LLaVA 0.269, Qwen-VL 0.565, GPT-4o-mini 0.779, and Gemini-2.5-flash-lite 0.790. In the 5-class consolidation, accuracies rose to 0.702 (Qwen-VL), 0.849 (GPT-4o-mini), and 0.858 (Gemini-2.5-flash-lite). Text-only OSM baselines were lower (≈ 0.60-0.68). Across 3,845 hexagons with OSM text, closed-source models agreed on 79% of labels; disagreements concentrated in mixed-use, office, and green classes. Error modes reflected area-dominant versus keyword-triggered reasoning, hybrid-parcel ambiguity, tag sparsity, and symbolic artifacts. Stabilized semantics support more robust computation of homestay, entropy, and activity space and are suitable for privacy-aware, cross-city reuse. In a day-level case study, minutes at Home related to lower stress; Green showed a U-shaped pattern.

Conclusions: H3-MOSAIC provides a scalable, auditable pipeline for semantic place detection from high-frequency GPS. Multimodal fusion markedly improves accuracy and consistency. Proprietary models are most robust on hard classes and open-source models are practical for coarse taxonomies. H3 day level exposures show stress patterns consistent with established mental health pathways, supporting face validity. The framework enables downstream exposure analyses with reduced misclassification and improved interpretability.

Background: Geospatial studies that consider the relationships between the built environment and health typically rely on researcher-led 'objective' measurement of geospatial attributes of the built environment. Some studies can fail to find expected associations between environments and health outcomes where the geospatial measures do not reflect the experiences or perceptions of people themselves. We took a participatory approach to work with older adults with a concern for falling to assess the built environment in order that we could understand how their assessments relate to researcher assessments. We also wanted to assess whether specific demographic characteristics explained differences in assessments of the built environment between participants. Age-friendly environments can contribute to healthy active ageing. Falling and a fear of falling can lead to restricted outdoor activity. Therefore, understanding how the built environment contributes to fear of falling is important for age-friendly environments.

Methods: The study is a cross-sectional retrospective observational study of the built environment. We worked with older adults in workshop settings to undertake community audits of the built environment in Google Street View. They assessed locations where a fall had occurred. Researchers separately audited the same locations. We used descriptive statistics and ordinal regression cumulative link mixed models to estimate the odds that community members would rank a location one level higher than the researchers.

Results: There are significant differences in researcher and community auditor assessments of locations of attractiveness. Site related and individual attributes explain variation in how difficult locations were rated for walking, and for concern about falling. Only individual attributes explained variation in site attractiveness. Locations with more trip hazards and steeper slopes were rated as being more difficult to walk and were associated with greater concern for falling.

Conclusions: Attributes of the built environment influence perceptions of difficulty walking and concern or falling at specific locations. Furthermore, there are some differences in how researchers and community auditors assess the same locations, meaning that geospatial studies which rely only on researcher assessments may be prone to bias. Involving older people in geospatial studies that measure age-friendly environments can make measurement more reflective of their experiences.

Background: Malaria, whose parasites are transmitted by Anopheles mosquitoes, remains a major public health burden in Madagascar despite the control measures led by the National Malaria Control Program. Understanding the population dynamics of Anopheles mosquitoes is therefore essential to optimize malaria surveillance and control. This study aimed to develop a model incorporating environmental, climatic and agricultural determinants of Anopheles abundance to predict their spatiotemporal distribution.

Methods: We developed a model of spatiotemporal dynamics for four Anopheles species, vectors of malaria parasite in Madagascar: Anopheles arabiensis, Anopheles coustani, Anopheles funestus and Anopheles gambiae. This model was based on the life cycle of Anopheles and accounted for both the aquatic and aerial phases of their development. It used a system of differential equations to estimate the number of Anopheles mosquitoes at each stage of development. The Ocelet language, dedicated to the modeling of spatial dynamics, was used to produce simulations based on climate and environmental data. The model explicitly integrates the agricultural calendar to adjust the environmental carrying capacity of larval habitats. Model outputs were validated with entomological data collected in Vohimasy (Farafangana districts, 2014-2017).

Results: 24 simulation outputs, from three Anopheles species and eight sites, were obtained and the validation revealed a significant correlation between field observations and model predictions: the correlation coefficients obtained ranged from 0.70 to 0.76. The predicted abundance of host-seeking Anopheles varied seasonally influenced by precipitation, temperature and environmental carrying capacity. The model exhibited robustness across sites with diverse climates and accurately reproduced interannual dynamics. The integration of the agricultural calendar significantly reduced the overestimation of the density of host-seeking adult females.

Conclusion: The developed Anopheles dynamics model provides a valuable tool for predicting mosquito abundance and distribution over time and space. It correctly predicted the abundance at villages with contrasting climates and reproduced interannual dynamics well. A distinctive aspect of this work lies in the explicit integration of seasonal agricultural practices into the estimation of larval habitat availability. This allows for a more accurate and transferable modeling of Anopheles population dynamics.

Background: Area-level socioeconomic status is a well-established determinant of geographical disparities in life expectancy. However, limited attention has been paid to spatial spillover effects, whereby socioeconomic conditions in neighbouring regions influence health outcomes. This study aimed to estimate the direct and spatial spillover effects of socioeconomic factors on life expectancy in Japan and to explore possible mechanisms underlying the observed spillover patterns.

Methods: Life expectancy at birth by sex at the municipal level in Japan for 2020 was the outcome variable. A spatial Durbin error model was used to estimate the direct and spatial spillover effects of ten regional socioeconomic factors, along with six control variables, on life expectancy. To ensure robustness, six spatial weight matrices were used. The results were compared with those obtained from a non-spatial linear regression model.

Results: Moran's I values for the residuals of the non-spatial model were statistically significant, indicating spatial autocorrelation. The unemployment rate and the proportion of individuals with no high school diploma showed negative direct and spillover effects, suggesting that being surrounded by regions with employment instability and low educational attainment is associated with lower life expectancy. Taxable income per capita showed no statistically significant spillover effects.

Conclusion: The findings indicate that socioeconomic conditions in neighbouring regions, in addition to those within a region, are associated with life expectancy. The observed spillover effects for employment and education support the role of collective resources in shaping regional health. These results indicate the need to incorporate interregional socioeconomic contexts into public health strategies to address geographical disparities in health.

Background: We 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.

Methods: The 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.

Results: Higher 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.

Conclusions: The 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.

Background: Environmental 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.

Methods: Four 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.

Results: Walking-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.

Conclusions: The 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.

Background: The 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.

Aims: There were two main aims. First, to evaluate the pre-pandemic (2019; [Formula: see text]) and post-pandemic (2021; [Formula: see text]) 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).

Method: We 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.

Results: The 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.

Conclusions: COVID-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.

Background: Pneumonia 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.

Methods: We 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.

Results: Our 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.

Conclusions: We 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.

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.