International Journal of Health Geographics最新文献

{"title":"Differential effects of neighborhood ambient PM_2.5 exposure and social vulnerability on cancer-related systemic inflammation by race in a large health care system population from 2000 to 2020.","authors":"Benjamin A Rybicki, Zihan Lin, Amber L Pearson","doi":"10.1186/s12942-026-00464-8","DOIUrl":"10.1186/s12942-026-00464-8","url":null,"abstract":"<p><strong>Background: </strong>The environment can impact cancer risk both directly, such as through the air we breathe, and indirectly, through the neighborhoods we live in. These risk factors often work in concert but can have disparate effects.</p><p><strong>Methods: </strong>To understand how air pollution, as measured by ambient particulate matter 2.5 (PM_2.5) levels and neighborhood disadvantage based on a geographically assigned social vulnerability index (SVI), may act together to impact cancer risk, we used 2000-2020 statewide cohort data from a large health system based in metropolitan Detroit, MI, USA (n = 245,438). Systemic inflammation was used as a surrogate indicator of cancer risk among study participants and was measured via the white blood cell count ratios of the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-monocyte ratio (NMR).</p><p><strong>Results: </strong>After adjusting for these and other confounding variables, PM_2.5 concentration had a greater positive association with the NMR than with the NLR (Z score = 37.7 vs. 21.8). According to the race-stratified multivariable models, PM_2.5 had a greater association with both inflammatory indices in White participants. PM_2.5 levels had the strongest positive linear relationship with both the Charlson comorbidity index and the SVI among Black participants. A PM_2.5 × SVI interaction term was found to be statistically significant only for White participants, suggesting that these two variables act synergistically to increase systemic inflammation in White participants, whereas in Black participants, there was evidence that the SVI may mediate the effects of PM_2.5 exposure on both inflammatory indices.</p><p><strong>Conclusion: </strong>At the population level, neighborhood environmental factors linked with both air pollution and neighborhood disadvantage appear to have an impact on systemic inflammation; however, these factors may act in a disparate fashion according to race.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13141369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147516186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying and assessing online food delivery swamps in major Chinese cities.","authors":"Sui Ye, Jianchao Xi, Ziqiang Li","doi":"10.1186/s12942-026-00463-9","DOIUrl":"10.1186/s12942-026-00463-9","url":null,"abstract":"<p><p>The rise of \"online food delivery swamps,\" driven by digital platforms, raises important questions about how urban food delivery systems affect nutritional equality. To overcome the limitations of traditional methods that rely on store classifications and physical proximity, this study introduces a high-resolution framework integrating dish-level nutritional data with digital accessibility. Applying this to 254,642 vendors across ten major Chinese cities, our analysis reveals a widespread structural imbalance in the food environment, marked by an oversupply of energy, fat, and carbohydrates. Spatially, all cities exhibit a stark \"core-oasis, periphery-swamp\" pattern, exposing significant intra-urban inequality. We uncover a critical mismatch between the geographical extent of these swamps and the actual residential proximity to nutritionally imbalanced food supply, identifying distinct urban patterns from \"extensive coverage-low residential exposure\" to \"high coverage-high residential exposure\". Furthermore, we demonstrate that \"food swamps\" are not monolithic; instead, they comprise heterogeneous subtypes with unique nutritional deviation profiles. This study uncovers new dimensions of urban nutritional supply inequality in the digital era, providing evidence that may inform the development of food environment policies.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13220461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147516210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal evolution pattern and convergence of healthy city development in China.","authors":"Fulei Jin, Yanli Geng, Xigang Zhang","doi":"10.1186/s12942-026-00466-6","DOIUrl":"10.1186/s12942-026-00466-6","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Urbanization-induced urban diseases threaten public health. The World Health Organization launched the Healthy City Initiative in 1986, and China has incorporated it into the Healthy China 2030 strategy. Currently, China faces significant regional development disparities, and issues regarding the development level of the healthy city in China, as well as its spatiotemporal pattern and convergence, remain to be clarified to promote the equalization of health services.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Using panel data of Chinese cities from 2011 to 2022, this study constructs an evaluation system covering 4 dimensions and 25 indicators, and applies the entropy weight method to measure the development level of China's healthy city development. Methods including exploratory spatial data analysis, spatial correlation analysis, trend surface analysis, kernel density analysis, Dagum Gini coefficient, and kernel density estimation are used to reveal the spatiotemporal pattern of China's healthy city development. The β-convergence model is employed to conduct the convergence test.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;From 2011 to 2022, the average level of healthy city development increases from 0.149 to 0.269, representing a growth rate of 80.5%. At the regional level, it exhibits a gradient differentiation characteristic described as \"the eastern region taking the lead, while the central and western regions showing similar levels below the national average\". Spatially, it presents a pattern of \"low levels in inland hinterlands and high levels in coastal and border areas\". The global Moran's I index ranges from 0.129 to 0.152 (P &lt; 0.001). The Gini coefficient fluctuates downward from 0.172 to 0.123, with the contribution rate of inter-regional disparities falling within the range of 55% to 60%. The kernel density distribution curves of all regions shift continuously to the right, with the main peak widening first and then narrowing; the degree of internal differentiation in the eastern region is higher than that in the central and western regions. Significant absolute β convergence and conditional β convergence are observed both at the national level and across the three major regions. In terms of convergence speed, the western region has the fastest convergence rate (1.37%) with a half-life of approximately 51 years, the central region has a convergence rate of 1.34% with a half-life of 52 years, and the eastern region has a relatively slower convergence rate (1.00%) with a half-life of 69 years. Regarding the convergence-driving factors, economic development and government intervention in the eastern region, urbanization in the central region, and financial development in the western region promote convergence, while opening-up in the western region inhibits convergence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Healthy city development in China shows an overall steady upward trend, but significant regional gradient disparities exis","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13134281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147505193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact analysis of flood-induced changes in geographical accessibility and coverage to healthcare in both public and private sector, 2024, Kenya.","authors":"Bibian N Robert, Samuel K Muchiri, Emma W Kahoro, Boneya H Hindada, Helen Kiarie, Emelda A Okiro, Peter M Macharia","doi":"10.1186/s12942-026-00461-x","DOIUrl":"10.1186/s12942-026-00461-x","url":null,"abstract":"<p><strong>Background: </strong>Climate change has caused more frequent and severe extreme weather events, threatening health system resilience worldwide. In April and May 2024, Kenya experienced unprecedented extensive floods with devastating outcomes. However, the quantitative impact of flooding on geographical access to healthcare remains unclear. This study, therefore, evaluates post-disaster accessibility to health facilities and quantifies geographical coverage losses resulting from flooding compounded by doctors' strike in Kenya.</p><p><strong>Methods: </strong>Geospatial datasets were assembled including health facility locations (public, private not-for-profit (PNfP), and private for-profit (PfP)), road network, land use/land cover, topography, population density, and flooding extents). A pre-flood baseline and three post-flood scenarios were defined using satellite-derived flooding extents (Sentinel 1 synthetic aperture radar (SAR) and National Oceanic and Atmospheric Administration - Visible Infrared Imaging Radiometer Suite (NOAA-VIIRS) satellites) and their combined maximal extents. Travel time (TT) to the nearest health facility by type was estimated using a least-cost path algorithm, accounting for ± 20% variations in travel speed and flood extent for sensitivity analysis. Population coverage was extracted within five 30-minute TT bands for each scenario, nationally and by subnational units (county).</p><p><strong>Results: </strong>A total of 10,995 health facilities were assembled (public = 5,586; PNfP = 855; PfP = 4,554). Pre-floods, average TT to the nearest facility was 19.6 min, with public facilities at 20.7 min, PfP at 37.8 min, and PNfP at 49.2 min. Post-floods average TT increased across all sectors, longest across PNfP at 113.5 min and shortest for public facilities at 48.5 min. Pre-floods, 94.0% (52.5 million) of the population had access within 30-min and 20 out of 47 counties with an average TT of < 2 h. Under the maximal flood extents, coverage dropped to 73% (40.9 million) and only 5 counties retained < 2 h TT. County-level 30-min coverage losses ranged from 1.0% (Nairobi) to 51.0% (Narok). In several arid counties, populations facing 2 + hours TT rose to 15-31%, up from 4 to 12% pre-floods.</p><p><strong>Conclusion: </strong>Kenya's health system is highly vulnerable to floods, causing unequal disruptions in geographical access across subnational region. Incorporating disaster preparedness into county health care planning to strengthen health system resilience nationwide is needed.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13122988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147482168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What is rural? Examining the relationship between human populations and their inter-connectedness in the context of communicable disease transmission.","authors":"Cassandra Boutelle, Patrick Corbett, Andrew Gibson, Frederic Lohr, Catherine Swedberg, Jesse Blanton, Ryan Wallace","doi":"10.1186/s12942-026-00456-8","DOIUrl":"10.1186/s12942-026-00456-8","url":null,"abstract":"<p><strong>Background: </strong>Rurality and urbanicity are recognized determinants of public health outcomes that influence policy and resource allocation. However, many commonly used methods to classify the rural-urban continuum, such as the Rural-Urban Commuting Area (RUCA) codes, lack applicability in low and middle-income countries (LMICs). This study introduces the Settlement Type and Road Connectivity (STARC) methodology, which offers a standardized and accessible approach to classifying regions along the rural-urban continuum.</p><p><strong>Methods: </strong>Leveraging open-source software and readily available data, STARC generates detailed maps composed of small hexagonal polygons. Each hexagon unit is assigned one of twenty-four categorical STARC codes based on its estimated population density and community-based road connectivity. Collectively, the hexagon units comprise the base layer STARC map. Additional metrics related to disease transmission can be overlayed onto each STARC hexagon unit in order to perform hotspot analysis via the local Gi* statistic and create transmission zones. All operations within the STARC process have been packaged into a publicly available tool on GitHub.</p><p><strong>Results: </strong>To demonstrate the STARC process, we executed the full STARC methodology at the local, national, and regional level for study areas in Central and Western Africa. Free roaming dog densities were selected as the metric of interest in order to identify hotspots and transmission clusters for dog-mediated rabies.</p><p><strong>Conclusions: </strong>In our analysis we demonstrate that STARC codes can be used to standardize the rural-urban continuum and better understand the distribution of connectedness of populations. Hotspot analysis of free roaming dogs in several African countries shows that dog populations, a key factor in rabies transmission, are often concentrated in urban and peri-urban areas, many of which span domestic and international boundaries. By providing a dynamic and data-driven approach to understanding the rural-urban landscape, STARC offers a valuable tool for public health interventions in LMICs.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13104494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating epidemiological modeling and time-series forecasting to optimize pandemic patient allocation.","authors":"Seyedreza Abazari, Onur Alisan, Omer Arda Vanli, Eren Erman Ozguven","doi":"10.1186/s12942-026-00455-9","DOIUrl":"10.1186/s12942-026-00455-9","url":null,"abstract":"<p><p>BACKGROUND: This paper addresses the challenge of allocating patients to healthcare facilities with limited capacities during infectious disease outbreaks. The method is based on a hierarchical time-series model to forecast hospital bed demand and a mixed-integer nonlinear mathematical model for allocating patients among a set of regions to minimize disease spread. METHODS: Our contributions include (1) a new hierarchical time-series model for forecasting hospital bed demand to enhance regional predictive accuracy, (2) a mathematical model that integrates the forecasting model with a Susceptible, Infected, Recovered (SIR) epidemic model to capture metapopulation dynamics and the impact of patient allocation on disease spread across regions, and (3) a sensitivity analysis to assess the importance of the optimization and forecasting parameters on allocation performance. RESULTS: The proposed approach is illustrated with a real-data case study from the COVID-19 pandemic in Florida which demonstrates that forecasting performance depends strongly on regional hospital capacity. The hierarchical model performs better in high-capacity regions, while the univariate model is more effective in regions with sparse bed availability. At the state level, both models yield comparable objective function values, but they lead to markedly different spatial distributions of unmet demand. The sensitivity analysis enables us to study the contributions of individual factors and shows that decision-making frequency plays a more critical role. Based on these findings, monthly decision intervals are recommended and forecasting model selection should be tailored to regional capacity. CONCLUSION: The results highlight the practical effectiveness of the proposed approach and its ability to capture trade-offs in patient allocation strategies. By explicitly modeling the additional disease transmissions resulting from patient reallocations across counties, the proposed framework offers actionable insights to support healthcare preparedness and operational decision-making during pandemics. </p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147391156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ethical guidelines for geoprivacy: a framework for researchers and ethics committees.","authors":"Milad Malekzadeh, Yan Kestens, Justine I Blanford, Camille Perchoux, Mir Abolfazl Mostafavi, Grant McKenzie, Eun-Kyeong Kim","doi":"10.1186/s12942-026-00460-y","DOIUrl":"10.1186/s12942-026-00460-y","url":null,"abstract":"<p><strong>Background: </strong>The increasing use of geographic data about individuals in health and social research raises ethical challenges that extend beyond existing legal frameworks. While regulations such as data protection laws define boundaries, they rarely provide researchers with sufficient practical guidance for addressing geoprivacy risks.</p><p><strong>Methods: </strong>We developed a structured, reflexive ethical framework tailored for research involving human-centered geographic data. The framework was designed using a lifecycle approach and informed by both a review of existing literature and the expertise of the multidisciplinary author team. It organizes ethical considerations into five research phases: data collection, storage, sharing, analysis, and results dissemination. To enhance usability, we translated these considerations into 60 guiding questions, each assigned an importance level (high, moderate, or low). An ethical review applicability matrix was also introduced to help determine the level of ethical scrutiny required, based on study characteristics such as data type, granularity, linkage potential, and participant vulnerability.</p><p><strong>Results: </strong>The framework offers a practical and scalable tool for embedding ethical reflection into research processes. It supports proportionate ethical review by aligning the sensitivity of specific research practices with the corresponding importance of guiding questions. To demonstrate its adaptability, we provide two case studies in the supplementary materials that apply the framework to different research scenarios with varying levels of geoprivacy sensitivity.</p><p><strong>Conclusions: </strong>By encouraging early and context-aware engagement with ethical risks, this framework safeguards participant dignity, fosters transparency, and advances ethically responsible research involving geographic data. It equips both researchers and ethics committees with a systematic approach for addressing geoprivacy challenges across diverse health and social science contexts.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13092162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147345524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping malaria vulnerability hotspots using multi-criteria decision analysis, GIS, and remote sensing: a case study in Abaya Woreda, West Guji Zone, Ethiopia.","authors":"Dechasa Diriba, Degu Demise, Birhanu Kenate, Dabesa Gobena, Melese Lemi, Natinael Teferi, Shankar Karuppannan, Gemechu Churiso","doi":"10.1186/s12942-026-00458-6","DOIUrl":"10.1186/s12942-026-00458-6","url":null,"abstract":"<p><strong>Introduction: </strong>Malaria is one of the world's most serious public health problems and remains a leading health burden in developing countries such as Ethiopia. Large parts of the country, especially lowland areas such as Abaya Woreda, are affected by this disease. The study area is highly endemic for malaria; therefore, identifying vulnerability hotspots and implementing targeted interventions are important for reducing disease burden and saving lives. The present study aims to identify malaria vulnerability hotspot areas in Abaya Woreda, West Guji, Ethiopia, using a comprehensive geospatial approach including GIS and remote sensing techniques.</p><p><strong>Methods: </strong>To generate a malaria vulnerability map, ten key factors representing climatic, topographic, environmental, and demographic determinants were derived from multi-source datasets, including Landsat 8, SRTM Digital Elevation Model (DEM), GPS field surveys, and CHIRPS precipitation data. The relative weights of these factors were determined using the Analytic Hierarchy Process (AHP) before being integrated via weighted overlay analysis in ArcGIS 10.8.</p><p><strong>Results: </strong>The results revealed that 38.3% of the study area is highly vulnerable to malaria. These areas are closely associated with lower elevations, wetlands, water bodies, greater distances from health facilities, and high population density. Furthermore, 49.1% of the area was identified as moderately vulnerable, while only 12.6% exhibited low vulnerability.</p><p><strong>Conclusion: </strong>The findings of this study provide critical, data-driven insights to support stakeholders and policymakers in designing, prioritizing, and implementing targeted, evidence-based malaria control and prevention strategies in Abaya Woreda, thereby significantly enhancing the efficiency and impact of public health interventions.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13049725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147318693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations between storm exposure patterns and metabolic syndrome risk in Chinese adults: a CHARLS-based prospective cohort study.","authors":"ZiJie Cai, LiXiang Gan, HongYing Tian, GuangPeng Zhang, YaLong Qiu, ChunZhci Tang","doi":"10.1186/s12942-026-00457-7","DOIUrl":"10.1186/s12942-026-00457-7","url":null,"abstract":"<p><p>BACKGROUND: To assess the nonlinear association between city-scale storm events and incident metabolic syndrome (MetS) in the China Health and Retirement Longitudinal Study (CHARLS) cohort of middle-aged and older adults, and to examine the effect modification and mediating effects of PM₂.₅, as well as spatial heterogeneity. METHODS: Based on CHARLS data from the 2011 baseline survey followed up to 2015, 4,085 participants without pre-existing MetS at baseline were included, with 512 incident MetS cases identified. Cox proportional hazards models were used to estimate risks; natural cubic splines (RCS) were applied to characterize the exposure–response relationship. RCS×RCS interactions was “storm peak rainfall × PM₂.₅,” with simple slope curves estimated at the 10th, 50th, and 90th percentiles (P10, P50, P90) of pollution levels. Parallel multiple mediation analysis assessed the indirect effects of PM₂.₅, and psychosocial factors. Global/local spatial autocorrelation and multiscale geographically weighted regression (MGWR) were employed to evaluate spatial clustering and non-stationarity. RESULTS: In quantile-based Cox models, compared with Q1 (lowest rainfall), participants in Q2–Q4 showed lower MetS risks (Model 2: HR_Q2 = 0.67, HR_Q3 = 0.61, HR_Q4 = 0.58; all significant). After full adjustment, associations attenuated but remained directionally consistent (Model 3: HR_Q2 = 0.76, HR_Q3 = 0.74, HR_Q4 = 0.81). PM₂.₅ was positively associated with MetS (HR ≈ 1.01 per unit), while annual mean temperature was protective (HR ≈ 0.96 per unit). The RCS revealed a U-shaped curve: in low storm peak rainfall areas (≈ 0–25 units), HR was slightly above 1; minimal risk occurred at moderate storm peak rainfall (≈ 50–150 units, HR ≈ 0.5–0.7); beyond 175 units, risk increased sharply (HR > 8 at ≈ 200 units). Significant effect modifications were observed in RCS×RCS analyses: PM₂.₅ stratification: At low pollution (P10 ≈ 37.1 µg/m³), the association increased with storm peak rainfall intensity; neutral at median (P50 ≈ 55.2 µg/m³); protective at high pollution (P90 ≈ 79.6 µg/m³). Mediation analysis indicated a significant indirect effect through PM₂.₅, and nonsignificant psychosocial pathways. Spatial analysis revealed clustering of MetS incidence and non-stationary storm effects—predominantly negative in northern/northeastern China, but neutral or positive in parts of central/southern coastal regions. CONCLUSION: City-scale storm peak rainfall exhibited a nonlinear association with incident MetS, where moderate intensity may be protective but extreme intensity harmful. PM2.5 served as the primary modifying and mediating pathway and marked regional variability. Health risk assessments of storm peak rainfall must consider both exposure ranges and geographic heterogeneity. </p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12964839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between long-term, short-term spatial land surface temperature variations and health-related quality of life in Lima, Peru.","authors":"Antonio Torres-Reyes, Pablo S C Santos, Yanetsy Elisa Rodríguez León, Estefanie Quispe Salas, Alessandra Gudila Rodriguez Mercado, Melvine Anyango Otieno, Elvis Ndikum Achiri, Jan W Kantelhardt, Andreas Wienke, Jan Christian Schlüter, Eva Johanna Kantelhardt","doi":"10.1186/s12942-026-00454-w","DOIUrl":"10.1186/s12942-026-00454-w","url":null,"abstract":"<p><strong>Background: </strong>The effect of environmental exposures on self-reported health has gained attention in the literature in recent years. Rising temperatures have become a public health concern due to climate change and urbanisation at a global scale. Findings vary strongly across regions, highlighting the need for further evidence to inform data-driven decisions.</p><p><strong>Methods: </strong>This study aimed to assess the impact of land surface temperature (LST) on health-related quality of life (HRQOL) among adults in Lima, Peru. We assessed 700 randomly selected adults using structured interviews covering HRQOL, sociodemographic factors, behavioural habits, and non-communicable disease (NCD) prevalence, between December 2023 and January 2024. LST was derived from satellite-based data.</p><p><strong>Results: </strong>We detected no association between LST and self-reported health for most of the exposure timeframes. However, we observed an association between spring and summer mean temperature for reporting perfect health and reporting problems in the usual activity and anxiety or depression dimension, respectively.</p><p><strong>Conclusions: </strong>These findings demonstrate the complex interplay of LST as an environmental exposure and HRQOL in urban settings. Insights from this study can optimise public health policies and interventions aimed at promoting healthy behaviours, improving environmental conditions, and enhancing population well-being. Further research should investigate the observed associations across diverse geographic settings and develop targeted strategies to improve HRQOL in the context of rising global temperatures.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13005491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146195797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}