Geohealth最新文献

{"title":"Inequitable Spatial and Temporal Patterns in the Distribution of Multiple Environmental Risks and Benefits in Metro Vancouver","authors":"Shuoqi Ren,&nbsp;Amanda Giang","doi":"10.1029/2024GH001157","DOIUrl":"https://doi.org/10.1029/2024GH001157","url":null,"abstract":"<p>The urban environment impacts residents' health and well-being in many ways. Environmental benefits and risks may be interactively and inequitably distributed across different populations in cities, and these patterns may change over time. Here, we assess the spatial distribution of environmental risks and benefits in pairs, considering synergies and trade-offs, in an illustrative metropolitan area (Metro Vancouver) in Canada in the years 2006 and 2016. We classify census dissemination areas as sweet, sour, risky, or medium spots based on relative exposures for six environmental combinations: Walkability and NO₂; heat stress and NO₂; vegetation coverage and NO₂; vegetation coverage and heat stress; walkability and accessibility to natural recreational areas; and heat stress and accessibility to natural recreational areas. We evaluate whether different population groups are disproportionately exposed to lower environmental quality based on linear regressions and other metrics. We find that while performance for individual environmental variables improved over the decade, considering their combinations, sweet spots became sweeter and sour spots became sourer. Residents with high material and social deprivation and visible minorities were disproportionately exposed to lower environmental quality in both years for most of the environmental combinations. Further, we find that these inequities were not improving over time for all groups: for instance, South Asian residents in the region faced higher disproportionate burdens or diminished access to benefits in 2016, as compared to 2006. Given these findings, we suggest considerations of cumulative exposure in prioritizing areas for intervention, targeting the sour and risky spots persistently experienced by overburdened populations.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"8 12","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868999","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":"Earth Observation Data to Support Environmental Justice: Linking Non-Permitted Poultry Operations to Social Vulnerability Indices","authors":"Mirela G. Tulbure,&nbsp;Júlio Caineta,&nbsp;Brooke Cox,&nbsp;Stephen V. Stehman,&nbsp;Ayse Ercumen,&nbsp;Rebecca Witter,&nbsp;Ryan Emanuel,&nbsp;Dana E. Powell,&nbsp;Kemp Burdette,&nbsp;Sherri White-Williamson,&nbsp;Shea Tuberty","doi":"10.1029/2024GH001179","DOIUrl":"10.1029/2024GH001179","url":null,"abstract":"<p>Concentrated Animal Feeding Operations (CAFOs) apply massive amounts of untreated waste to nearby farmlands, with severe environmental health impacts of swine CAFOs and proximity to disadvantaged communities well documented in some US regions. Most studies documenting the impacts of CAFOs rely almost exclusively on CAFO locations known from incomplete public records. Poultry CAFOs generate dry waste and operate without federal permits; thus, their environmental justice (EJ) impacts are undocumented. North Carolina (NC), a leading poultry producer, has seen a significant increase in poultry CAFOs, particularly since the 1997 swine CAFO moratorium. Using literature-derived heuristics, this study refined the locations of poultry CAFOs derived based on Earth Observation (EO) data and deep learning, reducing the overestimation of poultry CAFO density by 54% after heuristic adjustments. We removed 51.8% of misclassified features in NC and 61.5% across the US, significantly improving data set accuracy. Spatial analysis, including Local Indicators of Spatial Association, revealed that poultry CAFOs often cluster in census tracts with high Social Vulnerability Index (SVI) scores, indicating potential EJ issues. Notably, one-third of NC's census tracts with high poultry CAFO density also have high SVI, primarily in rural eastern regions. Similar patterns were observed in the South and Southeast of the US. However, not all high-density CAFO areas correspond with high SVI, suggesting a complex relationship between CAFO locations and community vulnerabilities. This study highlights the critical need for comprehensive, high-quality data on unpermitted poultry CAFOs derived using AI algorithms to fully understand their impacts on communities and accurately inform EJ evaluations.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"8 12","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11652945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856240","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":"The Analysis and the Impact of Surface Temperature Anomalies on the Health of Residents in the River Niger Basin Development Authority Area, West Africa","authors":"R. T. Akinnubi,&nbsp;K. J. Adegbo,&nbsp;M. O. Ojo,&nbsp;M. P. Ajakaiye,&nbsp;A. J. Sabejeje,&nbsp;J. O. Aramide,&nbsp;T. D. Akinnubi","doi":"10.1029/2024GH001069","DOIUrl":"10.1029/2024GH001069","url":null,"abstract":"<p>This study investigates the impact of surface temperature anomalies on the health of residents within the River Niger Basin Development Authority (RIBDA) enclave, which covers Nigeria, Niger, and Mali in West Africa, with a focus on the regional implications for public health. Historical climate data from 1985 to 2014, sourced from the Climatic Research Unit Time-Series, Version 3.22 (CRU TS 3.22), was analyzed to comprehend past climate patterns and establish a baseline for future comparisons. Predictions for future climate conditions (2015–2044) were derived by adjusting the CRU data using temperature projections from the Community Climate System Model 4 under the Representative Concentration Pathway 8.5 scenario. To assess the potential impacts of these climate changes, particularly during the boreal summer season of July-August-September (JAS), the study utilized the Hydrology, Entomology, and Malaria Transmission Simulator (HYDREMATS). Findings indicate that surface temperature can intricately influence disease transmission, with varied effects on parameters such as Ro, EIR, prevalence, and immunity index. Observations revealed fluctuations in temperature anomalies over the years, with negative anomalies in 1991–1995 and positive anomalies in subsequent years. Although precise predictions for 2016–2044 are challenging based solely on data trends from 1985 to 2015, continued temperature rises could potentially lead to increased disease prevalence and decreased immunity index. Moreover, the analysis identified a notable temporal increase in mean annual temperature and mean annual maximum temperature from 1999 to 2020, suggesting a faster warming trend in maximum temperatures compared to minimum temperatures. This increase in temperature variability may alter the onset and cessation dates of the rainy season, affecting water availability, accessibility, and consumption, consequently fostering conditions conducive to health-related diseases. By incorporating predicted long-term temperature changes due to greenhouse gas emissions while maintaining current inter-annual climate patterns, this approach allows researchers to anticipate potential future health implications in the studied regions.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"8 12","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11635307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819693","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":"Variation in the Impact of New York on Pause on Traffic Congestion by Racialized Economic Segregation and Environmental Burden","authors":"Jenni A. Shearston,&nbsp;Roheeni Saxena,&nbsp;Joan A. Casey,&nbsp;Marianthi-Anna Kioumourtzoglou,&nbsp;Markus Hilpert","doi":"10.1029/2024GH001050","DOIUrl":"10.1029/2024GH001050","url":null,"abstract":"<p>During the 2019 coronavirus pandemic, stay-at-home policies such as New York's (NY) NY on Pause dramatically reduced traffic congestion. Despite high traffic burden in NY's environmental justice communities, this reduction has not been evaluated through an environmental justice lens—our objective in this analysis. We obtained census tract-level traffic congestion data from Google traffic maps hourly for 2018–2020. We defined congestion as the percent of streets in a census tract with heavy traffic (red- or maroon-color). We used the Index of Concentration at the Extremes (ICE) to measure racialized economic segregation and the CDC's Environmental Justice Index (EJI) as a measure of combined environmental, social, and chronic disease burden. We divided census tracts into quintiles of ICE and EJI and used linear mixed models stratified by ICE and EJI quintile in an interrupted time series design. Prior to NY on Pause, less marginalized and burdened census tracts (Q5) tended to have higher levels of traffic congestion; during NY on Pause, this trend reversed. For both ICE and EJI, more marginalized and burdened (Q1–Q2 vs. Q4–Q5) tracts had smaller absolute decreases in percent traffic congestion. For example, percent traffic congestion in ICE Q5 decreased by 7.8% (% change: −36.6%), but in Q1, it decreased by 4.2% (% change: −51.7%). NY on Pause, while protecting residents during COVID-19, may have resulted in inequitable reductions in traffic congestion. It is critical that such inequities are measured and acknowledged so that future policies to reduce traffic congestion and respond to pandemics can enhance equity.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"8 12","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11632250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814699","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":"Effect of Regional Housing Hardship on Spatial Variation in Cancer Incidence: Does Housing Stress Increase Cancer Incidence?","authors":"Haishi Yu,&nbsp;Jinyu Huang,&nbsp;Yang Wang,&nbsp;Xiaoli Yue,&nbsp;Yingmei Wu,&nbsp;Hong'ou Zhang","doi":"10.1029/2024GH001140","DOIUrl":"https://doi.org/10.1029/2024GH001140","url":null,"abstract":"<p>Housing hardship can create a range of health issues. However, little attention has been paid to the relationship between housing hardship and cancer incidence. This study examines the Yangtze River Economic Belt (YREB) in China as a case study to develop a model of factors related to housing hardship that can affect cancer incidence. This study employs spatial regression models to investigate the correlation between housing hardship and cancer incidence and further explores the variation in the correlation between urban areas (UAs) and non-urban areas (NUAs). The research conclusions are as follows: (a) a palpable correlation exists between housing hardship and cancer incidence. The housing price-to-income ratio (HPIR) and the rental household proportion (RHP) are positively correlated to cancer incidence, whereas the per capita living area (PCLA) has a negative correlation with cancer incidence. (b) The differences in the impact of housing hardship on cancer incidence between the UAs and the NUAs are reflected mainly in the differences in the PCLA and the RHP. The PCLA has a strong association with cancer incidence in the UAs, whereas the RHP demonstrates a strong correlation with cancer incidence in the NUAs. (c) Significant spatial heterogeneity is observed in housing hardship in the YREB.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"8 12","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142758062","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":"Characteristics of the Spatiotemporal Distribution of Influenza Incidence and Its Driving Factors Among Residents in Mainland China From 2004 to 2018","authors":"Y. R. Yao,&nbsp;K. Ma,&nbsp;Y. S. Li,&nbsp;H. R. Tan,&nbsp;J. Y. Zhang,&nbsp;Z. M. Zhang,&nbsp;Y. S. Lin,&nbsp;F. M. Fang","doi":"10.1029/2024GH001181","DOIUrl":"https://doi.org/10.1029/2024GH001181","url":null,"abstract":"<p>Influenza is an acute respiratory infection that spreads rapidly and widely in densely populated areas with low vaccination coverage. The trends and drivers of the spatial and temporal dynamics of influenza incidence among residents of mainland China have not been systematically studied. This study comprehensively analyses the dynamics and spatial correlation of influenza using long-term scale data and spatial panel data. It then identifies the interactive process of socio-economic and natural elements on the incidence of influenza. The highest prevalence of influenza was found in the 0–4 years age group in mainland China (mean prevalence, 67.56/100,000). In addition, influenza in mainland China shows seasonality, with fall and winter being the periods of high incidence. Between 2014 and 2017, influenza incidence was clustered in Hubei and Anhui provinces, and the spatial clustering was statistically significant (<i>Z</i> value &gt; 1.96, <i>P</i> &lt; 0.05). Moreover, the directionality of influenza onset continued to increase each year. Specifically, the clustering of influenza onset was stronger in the northwest-southeast direction than in the southwest-northeast direction between 2014 and 2018. The significant role of socioeconomic factors as a primary influence on influenza incidence, while their interaction with natural factors, such as air quality (NO_x and PM_2.5) and climatic conditions can exacerbate regional outbreaks. This study provides a novel perspective for better prevention and control of influenza disease among mainland Chinese residents.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"8 12","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024GH001181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142758063","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":"Planetary Health Booms: Unpacking the Surge in Research Across the Globe Through Joint-Point Analysis","authors":"Gladson Vaghela,&nbsp;Zeeshan Ali Khan,&nbsp;Randa Elsheikh,&nbsp;Abdelrahman M. Makram,&nbsp;Arshia Fatima,&nbsp;Omar Mohamed Makram,&nbsp;Fei He,&nbsp;Nguyen Tien Huy","doi":"10.1029/2024GH001028","DOIUrl":"10.1029/2024GH001028","url":null,"abstract":"<p>Planetary Health (PH) is a relatively new field that studies the sciences behind the interaction between the environment, living organisms, and human health. The number of publications in this field has increased exponentially in the past few years. This study analyzed the trend of using the term “PH” in literature. We searched PubMed for all publications (APP) and those mentioning Planetary Health (APmPH) without limitations on text availability, dates, or species. The publication trend was estimated using the average annual percent change (AAPC). Joinpoint Regression Program 4.9.1.0 identified periods with statistically distinct log-linear trends in publication numbers over time. Bonferroni adjustment determined significant trend shifts. The time frame of the retrieved APP and APmPH results ranged from 2002 to 2022 with a publication boom since 2017. The most common study designs were reviews, followed by comments and editorials. The APP, APmPH, and the proportion of APmPH to APP steadily increased from 2002 to 2022, with an AAPC of 6.0% (95% CI: 4.4%–7.6%, <i>p</i> &lt; 0.001), 35.7 (95% CI: 21.3%–51.9%, <i>p</i> &lt; 0.001), and 28.1% (95% CI: 15.2–42.5, <i>p</i> &lt; 0.001), respectively. The term “PH” is increasingly prominent in academic literature, underscoring the need for interdisciplinary efforts. Its growing usage also highlights the need for recognition in Medical Subject Headings by the National Library of Medicine.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"8 11","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142630649","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":"Satellite-Derived, Smartphone-Delivered Geospatial Cholera Risk Information for Vulnerable Populations","authors":"Farah Nusrat,&nbsp;Ali S. Akanda,&nbsp;Abdullah Islam,&nbsp;Sonia Aziz,&nbsp;Emily L. Pakhtigian,&nbsp;Kevin Boyle,&nbsp;Syed Manzoor Ahmed Hanifi","doi":"10.1029/2024GH001039","DOIUrl":"10.1029/2024GH001039","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Cholera, an acute waterborne diarrheal disease, remains a major global health challenge. Despite being curable and preventable, it can be fatal if left untreated, especially for children. Bangladesh, a cholera-endemic country with a high disease burden, experiences two peaks annually, during the dry pre-monsoon spring and the wet post-monsoon fall seasons. An early warning system for disseminating cholera risk, which has potential to reduce the disease burden, currently does not exist in Bangladesh. Such systems can raise timely awareness and allow households in rural, riverine areas like Matlab to make behavioral adjustments with water usage and around water resources to reduce contracting and transmitting cholera. Current dissemination approaches typically target local government and public health organizations; however, the vulnerable rural populations largely remain outside the information chain. Here, we develop and evaluate the accuracy of an early warning system—CholeraMap that uses high-resolution earth observations to forecast cholera risk and disseminate geocoded risk maps directly to Matlab's population via a mobile smartphone application. Instead of relying on difficult to obtain station-based environmental and hydroclimatological data, this study offers a new opportunity to use remote sensing data sets for designing and operating a disease early warning system. CholeraMap delivers monthly, color-coded geospatial maps (1 km × 1 km spatial resolution) with household and community cholera risk information. Our results demonstrate that the satellite-derived local-scale risk model satisfactorily captured the seasonal cholera pattern for the Matlab region, and a detailed high-resolution picture of the spatial progression of at-risk areas during outbreak months.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"8 11","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11549691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142630654","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":"Upstream Oil and Gas Production and Community COVID-19 Case and Mortality Rates in California, USA","authors":"Helena Archer,&nbsp;David J. X. González,&nbsp;Julia Walsh,&nbsp;Paul English,&nbsp;Peggy Reynolds,&nbsp;W. John Boscardin,&nbsp;Catherine Carpenter,&nbsp;Rachel Morello-Frosch","doi":"10.1029/2024GH001070","DOIUrl":"10.1029/2024GH001070","url":null,"abstract":"<p>Higher concentrations of ambient air pollutants, including PM_2.5 and NO₂, and other pollutants have been found near active oil and gas wells and may be associated with adverse COVID-19 outcomes. We assessed whether residential exposure to nearby oil and gas production was associated with higher rates of the respiratory infection COVID-19 and related mortality using a population-based ecological study in California. Using gridded population estimates, we estimated area-level exposure to annual average oil and gas production volume from active wells within 1 kilometer (km) of populated areas within census block groups from 2018 to 2020. We geocoded confirmed cases and associated deaths to assess block group case and mortality rates from COVID-19 from February 2020 to January 2021. We fit hierarchical Poisson models with individual and area covariates (e.g., age, sex, socioeconomic disadvantage), and included time and other interactions to assess additional variation (e.g., testing, reporting rates). In the first 4 months of the study period (February–May 2020), block groups in the highest tertile of oil and gas production exposure had 34% higher case rates (IRR: 1.34 95% CI: 1.20, 1.49) and 55% higher mortality rates (MRR: 1.52 95%: CI: 1.14, 2.03) than those with no estimated production, after accounting for area-level covariates. Over the entire study period, we observed moderately higher mortality rates in the highest group (MRR: 1.16 95%: CI: 1.01, 1.33) and null associations for case rates.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"8 11","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11543630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142630659","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 of Long-Term Exposure to PM2.5 Constituents and Green Space With Arthritis and Rheumatoid Arthritis","authors":"Yi-Sheng He,&nbsp;Yi-Qing Xu,&nbsp;Fan Cao,&nbsp;Zhao-Xing Gao,&nbsp;Man Ge,&nbsp;Tian He,&nbsp;Peng Zhang,&nbsp;Chan-Na Zhao,&nbsp;Peng Wang,&nbsp;Zhiwei Xu,&nbsp;Hai-Feng Pan","doi":"10.1029/2024GH001132","DOIUrl":"10.1029/2024GH001132","url":null,"abstract":"<p>There is limited evidence regarding the effects of long-term exposure to PM_2.5 constituents on the risk of arthritis and rheumatoid arthritis, and the interaction between PM_2.5 and green space remains unclear. This study examined the relationship between long-term exposure to PM_2.5 constituents and the risk of arthritis and rheumatoid arthritis, with the exposure period extending from recruitment until self-reported outcomes, death, loss to follow-up, or end of follow-up. Additionally, the study assessed whether there was an interactive effect between PM_2.5 and green space on these risks. We gathered cohort data on 18,649 individuals aged ≥45 years. We applied generalized linear mixed-effects models to estimate the effects of PM_2.5 constituents, NDVI, and their interaction on arthritis and rheumatoid arthritis. The quantile g-computation and weighted quantile sum regression model were applied to estimate the combined effect of PM_2.5 constituents. Our results showed that exposure to single and mixed PM_2.5 constituents adversely affected arthritis and rheumatoid arthritis, and was mainly attributed to the black carbon component. We observed “U” or “J” shaped exposure-response curves for the effects of PM_2.5, OM, NO₃⁻ and NH₄⁺ exposure on the development of arthritis/rheumatoid arthritis. Additionally, the odds ratio of arthritis for per interquartile range (IQR) increase in PM_2.5 was 1.209 (95% CI:1.198, 1.221), per 0.1-unit decrease in NDVI was 1.091 (95% CI:1.033, 1.151), and the interaction term was 1.005 (95% CI:1.002, 1.007). These findings flesh out the existing evidence for PM_2.5 constituents, NDVI and arthritis, rheumatoid arthritis, but the underlying mechanisms still require further exploration.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"8 11","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11538738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591054","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}