International Journal of Health Geographics最新文献

{"title":"Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites.","authors":"Claire Teillet, Rodolphe Devillers, Annelise Tran, Thibault Catry, Renaud Marti, Nadine Dessay, Joseph Rwagitinywa, Johana Restrepo, Emmanuel Roux","doi":"10.1186/s12942-024-00378-3","DOIUrl":"10.1186/s12942-024-00378-3","url":null,"abstract":"<p><strong>Background: </strong>The spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models.</p><p><strong>Methods: </strong>We use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available.</p><p><strong>Results: </strong>Landscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available.</p><p><strong>Conclusion: </strong>This study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"18"},"PeriodicalIF":3.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11229250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555764","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":"An investigation of inequalities in exposure to PM_2.5 air pollution across small areas in Ireland.","authors":"Aislinn Hoy, Gretta Mohan, Anne Nolan","doi":"10.1186/s12942-024-00377-4","DOIUrl":"10.1186/s12942-024-00377-4","url":null,"abstract":"<p><p>The link between exposure to air pollution and adverse effects on human health is well documented. Yet, in a European context, research on the spatial distribution of air pollution and the characteristics of areas is relatively scarce, and there is a need for research using different spatial scales, a wider variety of socioeconomic indicators (such as ethnicity) and new methodologies to assess these relationships. This study uses comprehensive data on a wide range of demographic and socioeconomic indicators, matched to data on PM_2.5 concentrations for small areas in Ireland, to assess the relationship between social vulnerability and PM_2.5 air pollution. Examining a wide range of socioeconomic indicators revealed some differentials in PM_2.5 concentration levels by measure and by rural and urban classification. However, statistical modelling using concentration curves and concentration indices did not present substantial evidence of inequalities in PM_2.5 concentrations across small areas. In common with other western European countries, an overall decline in the levels of PM_2.5 between 2011 and 2016 was observed in Ireland, though the data indicates that almost all small areas in Ireland were found to have exceeded the World Health Organization (WHO)'s PM_2.5 annual guideline (of 5 µg/m³), calling for greater policy efforts to reduce air pollution in Ireland. The recent Clean Air Strategy contains a commitment to achieve the WHO guideline limits for PM_2.5 by 2040, with interim targets at various points over the next two decades. Achieving these targets will require policy measures to decarbonise home heating, promote active travel and the transition to electric vehicles, and further regulations on burning fossil fuels and enforcing environmental regulations more tightly. From a research and information-gathering perspective, installing more monitoring stations at key points could improve the quality and spatial dimension of the data collected and facilitate the assessment of the implementation of the measures in the Clean Air Strategy.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"17"},"PeriodicalIF":3.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11227186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538823","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 obesogenic environment through spatial clustering of body mass index among adults.","authors":"Kimberly Yuin Y'ng Wong, Foong Ming Moy, Aziz Shafie, Sanjay Rampal","doi":"10.1186/s12942-024-00376-5","DOIUrl":"10.1186/s12942-024-00376-5","url":null,"abstract":"<p><strong>Background: </strong>The escalating trend of obesity in Malaysia is surmounting, and the lack of evidence on the environmental influence on obesity is untenable. Obesogenic environmental factors often emerge as a result of shared environmental, demographic, or cultural effects among neighbouring regions that impact lifestyle. Employing spatial clustering can effectively elucidate the geographical distribution of obesity and pinpoint regions with potential obesogenic environments, thereby informing public health interventions and further exploration on the local environments. This study aimed to determine the spatial clustering of body mass index (BMI) among adults in Malaysia.</p><p><strong>Method: </strong>This study utilized information of respondents aged 18 to 59 years old from the National Health and Morbidity Survey (NHMS) 2014 and 2015 at Peninsular Malaysia and East Malaysia. Fast food restaurant proximity, district population density, and district median household income were determined from other sources. The analysis was conducted for total respondents and stratified by sex. Multilevel regression was used to produce the BMI estimates on a set of variables, adjusted for data clustering at enumeration blocks. Global Moran's I and Local Indicator of Spatial Association statistics were applied to assess the general clustering and location of spatial clusters of BMI, respectively using point locations of respondents and spatial weights of 8 km Euclidean radius or 5 nearest neighbours.</p><p><strong>Results: </strong>Spatial clustering of BMI independent of individual sociodemographic was significant (p < 0.001) in Peninsular and East Malaysia with Global Moran's index of 0.12 and 0.15, respectively. High-BMI clusters (hotspots) were in suburban districts, whilst the urban districts were low-BMI clusters (cold spots). Spatial clustering was greater among males with hotspots located closer to urban areas, whereas hotspots for females were in less urbanized areas.</p><p><strong>Conclusion: </strong>Obesogenic environment was identified in suburban districts, where spatial clusters differ between males and females in certain districts. Future studies and interventions on creating a healthier environment should be geographically targeted and consider gender differences.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"16"},"PeriodicalIF":3.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11201309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141460177","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 use of Virtual Reality (VR) to assess the impact of geographical environments on walking and cycling: a systematic literature review.","authors":"Marzieh Ghanbari, Martin Dijst, Roderick McCall, Camille Perchoux","doi":"10.1186/s12942-024-00375-6","DOIUrl":"10.1186/s12942-024-00375-6","url":null,"abstract":"<p><strong>Background: </strong>Geographical environments influence people's active mobility behaviors, contributing to their physical and mental health. The use of Virtual Reality (VR) in experimental research can unveil new insights into the relationship between exposure to geographic environments and active mobility behaviors. This systematic review aims to (1) identify environmental attributes investigated in relation with walking and cycling, using VR, (2) assess their impacts on active mobility behaviors and attitudes, and (3) identify research gaps, strengths and limitations in VR-based experimental research.</p><p><strong>Methods: </strong>Articles published between January 2010 and February 2022 within five databases (PubMed, Scopus, EBSCO, IEEE Xplore, and Cochrane Library) were explored using three keywords and their synonyms: Virtual Reality, Active mobility behavior, and Geographical environments. Studies focusing on indoor environments, driving simulation, disease-specific groups, non-relevant disciplines (e.g. military, emergency evacuation), VR methodology/software optimization, and those with static participants' involvement were excluded. The full protocol is available from PROSPERO (ID = CRD42022308366).</p><p><strong>Results: </strong>Out of 3255 articles, 18 peer-reviewed papers met the selection criteria, mostly focusing on walking (83%). Most studies used head-mounted displays (94%) and relied on convenience sampling (72% below 100 participants). Both static (33%) and dynamic (45%) environmental attributes have been investigated, with only 22% of them simultaneously in the same virtual environment. Greenness and crowd density were the most frequent attributes, rather consistently associated with emotional states and movement behaviors. Few studies have taken into account participant's previous VR experience (33%) and cybersickness (39%) while both are likely to affect an individual's perception and behavior.</p><p><strong>Conclusions: </strong>Future research should explore a broader range of environmental attributes, including static and dynamic ones, as well as a more complex integration of these attributes within a single experiment to mimic the effect of realistic environments on people's active mobility behaviors and attitudes. Larger and more diverse population samples are deemed required to improve result generalizability. Despite methodological challenges, VR emerges as a promising tool to disentangle the effect of complex environments on active mobility behaviors.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"15"},"PeriodicalIF":4.9,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11162039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141293850","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":"Longitudinal study of changes in greenness exposure, physical activity and sedentary behavior in the ORISCAV-LUX cohort study.","authors":"Juliette F E van Beek, Laurent Malisoux, Olivier Klein, Torsten Bohn, Marion Tharrey, Frank J Van Lenthe, Mariëlle A Beenackers, Martin Dijst, Camille Perchoux","doi":"10.1186/s12942-024-00374-7","DOIUrl":"10.1186/s12942-024-00374-7","url":null,"abstract":"<p><strong>Background: </strong>Greenness exposure has been associated with many health benefits, for example through the pathway of providing opportunities for physical activity (PA). Beside the limited body of longitudinal research, most studies overlook to what extent different types of greenness exposures may be associated with varying levels of PA and sedentary behavior (SB). In this study, we investigated associations of greenness characterized by density, diversity and vegetation type with self-reported PA and SB over a 9-year period, using data from the ORISCAV-LUX study (2007-2017, n = 628).</p><p><strong>Methods: </strong>The International Physical Activity Questionnaire (IPAQ) short form was used to collect PA and SB outcomes. PA was expressed as MET-minutes/week and log-transformed, and SB was expressed as sitting time in minutes/day. Geographic Information Systems (ArcGIS Pro, ArcMap) were used to collect the following exposure variables: Tree Cover Density (TCD), Soil-adjusted Vegetation Index (SAVI), and Green Land Use Mix (GLUM). The exposure variables were derived from publicly available sources using remote sensing and cartographic resources. Greenness exposure was calculated within 1000m street network buffers around participants' exact residential address.</p><p><strong>Results: </strong>Using Random Effects Within-Between (REWB) models, we found evidence of negative within-individual associations of TCD with PA (β = - 2.60, 95% CI - 4.75; - 0.44), and negative between-individual associations of GLUM and PA (β = - 2.02, 95% CI - 3.73; - 0.32). There was no evidence for significant associations between greenness exposure and SB. Significant interaction effects by sex were present for the associations between TCD and both PA and SB. Neighborhood socioeconomic status (NSES) did not modify the effect of greenness exposure on PA and SB in the 1000 m buffer.</p><p><strong>Discussion: </strong>Our results showed that the relationship between greenness exposure and PA depended on the type of greenness measure used, which stresses the need for the use of more diverse and complementary greenness measures in future research. Tree vegetation and greenness diversity, and changes therein, appeared to relate to PA, with distinct effects among men and women. Replication studies are needed to confirm the relevance of using different greenness measures to understand its' different associations with PA and SB.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"14"},"PeriodicalIF":4.9,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11110334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076756","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":"Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.","authors":"Fedra Trujillano, Gabriel Jimenez, Edgar Manrique, Najat F Kahamba, Fredros Okumu, Nombre Apollinaire, Gabriel Carrasco-Escobar, Brian Barrett, Kimberly Fornace","doi":"10.1186/s12942-024-00371-w","DOIUrl":"10.1186/s12942-024-00371-w","url":null,"abstract":"<p><strong>Background: </strong>In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies.</p><p><strong>Results: </strong>We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance.</p><p><strong>Conclusions: </strong>Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"13"},"PeriodicalIF":4.9,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141065797","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":"Investigating young children's physical activity through time and place.","authors":"T Remmers, P Koolwijk, I Fassaert, J Nolles, W de Groot, S B Vos, S I de Vries, R Mombarg, D H H Van Kann","doi":"10.1186/s12942-024-00373-8","DOIUrl":"10.1186/s12942-024-00373-8","url":null,"abstract":"<p><strong>Background: </strong>Previous research indicates the start of primary school (4-5-year-old) as an essential period for the development of children's physical activity (PA) patterns, as from this point, the age-related decline of PA is most often observed. During this period, young children are exposed to a wider variety of environmental- and social contexts and therefore their PA is influenced by more diverse factors. However, in order to understand children's daily PA patterns and identify relevant opportunities for PA promotion, it is important to further unravel in which (social) contexts throughout the day, PA of young children takes place.</p><p><strong>Methods: </strong>We included a cross-national sample of 21 primary schools from the Startvaardig study. In total, 248 children provided valid accelerometer and global positioning (GPS) data. Geospatial analyses were conducted to quantify PA in (social) environments based on their school and home. Transport-related PA was evaluated using GPS speed-algorithms. PA was analysed at different environments, time-periods and for week- and weekend days separately.</p><p><strong>Results: </strong>Children accumulated an average of 60 min of moderate-to-vigorous PA (MVPA), both during week- and weekend days. Schools contributed to approximately half of daily MVPA during weekdays. During weekends, environments within 100 m from home were important, as well as locations outside the home-school neighbourhood. Pedestrian trips contributed to almost half of the daily MVPA.</p><p><strong>Conclusions: </strong>We identified several social contexts relevant for children's daily MVPA. Schools have the potential to significantly contribute to young children's PA patterns and are therefore encouraged to systematically evaluate and implement parts of the school-system that stimulate PA and potentially also learning processes. Pedestrian trips also have substantial contribution to daily MVPA of young children, which highlights the importance of daily active transport in school- and parental routines.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"12"},"PeriodicalIF":4.9,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11092161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140921186","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":"Analysing the outbreaks of leptospirosis after floods in Kerala, India.","authors":"Oluwafemi John Ifejube, Sekhar L Kuriakose, T S Anish, Cees van Westen, Justine I Blanford","doi":"10.1186/s12942-024-00372-9","DOIUrl":"10.1186/s12942-024-00372-9","url":null,"abstract":"<p><p>A growing number of studies have linked the incidence of leptospirosis with the occurrence of flood events. Nevertheless, the interaction between flood and leptospirosis has not been extensively studied to understand the influence of flood attributes in inducing new cases. This study reviews leptospirosis cases in relation to multiple flood occurrences in Kerala, India. Leptospirosis data were obtained for three years: 2017 (non-flood year) and two years with flooding-2018 (heavy flooding) and 2019 (moderate flooding). We considered the severity of flood events using the discharge, duration and extent of each flooding event and compared them with the leptospirosis cases. The distribution of cases regarding flood discharge and duration was assessed through descriptive and spatiotemporal analyses, respectively. Furthermore, cluster analyses and spatial regression were completed to ascertain the relationship between flood extent and the postflood cases. This study found that postflood cases of leptospirosis can be associated with flood events in space and time. The total cases in both 2018 and 2019 increased in the post-flood phase, with the increase in 2018 being more evident. Unlike the 2019 flood, the flood of 2018 is a significant spatial indicator for postflood cases. Our study shows that flooding leads to an increase in leptospirosis cases, and there is stronger evidence for increased leptospirosis cases after a heavy flood event than after a moderate flooding event. Flood duration may be the most important factor in determining the increase in leptospirosis infections.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"11"},"PeriodicalIF":4.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11092194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140916413","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 the objective and perceived food environment and eating behavior in relation to socioeconomic status among adults in peri-urban settings: results from the CIVISANO study in Flanders, Belgium.","authors":"Yasemin Inaç, Suzannah D'Hooghe, Delfien Van Dyck, Sarah Dury, Stefanie Vandevijvere, Benedicte Deforche, Eva M De Clercq, Nico Van de Weghe, Karin De Ridder","doi":"10.1186/s12942-024-00369-4","DOIUrl":"10.1186/s12942-024-00369-4","url":null,"abstract":"<p><p>Obesity, a significant public health concern, disproportionately affects people with lower socioeconomic status (SES). Food environments have been identified as part of the causal chain of this disparity. This study investigated variations in the food environment across groups with different SES profiles residing in peri-urban municipal settings. In addition, it examined the association of the perceived and objective food environments with eating behaviour and assessed if these associations were moderated by SES. Utilizing GIS and survey data (n = 497, aged 25-65), results showed differences in the objective and perceived food environments based on SES. Respondents with higher SES perceived their food environments as better but resided farther from all food outlets compared to respondents with lower SES. However, there was no difference in outlet density or mRFEI between SES groups. SES moderated associations between the objective and perceived food environments and most eating behavior outcomes except fast food consumption frequency. For fruits and vegetables, SES moderated the association between neighborhood availability and consumption frequency (β0.23,CI0.03;0.49). Stratified analysis revealed a positive association for both lower (β0.15, CI0.03;0.27) and higher (β0.37, CI 0.12;0.63) SES groups. For snack foods, SES moderated the association between healthy outlet density and consumption frequency (β-0.60, CI-0.94; -0.23), showing statistical significance only for respondents with higher SES (β0.36,CI 0.18;0.55). Similarly, for sugar-sweetened beverages, a statistically significant interaction was observed between unhealthy outlet density in the 1000m buffer and consumption frequency (β 0.06, CI 0.02; 0.11). However, this association was only statistically significant for respondents with higher SES (β-0.02,CI -0.05;-0.0002). These results emphasize the significance of SES as a crucial element in comprehending the connection between the food environment and eating behaviour. Indicating the need for policymakers to take SES into account when implementing food environment interventions, particularly when focusing on the neighborhood food environment without considering residents' SES and their perceptions.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"10"},"PeriodicalIF":4.9,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11080110/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140900101","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":"How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data","authors":"Shuangming Zhao, Yuchen Fan, Pengxiang Zhao, Ali Mansourian, Hung Chak Ho","doi":"10.1186/s12942-024-00368-5","DOIUrl":"https://doi.org/10.1186/s12942-024-00368-5","url":null,"abstract":"Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability. This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers’ exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers’ activities. The taxi drivers’ weekday and weekend 24-h PM2.5 exposure was 83.60 μg/m3 and 55.62 μg/m3 respectively, 3.4 and 2.2 times than the WHO’s recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the “Inner Ring Road”, while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the “Third Ring Road”. These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"185 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}