International Journal of Health Geographics最新文献

Background: Addressing key behavioral risk factors for chronic diseases, such as diet, requires innovative methods to objectively measure dietary patterns and their upstream determinants, notably the food environment. Although GIS techniques have pushed the boundaries by mapping food outlet availability, they often simplify food access dynamics to the vicinity of home addresses, possibly misclassifying neighborhood effects. Leveraging Google Location History Timeline (GLH) data offers a novel approach to assess long-term patterns of food outlet utilization at an individual level, providing insights into the relationship between food environment interactions, diet quality, and health outcomes.

Methods: We leveraged GLH data previously collected from a sub-set of participants in the Washington State Twin Registry (WSTR). GLH included more than 287 million location records from 357 participants. We developed methods to identify visits to food outlets using outlet-specific buffer zones applied to the InfoUSA data on food outlet locations. This methodology involved the application of minimum and maximum stay durations, along with revisit intervals. We calculated metrics from the GLH data to detect frequency of visits to different food outlet classifications (e.g. grocery stores, fast food, convenience stores) important to health. Several sensitivity analyses were conducted to examine the robustness of our food outlet metrics and to examine visits occurring within 1 and 2.5 km of residential locations.

Results: We identified 156,405 specific food outlet visits for the 357 study participants. 60% were full-service restaurants, 15% limited-service restaurants, and 16% supermarkets. Mean visits per person per month to any food outlet was 12.795. Only 8, 10 and 11% of full-service restaurants, limited-service restaurants, and supermarkets, respectively, occurred within 1 km of residential locations.

Conclusions: GLH data presents a novel method to assess individual-level food utilization behaviors.

Background: Malaria is a major public health issue in Nekemte City, western Ethiopia, with various environmental and social factors influencing transmission patterns. Effective control and prevention strategies require precise identification of high-risk areas. This study aims to map malaria risk zones in Nekemte City using geospatial technologies, including remote sensing and Geographic Information Systems (GIS), to support targeted interventions and resource allocation.

Methods: The study integrated environmental and social factors to assess malaria risk in the city. Environmental factors, including climatic and geographic characteristics, such as elevation, rainfall patterns, temperature, slope, and proximity to river, were selected based on experts' opinions and literature review. These factors were weighted using the analytic hierarchy process according to their relative influence on malaria hazard susceptibility. Social factors considered within the GIS framework focused on human settlements and access to resources. These included population density, proximity to health facilities, and proximity to roads. The malaria risk analysis incorporated hazard and vulnerability layers, along with Land use/cover (LULC) data. A weighted overlay analysis method combined these layers and generate the final malaria risk map.

Results: The malaria risk map identified that 18.2% (10.5 km²) of the study area was at very high risk, 18.8% (10.9 km²) at high risk, 30.4% (17.8 km²) at moderate risk, 19.8% (11.5 km²) at low risk, and 12.6% (7.3 km²) at very low risk. A combined 37% (21.4 km²) of Nekemte City was classified as at high to very high malaria risk, highlighting key areas for intervention.

Conclusions: This malaria risk map offers a valuable tool for malaria control and elimination efforts in Nekemte City. By identifying high-risk areas, the map provides actionable insights that can guide local health strategies, optimize resource distribution, and improve the efficiency of interventions. These findings contribute to enhanced public health planning and can support future regional malaria control initiatives.

Mosquito-borne diseases pose a significant public health threat, prompting the need to pinpoint high-risk areas for targeted interventions and environmental control measures. Culex quinquefasciatus is the primary vector for several mosquito-borne pathogens, including West Nile virus. Using spatial analysis and modeling techniques, we investigated the geospatial distribution of Culex quinquefasciatus abundance in the large metropolis of Harris County, Texas, from 2020 to 2022. Our geospatial analysis revealed clusters of high mosquito abundance, predominantly located in central Houston and the north-northwestern regions of Harris County, with lower mosquito abundance observed in the western and southeastern areas. We identified persistent high mosquito abundance in some of Houston's oldest neighborhoods, highlighting the importance of considering socioeconomic factors, the built environment, and historical urban development patterns in understanding vector ecology. Additionally, we observed a positive correlation between mosquito abundance and neighborhood-level socioeconomic status with the area deprivation index explaining between 22 and 38% of the variation in mosquito abundance (p-value < 0.001). This further underscores the influence of the built environment on vector populations. Our study emphasizes the utility of spatial analysis, including hotspot analysis and geostatistical interpolation, for understanding mosquito abundance patterns to guide resource allocation and surveillance efforts. Using geostatistical analysis, we discerned fine-scale geospatial patterns of Culex quinquefasciatus abundance in Harris County, Texas, to inform targeted interventions in vulnerable communities, ultimately reducing the risk of mosquito exposure and mosquito-borne disease transmission. By integrating spatial analysis with epidemiologic risk assessment, we can enhance public health preparedness and response efforts to prevent and control mosquito-borne disease.

Background: A few studies have suggested that light at night (LAN) exposure, i.e. lighting during night hours, may increase dementia risk. We evaluated such association in a cohort of subjects diagnosed with mild cognitive impairment (MCI).

Methods: We recruited study participants between 2008 and 2014 at the Cognitive Neurology Clinic of Modena Hospital, Northern Italy and followed them for conversion to dementia up to 2021. We collected their residential history and we assessed outdoor artificial LAN exposure at subjects' residences using satellite imagery data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) for the period 2014-2022. We assessed the relation between LAN exposure and cerebrospinal fluid biomarkers. We used a Cox-proportional hazards model to compute the hazard ratio (HR) of dementia with 95% confidence interval (CI) according to increasing LAN exposure through linear, categorical, and non-linear restricted-cubic spline models, adjusting by relevant confounders.

Results: Out of 53 recruited subjects, 34 converted to dementia of any type and 26 converted to Alzheimer's dementia. Higher levels of LAN were positively associated with biomarkers of tau pathology, as well as with lower concentrations of amyloid β_1-42 assessed at baseline. LAN exposure was positively associated with dementia conversion using linear regression model (HR 1.04, 95% CI 1.01-1.07 for 1-unit increase). Using as reference the lowest tertile, subjects at both intermediate and highest tertiles of LAN exposure showed increased risk of dementia conversion (HRs 2.53, 95% CI 0.99-6.50, and 3.61, 95% CI 1.34-9.74). In spline regression analysis, the risk linearly increased for conversion to both any dementia and Alzheimer's dementia above 30 nW/cm²/sr of LAN exposure. Adding potential confounders including traffic-related particulate matter, smoking status, chronic diseases, and apolipoprotein E status to the multivariable model, or removing cases with dementia onset within the first year of follow-up did not substantially alter the results.

Conclusion: Our findings suggest that outdoor artificial LAN may increase dementia conversion, especially above 30 nW/cm²/sr, although the limited sample size suggests caution in the interpretation of the results, to be confirmed in larger investigations.

Background: Neighbourhood exposure to takeaways can contribute negatively to diet and diet-related health outcomes. Urban planners within local authorities (LAs) in England can modify takeaway exposure through denying planning permission to new outlets in management zones around schools. LAs sometimes refer to these as takeaway "exclusion zones". Understanding the long-term impacts of this intervention on the takeaway retail environment and health, an important policy question, requires methods to forecast future takeaway growth and subsequent population-level exposure to takeaways. In this paper we describe a novel two-stage method to achieve this.

Methods: We used historic data on locations of takeaways and a time-series auto-regressive integrated moving average (ARIMA) model, to forecast numbers of outlets within management zones to 2031, based on historical trends, in six LAs with different urban/rural characteristics across England. Forecast performance was evaluated based on root mean squared error (RMSE) and mean absolute scaled error (MASE) scores in time-series cross-validation. Using travel-to-work data from the 2011 UK census, we then translated these forecasts of the number of takeaways within management zones into population-level exposures across home, work and commuting domains.

Results: Our ARIMA models outperformed exponential smoothing equivalents according to RMSE and MASE. The model was able to forecast growth in the count of takeaways up to 2031 across all six LAs, with variable growth rates by RUC (min-max: 39.4-79.3%). Manchester (classified as a non-London urban with major conurbation LA) exhibited the highest forecast growth rate (79.3%, 95% CI 61.6, 96.9) and estimated population-level takeaway exposure within management zones, increasing by 65.5 outlets per capita to 148.2 (95% CI 133.6, 162.7) outlets. Overall, urban (vs. rural) LAs were forecast stronger growth and higher population exposures.

Conclusions: Our two-stage forecasting approach provides a novel way to estimate long-term future takeaway growth and population-level takeaway exposure. While Manchester exhibited the strongest growth, all six LAs were forecast marked growth that might be considered a risk to public health. Our methods can be used to model future growth in other types of retail outlets and in other areas.

Background: The creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV).

Methods: We trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math.

Results: The initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time.

Conclusions: The results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.

Background: The burden of malaria in Kenya was showing a declining trend, but appears to have reached a plateau in recent years. This study estimated changes in the geographical distribution of malaria parasite risk in the country between the years 2015 and 2020, and quantified the contribution of malaria control interventions and climatic/ environmental factors to these changes.

Methods: Bayesian geostatistical models were used to analyse the Kenyan 2015 and 2020 Malaria Indicator Survey (MIS) data. Bivariate models were fitted to identify the most important control intervention indicators and climatic/environmental predictors of parasitaemia risk by age groups (6-59 months and 5-14 years). Parasitaemia risk and the number of infected children were predicted over a 1 × 1 km² grid. The probability of the decline in parasitaemia risk in 2020 compared to 2015 was also evaluated over the gridded surface and factors associated with changes in parasitaemia risk between the two surveys were evaluated.

Results: There was a significant decline in the coverage of most malaria indicators related to Insecticide Treated Nets (ITN) and Artemisinin Combination Therapies (ACT) interventions. Overall, there was a 31% and 26% reduction in malaria prevalence among children aged < 5 and 5-14 years, respectively. Among younger children, the highest reduction (50%) and increase (41%) were in the low-risk and semi-arid epi zones, respectively; while among older children there was increased risk in both the low-risk (83%) and semi-arid (100%) epi zones. Increase in nightlights and the proportion of individuals using ITNs in 2020 were associated with reduced parasitaemia risk.

Conclusion: Increased nightlights and ITN use could have led to the reduction in parasitaemia risk. However, the reduction is heterogeneous and there was increased risk in northern Kenya. Taken together, these results suggest that constant surveillance and re-evaluation of parasite and vector control measures in areas with increased transmission is necessary. The methods used in this analysis can be employed in other settings.

Background: Lyme borreliosis is the most frequent zoonotic disease in the northern hemisphere and is transmitted by ticks of the genus Ixodes. Although many people are bitten by ticks in private yards, our understanding of the factors associated with their presence in these areas remains limited. To address this gap, we used a citizen science approach to identify the local and landscape features associated with tick presence in yards.

Methods: This study was conducted near Nancy, a city in northeastern France, from 2020 to 2022. Citizen scientists collected ticks in their yard on a single event (n = 185) and measured 13 yard features. Additionally, we computed 11 features related to the landscape composition and spatial configuration surrounding these yards. Using generalized linear mixed models, we determined the yard and landscape features associated with the presence of ticks and nymphal Ixodes ricinus (hereafter nymphs), the life stage, and species that mostly bite humans.

Results: Despite a low density, ticks were found in 32% of the yards, including yards in urbanized areas. At the transect level, the likelihood of finding a nymph was nearly three times higher in transects shaded by vegetation compared to those in open areas, with no relationship between nymph occurrence and transect location or grass height. At the yard level, the occurrence of ticks and nymphs was related to both yard and landscape characteristics. Nymph and tick occurrence were more than twice as high in yards with signs of deer and a wood/brush pile compared to those without these characteristics, and increased with the connectivity of vegetation areas and the percentage of forest areas in the landscape.

Conclusions: Our study reveals that private yards across an urbanization gradient are locations of tick exposure with tick presence linked to both yard and landscape factors. These findings emphasize the importance of public awareness regarding tick exposure in yards and provide crucial insights for future public health prevention campaigns.

Background: Gambling and its harmful effects on human health and well-being represent a significant public health concern in many countries, with electronic gambling machines (EGMs) recognized as one of the most detrimental forms of gambling. Previous research has established an association between EGM accessibility, expenditure, gambling harm, and the socioeconomic status (SES) of neighborhoods. However, there is limited understanding of the direct impact of SES and EGM accessibility on individual player expenditures. Prior estimations of expenditure often rely on self-reported data or venue-level revenue statistics. This study uses high spatial resolution socioeconomic data together with individual-level account-based location and expenditure (point of sales) data (71,669 players, 745 EGM venues) to explore the association between EGM accessibility and neighborhood SES and to examine whether the EGM expenditure of neighborhood residents is associated with EGM accessibility and neighborhood SES.

Data and methods: Player account data include information on the home location and expenditure of the entire EGM gambling population across every EGM venue located in the Helsinki region, Finland. High-resolution (250 × 250 m) grid-level data on socioeconomic variables were used to obtain the local socioeconomic conditions of the players. EGM accessibility was estimated for every grid cell using a calibrated gravity model derived from the player account data. Statistical analyses included correlation analysis, spatial autocorrelation analysis, and regression models.

Results: First, significantly higher levels of EGM accessibility were found in areas with lower local SES. Second, regression analysis revealed that both higher EGM accessibility and lower local SES were associated with higher annual losses per adult. These results, in combination with visual and spatial autocorrelation analyses, revealed that accessibility to EGM gambling is highly concentrated, especially in lower socioeconomic neighborhoods with higher levels of EGM expenditure.

Conclusions: The results lay the groundwork for future spatial research on gambling harm, expenditure, accessibility, and SES utilizing detailed account data on the interaction between players and venues. The results underscore the importance of spatial restrictions when regulating EGM accessibility, particularly in areas with vulnerable populations, as a crucial measure for public health and harm prevention. The results also enable targeted gambling harm prevention actions at the local level.

An important consideration in studies of the relationship between greenspace exposure and health is the use of mapped data to assign geographic exposures to participants. Previous studies have used validated data from municipal park departments to describe the boundaries of public greenspaces. However, this approach assumes that these data accurately describe park boundaries, that formal parks fully capture the park and greenspace exposure of residents, and (for studies that use personal GPS traces to assign participant exposures) that time spent within these boundaries represents time spent in greenspace. These assumptions are tested using a comparison and ground-truthing of four sources of mapped park and greenspace data in Philadelphia, Pennsylvania: PAD-US-AR, Philadelphia Parks and Recreation, the Delaware Valley Regional Planning Commission, and Open Street Maps. We find several important differences and tradeoffs in these data: the incorporation of highways and building lots within park boundaries, the inclusion or exclusion of formal park spaces (federal, state, and nonprofit), the exclusion of informal parks and greenspaces, and inconsistent boundaries for a linear park. Health researchers may wish to consider these issues when conducting studies using boundary data to assign park exposure.