Identification and analysis of accident black spots using Geographic Information System (GIS): A study on Kushtia-Jhenaidah national highway (N704), Bangladesh
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PROVIDER: S-EPMC10873738 | biostudies-literature | 2024 Feb
REPOSITORIES: biostudies-literature
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Project description:Good nutrition reduces the risk of chronic disease, but many vulnerable populations do not have equitable access to healthy food. People who are lower-income, non-White, older, or disabled, have increased likelihood of not owning a vehicle and of being dependent on public buses to source healthy, affordable food. Our study took place in three municipalities targeted by the Rhode Island Department of Health for equity-focused interventions. Our methodology aimed to overcome limitations of prior analyses of public transit food access. We determined healthy food sources, stratifying their affordability with market basket pricing data and community input. Using a geographic information system (GIS), we identified the transit bus service areas of the sources. Our novel approach included employing a fixed bus wait time, determining bus travel times based on schedule combined with historical data, and determining acceptable travel times through a community leader survey. We analyzed access by subgroups including those experiencing poverty, older adults, those without cars, and those with disabilities. We found that 45% of the population in the most urban municipality had bus access to discounted healthy food, and no one in the two less urban municipalities did. Bus access to six food pantries was limited to 15%-28% of the population. Our approach, combining spatial analysis informed by surveys and community input, can increase the ease and accuracy of analyzing bus access to healthy, affordable food and is replicable in other regions and extensible to other types of destinations and services and to rail transit.
| S-EPMC11787688 | biostudies-literature
Project description:Dengue is one of the most important vector-borne infection in Sri Lanka currently leading to vast economic and social burden. Neither a vaccine nor drug is still not being practiced, vector controlling is the best approach to control disease transmission in the country. Therefore, early warning systems are imminent requirement. The aim of the study was to develop Geographic Information System (GIS)-based multivariate analysis model to detect risk hotspots of dengue in the Gampaha District, Sri Lanka to control diseases transmission. A risk model and spatial Poisson point process model were developed using separate layers for patient incidence locations, positive breeding containers, roads, total buildings, public places, land use maps and elevation in four high risk areas in the district. Spatial correlations of each study layer with patient incidences was identified using Kernel density and Euclidean distance functions with minimum allowed distance parameter. Output files of risk model indicate that high risk localities are in close proximity to roads and coincide with vegetation coverage while the Poisson model highlighted the proximity of high intensity localities to public places and possibility of artificial reservoirs of dengue. The latter model further indicate that clustering of dengue cases in a radius of approximately 150 m in high risk areas indicating areas need intensive attention in future vector surveillances.
| S-EPMC7892844 | biostudies-literature
Project description:BackgroundLittle is known about the impact of neighborhood context on family caregivers, or how environmental factors combine with individual-level caregiver risk factors to affect caregiver outcomes.ObjectivesTo combine Geographic Information System (GIS) and survey methods to examine the effects of caregiver residence in disadvantaged/underserved neighborhoods on caregiver outcomes.Research design and methodsTelephone surveys with 758 caregivers from the Pittsburgh Regional Caregiver Survey geocoded for classification into Environmental Justice Areas (EJAs) and Medically Underserved Areas (MUAs). We examine the impact of EJA/MUA caregiver residence on care recipient unmet needs for care, caregiver depression and burden, and positive aspects of caregiving, adjusting for sociodemographics, caregiving context, care recipient disability level, caregiving intensity, and additional risk factors.ResultsThere was spatial clustering of caregiver depression and burden outside of the disadvantaged/underserved areas, while positive aspects of caregiving were clustered within EJAs/MUAs. Approximately 36% of caregivers lived in EJAs/MUAs, and they differed, sociodemographically, on caregiver risk factors and caregiver outcomes. Multivariable models showed that caregivers residing in EJAs/MUAs were less likely to be depressed and reported more positive aspects of caregiving after adjusting for known individual-level risk factors. Residence in disadvantaged/underserved areas also modified the effects of several risk factors on caregiver outcomes.Discussion and implicationsCaregiver outcomes show interesting spatial patterns. Unexpectedly, caregivers living in these potentially challenging environments were less depressed and reported more gains from caregiving after adjusting for known risk factors. Results suggest that socioeconomic disadvantage does not necessarily translate into poor caregiver outcomes. Understanding the mechanism for these effects is important to designing effective caregiver interventions. The paper also demonstrates the value of using GIS methods to study caregiving.
| S-EPMC6735773 | biostudies-literature
Project description:Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.
| S-EPMC5580968 | biostudies-literature
Project description:BackgroundBlack individuals in the U.S. face increasing racial disparities in drug overdose related to social determinants of health, including place-based features. Mobile outreach efforts work to mitigate social determinants by servicing geographic areas with low drug treatment and overdose prevention access but are often limited by convenience-based targets. Geographic information systems (GIS) are often used to characterize and visualize the overdose crisis and could be translated to community to guide mobile outreach services. The current study examines the initial acceptability and appropriateness of GIS to facilitate data-driven outreach for reducing overdose inequities facing Black individuals.MethodsWe convened a focus group of stakeholders (N = 8) in leadership roles at organizations conducting mobile outreach in predominantly Black neighborhoods of St. Louis, MO. Organizations represented provided adult mental health and substance use treatment or harm reduction services. Participants were prompted to discuss current outreach strategies and provided feedback on preliminary GIS-derived maps displaying regional overdose epidemiology. A reflexive approach to thematic analysis was used to extract themes.ResultsFour themes were identified that contextualize the acceptability and utility of an overdose visualization tool to mobile service providers in Black communities. They were: 1) importance of considering broader community context; 2) potential for awareness, engagement, and community collaboration; 3) ensuring data relevance to the affected community; and 4) data manipulation and validity concerns.ConclusionsThere are several perceived benefits of using GIS to map overdose among mobile providers serving Black communities that are overburdened by the overdose crisis but under resourced. Perceived potential benefits included informing location-based targets for services as well as improving awareness of the overdose crisis and facilitating collaboration, advocacy, and resource allocation. However, as GIS-enabled visualization of drug overdose grows in science, public health, and community settings, stakeholders must consider concerns undermining community trust and benefits, particularly for Black communities facing historical inequities and ongoing disparities.
| S-EPMC11299267 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC), the most common type of primary liver cancer, is associated with low survival. U.S. studies examining self-reported pesticide exposure in relation to HCC have demonstrated inconclusive results. We aimed to clarify the association between pesticide exposure and HCC by implementing a novel data linkage between Surveillance, Epidemiology, and End Results (SEER)-Medicare and California Pesticide Use Report (PUR) data using a geographic information system (GIS).MethodsControls were frequency-matched to HCC cases diagnosed between 2000 and 2009 in California by year, age, race, sex, and duration of residence in California. Potential confounders were extracted from Medicare claims. From 1974 to 2008, pounds (1 pound represents 0.45 kg) of applied organophosphate, organochlorine, and carbamate pesticides provided in PURs were aggregated to the ZIP Code level using area weighting in a GIS. ZIP Code exposure estimates were linked to subjects using Medicare-provided ZIP Codes to calculate pesticide exposure. Agricultural residents were defined as living in ZIP Codes with a majority area intersecting agricultural land cover according to the 1992, 2001, and 2006 National Land Cover Database (NLCD) rasters. Multivariable conditional logistic regression was used to estimate the association between pesticide exposure and HCC.ResultsAmong California residents of agriculturally intensive areas, previous annual ZIP Code-level exposure to over 14.53 kg/km(2) of organochlorine pesticides (75(th) percentile among controls) was associated with an increased risk of HCC after adjusting for liver disease and diabetes (adjusted odds ratio [OR] 1.87, 95% confidence interval [CI] 1.17, 2.99; p=0.0085). ZIP Code-level organochlorines were significantly associated with an increased risk of HCC among males (adjusted OR 2.76, 95% CI 1.58, 4.82; p=0.0004), but not associated with HCC among females (adjusted OR 0.83, 95% CI 0.35, 1.93; p=0.6600) (interaction p=0.0075).ConclusionsThis is the first epidemiologic study to use GIS-based exposure estimates to study pesticide exposure and HCC. Our results suggest that organochlorine pesticides are associated with an increase in HCC risk among males but not females.
| S-EPMC4641787 | biostudies-literature
Project description:GOALS OF THIS STUDY WERE TO: (1) develop distributional maps of modern rodent genera throughout the countries of South Africa, Lesotho, and Swaziland by georeferencing museum specimens; (2) assess habitat preferences for genera by cross-referencing locality position with South African vegetation; and (3) identify mean annual precipitation and temperature range where the genera are located. Conterminous South Africa including the countries of Lesotho and Swaziland Digital databases of rodent museum specimens housed in the Ditsong National Museum of Natural History, South Africa (DM), and the Division of Mammals, National Museum of Natural History, Smithsonian Institution, United States (NMNH), were acquired and then sorted into a subset of specimens with associated coordinate data. The coordinate data were then used to develop distributional maps for the rodent genera present within the study area. Percent habitat occupation and descriptive statistics for six climatic variables were then determined for each genus by cross-referencing locality positions with vegetation and climatic maps. This report presents a series of maps illustrating the distribution of 35 rodent genera based on 19,471 geo-referenced specimens obtained from two major collections. Inferred habitat use by taxon is provided for both locality and specimen percent occurrence at three hierarchical habitat levels: biome, bioregion, and vegetation unit. Descriptive statistics for six climatic variables are also provided for each genus based on locality and specimen percent incidence. As rodent faunas are commonly used in paleoenvironmental reconstructions, an accurate assessment of rodent environmental tolerance ranges is necessary before confidence can be placed in an actualistic model. While the data presented here represent only a subset of the modern geographic distributions for many of the taxa examined, a wide range of environmental regimes are observed, suggesting that more research is necessary in order to accurately reconstruct an environmental signature when these taxa are found in the fossil record.
| S-EPMC3501638 | biostudies-literature
Project description:ObjectivesGeographical Information Surveillance (GIS) is an advanced digital technology tool that maps location-based data and helps in epidemiological modeling. We applied GIS to analyze patterns of spread and hotspots of COVID-19 cases in the Vellore district in South India.MethodsLaboratory-confirmed COVID-19 cases from the Vellore district and neighboring taluks from March 2020 to June 2021 were geocoded and spatial maps were generated. Time trends exploring urban-rural burden with an age-sex distribution of cases and other variables were correlated with outcomes.ResultsA total of 45,401 cases of COVID-19 were detected, with 20,730 cases during the first wave and 24,671 cases during the second wave. The overall incidence rates of COVID-19 were 462.8 and 588.6 per 100,000 population during the first and second waves, respectively. The spread pattern revealed epicenters in densely populated urban areas with radial spread sparing rural areas in the first wave. The case fatality rate was 1.89% and 1.6% during the first and second waves, which increased with advancing age.ConclusionsModern surveillance systems like GIS can accurately predict the trends and spread patterns during future pandemics. In addition, real-time mapping can help design risk mitigation strategies, thereby preventing the spread to rural areas.
| S-EPMC9263687 | biostudies-literature
Project description:In the last decade, geographic information systems (GIS) have become accessible to researchers in developing countries, yet guidance remains sparse for developing a GIS. Drawing on experience in developing a GIS for a large community trial in rural Bangladesh, six stages for constructing, maintaining, and using a GIS for health research purposes were outlined. The system contains 0.25 million landmarks, including 150,000 houses, in an area of 435 sq km with over 650,000 people. Assuming access to reasonably accurate paper boundary maps of the intended working area and the absence of pre-existing digital local-area maps, the six stages are: to (a) digitize and update existing paper maps, (b) join the digitized maps into a large-area map, (c) reference this large-area map to a geographic coordinate system, (d) insert location landmarks of interest, (e) maintain the GIS, and (f) link it to other research databases. These basic steps can produce a household-level, updated, scaleable GIS that can both enhance field efficiency and support epidemiologic analyses of demographic patterns, diseases, and health outcomes.
| S-EPMC2754008 | biostudies-literature
Project description:Integration of microbiological data and geographical locations is necessary to understand the spatiotemporalpatterns of themicrobial diversity of an ecosystem. The Geographic Information System (GIS) to map and cataloguethe data ontheactinobacterial diversity of the Southern Ocean waterswas completed through sampling and analysis. Water samples collectedat two sampling stations viz.Polar Front 1(Station 1) andPolar Front 2(Station2)during7thIndian Scientific Expedition to the Indian Ocean Sector of the Southern Ocean (SOE-2012-13)were used for analysis. At the outset, two different genera of Actinobacteria were recorded at both sampling stations.Streptomyces was the dominantedwiththehigh score (> 60%), followed by Nocardiopsis (< 30%)at both the sampling stations-Polar Front 1 and Polar Front 2-along withother invasive genera such as Agrococcus, Arthrobacter, Cryobacterium, Curtobacterium,Microbacterium, Marisediminicola, Rhodococcus and Kocuria. This data will help to discriminate the diversity and distribution pattern of the Actinobacteria in the Polar Frontal Region of the Southern Ocean waters.Itis a novel approachusefulfor geospatial cataloguing of microbial diversityfromextreme nichesand in various environmental gradations.Furthermore,this research work will act as the milestone for bioprospecting of microbial communities and their products having potential applications in healthcare, agriculture and beneficial to mankind. Hence, this research work would have significance in creating a database on microbial communities of the Antarctic ecosystem.
| S-EPMC5988443 | biostudies-literature