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Thompson LK, Langholz B, Goldberg DW, Wilson JP, Ritz B, Tayour C, Cockburn M. Area-Based Geocoding: An Approach to Exposure Assessment Incorporating Positional Uncertainty. GEOHEALTH 2021; 5:e2021GH000430. [PMID: 34859166 PMCID: PMC8612311 DOI: 10.1029/2021gh000430] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 11/01/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
While the spatial resolution of exposure surfaces has greatly improved, our ability to locate people in space remains a limiting factor in accurate exposure assessment. In this case-control study, two approaches to geocoding participant locations were used to study the impact of geocoding uncertainty on the estimation of ambient pesticide exposure and breast cancer risk among women living in California's Central Valley. Residential and occupational histories were collected and geocoded using a traditional point-based method along with a novel area-based method. The standard approach to geocoding uses centroid points to represent all geocoded locations, and is unable to adapt exposure areas based on geocode quality, except through the exclusion of low-certainty locations. In contrast, area-based geocoding retains the complete area to which an address matched (the same area from which the centroid is returned), and therefore maintains the appropriate level of precision when it comes to assessing exposure by geography. Incorporating the total potential exposure area for each geocoded location resulted in different exposure classifications and resulting odds ratio estimates than estimates derived from the centroids of those same areas (using a traditional point-based geocoder). The direction and magnitude of these differences varied by pesticide, but in all cases odds ratios differed by at least 6% and up to 35%. These findings demonstrate the importance of geocoding in exposure estimation and suggest it is important to consider geocode certainty and quality throughout exposure assessment, rather than simply using the best available point geocodes.
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Affiliation(s)
- Laura K. Thompson
- Department of Population and Public Health SciencesKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Bryan Langholz
- Department of Population and Public Health SciencesKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Daniel W. Goldberg
- Department of GeographyCollege of GeosciencesTexas A&M UniversityCollege StationTXUSA
- Department of Computer Science and EngineeringCollege of GeosciencesTexas A&M UniversityCollege StationTXUSA
| | - John P. Wilson
- Spatial Sciences InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Beate Ritz
- Department of Epidemiology and Environmental SciencesFielding School of Public HealthUniversity of CaliforniaLos AngelesCAUSA
| | - Carrie Tayour
- Los Angeles County Department of Public HealthLos AngelesCAUSA
| | - Myles Cockburn
- Department of Population and Public Health SciencesKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCAUSA
- Spatial Sciences InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
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Faridah L, Mindra IGN, Putra RE, Fauziah N, Agustian D, Natalia YA, Watanabe K. Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk. Trop Med Health 2021; 49:44. [PMID: 34039439 PMCID: PMC8152360 DOI: 10.1186/s41182-021-00329-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 05/03/2021] [Indexed: 01/02/2023] Open
Abstract
Background Bandung, the fourth largest city in Indonesia and capital of West Java province, has been considered a major endemic area of dengue, and studies show that the incidence in this city could increase and spread rapidly. At the same time, estimation of incidence could be inaccurate due to a lack of reliable surveillance systems. To provide strategic information for the dengue control program in the face of limited capacity, this study used spatial pattern analysis of a possible outbreak of dengue cases, through the Geographic Information System (GIS). To further enhance the information needed for effective policymaking, we also analyzed the demographic pattern of dengue cases. Methods Monthly reports of dengue cases from January 2014 to December 2016 from 16 hospitals in Bandung were collected as the database, which consisted of address, sex, age, and code to anonymize the patients. The address was then transformed into geocoding and used to estimate the relative risk of a particular area’s developing a cluster of dengue cases. We used the kernel density estimation method to analyze the dynamics of change of dengue cases. Results The model showed that the spatial cluster of the relative risk of dengue incidence was relatively unchanged for 3 years. Dengue high-risk areas predominated in the southern and southeastern parts of Bandung, while low-risk areas were found mostly in its western and northeastern regions. The kernel density estimation showed strong cluster groups of dengue cases in the city. Conclusions This study demonstrated a strong pattern of reported cases related to specific demographic groups (males and children). Furthermore, spatial analysis using GIS also visualized the dynamic development of the aggregation of disease incidence (hotspots) for dengue cases in Bandung. These data may provide strategic information for the planning and design of dengue control programs.
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Affiliation(s)
- Lia Faridah
- Parasitology Division, Department of Biomedical Science, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia. .,Foreign Visiting Researcher at Department of Civil and Environmental Engineering, Ehime University, Matsuyama, Japan.
| | | | - Ramadhani Eka Putra
- School of Life Science and Technology, Institut Teknologi Bandung, Jl. Ganeca 10, Bandung, West Java, 40132, Indonesia
| | - Nisa Fauziah
- Parasitology Division, Department of Biomedical Science, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Dwi Agustian
- Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Yessika Adelwin Natalia
- Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia
| | - Kozo Watanabe
- Department of Civil and Environmental Engineering, Ehime University, Matsuyama, Japan
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Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041637. [PMID: 33572119 PMCID: PMC7915413 DOI: 10.3390/ijerph18041637] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 01/18/2021] [Accepted: 02/04/2021] [Indexed: 02/01/2023]
Abstract
Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), were geocoded in 2012–2016 and then again in 2019. We calculated distances between geocodes in the two periods. For a subset, we computed positional errors using “gold standard” rooftop coordinates (IA; N = 3566) or Global Positioning Systems (GPS) (IA and NC; N = 1258) and compared errors between periods. We used linear regression to model the change in positional error between time periods (improvement) by rural status and population density, and we used spatial relative risk functions to identify areas with significant improvement. Median improvement between time periods in IA was 41 m (interquartile range, IQR: −2 to 168) and 9 m (IQR: −80 to 133) based on rooftop coordinates and GPS, respectively. Median improvement in NC was 42 m (IQR: −1 to 109 m) based on GPS. Positional error was greater in rural and low-density areas compared to in towns and more densely populated areas. Areas of significant improvement in accuracy were identified and mapped across both states. Our findings underscore the importance of evaluating determinants and spatial distributions of errors in geocodes used in environmental epidemiology studies.
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Kinnee EJ, Tripathy S, Schinasi L, Shmool JLC, Sheffield PE, Holguin F, Clougherty JE. Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17165845. [PMID: 32806682 PMCID: PMC7459468 DOI: 10.3390/ijerph17165845] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 08/05/2020] [Accepted: 08/10/2020] [Indexed: 11/16/2022]
Abstract
Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km2), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates.
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Affiliation(s)
- Ellen J. Kinnee
- University Center for Social and Urban Research, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Correspondence: ; Tel.: +1-412-385-5105
| | - Sheila Tripathy
- Department of Environmental and Occupational Health, Drexel University Dornsife School of Public Health, Philadelphia, PA 19104, USA; (S.T.); (L.S.); (J.E.C.)
| | - Leah Schinasi
- Department of Environmental and Occupational Health, Drexel University Dornsife School of Public Health, Philadelphia, PA 19104, USA; (S.T.); (L.S.); (J.E.C.)
- Drexel University Urban Health Collaborative (UHC), Drexel University Dornsife School of Public Health, Philadelphia, PA 19104, USA
| | - Jessie L. C. Shmool
- Department of Environmental and Occupational Health, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA 15260, USA;
| | - Perry E. Sheffield
- Environmental Medicine and Public Health and Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Fernando Holguin
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA;
| | - Jane E. Clougherty
- Department of Environmental and Occupational Health, Drexel University Dornsife School of Public Health, Philadelphia, PA 19104, USA; (S.T.); (L.S.); (J.E.C.)
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Prevalence and spatio-temporal variation of an alopecia syndrome in polar bears (Ursus maritimus) of the southern Beaufort Sea. J Wildl Dis 2015; 51:48-59. [PMID: 25375943 DOI: 10.7589/2013-11-301] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Alopecia (hair loss) has been observed in several marine mammal species and has potential energetic consequences for sustaining a normal core body temperature, especially for Arctic marine mammals routinely exposed to harsh environmental conditions. Polar bears (Ursus maritimus) rely on a thick layer of adipose tissue and a dense pelage to ameliorate convective heat loss while moving between sea ice and open water. From 1998 to 2012, we observed an alopecia syndrome in polar bears from the southern Beaufort Sea of Alaska that presented as bilaterally asymmetrical loss of guard hairs and thinning of the undercoat around the head, neck, and shoulders, which, in severe cases, was accompanied by exudation and crusted skin lesions. Alopecia was observed in 49 (3.45%) of the bears sampled during 1,421 captures, and the apparent prevalence varied by years with peaks occurring in 1999 (16%) and 2012 (28%). The probability that a bear had alopecia was greatest for subadults and for bears captured in the Prudhoe Bay region, and alopecic individuals had a lower body condition score than unaffected individuals. The cause of the syndrome remains unknown and future work should focus on identifying the causative agent and potential effects on population vital rates.
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Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses. SPATIAL DEMOGRAPHY 2015; 4:155-173. [PMID: 27453935 DOI: 10.1007/s40980-015-0014-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
We evaluate the impacts of random spatial displacements on analyses that involve distance measures from displaced Demographic and Health Survey (DHS) clusters to nearest ancillary point or line features, such as health resources or roads. We use simulation and case studies to address the effects of this introduced error, and propose use of regression calibration (RC) to reduce its impact. Results suggest that RC outperforms analyses involving naive distance-based covariate assignments by reducing the bias and MSE of the main estimator in most settings. Proposed guidelines also address the effect of the spatial density of destination features on observed bias.
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Lyseen AK, Nøhr C, Sørensen EM, Gudes O, Geraghty EM, Shaw NT, Bivona-Tellez C. A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies. Yearb Med Inform 2014; 9:110-24. [PMID: 25123730 DOI: 10.15265/iy-2014-0008] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
OBJECTIVES The application of GIS in health science has increased over the last decade and new innovative application areas have emerged. This study reviews the literature and builds a framework to provide a conceptual overview of the domain, and to promote strategic planning for further research of GIS in health. METHOD The framework is based on literature from the library databases Scopus and Web of Science. The articles were identified based on keywords and initially selected for further study based on titles and abstracts. A grounded theory-inspired method was applied to categorize the selected articles in main focus areas. Subsequent frequency analysis was performed on the identified articles in areas of infectious and non-infectious diseases and continent of origin. RESULTS A total of 865 articles were included. Four conceptual domains within GIS in health sciences comprise the framework: spatial analysis of disease, spatial analysis of health service planning, public health, health technologies and tools. Frequency analysis by disease status and location show that malaria and schistosomiasis are the most commonly analyzed infectious diseases where cancer and asthma are the most frequently analyzed non-infectious diseases. Across categories, articles from North America predominate, and in the category of spatial analysis of diseases an equal number of studies concern Asia. CONCLUSION Spatial analysis of diseases and health service planning are well-established research areas. The development of future technologies and new application areas for GIS and data-gathering technologies such as GPS, smartphones, remote sensing etc. will be nudging the research in GIS and health.
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Affiliation(s)
- A K Lyseen
- Anders Knørr Lyseen, Department of Development and Planning, Aalborg University, Aalborg, Denmark, E-mail:
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Abstract
Until recently, little attention has been paid to geocoding positional accuracy and its impacts on accessibility measures; estimates of disease rates; findings of disease clustering; spatial prediction and modeling of health outcomes; and estimates of individual exposures based on geographic proximity to pollutant and pathogen sources. It is now clear that positional errors can result in flawed findings and poor public health decisions. Yet the current state-of-practice is to ignore geocoding positional uncertainty, primarily because of a lack of theory, methods and tools for quantifying, modeling, and adjusting for geocoding positional errors in health analysis. This paper proposes a research agenda to address this need. It summarizes the basics of the geocoding process, its assumptions, and empirical evidence describing the magnitude of geocoding positional error. An overview of the impacts of positional error in health analysis, including accessibility, disease clustering, exposure reconstruction, and spatial weights estimation is presented. The proposed research agenda addresses five key needs: (1) a lack of standardized, open-access geocoding resources for use in health research; (2) a lack of geocoding validation datasets that will allow the evaluation of alternative geocoding engines and procedures; (3) a lack of spatially explicit geocoding positional error models; (4) a lack of resources for assessing the sensitivity of spatial analysis results to geocoding positional error; (5) a lack of demonstration studies that illustrate the sensitivity of health policy decisions to geocoding positional error.
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Error propagation models to examine the effects of geocoding quality on spatial analysis of individual-level datasets. Spat Spatiotemporal Epidemiol 2012; 3:69-82. [PMID: 22469492 DOI: 10.1016/j.sste.2012.02.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The quality of geocoding has received substantial attention in recent years. A synthesis of published studies shows that the positional errors of street geocoding are somewhat unique relative to those of other types of spatial data: (1) the magnitude of error varies strongly across urban-rural gradients; (2) the direction of error is not uniform, but strongly associated with the properties of local street segments; (3) the distribution of errors does not follow a normal distribution, but is highly skewed and characterized by a substantial number of very large error values; and (4) the magnitude of error is spatially autocorrelated and is related to properties of the reference data. This makes it difficult to employ analytic approaches or Monte Carlo simulations for error propagation modeling because these rely on generalized statistical characteristics. The current paper describes an alternative empirical approach to error propagation modeling for geocoded data and illustrates its implementation using three different case-studies of geocoded individual-level datasets. The first case-study consists of determining the land cover categories associated with geocoded addresses using a point-in-raster overlay. The second case-study consists of a local hotspot characterization using kernel density analysis of geocoded addresses. The third case-study consists of a spatial data aggregation using enumeration areas of varying spatial resolution. For each case-study a high quality reference scenario based on address points forms the basis for the analysis, which is then compared to the result of various street geocoding techniques. Results show that the unique nature of the positional error of street geocoding introduces substantial noise in the result of spatial analysis, including a substantial amount of bias for some analysis scenarios. This confirms findings from earlier studies, but expands these to a wider range of analytical techniques.
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