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O’Connor C, Prusinski MA, Aldstadt J, Falco RC, Oliver J, Haight J, Tober K, Sporn LA, White J, Brisson D, Backenson PB. Assessing the impact of areal unit selection and the modifiable areal unit problem on associative statistics between cases of tick-borne disease and entomological indices. J Med Entomol 2024; 61:331-344. [PMID: 38157309 PMCID: PMC10936173 DOI: 10.1093/jme/tjad157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 11/13/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024]
Abstract
The modifiable areal unit problem (MAUP) is a cause of statistical and visual bias when aggregating data according to spatial units, particularly when spatial units may be changed arbitrarily. The MAUP is a concern in vector-borne disease research when entomological metrics gathered from point-level sampling data are related to epidemiological data aggregated to administrative units like counties or ZIP Codes. Here, we assess the statistical impact of the MAUP when calculating correlations between randomly aggregated cases of anaplasmosis in New York State during 2017 and a geostatistical layer of an entomological risk index for Anaplasma phagocytophilum in blacklegged ticks (Ixodes scapularis Say, Acari: Ixodidae) collected during the fall of 2017. Correlations were also calculated using various administrative boundaries for comparison. We also demonstrate the impact of the MAUP on data visualization using choropleth maps and offer pycnophylactic interpolation as an alternative. Polygon simulations indicate that increasing the number of polygons decreases correlation coefficients and their variability. Correlation coefficients calculated using ZIP Code tabulation area and Census tract polygons were beyond 4 standard deviations from the mean of the simulated correlation coefficients. These results indicate that using smaller polygons may not best incorporate the geographical context of the tick-borne disease system, despite the tendency of researchers to strive for more granular spatial data and associations.
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Affiliation(s)
- Collin O’Connor
- New York State Department of Health, Bureau of Communicable Disease Control, Buffalo, NY, USA
- Department of Geography, State University of New York, University at Buffalo, Buffalo, NY, USA
| | - Melissa A Prusinski
- New York State Department of Health, Bureau of Communicable Disease Control, Albany, NY, USA
| | - Jared Aldstadt
- Department of Geography, State University of New York, University at Buffalo, Buffalo, NY, USA
| | - Richard C Falco
- New York State Department of Health, Vector Ecology Laboratory, Fordham University, Armonk, NY, USA
| | - JoAnne Oliver
- New York State Department of Health, Bureau of Communicable Disease Control, Syracuse, NY, USA
| | - Jamie Haight
- New York State Department of Health, Bureau of Communicable Disease Control, Falconer, NY, USA
| | - Keith Tober
- New York State Department of Health, Bureau of Communicable Disease Control, Buffalo, NY, USA
| | - Lee Ann Sporn
- Natural Science Department, Paul Smith’s College, Paul Smiths, NY, USA
| | - Jennifer White
- New York State Department of Health, Bureau of Communicable Disease Control, Albany, NY, USA
| | - Dustin Brisson
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - P Bryon Backenson
- New York State Department of Health, Bureau of Communicable Disease Control, Albany, NY, USA
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2
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Zormpas E, Queen R, Comber A, Cockell SJ. Mapping the transcriptome: Realizing the full potential of spatial data analysis. Cell 2023; 186:5677-5689. [PMID: 38065099 DOI: 10.1016/j.cell.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 09/04/2023] [Accepted: 11/02/2023] [Indexed: 12/24/2023]
Abstract
RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.
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Affiliation(s)
- Eleftherios Zormpas
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Rachel Queen
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; Bioinformatics Support Unit, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Alexis Comber
- School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds LS2 9NL, UK
| | - Simon J Cockell
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; School of Biomedical, Nutritional and Sport Sciences, Faculty of Medical Sciences, Newcastle upon Tyne NE2 4HH, UK.
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Lambio C, Schmitz T, Elson R, Butler J, Roth A, Feller S, Savaskan N, Lakes T. Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln. Int J Environ Res Public Health 2023; 20:ijerph20105830. [PMID: 37239558 DOI: 10.3390/ijerph20105830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/28/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023]
Abstract
Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.
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Affiliation(s)
- Christoph Lambio
- Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
| | - Tillman Schmitz
- Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
| | - Richard Elson
- UK Health Security Agency, 61, Colindale Avenue, London NW9 5EQ, UK
- School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
| | - Jeffrey Butler
- Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
| | - Alexandra Roth
- Local Health Department Berlin-Neukölln, Gesundheitsamt Neukölln, Blaschkoallee 32, 12359 Berlin, Germany
| | - Silke Feller
- Local Health Department Berlin-Neukölln, Gesundheitsamt Neukölln, Blaschkoallee 32, 12359 Berlin, Germany
| | - Nicolai Savaskan
- Local Health Department Berlin-Neukölln, Gesundheitsamt Neukölln, Blaschkoallee 32, 12359 Berlin, Germany
| | - Tobia Lakes
- Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
- IRI THESys, Integrative Research Institute on Transformations of Human-Environment Systems, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
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Zhang HZ, Cui WG, Liu SH, Cui HW, Huang YM. [Identifying Driving Factors and Their Interacting Effects on Sources of Heavy Metal in Farmland Soils with Geodetector and Multi-source Data]. Huan Jing Ke Xue 2023; 44:2177-2191. [PMID: 37040967 DOI: 10.13227/j.hjkx.202205201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
The identification of heavy metal sources in farmland soils is essential for the rational health condition management and sustainable development of soil. Using source resolution results(source component spectrum and source contribution)of a positive matrix factorization(PMF)model, historical survey data, and time-series remote sensing data, integrating a geodetector(GD), an optimal parameters-based geographical detector(OPGD), a spatial association detector(SPADE), and an interactive detector for spatial associations(IDSA)model, this study explored the modifiable areal unit problem(MAUP) of spatial heterogeneity of soil heavy metal sources and identified the driving factors and their interacting effects on the spatial heterogeneity of soil heavy metal sources in categorical and continuous variables, respectively. The results showed that the spatial heterogeneity of soil heavy metal sources at small and medium scales was affected by the spatial scale, and the optional spatial unit was 0.08 km2 for detecting spatial heterogeneity of soil heavy metal sources in the study region. Considering spatial correlation and discretization level, the combination of the quantile method and discretization parameters with an interruption number of 10 could be implied to reduce the partitioning effects on continuous variables in the detection of spatial heterogeneity of soil heavy metal sources. Within categorical variables, strata(PD 0.12-0.48) controlled the spatial heterogeneity of soil heavy metal sources, the interaction between strata and watersheds explained 27.28%-60.61% of each source, and the high-risk areas of each source were distributed in the lower sinian system, upper cretaceous in strata, mining land in land use, and haplic acrisols in soil types. Within continuous variables, population (PSD 0.40-0.82) controlled the spatial variation in soil heavy metal sources, and the explanatory power of spatial combinations of continuous variables for each source ranged from 61.77% to 78.46%. The high-risk areas of each source were distributed in evapotranspiration (41.2-43 kg·m-2), distance from the river (315-398 m), enhanced vegetation index (0.796-0.995), and distance from the river (499-605 m). The results of this study provide a reference for the research of the drivers of heavy metal sources and their interactions in arable soils and provide an important scientific basis for the management of arable soil and its sustainable development in karst areas.
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Affiliation(s)
- Hong-Ze Zhang
- School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550025, China
- Guizhou Mountain Resources and Environmental Remote Sensing Application Laboratory, Guiyang 550001, China
| | - Wen-Gang Cui
- School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550025, China
- Guizhou Mountain Resources and Environmental Remote Sensing Application Laboratory, Guiyang 550001, China
| | - Sui-Hua Liu
- School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550025, China
- Guizhou Mountain Resources and Environmental Remote Sensing Application Laboratory, Guiyang 550001, China
| | - Han-Wen Cui
- School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550025, China
- Guizhou Mountain Resources and Environmental Remote Sensing Application Laboratory, Guiyang 550001, China
| | - Yue-Mei Huang
- School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550025, China
- Guizhou Mountain Resources and Environmental Remote Sensing Application Laboratory, Guiyang 550001, China
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Joseph N, Propper CR, Goebel M, Henry S, Roy I, Kolok AS. Investigation of Relationships Between the Geospatial Distribution of Cancer Incidence and Estimated Pesticide Use in the U.S. West. Geohealth 2022; 6:e2021GH000544. [PMID: 35599961 PMCID: PMC9121053 DOI: 10.1029/2021gh000544] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 03/31/2022] [Accepted: 05/04/2022] [Indexed: 05/24/2023]
Abstract
The objective of the study was to evaluate the potential geospatial relationship between agricultural pesticide use and two cancer metrics (pediatric cancer incidence and total cancer incidence) across each of the 11 contiguous states in the Western United States at state and county resolution. The pesticide usage data were collected from the U.S. Geological Survey Pesticide National Synthesis Project database, while cancer data for each state were compiled from the National Cancer Institute State Cancer Profiles. At the state spatial scale, this study identified a significant positive association between the total mass of fumigants and pediatric cancer incidence, and also between the mass of one fumigant in particular, metam, and total cancer incidence (P-value < 0.05). At the county scale, the relationship of all cancer incidence to pesticide usage was evaluated using a multilevel model including pesticide mass and pesticide mass tertiles. Low pediatric cancer rates in many counties precluded this type of evaluation in association with pesticide usage. At the county scale, the multilevel model using fumigant mass, fumigant mass tertiles, county, and state predicted the total cancer incidence (R-squared = 0.95, NSE = 0.91, and Sum of square of residuals [SSR] = 8.22). Moreover, this study identified significant associations between total fumigant mass, high and medium tertiles of fumigant mass, total pesticide mass, and high tertiles of pesticide mass relative to total cancer incidence across counties. Fumigant application rate was shown to be important relative to the incidence of total cancer and pediatric cancer, at both state and county scales.
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Affiliation(s)
- Naveen Joseph
- Idaho Water Resources Research InstituteUniversity of IdahoMoscowIDUSA
| | | | - Madeline Goebel
- Idaho Water Resources Research InstituteUniversity of IdahoMoscowIDUSA
| | - Shantel Henry
- Department of Biological SciencesNorthern Arizona UniversityFlagstaffAZUSA
| | - Indrakshi Roy
- Center for Health Equity ResearchNorthern Arizona UniversityFlagstaffAZUSA
| | - Alan S. Kolok
- Idaho Water Resources Research InstituteUniversity of IdahoMoscowIDUSA
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Jiang M, Wu Y, Chang Z, Shi K. The Effects of Urban Forms on the PM 2.5 Concentration in China: A Hierarchical Multiscale Analysis. Int J Environ Res Public Health 2021; 18:3785. [PMID: 33916395 PMCID: PMC8038580 DOI: 10.3390/ijerph18073785] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/26/2021] [Accepted: 04/02/2021] [Indexed: 11/19/2022]
Abstract
For a better environment and sustainable development of China, it is indispensable to unravel how urban forms (UF) affect the fine particulate matter (PM2.5) concentration. However, research in this area have not been updated consider multiscale and spatial heterogeneities, thus providing insufficient or incomplete results and analyses. In this study, UF at different scales were extracted and calculated from remote sensing land-use/cover data, and panel data models were then applied to analyze the connections between UF and PM2.5 concentration at the city and provincial scales. Our comparison and evaluation results showed that the PM2.5 concentration could be affected by the UF designations, with the largest patch index (LPI) and landscape shape index (LSI) the most influential at the provincial and city scales, respectively. The number of patches (NP) has a strong negative influence (-0.033) on the PM2.5 concentration at the provincial scale, but it was not statistically significant at the city scale. No significant impact of urban compactness on the PM2.5 concentration was found at the city scale. In terms of the eastern and central provinces, LPI imposed a weighty positive influence on PM2.5 concentration, but it did not exert a significant effect in the western provinces. In the western cities, if the urban layout were either irregular or scattered, exposure to high PM2.5 pollution levels would increase. This study reveals distinct ties of the different UF and PM2.5 concentration at the various scales and helps to determine the reasonable UF in different locations, aimed at reducing the PM2.5 concentration.
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Affiliation(s)
- Mingyue Jiang
- School of Geographical Sciences, State Cultivation Base of Eco-Agriculture for Southwest Mountainous Land, Southwest University, Chongqing 400715, China; (M.J.); (Y.W.); (Z.C.)
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Centre for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
| | - Yizhen Wu
- School of Geographical Sciences, State Cultivation Base of Eco-Agriculture for Southwest Mountainous Land, Southwest University, Chongqing 400715, China; (M.J.); (Y.W.); (Z.C.)
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Centre for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
| | - Zhijian Chang
- School of Geographical Sciences, State Cultivation Base of Eco-Agriculture for Southwest Mountainous Land, Southwest University, Chongqing 400715, China; (M.J.); (Y.W.); (Z.C.)
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Centre for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
| | - Kaifang Shi
- School of Geographical Sciences, State Cultivation Base of Eco-Agriculture for Southwest Mountainous Land, Southwest University, Chongqing 400715, China; (M.J.); (Y.W.); (Z.C.)
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Centre for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
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7
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Kok MR, Tuson M, Yap M, Turlach B, Boruff B, Vickery A, Whyatt D. Impact of the modifiable areal unit problem in assessing determinants of emergency department demand. Emerg Med Australas 2021; 33:794-802. [PMID: 33517585 DOI: 10.1111/1742-6723.13727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 12/22/2020] [Accepted: 01/01/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To examine the impact of the modifiable areal unit problem (MAUP) in an investigation of factors associated with ED demand in Perth, Western Australia, in 2016. Furthermore, to advocate a means of avoiding this impact. METHODS ED presentations were classified as: urgent medical, non-urgent medical, urgent trauma or non-urgent trauma. In each group, sex-stratified, age-adjusted multivariate associations with socio-economic status and distance to the nearest ED and general practitioner (GP) were estimated. Modelling was undertaken using different sets of spatial units: Australian Bureau of Statistics (ABS) Statistical Areas Level 1 (SA1s) and numerous aggregate-level zonations of SA1s (ABS SA2s and others). RESULTS Estimates obtained using the different units often varied widely: for seven (30%) of 24 strata defined by combinations of sex, ED type and covariate, the smallest and largest effect sizes differed in terms of direction; further, for 11 (65%) of the remaining 17 strata, the largest effect size was at least twice as high as the smallest. This demonstrates the MAUP's impact and that analyses based on a single set of spatial units are unreliable. To resolve the observed variation, we highlight the SA1-level estimates. CONCLUSIONS When formulating interventions targeting reduced ED utilisation, policy planners should be guided by evidence based on analysis of appropriate spatial units. This ideal is undermined by the widespread lack of acknowledgement of the MAUP in studies examining drivers of ED demand using spatially aggregated data. To avoid the MAUP, only estimates obtained through examining a minimal geographic unit should be relied upon.
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Affiliation(s)
- Mei Ruu Kok
- Division of General Practice, Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Matthew Tuson
- Division of General Practice, Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia.,Department of Mathematics and Statistics, Faculty of Engineering and Mathematical Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Matthew Yap
- Division of General Practice, Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Berwin Turlach
- Department of Mathematics and Statistics, Faculty of Engineering and Mathematical Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Bryan Boruff
- Department of Geography, Faculty of Arts, Business, Law and Education, The University of Western Australia, Perth, Western Australia, Australia.,UWA School of Agriculture and Environment, Faculty of Science, The University of Western Australia, Perth, Western Australia, Australia
| | - Alistair Vickery
- Division of General Practice, Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - David Whyatt
- Division of General Practice, Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia
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Yap M, Tuson M, Turlach B, Boruff B, Whyatt D. Modelling the Relationship between Rainfall and Mental Health Using Different Spatial and Temporal Units. Int J Environ Res Public Health 2021; 18:ijerph18031312. [PMID: 33535674 PMCID: PMC7908580 DOI: 10.3390/ijerph18031312] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/22/2021] [Accepted: 01/26/2021] [Indexed: 11/27/2022]
Abstract
Drought is thought to impact upon the mental health of agricultural communities, but studies of this relationship have reported inconsistent results. A source of inconsistency could be the aggregation of data by a single spatiotemporal unit of analysis, which induces the modifiable areal and temporal unit problems. To investigate this, mental health-related emergency department (MHED) presentations among residents of the Wheat Belt region of Western Australia, between 2002 and 2017, were examined. Average daily rainfall was used as a measure of drought. Associations between MHED presentations and rainfall were estimated based on various spatial aggregations of underlying data, at multiple temporal windows. Wide variation amongst results was observed. Despite this, two key features were found: Associations between MHED presentations and rainfall were generally positive when rainfall was measured in summer months (rate ratios up to 1.05 per 0.5 mm of daily rainfall) and generally negative when rainfall was measured in winter months (rate ratios as low as 0.96 per 0.5 mm of daily rainfall). These results demonstrate that the association between drought and mental health is quantifiable; however, the effect size is small and varies depending on the spatial and temporal arrangement of the underlying data. To improve understanding of this association, more studies should be undertaken with longer time spans and examining specific mental health outcomes, using a wide variety of spatiotemporal units.
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Affiliation(s)
- Matthew Yap
- Medical School, University of Western Australia, Crawley 6009, Australia; (M.Y.); (M.T.)
| | - Matthew Tuson
- Medical School, University of Western Australia, Crawley 6009, Australia; (M.Y.); (M.T.)
- Department of Mathematics and Statistics, University of Western Australia, Crawley 6009, Australia;
| | - Berwin Turlach
- Department of Mathematics and Statistics, University of Western Australia, Crawley 6009, Australia;
| | - Bryan Boruff
- Department of Geography, University of Western Australia, Crawley 6009, Australia;
- UWA School of Agriculture and Environment, University of Western Australia, Crawley 6009, Australia
| | - David Whyatt
- Medical School, University of Western Australia, Crawley 6009, Australia; (M.Y.); (M.T.)
- Correspondence:
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Abstract
Genetic clustering is a popular method for characterizing variation in transmission rates for rapidly evolving viruses, and could potentially be used to detect outbreaks in 'near real time'. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here, we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analysed the pairwise Tamura-Nei (TN93) genetic distances for anonymized HIV-1 subtype B pol sequences from Seattle (n = 1,653) and Middle Tennessee, USA (n = 2,779), and northern Alberta, Canada (n = 809). Under varying TN93 thresholds, we fit two models to the distributions of new cases relative to clusters of known cases: 1, a null model that assumes cluster growth is strictly proportional to cluster size, i.e. no variation in transmission rates among individuals; and 2, a weighted model that incorporates individual-level covariates, such as recency of diagnosis. The optimal threshold maximizes the difference in information loss between models, where covariates are used most effectively. Optimal TN93 thresholds varied substantially between data sets, e.g. 0.0104 in Alberta and 0.016 in Seattle and Tennessee, such that the optimum for one population would potentially misdirect prevention efforts in another. For a given population, the range of thresholds where the weighted model conferred greater predictive accuracy tended to be narrow (±0.005 units), and the optimal threshold tended to be stable over time. Our framework also indicated that variation in the recency of HIV diagnosis among clusters was significantly more predictive of new cases than sample collection dates (ΔAIC > 50). These results suggest that one cannot rely on historical precedence or convention to configure genetic clustering methods for public health applications, especially when translating methods between settings of low-level and generalized epidemics. Our framework not only enables investigators to calibrate a clustering method to a specific public health setting, but also provides a variable selection procedure to evaluate different predictive models of cluster growth.
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Affiliation(s)
- Connor Chato
- Department of Pathology and Laboratory Medicine, Western University, Dental Sciences Building DSB4044, London N6A 5C1, Canada
| | - Marcia L Kalish
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, 1161 21st Ave S, Nashville, TN 37232, USA
| | - Art F Y Poon
- Department of Pathology and Laboratory Medicine, Western University, Dental Sciences Building DSB4044, London N6A 5C1, Canada
- Department of Applied Mathematics, Western University, Middlesex College MC255, London N6A 5B7, Canada
- Department of Microbiology and Immunology, Western University, Dental Science Building DSB3014, London N6A 5C1, Canada
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Ho HC, Knudby A, Huang W. A Spatial Framework to Map Heat Health Risks at Multiple Scales. Int J Environ Res Public Health 2015; 12:16110-23. [PMID: 26694445 DOI: 10.3390/ijerph121215046] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Revised: 12/08/2015] [Accepted: 12/15/2015] [Indexed: 11/17/2022]
Abstract
In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord Gi index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord Gi index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.
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Duncan DT, Kawachi I, Subramanian SV, Aldstadt J, Melly SJ, Williams DR. Examination of how neighborhood definition influences measurements of youths' access to tobacco retailers: a methodological note on spatial misclassification. Am J Epidemiol 2014; 179:373-81. [PMID: 24148710 DOI: 10.1093/aje/kwt251] [Citation(s) in RCA: 97] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Measurements of neighborhood exposures likely vary depending on the definition of "neighborhood" selected. This study examined the extent to which neighborhood definition influences findings regarding spatial accessibility to tobacco retailers among youth. We defined spatial accessibility to tobacco retailers (i.e., tobacco retail density, closest tobacco retailer, and average distance to the closest 5 tobacco retailers) on the basis of circular and network buffers of 400 m and 800 m, census block groups, and census tracts by using residential addresses from the 2008 Boston Youth Survey Geospatial Dataset (n = 1,292). Friedman tests (to compare overall differences in neighborhood definitions) were applied. There were differences in measurements of youths' access to tobacco retailers according to the selected neighborhood definitions, and these were marked for the 2 spatial proximity measures (both P < 0.01 for all differences). For example, the median average distance to the closest 5 tobacco retailers was 381.50 m when using specific home addresses, 414.00 m when using census block groups, and 482.50 m when using census tracts, illustrating how neighborhood definition influences the measurement of spatial accessibility to tobacco retailers. These analyses suggest that, whenever possible, egocentric neighborhood definitions should be used. The use of larger administrative neighborhood definitions can bias exposure estimates for proximity measures.
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Mobley LR, Kuo TMM, Andrews L. How sensitive are multilevel regression findings to defined area of context?: a case study of mammography use in California. Med Care Res Rev 2008; 65:315-37. [PMID: 18259047 PMCID: PMC2678861 DOI: 10.1177/1077558707312501] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The authors develop a hybrid model of health care use that blends features of the traditional Aday-Andersen behavioral model with the socioecological modeling perspective. They use the model to conceptualize the various levels of influence expected from socioecological variables in individuals' mammography use decisions, build contextual variables from fine-grained data into four different types of geographic areas, and then use two- and three-level modeling of personal and area-level contextual factors to explain observed behavior. The central focus is on whether differentiating the conceptualized levels of influence seems to materially affect regression findings. The test could conceivably be confounded by the modifiable areal unit problem, but little evidence for this is found. Findings for California women suggest that distinctions do matter in how the levels of influence are defined for local neighborhood contextual factors. Studies using only county-level contextual factors will miss some meaningful associations related to interpersonal/proximate-level factors.
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Affiliation(s)
- Lee R Mobley
- RTI International, Research Triangle Park, NC 27709-2194, USA.
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