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Napier G, Lee D, Robertson C, Lawson A. A Bayesian space-time model for clustering areal units based on their disease trends. Biostatistics 2020; 20:681-697. [PMID: 29917057 PMCID: PMC6797054 DOI: 10.1093/biostatistics/kxy024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 04/13/2018] [Accepted: 04/29/2018] [Indexed: 11/27/2022] Open
Abstract
Population-level disease risk across a set of non-overlapping areal units varies in space and time, and a large research literature has developed methodology for identifying clusters of areal units exhibiting elevated risks. However, almost no research has extended the clustering paradigm to identify groups of areal units exhibiting similar temporal disease trends. We present a novel Bayesian hierarchical mixture model for achieving this goal, with inference based on a Metropolis-coupled Markov chain Monte Carlo ((MC)\documentclass[12pt]{minimal}
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}{}$^3$\end{document}) algorithm. The effectiveness of the (MC)\documentclass[12pt]{minimal}
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}{}$^3$\end{document} algorithm compared to a standard Markov chain Monte Carlo implementation is demonstrated in a simulation study, and the methodology is motivated by two important case studies in the United Kingdom. The first concerns the impact on measles susceptibility of the discredited paper linking the measles, mumps, and rubella vaccination to an increased risk of Autism and investigates whether all areas in the Scotland were equally affected. The second concerns respiratory hospitalizations and investigates over a 10 year period which parts of Glasgow have shown increased, decreased, and no change in risk.
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Affiliation(s)
- Gary Napier
- School of Mathematics and Statistics, University of Glasgow, University Place, Glasgow, UK
| | - Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, University Place, Glasgow, UK
| | - Chris Robertson
- Department of Mathematics and Statistics, University of Strathclyde, 26 Richmond Street, Glasgow, UK
| | - Andrew Lawson
- Department of Public Health Sciences, Medical University of South Carolina, South Carolina, USA
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Berchuck SI, Mwanza JC, Warren JL. Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method. J Am Stat Assoc 2019; 114:1063-1074. [PMID: 31662589 PMCID: PMC6818507 DOI: 10.1080/01621459.2018.1537911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2017] [Revised: 09/23/2018] [Accepted: 10/11/2018] [Indexed: 10/27/2022]
Abstract
Diagnosing glaucoma progression is critical for limiting irreversible vision loss. A common method for assessing glaucoma progression uses a longitudinal series of visual fields (VF) acquired at regular intervals. VF data are characterized by a complex spatiotemporal structure due to the data generating process and ocular anatomy. Thus, advanced statistical methods are needed to make clinical determinations regarding progression status. We introduce a spatiotemporal boundary detection model that allows the underlying anatomy of the optic disc to dictate the spatial structure of the VF data across time. We show that our new method provides novel insight into vision loss that improves diagnosis of glaucoma progression using data from the Vein Pulsation Study Trial in Glaucoma and the Lions Eye Institute trial registry. Simulations are presented, showing the proposed methodology is preferred over existing spatial methods for VF data. Supplementary materials for this article are available online and the method is implemented in the R package womblR.
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Affiliation(s)
- Samuel I Berchuck
- Department of Statistical Science and Forge, Duke University, NC 27708
| | - Jean-Claude Mwanza
- Department of Ophthalmology, University of North Carolina-Chapel Hill (DO, UNC-CH), NC 27517
| | - Joshua L Warren
- Department of Biostatistics, Yale University, New Haven, CT, 06520
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Bertazzon S, Shahid R. Schools, Air Pollution, and Active Transportation: An Exploratory Spatial Analysis of Calgary, Canada. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14080834. [PMID: 28757577 PMCID: PMC5580538 DOI: 10.3390/ijerph14080834] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 07/20/2017] [Accepted: 07/23/2017] [Indexed: 11/30/2022]
Abstract
An exploratory spatial analysis investigates the location of schools in Calgary (Canada) in relation to air pollution and active transportation options. Air pollution exhibits marked spatial variation throughout the city, along with distinct spatial patterns in summer and winter; however, all school locations lie within low to moderate pollution levels. Conversely, the study shows that almost half of the schools lie in low walkability locations; likewise, transitability is low for 60% of schools, and only bikability is widespread, with 93% of schools in very bikable locations. School locations are subsequently categorized by pollution exposure and active transportation options. This analysis identifies and maps schools according to two levels of concern: schools in car-dependent locations and relatively high pollution; and schools in locations conducive of active transportation, yet exposed to relatively high pollution. The findings can be mapped and effectively communicated to the public, health practitioners, and school boards. The study contributes with an explicitly spatial approach to the intra-urban public health literature. Developed for a moderately polluted city, the methods can be extended to more severely polluted environments, to assist in developing spatial public health policies to improve respiratory outcomes, neurodevelopment, and metabolic and attention disorders in school-aged children.
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Affiliation(s)
- Stefania Bertazzon
- Department of Geography, University of Calgary, University of Calgary, Calgary, AB T2N 1N4, Canada.
| | - Rizwan Shahid
- Department of Geography, University of Calgary, University of Calgary, Calgary, AB T2N 1N4, Canada.
- Alberta Health Services, Calgary, AB T2W 3N2, Canada.
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Pannullo F, Lee D, Neal L, Dalvi M, Agnew P, O’Connor FM, Mukhopadhyay S, Sahu S, Sarran C. Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England. Environ Health 2017; 16:29. [PMID: 28347336 PMCID: PMC5368918 DOI: 10.1186/s12940-017-0237-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2016] [Accepted: 03/20/2017] [Indexed: 05/21/2023]
Abstract
BACKGROUND Estimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future. METHODS We conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO2); ozone (O3); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5), and less than 10 micrometers (PM10); and sulphur dioxide (SO2). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP). RESULTS NO2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO2 emissions and concentrations. CONCLUSIONS NO2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.
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Affiliation(s)
- Francesca Pannullo
- School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QW UK
| | - Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QW UK
| | - Lucy Neal
- Met Office, FitzRoy Road, Exeter, EX1 3PB UK
| | - Mohit Dalvi
- Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB UK
| | - Paul Agnew
- Met Office, FitzRoy Road, Exeter, EX1 3PB UK
| | | | | | - Sujit Sahu
- Mathematical Sciences, University of Southampton, Highfield, Southampton, SO17 1BJ UK
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Rushworth A, Lee D, Sarran C. An adaptive spatiotemporal smoothing model for estimating trends and step changes in disease risk. J R Stat Soc Ser C Appl Stat 2016. [DOI: 10.1111/rssc.12155] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Sparks C. An examination of disparities in cancer incidence in Texas using Bayesian random coefficient models. PeerJ 2015; 3:e1283. [PMID: 26421245 PMCID: PMC4586809 DOI: 10.7717/peerj.1283] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 09/09/2015] [Indexed: 01/05/2023] Open
Abstract
Disparities in cancer risk exist between ethnic groups in the United States. These disparities often result from differential access to healthcare, differences in socioeconomic status and differential exposure to carcinogens. This study uses cancer incidence data from the population based Texas Cancer Registry to investigate the disparities in digestive and respiratory cancers from 2000 to 2008. A Bayesian hierarchical regression approach is used. All models are fit using the INLA method of Bayesian model estimation. Specifically, a spatially varying coefficient model of the disparity between Hispanic and Non-Hispanic incidence is used. Results suggest that a spatio-temporal heterogeneity model best accounts for the observed Hispanic disparity in cancer risk. Overall, there is a significant disadvantage for the Hispanic population of Texas with respect to both of these cancers, and this disparity varies significantly over space. The greatest disparities between Hispanics and Non-Hispanics in digestive and respiratory cancers occur in eastern Texas, with patterns emerging as early as 2000 and continuing until 2008.
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Affiliation(s)
- Corey Sparks
- Department of Demography, The University of Texas at San Antonio , San Antonio, TX , USA
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Coker E, Ghosh J, Jerrett M, Gomez-Rubio V, Beckerman B, Cockburn M, Liverani S, Su J, Li A, Kile ML, Ritz B, Molitor J. Modeling spatial effects of PM(2.5) on term low birth weight in Los Angeles County. ENVIRONMENTAL RESEARCH 2015. [PMID: 26196780 DOI: 10.1016/j.envres.2015.06.044] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.
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Affiliation(s)
- Eric Coker
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA.
| | - Jokay Ghosh
- School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Michael Jerrett
- School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | | | - Bernardo Beckerman
- School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Myles Cockburn
- Preventive Medicine and Spatial Sciences, University of Southern California, Los Angeles, CA, USA
| | - Silvia Liverani
- Department of Mathematics, Brunel University, London, United Kingdom
| | - Jason Su
- School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Arthur Li
- Department of Information Science, City of Hope National Cancer Center, Duarte, CA, USA
| | - Molly L Kile
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Beate Ritz
- School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - John Molitor
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
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Bertazzon S, Johnson M, Eccles K, Kaplan GG. Accounting for spatial effects in land use regression for urban air pollution modeling. Spat Spatiotemporal Epidemiol 2015; 14-15:9-21. [PMID: 26530819 DOI: 10.1016/j.sste.2015.06.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2014] [Revised: 04/07/2015] [Accepted: 06/24/2015] [Indexed: 11/30/2022]
Abstract
In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models.
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Affiliation(s)
- Stefania Bertazzon
- Department of Geography, University of Calgary, Calgary Alberta, Canada.
| | - Markey Johnson
- Air Health Science Division, Health Canada, 269 Laurier Avenue West, Ottawa, Ontario, Canada.
| | - Kristin Eccles
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.
| | - Gilaad G Kaplan
- Department of Medicine, University of Calgary, Calgary Alberta, Canada; Department of Community Health Sciences, University of Calgary, Calgary Alberta, Canada.
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