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Rundle AG, Bader MDM, Mooney SJ. Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies. CURR EPIDEMIOL REP 2022; 9:175-182. [PMID: 35789918 PMCID: PMC9244309 DOI: 10.1007/s40471-022-00296-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2022] [Indexed: 11/30/2022]
Abstract
Purpose of review Innovations in information technology, initiatives by local governments to share administrative data, and growing inventories of data available from commercial data aggregators have immensely expanded the information available to describe neighborhood environments, supporting an approach to research we call Urban Health Informatics. This review evaluates the application of machine learning to this new wealth of data for studies of the effects of neighborhood environments on health. Recent findings Prominent machine learning applications in this field include automated image analysis of archived imagery such as Google Street View images, variable selection methods to identify neighborhood environment factors that predict health outcomes from large pools of exposure variables, and spatial interpolation methods to estimate neighborhood conditions across large geographic areas. Summary In each domain, we highlight successes and cautions in the application of machine learning, particularly highlighting legal issues in applying machine learning approaches to Google’s geo-spatial data.
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Affiliation(s)
- Andrew G. Rundle
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York City, NY USA
| | | | - Stephen J. Mooney
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA USA
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Plascak JJ, Llanos AAM, Mooney SJ, Rundle AG, Qin B, Lin Y, Pawlish KS, Hong CC, Demissie K, Bandera EV. Pathways between objective and perceived neighborhood factors among Black breast cancer survivors. BMC Public Health 2021; 21:2031. [PMID: 34742279 PMCID: PMC8572419 DOI: 10.1186/s12889-021-12057-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 10/19/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mounting evidence supports associations between objective neighborhood disorder, perceived neighborhood disorder, and health, yet alternative explanations involving socioeconomic and neighborhood social cohesion have been understudied. We tested pathways between objective and perceived neighborhood disorder, perceived neighborhood social cohesion, and socioeconomic factors within a longitudinal cohort. METHODS Demographic and socioeconomic information before diagnosis was obtained at interviews conducted approximately 10 months post-diagnosis from participants in the Women's Circle of Health Follow-up Study - a cohort of breast cancer survivors self-identifying as African American or Black women (n = 310). Neighborhood perceptions were obtained during follow-up interviews conducted approximately 24 months after diagnosis. Objective neighborhood disorder was from 9 items audited across 23,276 locations using Google Street View and scored to estimate disorder values at each participant's residential address at diagnosis. Census tract socioeconomic and demographic composition covariates were from the 2010 U.S. Census and American Community Survey. Pathways to perceived neighborhood disorder were built using structural equation modelling. Model fit was assessed from the comparative fit index and root mean square error approximation and associations were reported as standardized coefficients and 95% confidence intervals. RESULTS Higher perceived neighborhood disorder was associated with higher objective neighborhood disorder (β = 0.20, 95% CI: 0.06, 0.33), lower neighborhood social cohesion, and lower individual-level socioeconomic factors (final model root mean square error approximation 0.043 (90% CI: 0.013, 0.068)). Perceived neighborhood social cohesion was associated with individual-level socioeconomic factors and objective neighborhood disorder (β = - 0.11, 95% CI: - 0.24, 0.02). CONCLUSION Objective neighborhood disorder might be related to perceived disorder directly and indirectly through perceptions of neighborhood social cohesion.
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Affiliation(s)
- Jesse J. Plascak
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH USA
- Division of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, 1590 North High Street, Suite 525, Columbus, OH 43201 USA
| | - Adana A. M. Llanos
- Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY USA
| | - Stephen J. Mooney
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington USA
| | - Andrew G. Rundle
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY USA
| | - Bo Qin
- Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ USA
| | - Yong Lin
- Department of Biostatistics and Epidemiology, School of Public Health, Rutgers, The State University of New Jersey, Piscataway, NJ USA
| | - Karen S. Pawlish
- New Jersey State Cancer Registry, New Jersey Department of Health, Trenton, NJ USA
| | - Chi-Chen Hong
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, New York USA
| | - Kitaw Demissie
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY USA
| | - Elisa V. Bandera
- Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ USA
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Plascak JJ, Rundle AG, Xu X, Mooney SJ, Schootman M, Lu B, Roy J, Stroup AM, Llanos AAM. Associations between neighborhood disinvestment and breast cancer outcomes within a populous state registry. Cancer 2021; 128:131-138. [PMID: 34495547 PMCID: PMC9070603 DOI: 10.1002/cncr.33900] [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: 03/02/2021] [Revised: 07/07/2021] [Accepted: 08/05/2021] [Indexed: 11/09/2022]
Abstract
BACKGROUND Breast cancer (BrCa) outcomes vary by social environmental factors, but the role of built-environment factors is understudied. The authors investigated associations between environmental physical disorder-indicators of residential disrepair and disinvestment-and BrCa tumor prognostic factors (stage at diagnosis, tumor grade, triple-negative [negative for estrogen receptor, progesterone receptor, and HER2 receptor] BrCa) and survival within a large state cancer registry linkage. METHODS Data on sociodemographic, tumor, and vital status were derived from adult women who had invasive BrCa diagnosed from 2008 to 2017 ascertained from the New Jersey State Cancer Registry. Physical disorder was assessed through virtual neighborhood audits of 23,276 locations across New Jersey, and a personalized measure for the residential address of each woman with BrCa was estimated using universal kriging. Continuous covariates were z scored (mean ± standard deviation [SD], 0 ± 1) to reduce collinearity. Logistic regression models of tumor factors and accelerated failure time models of survival time to BrCa-specific death were built to investigate associations with physical disorder adjusted for covariates (with follow-up through 2019). RESULTS There were 3637 BrCa-specific deaths among 40,963 women with a median follow-up of 5.3 years. In adjusted models, a 1-SD increase in physical disorder was associated with higher odds of late-stage BrCa (odds ratio, 1.09; 95% confidence interval, 1.02-1.15). Physical disorder was not associated with tumor grade or triple-negative tumors. A 1-SD increase in physical disorder was associated with a 10.5% shorter survival time (95% confidence interval, 6.1%-14.6%) only among women who had early stage BrCa. CONCLUSIONS Physical disorder is associated with worse tumor prognostic factors and survival among women who have BrCa diagnosed at an early stage.
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Affiliation(s)
- Jesse J Plascak
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio.,Division of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Andrew G Rundle
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Xinyi Xu
- Department of Statistics, College of Arts and Sciences, Columbus, Ohio
| | - Stephen J Mooney
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
| | - Mario Schootman
- Department of Clinical Analytics, SSM Health, St Louis, Missouri
| | - Bo Lu
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio.,Division of Biostatistics, College of Public Health, Columbus, Ohio
| | - Jason Roy
- Department of Biostatistics and Epidemiology, School of Public Health, Piscataway, New Jersey
| | - Antoinette M Stroup
- Department of Biostatistics and Epidemiology, School of Public Health, Piscataway, New Jersey.,Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey.,New Jersey State Cancer Registry, New Jersey Department of Health, Trenton, New Jersey
| | - Adana A M Llanos
- Department of Biostatistics and Epidemiology, School of Public Health, Piscataway, New Jersey.,Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
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Abstract
Virtual audits using Google Street View are an increasingly popular method of assessing neighborhood environments for health and urban planning research. However, the validity of these studies may be threatened by issues of image availability, image age, and variance of image age, particularly in the Global South. This study identifies patterns of Street View image availability, image age, and image age variance across cities in Latin America and assesses relationships between these measures and measures of resident socioeconomic conditions. Image availability was assessed at 530,308 near-road points within the boundaries of 371 Latin American cities described by the SALURBAL (Salud Urbana en America Latina) project. At the subcity level, mixed-effect linear and logistic models were used to assess relationships between measures of socioeconomic conditions and image availability, average image age, and the standard deviation of image age. Street View imagery was available at 239,394 points (45.1%) of the total sampled, and rates of image availability varied widely between cities and countries. Subcity units with higher scores on measures of socioeconomic conditions had higher rates of image availability (OR = 1.11 per point increase of combined index, p < 0.001) and the imagery was newer on average (- 1.15 months per point increase of combined index, p < 0.001), but image capture date within these areas varied more (0.59-month increase in standard deviation of image age per point increase of combined index, p < 0.001). All three assessed threats to the validity of Street View virtual audit studies spatially covary with measures of socioeconomic conditions in Latin American cities. Researchers should be attentive to these issues when using Street View imagery.
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Affiliation(s)
- Dustin Fry
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, 3600 Market Street 7th Floor, Philadelphia, PA 19104 USA
| | - Stephen J. Mooney
- Department of Epidemiology, University of Washington School of Public Health, 1959 NE Pacific Street, Seattle, WA 98195 USA
| | - Daniel A. Rodríguez
- Department of City & Regional Planning, University of California–Berkeley College of Environmental Design, 230 Wurster Hall, Berkeley, CA 94720 USA
| | - Waleska T. Caiaffa
- Department of Preventive and Social Medicine, Federal University of Minas Gerais Observatory for Urban Health in Belo Horizonte, Av. Alfredo Balena, 190, Belo Horizonte, CEP: 30130-100 Brazil
| | - Gina S. Lovasi
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, 3600 Market Street 7th Floor, Philadelphia, PA 19104 USA
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Plascak JJ, Rundle AG, Babel RA, Llanos AAM, LaBelle CM, Stroup AM, Mooney SJ. Drop-And-Spin Virtual Neighborhood Auditing: Assessing Built Environment for Linkage to Health Studies. Am J Prev Med 2020; 58:152-160. [PMID: 31862100 PMCID: PMC6927542 DOI: 10.1016/j.amepre.2019.08.032] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Revised: 08/18/2019] [Accepted: 08/19/2019] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Various built environment factors might influence certain health behaviors and outcomes. Reliable, resource-efficient methods that are feasible for assessing built environment characteristics across large geographies are needed for larger, more robust studies. This paper reports the item response prevalence, reliability, and rating time of a new virtual neighborhood audit protocol, drop-and-spin auditing, developed for assessment of walkability and physical disorder characteristics across large geographic areas. METHODS Drop-and-spin auditing, a method where a Google Street View scene was rated by spinning 360° around a point location, was developed using a modified version of the virtual audit tool Computer Assisted Neighborhood Visual Assessment System. Approximately 8,000 locations within Essex County, New Jersey were assessed by 11 trained auditors. Using a standardized protocol, 32 built environment items per a location within Google Street View were audited. Test-retest and inter-rater κ statistics were from a 5% subsample of locations. Data were collected in 2017-2018 and analyzed in 2018. RESULTS Roughly 70% of Google Street View scenes had sidewalks. Among those, two thirds were in good condition. At least 5 obvious items of garbage or litter were present in 41% of Google Street View scenes. Maximum test-retest reliability indicated substantial agreement (κ ≥0.61) for all items. Inter-rater reliability of each item, generally, was lower than test-retest reliability. The median time to rate each item was 7.3 seconds. CONCLUSIONS Compared with segment-based protocols, drop-and-spin virtual neighborhood auditing is quicker and similarly reliable for assessing built environment characteristics. Assessment of large geographies may be more feasible using drop-and-spin virtual auditing.
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Affiliation(s)
- Jesse J Plascak
- Department of Biostatistics and Epidemiology, School of Public Health, Rutgers, The State University of New Jersey, Piscataway, New Jersey; Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey.
| | - Andrew G Rundle
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Riddhi A Babel
- Department of Biostatistics and Epidemiology, School of Public Health, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Adana A M Llanos
- Department of Biostatistics and Epidemiology, School of Public Health, Rutgers, The State University of New Jersey, Piscataway, New Jersey; Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
| | - Celine M LaBelle
- Edward J. Bloustein School of Planning and Public Policy, Rutgers, The State University of New Jersey, New Brunswick, New Jersey
| | - Antoinette M Stroup
- Department of Biostatistics and Epidemiology, School of Public Health, Rutgers, The State University of New Jersey, Piscataway, New Jersey; Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey; New Jersey State Cancer Registry, New Jersey Department of Health, Trenton, New Jersey
| | - Stephen J Mooney
- Department of Epidemiology, University of Washington, Seattle, Washington
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Wang R, Liu Y, Lu Y, Yuan Y, Zhang J, Liu P, Yao Y. The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques. Int J Health Geogr 2019; 18:18. [PMID: 31345233 PMCID: PMC6659285 DOI: 10.1186/s12942-019-0182-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Accepted: 07/19/2019] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Neighbourhood environment characteristics have been found to be associated with residents' willingness to conduct physical activity (PA). Traditional methods to assess perceived neighbourhood environment characteristics are often subjective, costly, and time-consuming, and can be applied only on a small scale. Recent developments in deep learning algorithms and the recent availability of street view images enable researchers to assess multiple aspects of neighbourhood environment perceptions more efficiently on a large scale. This study aims to examine the relationship between each of six neighbourhood environment perceptual indicators-namely, wealthy, safe, lively, depressing, boring and beautiful-and residents' time spent on PA in Guangzhou, China. METHODS A human-machine adversarial scoring system was developed to predict perceptions of neighbourhood environments based on Tencent Street View imagery and deep learning techniques. Image segmentation was conducted using a fully convolutional neural network (FCN-8s) and annotated ADE20k data. A human-machine adversarial scoring system was constructed based on a random forest model and image ratings by 30 volunteers. Multilevel linear regressions were used to examine the association between each of the six indicators and time spent on PA among 808 residents living in 35 neighbourhoods. RESULTS Total PA time was positively associated with the scores for "safe" [Coef. = 1.495, SE = 0.558], "lively" [1.635, 0.789] and "beautiful" [1.009, 0.404]. It was negatively associated with the scores for "depressing" [- 1.232, 0.588] and "boring" [- 1.227, 0.603]. No significant linkage was found between total PA time and the "wealthy" score. PA was further categorised into three intensity levels. More neighbourhood perceptual indicators were associated with higher intensity PA. The scores for "safe" and "depressing" were significantly related to all three intensity levels of PA. CONCLUSIONS People living in perceived safe, lively and beautiful neighbourhoods were more likely to engage in PA, and people living in perceived boring and depressing neighbourhoods were less likely to engage in PA. Additionally, the relationship between neighbourhood perception and PA varies across different PA intensity levels. A combination of Tencent Street View imagery and deep learning techniques provides an accurate tool to automatically assess neighbourhood environment exposure for Chinese large cities.
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Affiliation(s)
- Ruoyu Wang
- School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275 China
- Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275 China
| | - Ye Liu
- School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275 China
- Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275 China
| | - Yi Lu
- Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, SAR China
| | - Yuan Yuan
- School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275 China
- Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275 China
| | - Jinbao Zhang
- School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275 China
- Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275 China
| | - Penghua Liu
- School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275 China
- Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275 China
| | - Yao Yao
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074 China
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