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Rose J, Dong W, Kim U, Hnath J, Statler A, Saroufim P, Song S, Ascha M, Menegay H, Tian Y, Beno M, Koroukian SM. An informatics infrastructure to catalyze cancer control research and practice. Cancer Causes Control 2022; 33:899-911. [PMID: 35380304 PMCID: PMC10865999 DOI: 10.1007/s10552-022-01571-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 03/07/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE A disconnect often exists between those with the expertise to manage and analyze complex, multi-source data sets, and the clinical, social services, advocacy, and public health professionals who can pose the most relevant questions and best apply the answers. We describe development and implementation of a cancer informatics infrastructure aimed at broadening the usability of community cancer data to inform cancer control research and practice; and we share lessons learned. METHODS We built a multi-level database known as The Ohio Cancer Assessment and Surveillance Engine (OH-CASE) to link data from Ohio's cancer registry with community data from the U.S. Census and other sources. Space-and place-based characteristics were assigned to individuals according to residential address. Stakeholder input informed development of an interface for generating queries based on geographic, demographic, and disease inputs and for outputting results aggregated at the state, county, municipality, or zip code levels. RESULTS OH-CASE contains data on 791,786 cancer cases diagnosed from 1/1/2006 to 12/31/2018 across 88 Ohio counties containing 1215 municipalities and 1197 zip codes. Stakeholder feedback from cancer center community outreach teams, advocacy organizations, public health, and researchers suggests a broad range of uses of such multi-level data resources accessible via a user interface. CONCLUSION OH-CASE represents a prototype of a transportable model for curating and synthesizing data to understand cancer burden across communities. Beyond supporting collaborative research, this infrastructure can serve the clinical, social services, public health, and advocacy communities by enabling targeting of outreach, funding, and interventions to narrow cancer disparities.
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Affiliation(s)
- Johnie Rose
- Case Western Reserve University Center for Community Health Integration, 11000 Cedar Ave., Ste. 402, Cleveland, OH, 44106-7136, USA.
- Case Comprehensive Cancer Center, Cleveland, OH, USA.
| | - Weichuan Dong
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Uriel Kim
- Case Western Reserve University Center for Community Health Integration, 11000 Cedar Ave., Ste. 402, Cleveland, OH, 44106-7136, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Joseph Hnath
- Case Western Reserve University Center for Community Health Integration, 11000 Cedar Ave., Ste. 402, Cleveland, OH, 44106-7136, USA
| | - Abby Statler
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Taussig Cancer Institute, The Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Paola Saroufim
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Sunah Song
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Mustafa Ascha
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Harry Menegay
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Ye Tian
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Mark Beno
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Siran M Koroukian
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
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Uncertainty in geospatial health: challenges and opportunities ahead. Ann Epidemiol 2021; 65:15-30. [PMID: 34656750 DOI: 10.1016/j.annepidem.2021.10.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few. METHODS We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis. RESULTS We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health. CONCLUSIONS Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.
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Tangka F, Kenny K, Miller J, Howard DH. The eligibility and reach of the national breast and cervical cancer early detection program after implementation of the affordable care act. Cancer Causes Control 2020; 31:473-489. [PMID: 32157463 DOI: 10.1007/s10552-020-01286-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 02/19/2020] [Indexed: 01/17/2023]
Abstract
INTRODUCTION The uninsured rate declined following passage of the Affordable Care Act in 2010. It is unclear how this decrease affected the size of the population eligible for existing safety net programs. We evaluated trends in the number of women eligible for breast and cervical cancer screening and diagnostic services under the National Breast and Cervical Cancer Early Detection Program (NBCCEDP) and the reach of the program. METHODS Using the Census Bureau's Small Area Health Insurance Estimates data, we calculated the number of women who met the NBCCEDP eligibility criteria based on age, income, and insurance status. We used these data in conjunction with program to estimate the proportion of eligible women served by the NBCCEDP. RESULTS The number of women eligible for breast cancer screening and diagnostic services under the program declined from 5.4 (90% CI 5.2-5.6) to 2.8 (90% CI 2.6-3.0) million from 2011 to 2017. The number of women eligible for cervical cancer screening and diagnostic services declined from 10.3 (90% CI 10.0-10.6) to 5.3 (90% CI 5.1-5.6) million. The share of eligible women served by the program was 15.0% (90% CI 14.8-15.1%) for breast services in 2016-2017 and 6.8% (90% CI 6.7-6.8%) for cervical services in 2015-2017. CONCLUSION Insurance coverage expansions may have contributed to a decrease in the number of program-eligible women. There are many more women eligible for the program than are served.
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Affiliation(s)
- Florence Tangka
- Division of Cancer Prevention and Control, Winship Cancer Center, Emory University, Atlanta, GA, 30030, USA
| | - Kristy Kenny
- Division of Cancer Prevention and Control, Winship Cancer Center, Emory University, Atlanta, GA, 30030, USA
| | - Jacqueline Miller
- Division of Cancer Prevention and Control, Winship Cancer Center, Emory University, Atlanta, GA, 30030, USA
| | - David H Howard
- Department of Health Policy and Management, Winship Cancer Center, Emory University, 1518 Clifton Road NE, Atlanta, GA, 30030, USA.
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Lou Z, Fei X, Christakos G, Yan J, Wu J. Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China. Sci Rep 2017; 7:3188. [PMID: 28600508 PMCID: PMC5466684 DOI: 10.1038/s41598-017-03524-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 05/08/2017] [Indexed: 11/21/2022] Open
Abstract
Breast cancer (BC) is the main cause of death of female cancer patients in China. Mainstream mapping techniques, like spatiotemporal ordinary kriging (STOK), generate disease incidence maps that improve our understanding of disease distribution. Yet, the implementation of these techniques experiences substantive and technical complications (due mainly to the different characteristics of space and time). A new spatiotemporal projection (STP) technique that is free of the above complications was implemented to model the space-time distribution of BC incidence in Hangzhou city and to estimate incidence values at locations-times for which no BC data exist. For comparison, both the STP and the STOK techniques were used to generate BC incidence maps in Hangzhou. STP performed considerably better than STOK in terms of generating more accurate incidence maps showing a closer similarity to the observed incidence distribution, and providing an improved assessment of the space-time BC correlation structure. In sum, the inter-connections between space, time, BC incidence and spread velocity established by STP allow a more realistic representation of the actual incidence distribution, and generate incidence maps that are more accurate and more informative, at a lower computational cost and involving fewer approximations than the incidence maps produced by mainstream space-time techniques.
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Affiliation(s)
- Zhaohan Lou
- Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan, China
| | - Xufeng Fei
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
| | - George Christakos
- Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan, China. .,Department of Geography, San Diego State University, San Diego, CA, USA.
| | - Jianbo Yan
- Zhoushan Center for Disease Control and Prevention, Zhoushan, China
| | - Jiaping Wu
- Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan, China.
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Fei X, Lou Z, Christakos G, Liu Q, Ren Y, Wu J. A Geographic Analysis about the Spatiotemporal Pattern of Breast Cancer in Hangzhou from 2008 to 2012. PLoS One 2016; 11:e0147866. [PMID: 26808895 PMCID: PMC4726732 DOI: 10.1371/journal.pone.0147866] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 01/08/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Breast cancer (BC) is the most common female malignant tumor. Previous studies have suggested a big incidence disparity among different cities in China. The present work selected a typical city, Hangzhou, to study BC incidence disparity within the city. METHODS Totally, 8784 female breast cancer cases were obtained from the Hangzhou Center for Disease Control and Prevention during the period 2008-2012. Analysis of Variance and Poisson Regression were the statistical tools implemented to compare incidence disparity in the space-time domain (reference group: township residents during 2008, area: subdistrict, town, and township, time frame: 2008-2012), space-time scan statistics was employed to detect significant spatiotemporal clusters of BC compared to the null hypothesis that the probability of cases diagnosed at a particular location was equal to the probability of cases diagnosed in the whole study area. Geographical Information System (GIS) was used to generate BC spatial distribution and cluster maps at the township level. RESULTS The subdistrict populations were found to have the highest and most stable BC incidence. Although town and township populations had a relatively low incidence, it displayed a significant increasing trend from 2008 to 2012. The BC incidence distribution was spatially heterogeneous and clustered with a trend-surface from the southwest low area to the northeast high area. High clusters were located in the northeastern Hangzhou area, whereas low clusters were observed in the southwestern area during the time considered. CONCLUSIONS Better healthcare service and lifestyle changes may be responsible for the increasing BC incidence observed in towns and townships. One high incidence cluster (Linping subdistrict) and two low incidence clusters (middle Hangzhou) were detected. The low clusters may be attributable mainly to developmental level disparity, whereas the high cluster could be associated with other risk factors, such as environmental pollution.
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Affiliation(s)
- Xufeng Fei
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
| | - Zhaohan Lou
- Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan, China
| | - George Christakos
- Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan, China
| | - Qingmin Liu
- Hangzhou Center for Disease Control and Prevention, Hangzhou, China
| | - Yanjun Ren
- Hangzhou Center for Disease Control and Prevention, Hangzhou, China
| | - Jiaping Wu
- Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan, China
- * E-mail:
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Tangka FKL, Howard DH, Royalty J, Dalzell LP, Miller J, O'Hara BJ, Sabatino SA, Joseph K, Kenney K, Guy GP, Hall IJ. Erratum to: Cervical cancer screening of underserved women in the United States: results from the National Breast and Cervical Cancer Early Detection Program, 1997-2012. Cancer Causes Control 2015; 26:687. [PMID: 25929885 PMCID: PMC4643590 DOI: 10.1007/s10552-015-0584-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Florence K L Tangka
- Division of Cancer Prevention and Control, Centers for Disease Control and Prevention (CDC), 4770 Buford Highway NE, Mailstop F-76, Atlanta, GA, 30341-3717, USA,
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