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Wiese D, DuBois TD, Sorice KA, Fang CY, Ragin C, Daly M, Reese AC, Henry KA, Lynch SM. An exploratory analysis of the impact of area-level exposome on geographic disparities in aggressive prostate cancer. Sci Rep 2024; 14:16900. [PMID: 39075110 PMCID: PMC11286755 DOI: 10.1038/s41598-024-63726-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 05/31/2024] [Indexed: 07/31/2024] Open
Abstract
Numbers of aggressive prostate cancer (aPC) cases are rising, but only a few risk factors have been identified. In this study, we introduce a systematic approach to integrate geospatial data into external exposome research using aPC cases from Pennsylvania. We demonstrate the association between several area-level exposome measures across five Social Determinants of Health domains (SDOH) and geographic areas identified as having elevated odds of aPC. Residential locations of Pennsylvania men diagnosed with aPC from 2005 to 2017 were linked to 37 county-/tract-level SDOH exosome measures. Variable reduction processes adopted from neighborhood-wide association study along with Bayesian geoadditive logistic regression were used to identify areas with elevated odds of aPC and exposome factors that significantly attenuated the odds and reduced the size of identified areas. Areas with significantly higher odds of aPC were explained by various SDOH exposome measures, though the extent of the reduction depended on geographic location. Some areas were associated with race (social context), health insurance (access), or tract-level poverty (economics), while others were associated with either county-level water quality or a combination of factors. Area-level exposome measures can guide future patient-level external exposome research and help design targeted interventions to reduce local cancer burden.
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Affiliation(s)
- Daniel Wiese
- Department of Geography, Temple University, Philadelphia, PA, USA
| | - Tesla D DuBois
- Department of Geography, Temple University, Philadelphia, PA, USA
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Kristen A Sorice
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Carolyn Y Fang
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Camille Ragin
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Mary Daly
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
| | | | - Kevin A Henry
- Department of Geography, Temple University, Philadelphia, PA, USA
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Shannon M Lynch
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA.
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2
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Zhang X, Blackwell CK, Moore J, Liu SH, Liu C, Forrest CB, Ganiban J, Stroustrup A, Aschner JL, Trasande L, Deoni SCL, Elliott AJ, Angal J, Karr CJ, Lester BM, McEvoy CT, O'Shea TM, Fry RC, Shipp GM, Gern JE, Herbstman J, Carroll KN, Teitelbaum SL, Wright RO, Wright RJ. Associations between neighborhood characteristics and child well-being before and during the COVID-19 pandemic: A repeated cross-sectional study in the Environmental influences on Child Health Outcomes (ECHO) program. ENVIRONMENTAL RESEARCH 2024; 252:118765. [PMID: 38548252 DOI: 10.1016/j.envres.2024.118765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 02/29/2024] [Accepted: 03/19/2024] [Indexed: 04/11/2024]
Abstract
The corona virus disease (COVID-19) pandemic disrupted daily life worldwide, and its impact on child well-being remains a major concern. Neighborhood characteristics affect child well-being, but how these associations were affected by the pandemic is not well understood. We analyzed data from 1039 children enrolled in the Environmental influences on Child Health Outcomes Program whose well-being was assessed using the Patient-Reported Outcomes Measurement Information System Global Health questionnaire and linked these data to American Community Survey (ACS) data to evaluate the impacts of neighborhood characteristics on child well-being before and during the pandemic. We estimated the associations between more than 400 ACS variables and child well-being t-scores stratified by race/ethnicity (non-Hispanic white vs. all other races and ethnicities) and the timing of outcome data assessment (pre-vs. during the pandemic). Network graphs were used to visualize the associations between ACS variables and child well-being t-scores. The number of ACS variables associated with well-being t-scores decreased during the pandemic period. Comparing non-Hispanic white with other racial/ethnic groups during the pandemic, different ACS variables were associated with child well-being. Multiple ACS variables representing census tract-level housing conditions and neighborhood racial composition were associated with lower well-being t-scores among non-Hispanic white children during the pandemic, while higher percentage of Hispanic residents and higher percentage of adults working as essential workers in census tracts were associated with lower well-being t-scores among non-white children during the same study period. Our study provides insights into the associations between neighborhood characteristics and child well-being, and how the COVID-19 pandemic affected this relationship.
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Affiliation(s)
- Xueying Zhang
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Climate Change, Environmental Health and Exposomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | | | | | - Shelley H Liu
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chang Liu
- Department of Psychology, Washington State University, WA, USA
| | | | - Jody Ganiban
- Department of Psychological and Brain Sciences, George Washington University, Washington D.C, USA
| | - Annemarie Stroustrup
- Departments of Pediatrics and Occupational Medicine, Epidemiology & Prevention, Zucker School of Medicine at Hofstra / Northwell and Cohen Children's Hospital, New Hyde Park, NY, USA
| | - Judy L Aschner
- Departments of Pediatrics, Hackensack Meridian School of Medicine, Nutley NJ and Albert Einstein College of Medicine, Bronx, NY, USA
| | | | - Sean C L Deoni
- Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Amy J Elliott
- Avera Research Institute and University of South Dakota School of Medicine, Sioux Falls, SD, USA
| | - Jyoti Angal
- Avera Research Institute and University of South Dakota School of Medicine, Sioux Falls, SD, USA
| | - Catherine J Karr
- Department of Environmental and Occupational Health Sciences, University of Washington, WA, USA
| | - Barry M Lester
- Department of Psychiatry and Human Behavior/ Department of Pediatrics, Alpert Medical School of Brown University, Providence, RI, USA
| | - Cindy T McEvoy
- Department of Pediatrics and Papé Pediatric Research Institute, Oregon Health & Science University, Portland, OR, USA
| | - T Michael O'Shea
- Department of Pediatrics, School of Medicine, University of North Carolina at Chapel Hill, NC, USA
| | - Rebecca C Fry
- Department of Environmental Sciences and Engineering, Gillings School of Public Health, University of North Carolina at Chapel Hill, NC, USA
| | - Gayle M Shipp
- Chareles Stewart Mott Department of Public Health, Michigan State University, MI, USA
| | - James E Gern
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin-Madison, WI, USA
| | - Julie Herbstman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, NY, USA
| | - Kecia N Carroll
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Climate Change, Environmental Health and Exposomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Susan L Teitelbaum
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Climate Change, Environmental Health and Exposomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert O Wright
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Climate Change, Environmental Health and Exposomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rosalind J Wright
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Climate Change, Environmental Health and Exposomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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3
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Boyle J, Yau J, Slade JL, Butts DA, Zhang Y, Legesse TB, Cellini A, Clark K, Park JY, Wimbush J, Ambulos N, Yin J, Hussain A, Onukwugha E, Knott CL, Wheeler DC, Barry KH. Neighborhood Disadvantage and Prostate Tumor RNA Expression of Stress-Related Genes. JAMA Netw Open 2024; 7:e2421903. [PMID: 38995644 PMCID: PMC11245728 DOI: 10.1001/jamanetworkopen.2024.21903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 05/09/2024] [Indexed: 07/13/2024] Open
Abstract
Importance African American men experience greater prostate cancer incidence and mortality than White men. Growing literature supports associations of neighborhood disadvantage, which disproportionately affects African American men, with aggressive prostate cancer; chronic stress and downstream biological impacts (eg, increased inflammation) may contribute to these associations. Objective To examine whether several neighborhood disadvantage metrics are associated with prostate tumor RNA expression of stress-related genes. Design, Setting, and Participants This cross-sectional study leveraged prostate tumor transcriptomic data for African American and White men with prostate cancer who received radical prostatectomy at the University of Maryland Medical Center between August 1992 and January 2021. Data were analyzed from May 2023 to April 2024. Exposures Using addresses at diagnosis, 2 neighborhood deprivation metrics (Area Deprivation Index [ADI] and validated bayesian Neighborhood Deprivation Index) as well as the Racial Isolation Index (RI) and historical redlining were applied to participants' addresses. Self-reported race was determined using electronic medical records. Main Outcomes and Measures A total of 105 stress-related genes were evaluated with each neighborhood metric using linear regression, adjusting for race, age, and year of surgery. Genes in the Conserved Transcriptional Response to Adversity (CTRA) and stress-related signaling genes were included. Results A total of 218 men (168 [77%] African American, 50 [23%] White) with a median (IQR) age of 58 (53-63) years were included. African American participants experienced greater neighborhood disadvantage than White participants (median [IQR] ADI, 115 [100-130] vs 92 [83-104]; median [IQR] RI, 0.68 [0.34-0.87] vs 0.11 [0.06-0.14]). ADI was positively associated with expression for 11 genes; HTR6 (serotonin pathway) remained significant after multiple-comparison adjustment (β = 0.003; SE, 0.001; P < .001; Benjamini-Hochberg q value = .01). Several genes, including HTR6, were associated with multiple metrics. We observed higher expression of 5 proinflammatory genes in the CTRA with greater neighborhood disadvantage (eg, CXCL8 and ADI, β = 0.008; SE, 0.003; P = .01; q value = .21). Conclusions and Relevance In this cross-sectional study, the expression of several stress-related genes in prostate tumors was higher among men residing in disadvantaged neighborhoods. This study is one of the first to suggest associations of neighborhood disadvantage with prostate tumor RNA expression. Additional research is needed in larger studies to replicate findings and further investigate interrelationships of neighborhood factors, tumor biology, and aggressive prostate cancer to inform interventions to reduce disparities.
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Affiliation(s)
- Joseph Boyle
- Department of Biostatistics, Virginia Commonwealth University, Richmond
- Massey Comprehensive Cancer Center, Richmond, Virginia
| | - Jessica Yau
- Department of Cellular and Molecular Biomedical Science, University of Maryland School of Medicine, Baltimore
| | - Jimmie L. Slade
- Maryland Community Health Engagement Partnership, Upper Marlboro
| | | | - Yuji Zhang
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore
- Program in Oncology, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore
| | - Teklu B. Legesse
- Department of Pathology, University of Maryland School of Medicine, Baltimore
| | - Ashley Cellini
- Pathology Biorepository Shared Service, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore
| | - Kimberly Clark
- Pathology Biorepository Shared Service, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore
| | - Jong Y. Park
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center, Tampa, Florida
| | - Jessica Wimbush
- University of Maryland Greenebaum Comprehensive Cancer Center Tumor Registry, Baltimore
| | - Nicholas Ambulos
- Program in Oncology, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore
| | - Jing Yin
- Program in Oncology, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore
| | - Arif Hussain
- Program in Oncology, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore
- Department of Medicine, University of Maryland School of Medicine, Baltimore
- Baltimore Veterans Administration Medical Center, Baltimore, Maryland
| | - Eberechukwu Onukwugha
- Program in Oncology, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore
- Department of Practice, Sciences, and Health Outcomes Research, University of Maryland School of Pharmacy, Baltimore
| | - Cheryl L. Knott
- Program in Oncology, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore
- Department of Behavioral and Community Health, University of Maryland, College Park, College Park
| | - David C. Wheeler
- Department of Biostatistics, Virginia Commonwealth University, Richmond
- Massey Comprehensive Cancer Center, Richmond, Virginia
| | - Kathryn Hughes Barry
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore
- Program in Oncology, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore
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Stangis MM, Chen Z, Min J, Glass SE, Jackson JO, Radyk MD, Hoi XP, Brennen WN, Yu M, Dinh HQ, Coffey RJ, Shrubsole MJ, Chan KS, Grady WM, Yegnasubramanian S, Lyssiotis CA, Maitra A, Halberg RB, Dey N, Lau KS. The Hallmarks of Precancer. Cancer Discov 2024; 14:683-689. [PMID: 38571435 PMCID: PMC11170686 DOI: 10.1158/2159-8290.cd-23-1550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Research on precancers, as defined as at-risk tissues and early lesions, is of high significance given the effectiveness of early intervention. We discuss the need for risk stratification to prevent overtreatment, an emphasis on the role of genetic and epigenetic aging when considering risk, and the importance of integrating macroenvironmental risk factors with molecules and cells in lesions and at-risk normal tissues for developing effective intervention and health policy strategies.
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Affiliation(s)
- Mary M. Stangis
- Department of Oncology – McArdle Laboratory for Cancer Research, University of Wisconsin-Madison
- Department of Medicine – Gastroenterology Division, University of Wisconsin-Madison
- Carbone Cancer Center, University of Wisconsin-Madison
| | - Zhengyi Chen
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine
- Epithelial Biology Center, Vanderbilt University Medical Center
| | - Jimin Min
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center
- Sheikh Ahmed Center for Pancreatic Cancer Research, University of Texas MD Anderson Cancer Center
| | - Sarah E. Glass
- Epithelial Biology Center, Vanderbilt University Medical Center
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine
| | - Jordan O. Jackson
- Department of Laboratory Medicine and Pathology, University of Washington
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center
| | - Megan D. Radyk
- Department of Molecular & Integrative Physiology, University of Michigan Medical School
| | - Xen Ping Hoi
- Department of Urology, Houston Methodist Research Institute
- Neal Cancer Center, Houston Methodist Research Institute
| | - W. Nathaniel Brennen
- Department of Oncology – Genitourinary Cancer Disease Division, Johns Hopkins Medicine
- Department of Pharmacology and Molecular Sciences, Johns Hopkins Medicine
- Department of Urology, Johns Hopkins Medicine
| | - Ming Yu
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center
- Department of Medicine – Division of Gastroenterology, University of Washington
- Public Health Sciences Division, Fred Hutchinson Cancer Center
| | - Huy Q. Dinh
- Department of Oncology – McArdle Laboratory for Cancer Research, University of Wisconsin-Madison
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
| | - Robert J. Coffey
- Epithelial Biology Center, Vanderbilt University Medical Center
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine
- Department of Medicine – Division of Gastroenterology, Hepatology, & Nutrition, Vanderbilt University Medical Center
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center
| | - Martha J. Shrubsole
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center
- Department of Medicine – Division of Epidemiology, Vanderbilt University Medical Center
| | - Keith S. Chan
- Department of Urology, Houston Methodist Research Institute
- Neal Cancer Center, Houston Methodist Research Institute
| | - William M. Grady
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center
- Department of Medicine – Division of Gastroenterology, University of Washington
- Public Health Sciences Division, Fred Hutchinson Cancer Center
| | - Srinivasan Yegnasubramanian
- Department of Oncology – Genitourinary Cancer Disease Division, Johns Hopkins Medicine
- Radiation Oncology and Molecular Radiation Sciences – Molecular Radiation Science Division, Johns Hopkins Medicine
- Department of Pathology – Kidney-Urologic Pathology Division, Johns Hopkins Medicine
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine
| | - Costas A. Lyssiotis
- Department of Molecular & Integrative Physiology, University of Michigan Medical School
- Internal Medicine – Division of Gastroenterology, University of Michigan Medical School
- Rogel Cancer Center, University of Michigan Medical School
| | - Anirban Maitra
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center
- Sheikh Ahmed Center for Pancreatic Cancer Research, University of Texas MD Anderson Cancer Center
| | - Richard B. Halberg
- Department of Oncology – McArdle Laboratory for Cancer Research, University of Wisconsin-Madison
- Department of Medicine – Gastroenterology Division, University of Wisconsin-Madison
- Carbone Cancer Center, University of Wisconsin-Madison
| | - Neelendu Dey
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center
- Department of Medicine – Division of Gastroenterology, University of Washington
| | - Ken S. Lau
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine
- Epithelial Biology Center, Vanderbilt University Medical Center
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center
- Department of Surgery, Vanderbilt University Medical Center
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5
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Aktary ML, Shewchuk B, Wang Q, Hyndman E, Shack L, Robson PJ, Kopciuk KA. Health-Related and Psychosocial Factors Associated with Prostate Cancer Stage at Diagnosis among Males Participating in Alberta's Tomorrow Project. Prostate Cancer 2023; 2023:4426167. [PMID: 38020965 PMCID: PMC10656198 DOI: 10.1155/2023/4426167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 10/20/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
Prostate cancer (PCa) stage at diagnosis is an important predictor of cancer prognosis. In Canada, over one-quarter of males are diagnosed with advanced-stage PCa. Studies have identified several factors associated with PCa stage at diagnosis; however, evidence from Canada is limited. This study aimed to examine associations between sociodemographic characteristics, health history, health practices, and psychosocial factors and PCa stage at diagnosis among males participating in Alberta's Tomorrow Project (ATP), a prospective cohort in Alberta, Canada. The study included males aged 35-69 years who developed PCa until January 2018. Factors associated with PCa stage at diagnosis were examined using partial proportional odds (PPO) ordinal regression models. A total of 410 males were diagnosed with PCa over the study period. A higher number of lifetime prostate-specific antigen tests were associated with earlier-stage PCa (OR 0.91, p = 0.02, 95% CI 0.83-0.99), while higher abdominal circumference (OR 1.02, p = 0.05, 95% CI 1.00-1.03), lower social support (OR 2.34, p < 0.01, 95% CI 1.31-4.17), and having children (OR 2.67, p < 0.01, 95% CI 1.38-5.16) were associated with later-stage disease. This study identified factors previously found in the literature as well as novel factors associated with PCa stage at diagnosis, which can help inform targets for cancer prevention programs to improve PCa prognosis.
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Affiliation(s)
- Michelle L. Aktary
- Faculty of Kinesiology, University of Calgary, 2500 University Drive NW, T2N 1N4, Calgary, Alberta, Canada
| | - Brittany Shewchuk
- Cancer Epidemiology and Prevention Research, Cancer Care Alberta, Alberta Health Services, T2S 3C3, Calgary, Alberta, Canada
| | - Qinggang Wang
- Cancer Epidemiology and Prevention Research, Cancer Care Alberta, Alberta Health Services, T2S 3C3, Calgary, Alberta, Canada
| | - Eric Hyndman
- Department of Surgery, Urology Section, University of Calgary, 1403 29 Street NW, T2N 2T9, Calgary, Alberta, Canada
- Southern Alberta Institute of Urology, Office 6635, 7007 14 Street SW, T2V 1P9, Calgary, Alberta, Canada
| | - Lorraine Shack
- Cancer Surveillance and Reporting, Alberta Health Services, 1400-10123 99 Street Edmonton, T5J 3H1, Calgary, AB, Canada
| | - Paula J. Robson
- Department of Agricultural, Food and Nutritional Science and School of Public Health, University of Alberta, 116 Street & 85 Avenue, T6G 2R3, Edmonton, Alberta, Canada
- Cancer Care Alberta, Alberta Health Services, 10030-107 Street NW, T5J 3E4, Edmonton, Alberta, Canada
| | - Karen A. Kopciuk
- Cancer Epidemiology and Prevention Research, Cancer Care Alberta, Alberta Health Services, T2S 3C3, Calgary, Alberta, Canada
- Departments of Oncology, Community Health Sciences, and Mathematics and Statistics, University of Calgary, 2500 University Drive NW, T2N 1N4, Calgary, Alberta, Canada
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6
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Bharmjeet, Das A. Racial disparities in cancer care, an eyeopener for developing better global cancer management strategies. Cancer Rep (Hoboken) 2023; 6 Suppl 1:e1807. [PMID: 36971312 PMCID: PMC10440846 DOI: 10.1002/cnr2.1807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/05/2023] [Accepted: 03/01/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND In the last few decades, advancements in cancer research, both in the field of cancer diagnostics as well as treatment of the disease have been extensive and multidimensional. Increased availability of health care resources and growing awareness has resulted in the reduction of consumption of carcinogens such as tobacco; adopting various prophylactic measures; cancer testing on regular basis and improved targeted therapies have greatly reduced cancer mortality among populations, globally. However, this notable reduction in cancer mortality is discriminate and reflective of disparities between various ethnic populations and economic classes. Several factors contribute to this systemic inequity, at the level of diagnosis, cancer prognosis, therapeutics, and even point-of-care facilities. RECENT FINDINGS In this review, we have highlighted cancer health disparities among different populations around the globe. It encompasses social determinants such as status in society, poverty, education, diagnostic approaches including biomarkers and molecular testing, treatment as well as palliative care. Cancer treatment is an active area of constant progress and newer targeted treatments like immunotherapy, personalized treatment, and combinatorial therapies are emerging but these also show biases in their implementation in various sections of society. The involvement of populations in clinical trials and trial management is also a hotbed for racial discrimination. The immense progress in cancer management and its worldwide application needs a careful evaluation by identifying the biases in racial discrimination in healthcare facilities. CONCLUSION Our review gives a comprehensive evaluation of this global racial discrimination in cancer care and would be helpful in designing better strategies for cancer management and decreasing mortality.
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Affiliation(s)
- Bharmjeet
- Department of BiotechnologyDelhi Technological UniversityDelhi110042India
| | - Asmita Das
- Department of BiotechnologyDelhi Technological UniversityDelhi110042India
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7
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Salmon C, Quesnel-Vallée A, Barnett TA, Benedetti A, Cloutier MS, Datta GD, Kestens Y, Nicolau B, Parent MÉ. Neighbourhood social deprivation and risk of prostate cancer. Br J Cancer 2023; 129:335-345. [PMID: 37188877 PMCID: PMC10338528 DOI: 10.1038/s41416-023-02299-7] [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: 07/13/2022] [Revised: 04/24/2023] [Accepted: 04/28/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Striking geographic variations in prostate cancer incidence suggest an aetiological role for spatially-distributed factors. We assessed whether neighbourhood social deprivation, which can reflect limited social contacts, unfavourable lifestyle and environmental exposures, is associated with prostate cancer risk. METHODS In 2005-2012, we recruited 1931 incident prostate cancer cases and 1994 controls in a case-control study in Montreal, Canada. Lifetime residential addresses were linked to an area-based social deprivation index around recruitment (2006) and about 10 years earlier (1996). Logistic regression estimated adjusted odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS Men residing in areas characterised by greater social deprivation had elevated prostate cancer risks (ORs of 1.54 and 1.60 for recent and past exposures, respectively; highest vs lowest quintiles), independently from area- and individual-level confounders and screening patterns. The increase in risk with recent high social deprivation was particularly elevated for high-grade prostate cancer at diagnosis (OR 1.87, 95% CI 1.32-2.64). Associations were more pronounced for neighbourhoods with higher proportions of separated/divorced or widowed individuals in the past, and with higher percentages of residents living alone recently. CONCLUSIONS These novel findings, suggesting that neighbourhood-level social deprivation increases the risk of prostate cancer, point out to potential targeted public health interventions.
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Affiliation(s)
- Charlotte Salmon
- Unité d'épidémiologie et de biostatistique, Centre Armand-Frappier Santé Biotechnologie, Institut national de la recherche scientifique, Université du Québec, 531 Boulevard des Prairies, Laval, QC, H7V 1B7, Canada
| | - Amélie Quesnel-Vallée
- Department of Sociology, McGill University, 3460 McTavish Street, Montreal, QC, H3A 0E6, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 2001 McGill College Avenue, Montreal, QC, H3A 1G1, Canada
| | - Tracie A Barnett
- Department of Family Medicine, McGill University, 5858 Chemin de la Côte-des-Neiges, Montreal, QC, H3S 1Z1, Canada
| | - Andrea Benedetti
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 2001 McGill College Avenue, Montreal, QC, H3A 1G1, Canada
- Respiratory Epidemiology and Clinical Research Unit, Research Institute of the McGill University Health Centre, 5252 Maisonneuve Boulevard, Montreal, QC, H4A 3S5, Canada
| | - Marie-Soleil Cloutier
- Centre Urbanisation Culture Société, Institut national de la recherche scientifique, Université du Québec, 385 Sherbrooke Street East, Montreal, QC, H2X 1E3, Canada
| | - Geetanjali D Datta
- Department of Medicine and Cancer Research Center for Health Equity, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Département de médecine sociale et préventive, École de santé publique, Université de Montréal, 7101 Avenue du Parc, Montreal, QC, H3N 1X9, Canada
- Centre de recherche en santé publique, 7101 Avenue du Parc, Montreal, QC, H3N 1X9, Canada
| | - Yan Kestens
- Département de médecine sociale et préventive, École de santé publique, Université de Montréal, 7101 Avenue du Parc, Montreal, QC, H3N 1X9, Canada
- Centre de recherche en santé publique, 7101 Avenue du Parc, Montreal, QC, H3N 1X9, Canada
| | - Belinda Nicolau
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, 2001 McGill College Avenue, Montreal, QC, H3A 1G1, Canada
| | - Marie-Élise Parent
- Unité d'épidémiologie et de biostatistique, Centre Armand-Frappier Santé Biotechnologie, Institut national de la recherche scientifique, Université du Québec, 531 Boulevard des Prairies, Laval, QC, H7V 1B7, Canada.
- Département de médecine sociale et préventive, École de santé publique, Université de Montréal, 7101 Avenue du Parc, Montreal, QC, H3N 1X9, Canada.
- Research Centre of the Centre Hospitalier de l'Université de Montréal, 850 rue Saint-Denis, Montreal, QC, H2X 0A9, Canada.
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8
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Hu H, Liu X, Zheng Y, He X, Hart J, James P, Laden F, Chen Y, Bian J. Methodological Challenges in Spatial and Contextual Exposome-Health Studies. CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2023; 53:827-846. [PMID: 37138645 PMCID: PMC10153069 DOI: 10.1080/10643389.2022.2093595] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The concept of the exposome encompasses the totality of exposures from a variety of external and internal sources across an individual's life course. The wealth of existing spatial and contextual data makes it appealing to characterize individuals' external exposome to advance our understanding of environmental determinants of health. However, the spatial and contextual exposome is very different from other exposome factors measured at the individual-level as spatial and contextual exposome data are more heterogenous with unique correlation structures and various spatiotemporal scales. These distinctive characteristics lead to multiple unique methodological challenges across different stages of a study. This article provides a review of the existing resources, methods, and tools in the new and developing field for spatial and contextual exposome-health studies focusing on four areas: (1) data engineering, (2) spatiotemporal data linkage, (3) statistical methods for exposome-health association studies, and (4) machine- and deep-learning methods to use spatial and contextual exposome data for disease prediction. A critical analysis of the methodological challenges involved in each of these areas is performed to identify knowledge gaps and address future research needs.
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Affiliation(s)
- Hui Hu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Xiaokang Liu
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yi Zheng
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Xing He
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jaime Hart
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Peter James
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Population Medicine, Harvard Pilgrim Healthcare, Boston, Massachusetts, USA
| | - Francine Laden
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
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9
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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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10
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Mamouei M, Zhu Y, Nazarzadeh M, Hassaine A, Salimi-Khorshidi G, Cai Y, Rahimi K. Investigating the association of environmental exposures and all-cause mortality in the UK Biobank using sparse principal component analysis. Sci Rep 2022; 12:9239. [PMID: 35654993 PMCID: PMC9163152 DOI: 10.1038/s41598-022-13362-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/13/2022] [Indexed: 11/18/2022] Open
Abstract
Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations. An alternative approach to address this problem is the use of principal component analysis. This method, combines and projects a group of correlated variables onto a new orthogonal space. While this resolves the multicollinearity problem, it poses another challenge in relation to interpretability of results. Standard hypothesis testing methods can be used to evaluate the association of projected predictors, called principal components, with the outcomes of interest, however, there is no established way to trace the significance of principal components back to individual variables. To address this problem, we investigated the use of sparse principal component analysis which enforces a parsimonious projection. We hypothesise that this parsimony could facilitate the interpretability of findings. To this end, we investigated the association of 20 environmental predictors with all-cause mortality adjusting for demographic, socioeconomic, physiological, and behavioural factors. The study was conducted in a cohort of 379,690 individuals in the UK. During an average follow-up of 8.05 years (3,055,166 total person-years), 14,996 deaths were observed. We used Cox regression models to estimate the hazard ratio (HR) and 95% confidence intervals (CI). The Cox models were fitted to the standardised environmental predictors (a) without any transformation (b) transformed with PCA, and (c) transformed with SPCA. The comparison of findings underlined the potential of SPCA for conducting inference in scenarios where multicollinearity can increase the risk of Type II error. Our analysis unravelled a significant association between average noise pollution and increased risk of all-cause mortality. Specifically, those in the upper deciles of noise exposure have between 5 and 10% increased risk of all-cause mortality compared to the lowest decile.
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Affiliation(s)
- Mohammad Mamouei
- Deep Medicine, Nuffield Department of Women's & Reproductive Health, Oxford Martin School, University of Oxford, 1st Floor, Haye House, 75 George Street, Oxford, OX1 2BQ, UK.
| | - Yajie Zhu
- Deep Medicine, Nuffield Department of Women's & Reproductive Health, Oxford Martin School, University of Oxford, 1st Floor, Haye House, 75 George Street, Oxford, OX1 2BQ, UK
| | - Milad Nazarzadeh
- Deep Medicine, Nuffield Department of Women's & Reproductive Health, Oxford Martin School, University of Oxford, 1st Floor, Haye House, 75 George Street, Oxford, OX1 2BQ, UK
| | - Abdelaali Hassaine
- Deep Medicine, Nuffield Department of Women's & Reproductive Health, Oxford Martin School, University of Oxford, 1st Floor, Haye House, 75 George Street, Oxford, OX1 2BQ, UK
| | - Gholamreza Salimi-Khorshidi
- Deep Medicine, Nuffield Department of Women's & Reproductive Health, Oxford Martin School, University of Oxford, 1st Floor, Haye House, 75 George Street, Oxford, OX1 2BQ, UK
| | - Yutong Cai
- Deep Medicine, Nuffield Department of Women's & Reproductive Health, Oxford Martin School, University of Oxford, 1st Floor, Haye House, 75 George Street, Oxford, OX1 2BQ, UK
| | - Kazem Rahimi
- Deep Medicine, Nuffield Department of Women's & Reproductive Health, Oxford Martin School, University of Oxford, 1st Floor, Haye House, 75 George Street, Oxford, OX1 2BQ, UK
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11
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Sorice KA, Fang CY, Wiese D, Ortiz A, Chen Y, Henry KA, Lynch SM. Systematic review of neighborhood socioeconomic indices studied across the cancer control continuum. Cancer Med 2022; 11:2125-2144. [PMID: 35166051 PMCID: PMC9119356 DOI: 10.1002/cam4.4601] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/03/2021] [Accepted: 12/28/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND There is extensive interest in understanding how neighborhood socioeconomic status (nSES) may affect cancer incidence or survival. However, variability regarding items included and approaches used to form a composite nSES index presents challenges in summarizing overall associations with cancer. Given recent calls for standardized measures of neighborhood sociodemographic effects in cancer disparity research, the objective of this systematic review was to identify and compare existing nSES indices studied across the cancer continuum (incidence, screening, diagnosis, treatment, survival/mortality) and summarize associations by race/ethnicity and cancer site to inform future cancer disparity studies. METHODS Using PRISMA guidelines, peer-reviewed articles published between 2010 and 2019 containing keywords related to nSES and cancer were identified in PubMed. RESULTS Twenty-four nSES indices were identified from 75 studies. In general, findings indicated a significant association between nSES and cancer outcomes (n = 64/75 studies; 85.33%), with 42/64 (65.63%) adjusting for highly-correlated individual SES factors (e.g., education). However, the direction of association differed by cancer site, race/ethnicity, and nSES index. CONCLUSIONS This review highlights several methodologic and conceptual issues surrounding nSES measurement and potential associations with cancer disparities. Recommendations pertaining to the selection of nSES measures are provided, which may help inform disparity-related disease processes and improve the identification of vulnerable populations in need of intervention.
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Affiliation(s)
- Kristen A. Sorice
- Cancer Prevention and ControlFox Chase Cancer CenterPhiladelphiaPAUSA
| | - Carolyn Y. Fang
- Cancer Prevention and ControlFox Chase Cancer CenterPhiladelphiaPAUSA
| | - Daniel Wiese
- Geography and Urban StudiesTemple UniversityPhiladelphiaPAUSA
| | - Angel Ortiz
- Cancer Prevention and ControlFox Chase Cancer CenterPhiladelphiaPAUSA
| | - Yuku Chen
- Cancer Prevention and ControlFox Chase Cancer CenterPhiladelphiaPAUSA
| | - Kevin A. Henry
- Geography and Urban StudiesTemple UniversityPhiladelphiaPAUSA
| | - Shannon M. Lynch
- Cancer Prevention and ControlFox Chase Cancer CenterPhiladelphiaPAUSA
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12
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K. C. M, Oral E, Rung AL, Trapido EJ, Rozek LS, Fontham ETH, Bensen JT, Farnan L, Steck SE, Song L, Mohler JL, Peters ES. Neighborhood deprivation and risk of mortality among men with prostate cancer: Findings from a long-term follow-up study. Prostate 2022; 82:783-792. [PMID: 35201637 PMCID: PMC9306458 DOI: 10.1002/pros.24320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 01/10/2022] [Accepted: 02/04/2022] [Indexed: 12/22/2022]
Abstract
BACKGROUND The overall survival rate of prostate cancer (PCa) has improved over the past decades. However, huge socioeconomic and racial disparities in overall and prostate cancer-specific mortality exist. The neighborhood-level factors including socioeconomic disadvantage and lack of access to care may contribute to disparities in cancer mortality. This study examines the impact of neighborhood deprivation on mortality among PCa survivors. METHODS North Carolina-Louisiana Prostate Cancer Project (PCaP) data were used. A total of 2113 men, 1046 AA and 1067 EA, with PCa were included in the analysis. Neighborhood deprivation was measured by the Area Deprivation Index (ADI) at the census block group level using data from the US Census Bureau. Quintiles of ADI were created. Cox proportional hazards and competing risk models with mixed effects were performed to estimate the effect of neighborhood deprivation on all-cause and PCa-specific mortality adjusted for age, race, study site, insurance status, and comorbidities. RESULTS Participants living in the most deprived neighborhoods had an increased risk for all-cause mortality (quintiles 4 + 5: adjusted hazard ratio [aHR] = 1.51, 95% confidence interval [CI] = 1.16-1.96) compared to those in the least deprived (quintile 1) neighborhoods. The risk of prostate cancer-specific mortality was also higher among those living in the deprived neighborhoods (quintiles 4 + 5: aHR = 1.90, 95% CI = 1.10-3.50) than those in the least deprived neighborhood. CONCLUSIONS The findings suggest neighborhood-level resources or health interventions are essential to improve survival among men with PCa. Additional research should focus on the mechanisms of how the neighborhood environment affects mortality.
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Affiliation(s)
- Madhav K. C.
- Department of Internal Medicine, Yale School of MedicineCancer Outcomes, Public Policy, and Effectiveness Research (COPPER) CenterNew HavenConnecticutUSA
- Department of Epidemiology, School of public HealthEpidemiology Program, Louisiana State University Health Sciences Center‐New OrleansNew OrleansLouisianaUSA
| | - Evrim Oral
- Department of Biostatistics, School of Public HealthBiostatistics Program, Louisiana State University Health Sciences Center‐New OrleansNew OrleansLouisianaUSA
| | - Ariane L. Rung
- Department of Epidemiology, School of public HealthEpidemiology Program, Louisiana State University Health Sciences Center‐New OrleansNew OrleansLouisianaUSA
| | - Edward J. Trapido
- Department of Epidemiology, School of public HealthEpidemiology Program, Louisiana State University Health Sciences Center‐New OrleansNew OrleansLouisianaUSA
| | - Laura S. Rozek
- Department of Environmental Health SciencesUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
| | - Elizabeth T. H. Fontham
- Department of Epidemiology, School of public HealthEpidemiology Program, Louisiana State University Health Sciences Center‐New OrleansNew OrleansLouisianaUSA
| | - Jeannette T. Bensen
- Department of EpidemiologyGillings School of Global Public Health, University of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Lineberger Comprehensive Cancer Center, School of MedicineUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Laura Farnan
- Lineberger Comprehensive Cancer Center, School of MedicineUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Susan E. Steck
- Department of Epidemiology and BiostatisticsArnold School of Public Health, University of South CarolinaColumbiaSouth CarolinaUSA
| | - Lixin Song
- School of NursingUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - James L. Mohler
- Department of UrologyRoswell Park Comprehensive Cancer CenterNew YorkNew YorkUSA
| | - Edward S. Peters
- Department of Epidemiology, School of public HealthEpidemiology Program, Louisiana State University Health Sciences Center‐New OrleansNew OrleansLouisianaUSA
- Department of EpidemiologyCollege of Public HealthUniversity of Nebraska Medical CenterOmahaNebraskaUSA
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13
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Press DJ, Shariff-Marco S, Lichtensztajn DY, Lauderdale D, Murphy AB, Inamdar PP, DeRouen MC, Hamilton AS, Yang J, Lin K, Hedeker D, Haiman CA, Cheng I, Gomez SL. Contributions of Social Factors to Disparities in Prostate Cancer Risk Profiles among Black Men and Non-Hispanic White Men with Prostate Cancer in California. Cancer Epidemiol Biomarkers Prev 2022; 31:404-412. [PMID: 34853020 PMCID: PMC8825684 DOI: 10.1158/1055-9965.epi-21-0697] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/20/2021] [Accepted: 11/22/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Black men are more likely than Non-Hispanic White (NHW) men to be diagnosed with high-risk prostate cancer. We examined the extent to which social factors were associated with differences in prostate cancer risk profiles between Black men and NHW men [using a modification to the original D'Amico risk groups based on prostate specific antigen (PSA), Gleason score (GS), and TNM stage (stage)], based on individual and combined clinicopathologic characteristics. METHODS We conducted a cross-sectional population-based study of 23,555 Black men and 146,889 NHW men diagnosed with prostate cancer in the California Cancer Registry from 2004 to 2017. We conducted multivariable logistic regression to examine the association of year of diagnosis, block group-level neighborhood socioeconomic status (nSES), marital status, and insurance type on differences in prostate cancer risk profiles between Black and NHW men. RESULTS High PSA (>20 ng/mL), GS, stage, individually and combined prostate cancer risk profiles were more common among Black men versus NHW men. In fully adjusted models, relative to NHW men, we observed a persistent 67% increased odds of high PSA among Black men. nSES was the factor most strongly associated with racial disparity in high PSA, accounting for 25% of the difference. Marital status was the factor that was second most associated with a racial disparity. CONCLUSIONS nSES was the factor most strongly associated with racial disparities in high PSA prostate cancer. IMPACT The influence of nSES on racial disparities in PSA, GS, stage, and prostate cancer risk profiles warrants further consideration.
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Affiliation(s)
- David J Press
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois.
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago Illinois
- The Center for Health Information Partnerships (CHiP), Institute of Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Salma Shariff-Marco
- Greater Bay Area Cancer Registry, Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, San Francisco, California
| | - Daphne Y Lichtensztajn
- Greater Bay Area Cancer Registry, Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
| | - Diane Lauderdale
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois
| | - Adam B Murphy
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Pushkar P Inamdar
- Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
| | - Mindy C DeRouen
- Greater Bay Area Cancer Registry, Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, San Francisco, California
| | - Ann S Hamilton
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California (USC), Los Angeles, California
| | - Juan Yang
- Greater Bay Area Cancer Registry, Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
| | - Katherine Lin
- Greater Bay Area Cancer Registry, Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
| | - Donald Hedeker
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California (USC), Los Angeles, California
| | - Iona Cheng
- Greater Bay Area Cancer Registry, Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, San Francisco, California
| | - Scarlett Lin Gomez
- Greater Bay Area Cancer Registry, Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, San Francisco, California
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14
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Singh S, Sridhar P. A narrative review of sociodemographic risk and disparities in screening, diagnosis, treatment, and outcomes of the most common extrathoracic malignancies in the United States. J Thorac Dis 2021; 13:3827-3843. [PMID: 34277073 PMCID: PMC8264686 DOI: 10.21037/jtd-21-87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/20/2021] [Indexed: 11/06/2022]
Abstract
There is a well-established association between multiple sociodemographic risk factors and disparities in cancer care. These risk factors include minority race and ethnicity, low socioeconomic status (SES) including low income and education level, non-English primary language, immigrant status, and residential segregation, and distance to facilities that deliver cancer care. As cancer care advances, existing disparities in screening, treatment, and outcomes have become more evident. Lung cancer remains the most common and fatal malignancy in the United States, with breast, colorectal, and prostate cancer being the three most common and deadly extrathoracic malignancies. Achieving the best outcomes for patients with these malignancies relies on strong physician-patient relationships leading to robust screening, early diagnosis, and early referral to facilities that can deliver multidisciplinary care and multimodal therapy. It is likely that challenges experienced in developing patient trust and understanding, providing access to screening, and building referral pipelines for definitive therapy in lung cancer care to vulnerable populations are paralleled by those in extrathoracic malignancies. Likewise, progress made in delivering optimal care to all patients across sociodemographic and geographic barriers can serve as a roadmap. Therefore, we provide a narrative review of current disparities in screening, treatment, and outcomes for patients with breast, prostate, and colorectal malignancies.
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Affiliation(s)
- Sarah Singh
- Division of Thoracic Surgery, Department of Surgery, Boston University School of Medicine, Boston Medical Center, Boston, MA, USA
| | - Praveen Sridhar
- Division of Thoracic Surgery, Department of Surgery, Boston University School of Medicine, Boston Medical Center, Boston, MA, USA
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15
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Zhang X, Smith N, Spear E, Stroustrup A. Neighborhood characteristics associated with COVID-19 burden-the modifying effect of age. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2021; 31:525-537. [PMID: 33947953 PMCID: PMC8095472 DOI: 10.1038/s41370-021-00329-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 04/01/2021] [Accepted: 04/07/2021] [Indexed: 05/06/2023]
Abstract
BACKGROUND Neighborhood characteristics have been linked to community incidence of COVID-19, but the modifying effect of age has not been examined. OBJECTIVE We adapted a neighborhood-wide analysis study (NWAS) design to systematically examine associations between neighborhood characteristics and COVID-19 incidence among different age groups. METHODS The number of daily cumulative cases of COVID-19 by zip code area in Illinois has been made publicly available by the Illinois Department of Public Health. The number of COVID-19 cases was reported for eight age groups (under 20, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+). We reviewed this data published from May 23 through June 17, 2020 with complete data for all eight age groups and linked the data to neighborhood characteristics measured by the American Community Survey (ACS). Geographic age-specific cumulative incidence (cases per 1000 people) of COVID-19 was calculated by dividing the number of daily cumulative cases by the population of the same age group at each zip code area. The association between individual characteristics and COVID-19 incidence was examined using Poisson regression models. RESULTS At the zip code level, neighborhood socioeconomic status was a more important risk factor of COVID-19 incidence in children and working-age adults than in seniors. Social demographics and housing conditions were important risk factors of COVID-19 incidence in older age groups. We additionally observed significant associations between transportation-related variables and COVID-19 incidences in multiple age groups. SIGNIFICANCE We concluded that age modified the association between neighborhood characteristics and COVID-19 incidence.
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Affiliation(s)
- Xueying Zhang
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Norah Smith
- The Bronx High School of Science, Bronx, NY, USA
| | - Emily Spear
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Annemarie Stroustrup
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Neonatology, Cohen Children's Medical Center, Northwell Health, New Hyde Park, NY, USA
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16
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Wiese D, Stroup AM, Maiti A, Harris G, Lynch SM, Vucetic S, Gutierrez-Velez VH, Henry KA. Measuring Neighborhood Landscapes: Associations between a Neighborhood's Landscape Characteristics and Colon Cancer Survival. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18094728. [PMID: 33946680 PMCID: PMC8124655 DOI: 10.3390/ijerph18094728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/21/2021] [Accepted: 04/27/2021] [Indexed: 12/11/2022]
Abstract
Landscape characteristics have been shown to influence health outcomes, but few studies have examined their relationship with cancer survival. We used data from the National Land Cover Database to examine associations between regional-stage colon cancer survival and 27 different landscape metrics. The study population included all adult New Jersey residents diagnosed between 2006 and 2011. Cases were followed until 31 December 2016 (N = 3949). Patient data were derived from the New Jersey State Cancer Registry and were linked to LexisNexis to obtain residential histories. Cox proportional hazard regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI95) for the different landscape metrics. An increasing proportion of high-intensity developed lands with 80–100% impervious surfaces per cell/pixel was significantly associated with the risk of colon cancer death (HR = 1.006; CI95 = 1.002–1.01) after controlling for neighborhood poverty and other individual-level factors. In contrast, an increase in the aggregation and connectivity of vegetation-dominated low-intensity developed lands with 20–<40% impervious surfaces per cell/pixel was significantly associated with the decrease in risk of death from colon cancer (HR = 0.996; CI95 = 0.992–0.999). Reducing impervious surfaces in residential areas may increase the aesthetic value and provide conditions more advantageous to a healthy lifestyle, such as walking. Further research is needed to understand how these landscape characteristics impact survival.
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Affiliation(s)
- Daniel Wiese
- Department of Geography and Urban Studies, Temple University, Philadelphia, PA 19122, USA; (V.H.G.-V.); (K.A.H.)
- Correspondence:
| | - Antoinette M. Stroup
- New Jersey Department of Health, New Jersey State Cancer Registry, Trenton, NJ 08625, USA; (A.M.S.); (G.H.)
- Rutgers School of Public Health, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Aniruddha Maiti
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA; (A.M.); (S.V.)
| | - Gerald Harris
- New Jersey Department of Health, New Jersey State Cancer Registry, Trenton, NJ 08625, USA; (A.M.S.); (G.H.)
| | - Shannon M. Lynch
- Fox Chase Cancer Center, Division of Cancer Prevention and Control, Philadelphia, PA 19111, USA;
| | - Slobodan Vucetic
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA; (A.M.); (S.V.)
| | - Victor H. Gutierrez-Velez
- Department of Geography and Urban Studies, Temple University, Philadelphia, PA 19122, USA; (V.H.G.-V.); (K.A.H.)
| | - Kevin A. Henry
- Department of Geography and Urban Studies, Temple University, Philadelphia, PA 19122, USA; (V.H.G.-V.); (K.A.H.)
- Fox Chase Cancer Center, Division of Cancer Prevention and Control, Philadelphia, PA 19111, USA;
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17
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Handorf E, Yin Y, Slifker M, Lynch S. Variable selection in social-environmental data: sparse regression and tree ensemble machine learning approaches. BMC Med Res Methodol 2020; 20:302. [PMID: 33302880 PMCID: PMC7727197 DOI: 10.1186/s12874-020-01183-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 11/27/2020] [Indexed: 11/18/2022] Open
Abstract
Background Social-environmental data obtained from the US Census is an important resource for understanding health disparities, but rarely is the full dataset utilized for analysis. A barrier to incorporating the full data is a lack of solid recommendations for variable selection, with researchers often hand-selecting a few variables. Thus, we evaluated the ability of empirical machine learning approaches to identify social-environmental factors having a true association with a health outcome. Methods We compared several popular machine learning methods, including penalized regressions (e.g. lasso, elastic net), and tree ensemble methods. Via simulation, we assessed the methods’ ability to identify census variables truly associated with binary and continuous outcomes while minimizing false positive results (10 true associations, 1000 total variables). We applied the most promising method to the full census data (p = 14,663 variables) linked to prostate cancer registry data (n = 76,186 cases) to identify social-environmental factors associated with advanced prostate cancer. Results In simulations, we found that elastic net identified many true-positive variables, while lasso provided good control of false positives. Using a combined measure of accuracy, hierarchical clustering based on Spearman’s correlation with sparse group lasso regression performed the best overall. Bayesian Adaptive Regression Trees outperformed other tree ensemble methods, but not the sparse group lasso. In the full dataset, the sparse group lasso successfully identified a subset of variables, three of which replicated earlier findings. Conclusions This analysis demonstrated the potential of empirical machine learning approaches to identify a small subset of census variables having a true association with the outcome, and that replicate across empiric methods. Sparse clustered regression models performed best, as they identified many true positive variables while controlling false positive discoveries. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-020-01183-9.
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Affiliation(s)
- Elizabeth Handorf
- Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Reimann 383, 333 Cottman Ave, Philadelphia, PA, 19111, USA.
| | - Yinuo Yin
- Cancer Prevention and Control, Fox Chase Cancer Center, Young Pavilion, 333 Cottman Ave, Philadelphia, PA, 19111, USA
| | - Michael Slifker
- Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Reimann 383, 333 Cottman Ave, Philadelphia, PA, 19111, USA
| | - Shannon Lynch
- Cancer Prevention and Control, Fox Chase Cancer Center, Young Pavilion, 333 Cottman Ave, Philadelphia, PA, 19111, USA
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Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania-A Geo-Additive Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17207526. [PMID: 33081168 PMCID: PMC7588924 DOI: 10.3390/ijerph17207526] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 10/04/2020] [Accepted: 10/12/2020] [Indexed: 02/07/2023]
Abstract
Many neighborhood socioeconomic index measures (nSES) that capture neighborhood deprivation exist but the impact of measure selection on liver cancer (LC) geographic disparities remains unclear. We introduce a Bayesian geoadditive modeling approach to identify clusters in Pennsylvania (PA) with higher than expected LC incidence rates, adjusted for individual-level factors (age, sex, race, diagnosis year) and compared them to models with 7 different nSES index measures to elucidate the impact of nSES and measure selection on LC geospatial variation. LC cases diagnosed from 2007–2014 were obtained from the PA Cancer Registry and linked to nSES measures from U.S. census at the Census Tract (CT) level. Relative Risks (RR) were estimated for each CT, adjusted for individual-level factors (baseline model). Each nSES measure was added to the baseline model and changes in model fit, geographic disparity and state-wide RR ranges were compared. All 7 nSES measures were strongly associated with high risk clusters. Tract-level RR ranges and geographic disparity from the baseline model were attenuated after adjustment for nSES measures. Depending on the nSES measure selected, up to 60% of the LC burden could be explained, suggesting methodologic evaluations of multiple nSES measures may be warranted in future studies to inform LC prevention efforts.
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Madnick D, Handorf E, Ortiz A, Sorice K, Nagappan L, Moccia M, Cheema K, Vijayvergia N, Dotan E, Lynch SM. Investigating disparities: the effect of social environment on pancreatic cancer survival in metastatic patients. J Gastrointest Oncol 2020; 11:633-643. [PMID: 32953147 DOI: 10.21037/jgo-20-39] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Background Pancreatic adenocarcinoma (PCA) incidence is higher in Black compared to White patients. Beyond race, neighborhood socioeconomic status (nSES) may also inform disparities. However, these effects on metastatic pancreatic adenocarcinoma (mPCA) are not well-studied. The aim of this study was to explore whether nSES influences survival in patients with mPCA. Methods nSES measures were derived from U.S. census data at the census tract (CT) level. We correlated medical records of mPCA patients (diagnosed 2010-2016; n=370) to nSES measures retrospectively via a geocode derived from patient address. Multivariable cox proportional hazards models were used to identify patient-level (age, sex, race, marital status, treatment (radiation/chemo/surgery), PCA family history, stage, Jewish ancestry, tobacco use, BMI, diabetes, and statin use) and nSES measures (deprivation, racial concentration, stability, transportation access, immigration) associated with mPCA survival; P values <0.05 were significant. Results Eighty-two percent of patients were White; less than one-third of patients resided in highly deprived neighborhoods. Three hundred thirty-three mPCA patient deaths occurred, with a survival ranging from 7-9 months (median 8 months). Patient-level factors including younger age, receipt of chemotherapy or initial surgery and statin use, were associated with improved survival, whereas neighborhood stability (i.e., a higher % of residents still living in the same house as 1 year ago) was significantly associated with poor pancreatic survival. Conclusions Our findings suggest nSES has limited effect on survival of mPCA patients as compared to clinical variables. This may be due to the aggressive nature of this cancer, however, additional studies with larger, more diverse cohorts are needed to better understand the effect of nSES on survival of patients with mPCA.
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Affiliation(s)
- David Madnick
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Elizabeth Handorf
- Population Studies Facility, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Angel Ortiz
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Kristen Sorice
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Lavanya Nagappan
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Matthew Moccia
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Khadija Cheema
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA.,Department of Medical Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Namrata Vijayvergia
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA.,Department of Medical Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Efrat Dotan
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA.,Department of Medical Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Shannon M Lynch
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA.,Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
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20
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Lynch SM, Handorf E, Sorice KA, Blackman E, Bealin L, Giri VN, Obeid E, Ragin C, Daly M. The effect of neighborhood social environment on prostate cancer development in black and white men at high risk for prostate cancer. PLoS One 2020; 15:e0237332. [PMID: 32790761 PMCID: PMC7425919 DOI: 10.1371/journal.pone.0237332] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 07/23/2020] [Indexed: 12/24/2022] Open
Abstract
INTRODUCTION Neighborhood socioeconomic (nSES) factors have been implicated in prostate cancer (PCa) disparities. In line with the Precision Medicine Initiative that suggests clinical and socioenvironmental factors can impact PCa outcomes, we determined whether nSES variables are associated with time to PCa diagnosis and could inform PCa clinical risk assessment. MATERIALS AND METHODS The study sample included 358 high risk men (PCa family history and/or Black race), aged 35-69 years, enrolled in an early detection program. Patient variables were linked to 78 nSES variables (employment, income, etc.) from previous literature via geocoding. Patient-level models, including baseline age, prostate specific antigen (PSA), digital rectal exam, as well as combined models (patient plus nSES variables) by race/PCa family history subgroups were built after variable reduction methods using Cox regression and LASSO machine-learning. Model fit of patient and combined models (AIC) were compared; p-values<0.05 were significant. Model-based high/low nSES exposure scores were calculated and the 5-year predicted probability of PCa was plotted against PSA by high/low neighborhood score to preliminarily assess clinical relevance. RESULTS In combined models, nSES variables were significantly associated with time to PCa diagnosis. Workers mode of transportation and low income were significant in White men with a PCa family history. Homeownership (%owner-occupied houses with >3 bedrooms) and unemployment were significant in Black men with and without a PCa family history, respectively. The 5-year predicted probability of PCa was higher in men with a high neighborhood score (weighted combination of significant nSES variables) compared to a low score (e.g., Baseline PSA level of 4ng/mL for men with PCa family history: White-26.7% vs 7.7%; Black-56.2% vs 29.7%). DISCUSSION Utilizing neighborhood data during patient risk assessment may be useful for high risk men affected by disparities. However, future studies with larger samples and validation/replication steps are needed.
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Affiliation(s)
- Shannon M. Lynch
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
- * E-mail:
| | - Elizabeth Handorf
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
| | - Kristen A. Sorice
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
| | - Elizabeth Blackman
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
| | - Lisa Bealin
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
| | - Veda N. Giri
- Cancer Risk Assessment and Clinical Cancer Genetics Program, Departments of Medical Oncology, Cancer Biology, and Urology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, United States of America
| | - Elias Obeid
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
| | - Camille Ragin
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
| | - Mary Daly
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
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21
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Hu H, Zhao J, Savitz DA, Prosperi M, Zheng Y, Pearson TA. An external exposome-wide association study of hypertensive disorders of pregnancy. ENVIRONMENT INTERNATIONAL 2020; 141:105797. [PMID: 32413622 PMCID: PMC7336837 DOI: 10.1016/j.envint.2020.105797] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 04/22/2020] [Accepted: 05/03/2020] [Indexed: 05/11/2023]
Abstract
It is widely recognized that exogenous factors play an important role in the development of hypertensive disorders of pregnancy (HDP). However, only a few external environmental factors have been studied, often separately, with no attempt to examine the totality of the external environment, or the external exposome. We conducted an external exposome-wide association study (ExWAS) using the Florida Vital Statistics Birth Records including 819,399 women with live births in 2010-2013. A total of 5784 factors characterizing women's surrounding natural, built, and social environment during pregnancy from 10 data sources were collected, harmonized, integrated, and spatiotemporally linked to the women based on pregnancy periods using 250 m buffers around their geocoded residential addresses. A random 50:50 split divided the data into discovery and replication sets, and a 3-phase procedure was used. In phase 1, associations between HDP and individual factors were examined, and Bonferroni adjustment was performed. In phase 2, an elastic net model was used to perform variable selection among significant variables from phase 1. In phase 3, a multivariable logistic regression model including all variables selected by the elastic net model was fitted. Variables that were significant in both the discovery and replication sets were retained. Among the 528 and 490 variables identified in Phase 1, 232 and 224 were selected by the elastic net model in Phase 2, and 67 and 48 variables remained statistically significant in Phase 3 in the discovery and replication sets, respectively. A total of 12 variables were significant in both the discovery and replication sets, including air toxicants (e.g., 2,2,4-trimethylpentane), meteorological factors (e.g., omega or vertical velocity at 125mb pressure level), neighborhood crime and safety (e.g., burglary rate), and neighborhood sociodemographic status (e.g., urbanization). This is the first large external exposome study of HDP. It confirmed some of the previously reported associations and generated unexpected predictors within the environment that may warrant more focused evaluation.
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Affiliation(s)
- Hui Hu
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - David A Savitz
- Department of Epidemiology, School of Public Health, Brown University, Providence, Rhode Island, USA
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Yi Zheng
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Thomas A Pearson
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
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22
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Mooney SJ, Hurvitz PM, Moudon AV, Zhou C, Dalmat R, Saelens BE. Residential neighborhood features associated with objectively measured walking near home: Revisiting walkability using the Automatic Context Measurement Tool (ACMT). Health Place 2020; 63:102332. [PMID: 32543423 PMCID: PMC7306420 DOI: 10.1016/j.healthplace.2020.102332] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 03/26/2020] [Accepted: 03/31/2020] [Indexed: 10/24/2022]
Abstract
Many distinct characteristics of the social, natural, and built neighborhood environment have been included in walkability measures, and it is unclear which measures best describe the features of a place that support walking. We developed the Automatic Context Measurement Tool, which measures neighborhood environment characteristics from public data for any point location in the United States. We explored these characteristics in home neighborhood environments in relation to walking identified from integrated GPS, accelerometer, and travel log data from 681 residents of King Country, WA. Of 146 neighborhood characteristics, 92 (63%) were associated with walking bout counts after adjustment for individual characteristics and correction for false discovery. The strongest built environment predictor of walking bout count was housing unit count. Models using data-driven and a priori defined walkability measures exhibited similar fit statistics. Walkability measures consisting of different neighborhood characteristic measurements may capture the same underlying variation in neighborhood conditions.
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Affiliation(s)
- Stephen J Mooney
- Department of Epidemiology, University of Washington, Seattle, WA, United States.
| | - Philip M Hurvitz
- Urban Form Lab, University of Washington, Seattle, WA, United States; Center for Studies in Demography and Ecology, University of Washington, Seattle, WA, United States
| | | | - Chuan Zhou
- Seattle Children's Research Institute, Seattle, WA, United States; Department of Pediatrics, University of Washington, Seattle, WA, United States
| | - Ronit Dalmat
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Brian E Saelens
- Seattle Children's Research Institute, Seattle, WA, United States; Department of Pediatrics, University of Washington, Seattle, WA, United States
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23
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Wang M, Wasserman E, Geyer N, Carroll RM, Zhao S, Zhang L, Hohl R, Lengerich EJ, McDonald AC. Spatial patterns in prostate Cancer-specific mortality in Pennsylvania using Pennsylvania Cancer registry data, 2004-2014. BMC Cancer 2020; 20:394. [PMID: 32375682 PMCID: PMC7203834 DOI: 10.1186/s12885-020-06902-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 04/26/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Spatial heterogeneity of prostate cancer-specific mortality in Pennsylvania remains unclear. We utilized advanced geospatial survival regressions to examine spatial variation of prostate cancer-specific mortality in PA and evaluate potential effects of individual- and county-level risk factors. METHODS Prostate cancer cases, aged ≥40 years, were identified in the 2004-2014 Pennsylvania Cancer Registry. The 2018 County Health Rankings data and the 2014 U.S. Environmental Protection Agency's Environmental Quality Index were used to extract county-level data. The accelerated failure time models with spatial frailties for geographical correlations were used to assess prostate cancer-specific mortality rates for Pennsylvania and by the Penn State Cancer Institute (PSCI) 28-county catchment area. Secondary assessment based on estimated spatial frailties was conducted to identify potential health and environmental risk factors for mortality. RESULTS There were 94,274 cases included. The 5-year survival rate in PA was 82% (95% confidence interval, CI: 81.1-82.8%), with the catchment area having a lower survival rate 81% (95% CI: 79.5-82.6%) compared to the non-catchment area rate of 82.3% (95% CI: 81.4-83.2%). Black men, uninsured, more aggressive prostate cancer, rural and urban Appalachia, positive lymph nodes, and no definitive treatment were associated with lower survival. Several county-level health (i.e., poor physical activity) and environmental factors in air and land (i.e., defoliate chemical applied) were associated with higher mortality rates. CONCLUSIONS Spatial variations in prostate cancer-specific mortality rates exist in Pennsylvania with a higher risk in the PSCI's catchment area, in particular, rural-Appalachia. County-level health and environmental factors may contribute to spatial heterogeneity in prostate cancer-specific mortality.
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Affiliation(s)
- Ming Wang
- Department of Public Health Sciences, Penn State College of Medicine and Cancer Institute, 90 Hope Drive, Hershey, PA, 17033, USA.
- Penn State Cancer Institute, Hershey, PA, USA.
| | - Emily Wasserman
- Department of Public Health Sciences, Penn State College of Medicine and Cancer Institute, 90 Hope Drive, Hershey, PA, 17033, USA
| | - Nathaniel Geyer
- Department of Public Health Sciences, Penn State College of Medicine and Cancer Institute, 90 Hope Drive, Hershey, PA, 17033, USA
| | - Rachel M Carroll
- Department of Mathematics and Statistics, the University of North Carolina at Wilmington, Wilmington, NC, USA
| | - Shanshan Zhao
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Lijun Zhang
- Penn State Cancer Institute, Hershey, PA, USA
- Penn State Institute of Personalized Medicine, Hershey, PA, USA
| | - Raymond Hohl
- Penn State Cancer Institute, Hershey, PA, USA
- Penn State Milton S. Hershey Medical Center, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Eugene J Lengerich
- Department of Public Health Sciences, Penn State College of Medicine and Cancer Institute, 90 Hope Drive, Hershey, PA, 17033, USA
- Penn State Cancer Institute, Hershey, PA, USA
- Penn State Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Alicia C McDonald
- Department of Public Health Sciences, Penn State College of Medicine and Cancer Institute, 90 Hope Drive, Hershey, PA, 17033, USA
- Penn State Cancer Institute, Hershey, PA, USA
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Zheng Y, Chen Z, Pearson T, Zhao J, Hu H, Prosperi M. Design and methodology challenges of environment-wide association studies: A systematic review. ENVIRONMENTAL RESEARCH 2020; 183:109275. [PMID: 32105887 PMCID: PMC7346707 DOI: 10.1016/j.envres.2020.109275] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 02/18/2020] [Accepted: 02/18/2020] [Indexed: 05/09/2023]
Abstract
Environment-wide association studies (EWAS) are an untargeted, agnostic, and hypothesis-generating approach to exploring environmental factors associated with health outcomes, akin to genome-wide association studies (GWAS). While design, methodology, and replicability standards for GWAS are established, EWAS pose many challenges. We systematically reviewed published literature on EWAS to categorize scope, impact, types of analytical approaches, and open challenges in designs and methodologies. The Web of Science and PubMed databases were searched through multiple queries to identify EWAS articles between January 2010 and December 2018, and a systematic review was conducted following the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) reporting standard. Twenty-three articles met our inclusion criteria and were included. For each study, we categorized the data sources, the definitions of study outcomes, the sets of environmental variables, and the data engineering/analytical approaches, e.g. neighborhood definition, variable standardization, handling of multiple hypothesis testing, model selection, and validation. We identified limited exploitation of data sources, high heterogeneity in analytical approaches, and lack of replication. Despite of the promising utility of EWAS, further development of EWAS will require improved data sources, standardization of study designs, and rigorous testing of methodologies.
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Affiliation(s)
- Yi Zheng
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Zhaoyi Chen
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Thomas Pearson
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Hui Hu
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.
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25
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Bluethmann SM, Wang M, Wasserman E, Chen C, Zaorsky NG, Hohl RJ, McDonald AC. Prostate cancer in Pennsylvania: The role of older age at diagnosis, aggressiveness, and environmental risk factors on treatment and mortality using data from the Pennsylvania Cancer Registry. Cancer Med 2020; 9:3623-3633. [PMID: 32212232 PMCID: PMC7221418 DOI: 10.1002/cam4.3003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 02/27/2020] [Accepted: 02/28/2020] [Indexed: 12/15/2022] Open
Abstract
Background To assess: (a) cancer treatment in prostate cancer survivors (PCS) by age at diagnosis (ADx) and prostate cancer (PC) aggressiveness; (b) potential impact on PC mortality; and (c) these results in the context of environmental/behavioral risk factors on PCS in Pennsylvania. Methods Prostate cancer survivors ages ≥40 years were identified from the 2004‐2014 Pennsylvania Cancer Registry (PCR). Demographic/clinical descriptors and PC treatment were extracted from PCR. Prostate cancer aggressiveness was defined by clinical/pathologic Gleason score and tumor stage. Logistic and Cox regression analyses tested associations between treatment received and PC‐specific mortality. County‐level data from the Pennsylvania BRFSS were used to estimate cancer‐related behavioral risk factors (eg, smoking, physical inactivity, fruit/vegetable consumption [FV], alcohol use) and used as covariates. Results There were 90 694 PCS ages 40‐105 years (mean age = 66.19 years, SD = 9.25) included. Most were non‐Hispanic white men (83%). Prostate cancer survivors ≥75 years were least likely to receive any treatment but men ages 65‐74 were more likely to receive combined therapies (OR = 1.47; 95% CI 1.28, 1.69) vs PCS ages 40‐54 years, controlling for covariates. Prostate cancer survivors 55‐75+ with aggressive PC who received any treatment vs no definitive treatment had significantly reduced mortality. Men from counties with high obesity and smoking rates were significantly less likely to receive any treatment than men living in counties with lower rates of these risk factors. Prostate cancer survivors who lived in counties with high rates of physical inactivity and had high rates of sufficient FV consumption were slightly more likely to receive cancer treatment vs no definitive treatment compared to men who lived in counties with high rates of physical activity and lower FV consumption. Conclusions We observed a general age‐related decline in receipt of treatment. Prostate cancer survivors ages ≥75 years were significantly less likely to get any cancer treatment compared to younger PCS. However, most men with more aggressive disease who received any treatment had greatly reduced PC mortality, regardless of age. Considering environmental/behavioral risk factors may attenuate PC risk and inform treatment options.
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Affiliation(s)
- Shirley M Bluethmann
- The Pennsylvania State University College of Medicine, Hershey, PA, USA.,Penn State Cancer Institute, Hershey, PA, USA
| | - Ming Wang
- The Pennsylvania State University College of Medicine, Hershey, PA, USA.,Penn State Cancer Institute, Hershey, PA, USA
| | - Emily Wasserman
- The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Chixiang Chen
- The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Nicholas G Zaorsky
- The Pennsylvania State University College of Medicine, Hershey, PA, USA.,Penn State Cancer Institute, Hershey, PA, USA
| | - Raymond J Hohl
- The Pennsylvania State University College of Medicine, Hershey, PA, USA.,Penn State Cancer Institute, Hershey, PA, USA
| | - Alicia C McDonald
- The Pennsylvania State University College of Medicine, Hershey, PA, USA.,Penn State Cancer Institute, Hershey, PA, USA
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26
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Lynch SM, Sorice K, Tagai EK, Handorf EA. Use of empiric methods to inform prostate cancer health disparities: Comparison of neighborhood-wide association study "hits" in black and white men. Cancer 2020; 126:1949-1957. [PMID: 32012234 DOI: 10.1002/cncr.32734] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 12/30/2019] [Accepted: 01/03/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Black men are more likely to die of prostate cancer (PCa) compared with white men. Factors ranging from genetics to neighborhood environment contribute to these disparities. However, unlike genetics, agnostic investigations that identify candidate variables from large-scale data, and that allow for empiric investigations into differential associations between neighborhood and PCa by race/ethnicity, to the authors' knowledge have not been well explored. Thus, herein, the authors built on their previously developed, empiric neighborhood-wide association study (NWAS) in white men and conducted a NWAS in black men to determine whether findings differed by race. METHODS Pennsylvania Cancer Registry data were linked to US Census data. For the NWAS in non-Hispanic black men, the authors evaluated the association between 14,663 neighborhood census variables and advanced PCa (11 high-stage and/or high-grade cases and 8632 low-stage and/or low-grade cases), adjusting for age, diagnosis year, spatial correlation, and multiple testing. Odds ratios and 95% credible intervals were reported. Replication of NWAS findings across black and white races was assessed using Bayesian mixed effects models. RESULTS Five variables related to housing (3 variables), education (1 variable), and employment and/or transportation (1 variable) were found to be significantly associated with advanced PCa in black men compared with 17 socioeconomic variables (mostly related to poverty and/or income) in white men. The top hit in black men was related to crowding in renter-occupied housing (odds ratio, 1.10; 95% credible interval, 1.001-1.12). Nine of 22 NWAS hits (4 of 5 hits in black men) were replicated across racial/ethnic groups. CONCLUSIONS Different neighborhood variables, or "candidates," were identified across race-specific NWASs. These findings and empiric approaches warrant additional study and may inform PCa racial disparities, particularly future gene-environment studies aimed at identifying patients and/or communities at risk of advanced PCa.
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Affiliation(s)
- Shannon M Lynch
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Kristen Sorice
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Erin K Tagai
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Elizabeth A Handorf
- Population Studies Facility, Fox Chase Cancer Center, Philadelphia, Pennsylvania
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Coughlin SS. A review of social determinants of prostate cancer risk, stage, and survival. Prostate Int 2019; 8:49-54. [PMID: 32647640 PMCID: PMC7335972 DOI: 10.1016/j.prnil.2019.08.001] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 08/15/2019] [Indexed: 11/15/2022] Open
Abstract
Social determinants of health that have been examined in relation to prostate cancer incidence, stage at diagnosis, and survival include socioeconomic status (income, education), neighborhood disadvantage, immigration status, social support, and social network. Other social determinants of health include geographic factors such as neighborhood access to health services. Socioeconomic factors influence risk of prostate cancer. Prostate cancer incidence rates tend to be positively associated with socioeconomic status. On the other hand, low socioeconomic status is associated with increased risk of poorer survival. There are well-documented disparities in prostate cancer survival by socioeconomic status, race, education, and census tract-level poverty. The results of this review indicate that social determinants such as poverty, lack of education, immigration status, lack of social support, and social isolation play an important role in prostate cancer stage at diagnosis and survival. To address these social determinants and eliminate cancer disparities, effective interventions that account for the social and environmental contexts in which patients with cancer live and are treated are needed.
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Affiliation(s)
- Steven S Coughlin
- Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, GA, USA.,Institute of Public and Preventive Health, Augusta University, Augusta, GA, USA
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Abstract
The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.
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Affiliation(s)
- Stephen J Mooney
- Harborview Injury Prevention and Research Center, University of Washington, Seattle, Washington 98122, USA;
| | - Vikas Pejaver
- Department of Biomedical Informatics and Medical Education and the eScience Institute, University of Washington, Seattle, Washington 98109, USA;
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