A Neighborhood-Wide Association Study (NWAS): Example of prostate cancer aggressiveness

Purpose Cancer results from complex interactions of multiple variables at the biologic, individual, and social levels. Compared to other levels, social effects that occur geospatially in neighborhoods are not as well-studied, and empiric methods to assess these effects are limited. We propose a novel Neighborhood-Wide Association Study(NWAS), analogous to genome-wide association studies(GWAS), that utilizes high-dimensional computing approaches from biology to comprehensively and empirically identify neighborhood factors associated with disease. Methods Pennsylvania Cancer Registry data were linked to U.S. Census data. In a successively more stringent multiphase approach, we evaluated the association between neighborhood (n = 14,663 census variables) and prostate cancer aggressiveness(PCA) with n = 6,416 aggressive (Stage≥3/Gleason grade≥7 cases) vs. n = 70,670 non-aggressive (Stage<3/Gleason grade<7) cases in White men. Analyses accounted for age, year of diagnosis, spatial correlation, and multiple-testing. We used generalized estimating equations in Phase 1 and Bayesian mixed effects models in Phase 2 to calculate odds ratios(OR) and confidence/credible intervals(CI). In Phase 3, principal components analysis grouped correlated variables. Results We identified 17 new neighborhood variables associated with PCA. These variables represented income, housing, employment, immigration, access to care, and social support. The top hits or most significant variables related to transportation (OR = 1.05;CI = 1.001–1.09) and poverty (OR = 1.07;CI = 1.01–1.12). Conclusions This study introduces the application of high-dimensional, computational methods to large-scale, publically-available geospatial data. Although NWAS requires further testing, it is hypothesis-generating and addresses gaps in geospatial analysis related to empiric assessment. Further, NWAS could have broad implications for many diseases and future precision medicine studies focused on multilevel risk factors of disease.

[1]  Ali Amin Al Olama,et al.  Identification of seven new prostate cancer susceptibility loci through a genome-wide association study , 2009, Nature Genetics.

[2]  Matthew R. Cooperberg,et al.  Epidemiology of prostate cancer , 2017, World Journal of Urology.

[3]  V. Caron,et al.  United states. , 2018, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[4]  Jessica G. Burke,et al.  The Development of a Standardized Neighborhood Deprivation Index , 2006, Journal of Urban Health.

[5]  A. D. Diez Roux,et al.  Neighborhoods and health , 2010, Annals of the New York Academy of Sciences.

[6]  Y. Taylor,et al.  Racial differences in PSA screening interval and stage at diagnosis , 2010, Cancer Causes & Control.

[7]  Jarvis T. Chen,et al.  Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project. , 2002, American journal of epidemiology.

[8]  P. Fearnhead,et al.  Genome-wide association study of prostate cancer identifies a second risk locus at 8q24 , 2007, Nature Genetics.

[9]  G. Lockwood,et al.  Rates of Cancer Incidence Across Terciles of the Foreign-born Population in Canada From 2001–2006 , 2013, Canadian Journal of Public Health.

[10]  M. Aickin,et al.  Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. , 1996, American journal of public health.

[11]  Nicholas J Timpson,et al.  Genome-wide association study of three-dimensional facial morphology identifies a variant in PAX3 associated with nasion position. , 2012, American journal of human genetics.

[12]  Euclid,et al.  Statistical science : a review journal of the Institute of Mathematical Statistics. , 1986 .

[13]  Lorenzo L. Pesce,et al.  Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions , 2016, npj Genomic Medicine.

[14]  A. Geronimus.  Invited commentary: Using area-based socioeconomic measures--think conceptually, act cautiously. , 2006, American journal of epidemiology.

[15]  Douglas F Easton,et al.  Genome-wide association studies in common cancers--what have we learnt? , 2010, Current opinion in genetics & development.

[16]  R. Winn,et al.  Transportation as a barrier to cancer treatment. , 1997, Cancer practice.

[17]  Jason H. Moore,et al.  STUDENTJAMA. The challenges of whole-genome approaches to common diseases. , 2004, JAMA.

[18]  S. Gomez,et al.  Immigration factors and prostate cancer survival among Hispanic men in California: Does neighborhood matter? , 2014, Cancer.

[19]  John Bound,et al.  "Weathering" and age patterns of allostatic load scores among blacks and whites in the United States. , 2006, American journal of public health.

[20]  B. McEwen.  Protective and damaging effects of stress mediators. , 1998, The New England journal of medicine.

[21]  W. Raub.  From the National Institutes of Health. , 1990, JAMA.

[22]  R. Dietz.  The estimation of neighborhood effects in the social sciences: An , 2002 .

[23]  Terrence D. Hill,et al.  Neighborhood disorder, psychological distress, and heavy drinking. , 2005, Social science & medicine.

[24]  T. Byers,et al.  The impact of socioeconomic status on survival after cancer in the United States , 2008, Cancer.

[25]  T. Manolio,et al.  How to Interpret a Genome-wide Association Study Topic Collections , 2022 .

[26]  H. Rue,et al.  Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .

[27]  Shannon M. Lynch,et al.  Bridging the Gap between Biologic, Individual, and Macroenvironmental Factors in Cancer: A Multilevel Approach , 2013, Cancer Epidemiology, Biomarkers & Prevention.

[28]  Ali Amin Al Olama,et al.  Multiple newly identified loci associated with prostate cancer susceptibility , 2008, Nature Genetics.

[29]  N. Krause,et al.  Living alone and neighborhood characteristics as predictors of social support in late life. , 1998, The journals of gerontology. Series B, Psychological sciences and social sciences.

[30]  M. Petticrew,et al.  Housing improvements for health and associated socio-economic outcomes. , 2013, The Cochrane database of systematic reviews.

[31]  N. Jewell,et al.  To GEE or Not to GEE: Comparing Population Average and Mixed Models for Estimating the Associations Between Neighborhood Risk Factors and Health , 2010, Epidemiology.

[32]  Atul J. Butte,et al.  An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus , 2010, PloS one.

[33]  T. Ten Have,et al.  A difference-in-differences analysis of health, safety, and greening vacant urban space. , 2011, American journal of epidemiology.

[34]  R. Sampson,et al.  ASSESSING "NEIGHBORHOOD EFFECTS": Social Processes and New Directions in Research , 2002 .

[35]  PubMed Id,et al.  To replicate or not to replicate: the case of pharmacogenetic studies: Establishing validity of pharmacogenomic findings: from replication to triangulation. , 2013 .

[36]  A. D. Diez Roux,et al.  Neighborhood characteristics and components of the insulin resistance syndrome in young adults: the coronary artery risk development in young adults (CARDIA) study. , 2002, Diabetes care.

[37]  T. Rebbeck,et al.  Gender- and ethnic-specific associations with obesity: individual and neighborhood-level factors. , 2013, Journal of the National Medical Association.

[38]  T. Ricketts,et al.  Cancer in rural versus urban populations: a review. , 1992, The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association.

[39]  Indiana Strombom,et al.  Socioeconomic Risk Factors for Breast Cancer: Distinguishing Individual- and Community-Level Effects , 2004, Epidemiology.

[40]  M. Goddard,et al.  Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci. , 2000, Genetics.

[41]  D. Neal,et al.  Population based time trends and socioeconomic variation in use of radiotherapy and radical surgery for prostate cancer in a UK region: continuous survey , 2010, BMJ : British Medical Journal.

[42]  E. Crawford,et al.  Epidemiology of prostate cancer. , 2003, Urology.

[43]  Hong Wang,et al.  Factors Influencing Prostate Cancer Screening in Low-Income African Americans in Tennessee , 2010, Journal of health care for the poor and underserved.

[44]  Juan Pablo Lewinger,et al.  Methodological Issues in Multistage Genome-wide Association Studies. , 2009, Statistical science : a review journal of the Institute of Mathematical Statistics.

[45]  Timothy R. Rebbeck,et al.  Prostate Cancer Severity Associations with Neighborhood Deprivation , 2011, Prostate cancer.

[46]  Michelle C. Kondo,et al.  A Difference-In-Differences Study of the Effects of a New Abandoned Building Remediation Strategy on Safety , 2015, PloS one.