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

An exploratory analysis of the impact of area-level exposome on geographic disparities in aggressive prostate cancer

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.

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

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.

Neighborhood Disadvantage and Prostate Tumor RNA Expression of Stress-Related Genes

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.

The Hallmarks of Precancer

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.

Health-Related and Psychosocial Factors Associated with Prostate Cancer Stage at Diagnosis among Males Participating in Alberta's Tomorrow Project

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.

Racial disparities in cancer care, an eyeopener for developing better global cancer management strategies

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.

Neighbourhood social deprivation and risk of prostate cancer

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.

Methodological Challenges in Spatial and Contextual Exposome-Health Studies

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.

Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies

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.

Investigating the association of environmental exposures and all-cause mortality in the UK Biobank using sparse principal component analysis

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.

Systematic review of neighborhood socioeconomic indices studied across the cancer control continuum

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.

Neighborhood deprivation and risk of mortality among men with prostate cancer: Findings from a long-term follow-up study

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.

Contributions of Social Factors to Disparities in Prostate Cancer Risk Profiles among Black Men and Non-Hispanic White Men with Prostate Cancer in California

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.

A narrative review of sociodemographic risk and disparities in screening, diagnosis, treatment, and outcomes of the most common extrathoracic malignancies in the United States

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.

Neighborhood characteristics associated with COVID-19 burden-the modifying effect of age

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.

Measuring Neighborhood Landscapes: Associations between a Neighborhood's Landscape Characteristics and Colon Cancer Survival

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.

Variable selection in social-environmental data: sparse regression and tree ensemble machine learning approaches

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.

Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania-A Geo-Additive Approach

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.

Investigating disparities: the effect of social environment on pancreatic cancer survival in metastatic patients

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.

The effect of neighborhood social environment on prostate cancer development in black and white men at high risk for prostate cancer

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.

An external exposome-wide association study of hypertensive disorders of pregnancy

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.

Residential neighborhood features associated with objectively measured walking near home: Revisiting walkability using the Automatic Context Measurement Tool (ACMT)

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.

Spatial patterns in prostate Cancer-specific mortality in Pennsylvania using Pennsylvania Cancer registry data, 2004-2014

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.

Design and methodology challenges of environment-wide association studies: A systematic review

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.

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

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.

Use of empiric methods to inform prostate cancer health disparities: Comparison of neighborhood-wide association study "hits" in black and white men

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.

A review of social determinants of prostate cancer risk, stage, and survival

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.

Big Data in Public Health: Terminology, Machine Learning, and Privacy

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.