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.

Twitter as a Potential Data Source for Cardiovascular Disease Research.

IMPORTANCE: As society is increasingly becoming more networked, researchers are beginning to explore how social media can be used to study person-to-person communication about health and health care use. Twitter is an online messaging platform used by more than 300 million people who have generated several billion Tweets, yet little work has focused on the potential applications of these data for studying public attitudes and behaviors associated with cardiovascular health. OBJECTIVE: To describe the volume and content of Tweets associated with cardiovascular disease as well as the characteristics of Twitter users. DESIGN, SETTING, AND PARTICIPANTS: We used Twitter to access a random sample of approximately 10 billion English-language Tweets originating from US counties from July 23, 2009, to February 5, 2015, associated with cardiovascular disease. We characterized each Tweet relative to estimated user demographics. A random subset of 2500 Tweets was hand-coded for content and modifiers. MAIN OUTCOMES AND MEASURES: The volume of Tweets about cardiovascular disease and the content of these Tweets. RESULTS: Of 550 338 Tweets associated with cardiovascular disease, the terms diabetes (n = 239 989) and myocardial infarction (n = 269 907) were used more frequently than heart failure (n = 9414). Users who Tweeted about cardiovascular disease were more likely to be older than the general population of Twitter users (mean age, 28.7 vs 25.4 years; P < .01) and less likely to be male (59 082 of 124 896 [47.3%] vs 8433 of 17 270 [48.8%]; P < .01). Most Tweets (2338 of 2500 [93.5%]) were associated with a health topic; common themes of Tweets included risk factors (1048 of 2500 [41.9%]), awareness (585 of 2500 [23.4%]), and management (541 of 2500 [21.6%]) of cardiovascular disease. CONCLUSIONS AND RELEVANCE: Twitter offers promise for studying public communication about cardiovascular disease.