A Prostate Cancer Composite Score to Identify High Burden Neighborhoods.

This study presents a novel geo-based metric to identify neighborhoods with high burdens of prostate cancer, and compares this metric to other methods to prioritize neighborhoods for prostate cancer interventions. We geocoded prostate cancer patient data (n = 10,750) from the Pennsylvania cancer registry from 2005 to 2014 by Philadelphia census tract (CT) to create standardized incidence ratios (SIRs), mortality ratios (SMRs), and mean prostate cancer aggressiveness. We created a prostate cancer composite (PCa composite) variable to describe CTs by mean-centering and standard deviation-scaling the SMR, SIR, and mean aggressiveness variables and summing them. We mapped CTs with the 25 highest PCa composite scores and compared these neighborhoods to CTs with the 25 highest percent African American residents and the 25 lowest median household incomes. The mean PCa composite score among the 25 highest CTs was 4.65. Only seven CTs in Philadelphia had both one of the highest PCa composite scores and the highest percent African American residents. Only five CTs had both the highest PCa composites and the lowest median incomes. Mean PCa composite scores among CTs with the highest percent African American residents and lowest median incomes were 2.08 and 1.19, respectively. The PCa composite score is an accurate metric for prioritizing neighborhoods based on burden. If neighborhoods were prioritized based on percent African American or median income, priority neighborhoods would have been very different and not based on PCa burden. These methods can be utilized by public health decision-makers when tasked to prioritize and select neighborhoods for cancer interventions.

Secondhand Smoke Exposure and Smoke-free Policy in Philadelphia Public Housing.

OBJECTIVES: Multi-unit housing environments remain significant sources of secondhand smoke (SHS) exposure, especially for vulnerable populations in subsidized housing. In Philadelphia, the largest US housing authority to implement smoke-free policies, we measured baseline resident smoking-related behaviors and attitudes, and longitudinal exposures to airborne nicotine, during policy development and implementation. METHODS: In 4 communities, we collected data in 2013, 2014, and 2016, before and after introduction of comprehensive smoke-free policies, interviewing persons in 172 households, and monitoring air-borne nicotine in non-smoking homes and public areas. Average nicotine level differences across years were estimated with multi-level models. RESULTS: Fifty-six percent of respondents smoked. Only 37% of households were smoke-free, with another 41% restricting smoking by area or time of day. The number of locations with detectable nicotine did not differ before and after policy implementation, with approximately 20% of non-smoking homes and 70%-80% of public areas having detectable nicotine. However, public area nicotine levels were lower in 2016, after policy implementation, than in 2013 and 2014 (-0.19 µg/m3, p = .03). CONCLUSIONS: Findings suggest that initial policy implementation was associated with reduced SHS exposure in Philadelphia. As HUD strengthens smoke-free policies, SHS monitoring can be useful to educate stakeholders and build support for policy enforcement.