Impact of public housing assistance on modifiable cancer risk factors in adults

描述（由申请人提供）：尽管在过去二十年中癌症死亡率有所改善，但在种族，民族和社会经济地位（SES）亚人群中仍然存在显着差异。住房已被确定为一般健康差异和具体癌症相关差异的重要社会决定因素。公营房屋援助的目的，是改善社会经济状况最低的住户的房屋负担能力和质素。因此，PH代表了一种干预措施，以改善住房作为健康的社会决定因素。然而，很少有研究检查生活在PH对健康相关结果的影响。此外，一些研究人员提出了PH值影响健康的途径，但尚未进行实证检验。拟议的研究将使用1999-2013年收入动态小组研究（PSID）收集的数据来估计生活在PH中对年龄<62岁的成年人的四种主要可改变的癌症风险因素（吸烟、饮酒、缺乏体育活动和超重/肥胖）的影响。PSID提供了一个独特的机会来检查PH的影响，因为它是一项具有全国代表性的纵向调查，具有广泛的人口、家庭、住房、经济、就业、社会和健康变量；社区级别的变量也将使用地理编码进行链接。治疗组将只包括在研究期间进入PH的受访者（预计n=550）。比较组将限于符合PH收入资格的租房者（预计n=1650）。我们将倾向评分方法应用于PSID中丰富的基线特征集，以帮助控制观测数据中固有的选择偏差。治疗权重的逆概率将从估计的倾向得分中计算出来，并用于逻辑回归模型中，以估计基线后2年、4年和6年每个风险因素的平均治疗效果。将开发多种中介模型，通过个人层面的中介（例如，非住房收入、压力、居住稳定性）和社区层面的中介（例如，社区社会经济地位、进入超市的机会、可步行性）来检查PH对可改变的癌症风险因素的影响程度。研究结果将用于帮助澄清生活在PH环境中是改善还是恶化了可改变的癌症风险因素。重要的是，获得的有关这些影响发生的途径的信息可以为未来的住房和公共卫生研究、实践和政策提供信息。

DESCRIPTION (provided by applicant): Despite improvements in cancer mortality in the past two decades, significant disparities persist among racial, ethnic, and socioeconomic status (SES) subpopulations. Housing has been identified as an important social determinant for health disparities in general and cancer-related disparities specifically. Public housing (PH) assistance aims to improve housing affordability and quality for the lowest-SES households. Thus, PH represents an intervention to improve housing as a social determinant of health. However, few studies have examined the effects of living in PH on health-related outcomes. Additionally, several researchers have proposed pathways through which PH influences health but have not tested them empirically. The proposed study will use data collected from 1999-2013 in the Panel Study of Income Dynamics (PSID) to estimate the effect of living in PH on four leading modifiable risk factors for cancer (smoking, alcohol use, physical inactivity, and overweight/obesity) in adults <62 years of age. The PSID offers a unique opportunity to examine the effects of PH because it is a nationally representative, longitudinal survey with a wide range of demographic, household, housing, economic, employment, social, and health variables; neighborhood-level variables will also be linked using geocodes. The treatment group will include only respondents who move into PH during the study period (expected n=550). The comparison group will be limited to renters who are income-eligible for PH (expected n=1650). We will apply propensity score methods to the rich set of baseline characteristics available in the PSID to help control for selection bias inherent in observational data. Inverse probability of treatment weights will be calculated from estimated propensity scores and used in logistic regression models to estimate average treatment effects for each risk factor at 2, 4, and 6 years after baseline. Multiple mediation models will be developed to examine the extent to which PH influences modifiable cancer risk factors through individual-level mediators (e.g., non-housing income, stress, residential stability) and neighborhood-level mediators (e.g., neighborhood SES, access to supermarkets, walkability). Study results will be used to help clarify whether living in PH improves or worsens modifiable risk factors for cancer. Importantly, information obtained about pathways through which these effects occur can inform future housing and public health research, practice, and policy.