Changes in Reported Secondhand Smoke Incursions and Smoking Behavior after Implementation of a Federal Smoke-Free Rule in New York State Federally Subsidized Public Housing
International Journal of Environmental Research and Public Health(2022)
RTI Int
Abstract
This study assessed changes in smoking behavior and secondhand smoke (SHS) exposure after implementation of the U.S. Department of Housing and Urban Development (HUD) rule prohibiting the use of cigarettes, cigars, pipes, and waterpipes in all federally subsidized public housing, including within residential units (apartments). Using quantitative data from a repeated cross-sectional mail survey of New York State residents of five public housing authorities (N = 761 at Wave 1, N = 649 at Wave 2), we found evidence of policy compliance (99% decrease in odds of self-reported smoking in units, OR = 0.01, p < 0.01, CI: 0.00–0.16), reduced SHS incursions (77% decrease in odds of smelling smoke within developments, OR = 0.23, p < 0.01, CI: 0.13–0.44), and lower reported smoking rates in July 2018 (9.5%, down from 16.8%), 10 months after implementation of the rule. Despite evident success, one-fifth of residents reported smelling smoke inside their apartment at least a few times per week. This study provides insights into how the policy was implemented in selected New York public housing authorities, offers evidence of policy-intended effects, and highlights challenges to consistent and impactful policy implementation.
MoreTranslated text
Key words
public policy,secondhand smoke,anti-smoking
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined