Prevalence and factors associated with self-reported exposure to secondhand cannabis smoke in the United States and Canada in 2019

Little is known about exposure to secondhand cannabis smoke (SHCS) among residents of detached single-family homes and multiunit housing (MUH). Using data from the 2019 International Cannabis Policy Study, the prevalence of (a) self-reported exposure to SHCS at home (n = 33,024) and (b) self-reported SHCS incursions into MUH (defined as SHCS from another unit/the outdoors, n = 15,634) was estimated in (1) Canada; (2) US states where non-medical cannabis use was legal, and (3) US states where it remained illegal. Factors associated with exposures and incursions were assessed using weighted logistic regression. Overall, 16.9% of residents in Canada, 20.6% in US legal states, and 15.5% in US illegal states reported exposure to SHCS in their homes at least once in the previous month. One quarter (25.7%) of Canadian MUH residents, 26.6% from US legal states, and 20.1% from US illegal states reported at least monthly incursions. Sociodemographic factors associated with incursions suggested MUH residents reporting incursions lived in qualitatively different MUH than those not reporting incursions. Irrespective of the legality of non-medical cannabis use, smoke-free policies in MUH should protect residents from involuntary exposure to all types of secondhand smoke.

Drifting Tobacco Smoke Exposure among Young Adults in Multiunit Housing

This study evaluated young adults' exposure to drifting secondhand smoke in San Francisco County housing units using the 2014 Bay Area Young Adult Health Survey (N = 1363). Logistic and geographically weighted regression models were used to determine whether residing in multiunit housing or in areas with greater neighborhood disorder were risk factors for exposure, and how drifting smoke exposure varied spatially within San Francisco County. Residing in buildings with five or more units significantly increased the odds of reporting drifting smoke exposure [OR (3.5 1.3, 9.9)], but neighborhood disorder did not have a significant association in the fully adjusted logistic regression model. At the local level, however, neighborhood disorder was significantly associated with exposure in lower income residential and downtown areas. Multiunit housing was significantly associated with exposure across all neighborhoods.

Tobacco smoke is a likely source of lead and cadmium in settled house dust

INTRODUCTION

Environmental exposure to lead (Pb) and cadmium (Cd) are risk factors for adverse health outcomes in children and adults. This study examined whether thirdhand smoke residue contributes to Pb and Cd in settled house dust.

METHODS

Participants were 60 multiunit housing residents in San Diego, California. All had indoor smoking bans during the study period, and 55 were nonsmokers. Wipe samples from different surfaces and vacuum floor dust samples were analyzed for nicotine, a marker of thirdhand smoke, and for Pb and Cd using liquid chromatography-triple quadrupole mass spectrometry and inductively coupled plasma-mass spectrometry, respectively.

RESULTS

Examined in each sample type separately, Pb and Cd loadings were significantly correlated (r = 0.73, vacuum floor dust; 0.52, floor wipes; 0.72, window sill/trough wipes; all p < 0.0025). Pb and Cd loadings from different sample types were not correlated (all p > 0.30). Nicotine loading in dust was significantly correlated with Pb and Cd loading in dust (r = 0.49 for Pb; r = 0.39 for Cd, all p < 0.0025). Pb and Cd loadings on floor or window surfaces, showed no association with nicotine loading in dust, on floors, or on furniture (all p < 0.30).

CONCLUSIONS

Tobacco smoke is a likely source of Pb and Cd that accumulates in settled house dust in multiunit housing, suggesting that Pb and Cd are constituents of thirdhand smoke that lingers long after smoking has ended.

Secondhand smoke exposure in public and private high-rise multiunit housing serving low-income residents in New York City prior to federal smoking ban in public housing, 2018

BACKGROUND

Tobacco remains the leading cause of preventable death in the United States, with 41,000 deaths attributable to secondhand smoke (SHS) exposure. On July 30, 2018, the U.S. Department of Housing and Urban Development passed a rule requiring public housing authorities to implement smoke-free housing (SFH) policies.

OBJECTIVES

Prior to SFH policy implementation, we measured self-reported and objective SHS incursions in a purposeful sample of 21 high-rise buildings (>15 floors) in New York City (NYC): 10 public housing and 11 private sector buildings where most residents receive federal housing subsidies (herein 'Section 8' buildings).

METHODS

We conducted a baseline telephone survey targeting all residents living on the 3rd floor or higher of selected buildings: NYC Housing Authority (NYCHA) residents were surveyed in April-July 2018 (n = 559), and residents in 'Section 8' buildings in August-November 2018 (n = 471). We invited non-smoking household participants to enroll into a longitudinal air monitoring study to track SHS exposure using: (1) nicotine concentration from passive, bisulfate-coated nicotine filters and (2) particulate matter (PM2.5) from low-cost particle monitors. SHS was measured for 7-days in non-smoking households (NYCHA n = 157, Section 8 n = 118 households) and in building common areas (n = 91 hallways and stairwells).

RESULTS

Smoking prevalence among residents in the 21 buildings was 15.5%. Two-thirds of residents reported seeing people smoke in common areas in the past year (67%) and 60% reported smelling smoke in their apartments coming from elsewhere. Most stairwells (88%) and hallways (74%) had detectable nicotine levels, but nicotine was detected in only 9.9% of non-smoking apartments. Substantial variation in nicotine and PM2.5 was observed between and within buildings; on average nicotine concentrations were higher in NYCHA apartments and hallways than in Section 8 buildings (p < 0.05), and NYCHA residents reported seeing smokers in common areas more frequently.

CONCLUSIONS

SFH policies may help in successfully reducing SHS exposure in public housing, but widespread pre-policy incursions suggest achieving SFH will be challenging.

Geospatial mapping and resource utilization tool in support of a national smoke-free public housing rule

Objective

To advance public health support for the U.S. Department of Housing and Urban Development’s smoke-free rule, the Centers for Disease Control and Prevention collaborated with the Georgia Institute of Technology to develop a geospatial mapping tool. The objective was to create a tool state and local public health agencies could use to tailor smoke-free educational materials and cessation interventions for specific public housing development resident populations.

Results

The resulting “Extinguish Tool” includes an interactive map of U.S. public housing developments (PHDs) and healthcare facilities that provides detailed information on individual PHDs, their proximity to existing healthcare facilities, and the demographic characteristics of residents. The tool also estimates the number of PHD residents who smoke cigarettes and calculates crude estimates of the potential economic benefits of providing cessation interventions to these residents. The geospatial mapping tool project serves as an example of a collaborative and innovative public health approach to protecting the health and well-being of the nation’s two million public housing residents, including 760,000 children, from the harms of tobacco smoking and secondhand smoke exposure in the places where they live, play, and gather.

Seasonal variability in environmental tobacco smoke exposure in public housing developments

The risk of tobacco smoking and second-hand smoke (SHS) exposure combined are the leading contributors to disease burden in high-income countries. Recent studies and policies are focusing on reducing exposure to SHS in multiunit housing (MUH), especially public housing. We examined seasonal patterns of SHS levels within indoor common areas located on Boston Housing Authority (BHA) properties. We measured weekly integrated and continuous fine particulate matter (PM2.5) and passive airborne nicotine in six buildings of varying building and occupant characteristics in summer 2012 and winter 2013. The average weekly indoor PM2.5 concentration across all six developments was 9.2 μg/m3, higher during winter monitoring period (10.3 μg/m3) compared with summer (8.0 μg/m3). Airborne nicotine concentrations ranged from no detection to about 5000 ng/m3 (mean 311 ng/m3). Nicotine levels were significantly higher in the winter compared with summer (620 vs. 85 ng/m3; 95% CI: 72-998). Smoking-related exposures within Boston public housing vary by season, building types, and resident smoking policy. Our results represent exposure disparities that may contribute to health disparities in low-income communities and highlight the potential importance of efforts to mitigate SHS exposures during winter when outdoor-indoor exchange rates are low and smokers may tend to stay indoors. Our findings support the use of smoke-free policy as an effective tool to eliminate SHS exposure and protect non-smokers, especially residents of MUH.

Identifying and quantifying secondhand smoke in source and receptor rooms: logistic regression and chemical mass balance approaches

Identifying and quantifying secondhand tobacco smoke (SHS) that drifts between multiunit homes is critical to assessing exposure. Twenty-three different gaseous and particulate measurements were taken during controlled emissions from smoked cigarettes and six other common indoor source types in 60 single-room and 13 two-room experiments. We used measurements from the 60 single-room experiments for (i) the fitting of logistic regression models to predict the likelihood of SHS and (ii) the creation of source profiles for chemical mass balance (CMB) analysis to estimate source apportionment. We then applied these regression models and source profiles to the independent data set of 13 two-room experiments. Several logistic regression models correctly predicted the presence of cigarette smoke more than 80% of the time in both source and receptor rooms, with one model correct in 100% of applicable cases. CMB analysis of the source room provided significant PM2.5 concentration estimates of all true sources in 9 of 13 experiments and was half-correct (i.e., included an erroneous source or missed a true source) in the remaining four. In the receptor room, CMB provided significant estimates of all true sources in 9 of 13 experiments and was half-correct in another two.