Effects of electronic cigarettes and hookah (waterpipe) use on home air quality.

INTRODUCTION: A major site of secondhand smoke exposure for children and adults is the home. Few studies have evaluated the impact of e-cigarette or hookah use on home air quality, despite evidence finding toxic chemicals in secondhand e-cigarette aerosols and hookah smoke. We assessed the effect of e-cigarette and hookah use on home air quality and compared it with air quality in homes where cigarettes were smoked and where no smoking or e-cigarette use occurred. METHODS: Non-smoking homes and homes where e-cigarettes, hookah or cigarettes were used were recruited in the New York City area (n=57) from 2015 to 2019. Particulate matter with diameter less than 2.5 µm (PM2.5), black carbon and carbon monoxide (CO) were measured during a smoking or vaping session, both in a 'primary' smoking room and in an adjacent 'secondary' room where no smoking or vaping occurred. Log transformed data were compared with postanalysis of variance Tukey simultaneous tests. RESULTS: Use of hookah significantly increased PM2.5 levels compared with non-smoking homes, in both the primary and secondary rooms, while use of e-cigarettes increased PM2.5 levels only in primary rooms. Additionally, in-home use of hookah resulted in greater CO concentrations than the use of cigarettes in primary rooms. CONCLUSIONS: Use of e-cigarettes or hookah increases air pollution in homes. For hookah, increases in PM2.5 penetrated even into rooms adjacent to where smoking occurs. Extending smoke-free rules inside homes to include e-cigarette and hookah products is needed to protect household members and visitors from passive exposure to harmful aerosols and gases.

Air Pollution in American Indian Versus Non-American Indian Communities, 2000-2018.

Objectives. To compare fine particulate matter (PM2.5) concentrations in American Indian (AI)-populated with those in non-AI-populated counties over time (2000-2018) in the contiguous United States. Methods. We used a multicriteria approach to classify counties as AI- or non--AI-populated. We ran linear mixed effects models to estimate the difference in countywide annual PM2.5 concentrations from well-validated prediction models and monitoring sites (modeled and measured PM2.5, respectively) in AI- versus non-AI-populated counties. Results. On average, adjusted modeled PM2.5 concentrations in AI-populated counties were 0.38 micrograms per cubic meter (95% confidence interval [CI] = 0.23, 0.54) lower than in non-AI-populated counties. However, this difference was not constant over time: in 2000, modeled concentrations in AI-populated counties were 1.46 micrograms per cubic meter (95% CI = 1.25, 1.68) lower, and by 2018, they were 0.66 micrograms per cubic meter (95% CI = 0.45, 0.87) higher. Over the study period, adjusted modeled PM2.5 mean concentrations decreased by 2.13 micrograms per cubic meter in AI-populated counties versus 4.26 micrograms per cubic meter in non-AI-populated counties. Results were similar for measured PM2.5. Conclusions. This study highlights disparities in PM2.5 trends between AI- and non-AI-populated counties over time, underscoring the need to strengthen air pollution regulations and prevention implementation in tribal territories and areas where AI populations live. (Am J Public Health. 2022;112(4): 615-623. https://doi.org/10.2105/AJPH.2021.306650).

Evaluating State-Level Differences in E-cigarette and Cigarette Use Among Adults in the United States Between 2012 and 2014: Findings From the National Adult Tobacco Survey.

Objective: To examine the association between state-level tobacco control measures and current use estimates of both e-cigarettes and cigarettes, while accounting for socio-demographic correlates. Methods: Using the 2012-2013 and 2013-2014 National Adult Tobacco Survey (NATS), we assessed prevalence estimates of US adults' e-cigarette and cigarette current use. Four state groups were created based on the combined state-specific prevalence of both products: low cigarette/e-cigarette (n = 15), high cigarette/e-cigarette (n = 16), high cigarette/low e-cigarette (n = 11), and low cigarette/high e-cigarette) (n = 9). To evaluate the implementation of state-level tobacco control measures, Tobacco Control Index (TCI) was calculated using the State of Tobacco Control annual reports for 2012 and 2013. Multinomial logistic regression models were used to examine differences among the four groups on socio-demographic factors and TCI. Low cigarette/e-cigarette group was used as the referent group. Results: Current use estimates of each product varied substantially by state; current e-cigarette use was highest in Oklahoma (10.3%) and lowest in Delaware (2.7%), and current cigarette use was highest in West Virginia (26.1%), and lowest in Vermont (12.6%). Compared to low cigarette/e-cigarette, all other US-state categories had significantly lower TCI scores (high cigarette/e-cigarette: adjusted Relative Risk Ratio [aRRR] = 0.61; 95% confidence interval [CI]: 0.60-0.61, high cigarette/low e-cigarette: aRRR = 0.74; 95% CI: 0.73-0.74, and low cigarette/high e-cigarette: aRRR = 0.72; 95% CI: 0.71-073). Conclusions: Enforcing existing tobacco control measures likely interacts with e-cigarette use despite being cigarette-focused. Continuing to monitor e-cigarette use is critical to establish baseline use and evaluate future e-cigarette specific federal and state-level tobacco regulatory actions while accounting for the existing tobacco control environment. Implications: This study investigates state-level current use estimates of e-cigarettes and cigarettes among US adults; and their association with four existing tobacco control measures. The overall score of these measures was negatively associated with state-level current use estimates such that states with low current e-cigarette and cigarette use had the highest mean overall score. This study assesses the potential relationship between existing state-level tobacco control measures and e-cigarette use and calls for improving the enforcement of the known-to-work tobacco control measures across all US states, while developing evidence-based regulations and interventions specific to e-cigarettes within the existing US tobacco use environment.

Analysis of State-Specific Prevalence, Regional Differences, and Correlates of Hookah Use in U.S. Adults, 2012-2013.

OBJECTIVE: To investigate the state-specific prevalence, regional differences, and correlates of hookah use among U.S. adults. METHODS: We analyzed the most recent nationally representative data of adults from the National Adult Tobacco Survey (NATS) 2012-2013 (n = 60192). State-specific prevalence of lifetime and current hookah use was calculated and mapped. Multivariate logistic regression was performed to determine the association between sociodemographic characteristics, regional differences, and hookah use. RESULTS: Among U.S. adults (≥18 years), overall prevalence of lifetime hookah use was 12.3%, while current use was 3.9%. Mapping of state-specific prevalence revealed that the West tended to have higher rates of use, while the South tended to have lower ones. In the adjusted model, we observed that current hookah use was positively associated with younger adults, males, non-Hispanic adults, those with higher education and income statuses, being single, those living in the West, and current cigarette use. CONCLUSION: The prevalence of hookah use varies by state, region, and sociodemographic characteristics among adults. Future research, including longitudinal studies, are needed to identify geographic and sociodemographic characteristics and trends among hookah users, investigate hookah-related health outcomes, and evaluate targeted public health efforts aimed at this emerging threat. IMPLICATIONS: This study investigates state-level prevalence, regional differences, and sociodemographic characteristics of hookah use among U.S. adults, using the most recent NATS. Hookah use was positively associated with younger adults and those living in the West. This study adds to the understanding of the geographic and sociodemographic factors underlying hookah use, which can be used to develop much needed evidence-based regulations and programs that are responsive to the needs of different risk groups.

Discordant reporting of nonmedical opioid use in a nationally representative sample of US high school seniors.

BACKGROUND: Nonmedical opioid use has become a major public health concern due to increases in treatment admissions, overdoses, and deaths. Use has also been linked to heroin initiation. Reliable data on nonmedical opioid use are needed to continue to inform prevention. OBJECTIVE: To determine the prevalence and correlates of discordant self-report of nonmedical use of opioids in a national sample. METHODS: Utilizing a nationally representative sample of 31,149 American high school seniors in the Monitoring the Future study (2009-2013), discordant responses between self-reported 12-month nonmedical opioid use and self-reported 12-month nonmedical Vicodin and OxyContin use (reporting Vicodin/OxyContin use, but not reporting "opioid" use) were assessed. We also used multivariable logistic regression to determine the characteristics of students who were most likely to provide a discordant response. RESULTS: 37.1% of those reporting nonmedical Vicodin use and 28.2% of those reporting nonmedical OxyContin use did not report overall nonmedical opioid use. Prevalence of nonmedical opioid use (8.3%) would increase when factoring in Vicodin, OxyContin, or both, by 2.8%, 1.3%, and 3.3%, respectively. Females were more likely to provide a discordant response to Vicodin and highly religious students were more likely to provide a discordant response regarding OxyContin use. Those who reported cocaine or nonmedical tranquilizer use were at consistently low odds for discordant responses. Nonmedical amphetamine users were at low odds for providing a discordant Vicodin response. CONCLUSION: Prevalence of nonmedical opioid use may be underreported on some surveys, particularly among specific subpopulations. Further research on the effect of question order and skip-patterns (e.g., "gate" questions) is needed. Reliable data on nonmedical opioid use are needed to continue to accurately inform prevention.

Tampons as a source of exposure to metal(loid)s.

BACKGROUND: Between 52-86% of people who menstruate in the United States use tampons-cotton and/or rayon/viscose 'plugs'-to absorb menstrual blood in the vagina. Tampons may contain metals from agricultural or manufacturing processes, which could be absorbed by the vagina's highly absorptive tissue, resulting in systemic exposure. To our knowledge, no previous studies have measured metals in tampons. OBJECTIVES: We evaluated the concentrations of 16 metal(loid)s in 30 tampons from 14 tampon brands and 18 product lines and compared the concentrations by tampon characteristics. METHODS: About 0.2 - 0.3 g from each tampon (n = 60 samples) were microwave-acid digested and analyzed by inductively coupled plasma mass spectrometry (ICP-MS) to determine concentrations of arsenic, barium, calcium, cadmium, cobalt, chromium, copper, iron, manganese, mercury, nickel, lead, selenium, strontium, vanadium, and zinc. We compared concentrations by several tampon characteristics (region of purchase, organic material, brand type) using median quantile mixed models. RESULTS: We found measurable concentrations of all 16 metals assessed. We detected concentrations of several toxic metals, including elevated mean concentrations of lead (geometric mean [GM] = 120 ng/g), cadmium (GM = 6.74 ng/g), and arsenic (GM = 2.56 ng/g). Metal concentrations differed by region of tampon purchase (US versus European Union/United Kingdom), by organic versus non-organic material, and for store- versus name-brand tampons. Most metals differed by organic status; lead concentrations were higher in non-organic tampons while arsenic was higher in organic tampons. No categoriy had consistently lower concentrations of all or most metals. DISCUSSION: Tampon use is a potential source of metal exposure. We detected all 16 metals in at least one sampled tampon, including some toxic metals like lead that has no "safe" exposure level. Future research is needed to replicate our findings and determine whether metals can leach out of tampons and cross the vaginal epithelium into systemic circulation.

An environmental justice analysis of air pollution emissions in the United States from 1970 to 2010.

Over the last decades, air pollution emissions have decreased substantially; however, inequities in air pollution persist. We evaluate county-level racial/ethnic and socioeconomic disparities in emissions changes from six air pollution source sectors (industry [SO2], energy [SO2, NOx], agriculture [NH3], commercial [NOx], residential [particulate organic carbon], and on-road transportation [NOx]) in the contiguous United States during the 40 years following the Clean Air Act (CAA) enactment (1970-2010). We calculate relative emission changes and examine the differential changes given county demographics using hierarchical nested models. The results show racial/ethnic disparities, particularly in the industry and energy generation source sectors. We also find that median family income is a driver of variation in relative emissions changes in all sectors-counties with median family income >$75 K vs. less generally experience larger relative declines in industry, energy, transportation, residential, and commercial-related emissions. Emissions from most air pollution source sectors have, on a national level, decreased following the United States CAA. In this work, we show that the relative reductions in emissions varied across racial/ethnic and socioeconomic groups.

Can traffic-related air pollution trigger myocardial infarction within a few hours of exposure? Identifying hourly hazard periods.

INTRODUCTION: Traffic-related air pollution can trigger myocardial infarction (MI). However, the hourly hazard period of exposure to nitrogen dioxide (NO2), a common traffic tracer, for incident MI has not been fully evaluated. Thus, the current hourly US national air quality standard (100 ppb) is based on limited hourly-level effect estimates, which may not adequately protect cardiovascular health. OBJECTIVES: We characterized the hourly hazard period of NO2 exposure for MI in New York state (NYS), USA, from 2000 to 2015. METHODS: For nine cities in NYS, we obtained data on MI hospitalizations from the NYS Department of Health Statewide Planning and Research Cooperative System and hourly NO2 concentrations from the US Environmental Protection Agency's Air Quality System database. We used city-wide exposures and a case-crossover study design with distributed lag non-linear terms to assess the relationship between hourly NO2 concentrations over 24 h and MI, adjusting for hourly temperature and relative humidity. RESULTS: The mean NO2 concentration was 23.2 ppb (standard deviation: 12.6 ppb). In the six hours preceding MI, we found linearly increased risk with increasing NO2 concentrations. At lag hour 0, a 10 ppb increase in NO2 was associated with 0.2 % increased risk of MI (Rate Ratio [RR]: 1.002; 95 % Confidence Interval [CI]: 1.000, 1.004). We estimated a cumulative RR of 1.015 (95 % CI: 1.008, 1.021) for all 24 lag hours per 10 ppb increase in NO2. Lag hours 2-3 had consistently elevated risk ratios in sensitivity analyses. CONCLUSIONS: We found robust associations between hourly NO2 exposure and MI risk at concentrations far lower than current hourly NO2 national standards. Risk of MI was most elevated in the six hours after exposure, consistent with prior studies and experimental work evaluating physiologic responses after acute traffic exposure. Our findings suggest that current hourly standards may be insufficient to protect cardiovascular health.

Menstrual Products as a Source of Environmental Chemical Exposure: A Review from the Epidemiologic Perspective.

PURPOSE OF REVIEW: Menstrual bleeding is a regular, common occurrence in a substantial portion of the population. Menstruators may use more than 10,000 menstrual products over the lifetime. Given the potential for environmental chemicals in menstrual products to be absorbed by the vulvar and vaginal epithelium into systemic circulation, we reviewed the available data on menstrual products as a source of environmental chemical exposure. RECENT FINDINGS: Nearly two dozen studies have been conducted measuring environmental contaminants in menstrual products; all have detected environmental chemicals but had discrepant conclusions on exposure risks. Only three human studies have investigated menstrual product use and environmental chemical concentrations and all observed associations. Detection of environmental chemicals in menstrual products, in combination with challenges of exposure assessment, scarcity of human studies, and the exceedingly common occurrence of menstrual bleeding, motivates the need for further research. We provide recommendations to move this field forward.

An Epidemiologic Review of Menstrual Blood Loss as an Excretion Route for Per- and Polyfluoroalkyl Substances.

PURPOSE OF REVIEW: Menstrual blood loss, a common physiologic occurrence, provides an excretion route for per- and polyfluoroalkyl substances (PFAS) since these chemicals are bound to proteins in blood. To increase awareness of this relationship in environmental epidemiology, we reviewed the available epidemiologic data on menstrual bleeding and PFAS concentrations. RECENT FINDINGS: Initial epidemiologic studies reported generally higher PFAS concentrations in men, menopausal women, and those with a history of hysterectomy compared to premenopausal women. Although subsequent studies investigating menstrual cycle characteristics observed somewhat discrepant results for menstrual irregularity and cycle length, consistent associations have been observed between heavy menstrual bleeding and lower PFAS concentrations. This review highlights the important role of menstrual bleeding on the excretion of PFAS. Given the high prevalence of menstrual bleeding in the population and the implications for environmental epidemiology, we provide recommendations to move this field forward.

Quantifying diurnal changes in NO2 due to COVID-19 stay-at-home orders in New York City.

Introduction: Policy responses to the COVID-19 pandemic, such as the NY on Pause stay-at-home order (March 22 - June 8, 2020), substantially reduced traffic and traffic-related air pollution (TRAP) in New York City (NYC). We evaluated the magnitude of TRAP decreases and examined the role of modifying factors such as weekend/weekday, road proximity, location, and time-of-day. Methods: Hourly nitrogen dioxide (NO2) concentrations from January 1, 2018 through June 8, 2020 were obtained from the Environmental Protection Agency's Air Quality System for all six hourly monitors in the NYC area. We used an interrupted time series design to determine the impact of NY on Pause on NO2 concentrations, using a mixed effects model with random intercepts for monitor location, adjusted for meteorology and long-term trends. We evaluated effect modification through stratification. Results: NO2 concentrations decreased during NY on Pause by 19% (-3.2 ppb, 95% confidence interval [CI]: -3.5, -3.0), on average, compared to pre-Pause time trends. We found no evidence for modification by weekend/weekday, but greater decreases in NO2 at non-roadside monitors and weak evidence for modification by location. For time-of-day, we found the largest decreases for 5 am (27%, -4.5 ppb, 95% CI: -5.7, -3.3) through 7 am (24%, -4.0 ppb, 95% CI: -5.2, -2.8), followed by 6 pm and 7 pm (22%, -3.7 ppb, 95% CI: -4.8, -2.6 and 22%, -4.8, -2.5, respectively), while the smallest decreases occurred at 11 pm and 1 am (both: 11%, -1.9 ppb, 95% CI: -3.1, -0.7). Conclusion: NY on Pause's impact on TRAP varied greatly diurnally. Decreases during early morning and evening time periods are likely due to decreases in traffic. Our results may be useful for planning traffic policies that vary by time of day, such as congestion tolling policies.

Methods for Evaluating Environmental Health Impacts at Different Stages of the Policy Process in Cities.

PURPOSE OF REVIEW: Evaluating the environmental health impacts of urban policies is critical for developing and implementing policies that lead to more healthy and equitable cities. This article aims to (1) identify research questions commonly used when evaluating the health impacts of urban policies at different stages of the policy process, (2) describe commonly used methods, and (3) discuss challenges, opportunities, and future directions. RECENT FINDINGS: In the diagnosis and design stages of the policy process, research questions aim to characterize environmental problems affecting human health and to estimate the potential impacts of new policies. Simulation methods using existing exposure-response information to estimate health impacts predominate at these stages of the policy process. In subsequent stages, e.g., during implementation, research questions aim to understand the actual policy impacts. Simulation methods or observational methods, which rely on experimental data gathered in the study area to assess the effectiveness of the policy, can be applied at these stages. Increasingly, novel techniques fuse both simulation and observational methods to enhance the robustness of impact evaluations assessing implemented policies. The policy process consists of interdependent stages, from inception to end, but most reviewed studies focus on single stages, neglecting the continuity of the policy life cycle. Studies assessing the health impacts of policies using a multi-stage approach are lacking. Most studies investigate intended impacts of policies; focusing also on unintended impacts may provide a more comprehensive evaluation of policies.

The Mediating Effect of E-Cigarette Harm Perception in the Relationship between E-Cigarette Advertising Exposure and E-Cigarette Use.

Exposure to e-cigarette advertising is associated with e-cigarette use among young people. This study examined the mediating effect of e-cigarette harm perception on the above relationship. Cross-sectional survey data were collected from 2112 college students in New York City in 2017-2018. The analytic sample comprised 2078 participants (58.6% females) who provided completed data. Structural equal modeling was performed to examine if e-cigarette harm perception mediated the relationship between e-cigarette advertising exposure (via TV, radio, large signs, print media, and online) and ever e-cigarette use and susceptibility to e-cigarette use. About 17.1% of participants reported ever e-cigarette use. Of never users, 17.5% were susceptible to e-cigarette use. E-cigarette advertising exposure was mainly through online sources (31.5%). Most participants (59.4%) perceived e-cigarettes as equally or more harmful than cigarettes. Advertising exposure showed different effects on e-cigarette harm perception depending on the source of the advertising exposure, but perceiving e-cigarettes as less harmful than cigarettes was consistently associated with e-cigarette use and susceptibility. Low harm perception mediated the association between advertising exposure (via online, TV, and radio) and ever e-cigarette use and between online advertising exposure and e-cigarette use susceptibility. Regulatory actions are needed to address e-cigarette marketing, particularly on the Internet.

Supragingival microbiome alternations as a consequence of smoking different tobacco types and its relation to dental caries.

This study aimed to assess the effect of smoking different tobacco types on the supragingival microbiome and its relation to dental caries. Forty supragingival plaque samples were collected from smokers of a single tobacco type and non-smokers seeking treatment at the University Dental Hospital Sharjah, UAE. DMFT (decayed, missing and filled teeth) was determined for all participants who were divided into two groups: no-low caries (NC-LC: DMFT = 0-4; n = 18) and moderate-high caries (MC-HC: DMFT = 5-20; n = 22). 16S rRNA gene was sequenced using third-generation sequencing with Nanopore technology. Microbiome composition and diversity were compared. Caries was most common among cigarette smokers. Supragingival microbiota were significantly altered among smokers of different tobacco types. In cigarette smokers, cariogenic bacteria from genus Streptococcus (including S. mutans) were significantly more among subjects with NC-LC, while Lactobacilli (including L. fermentum) were more among subjects with MC-HC. In medwakh smokers, several periodontopathogens were significantly elevated in subjects with NC-LC, while other pathogenic bacteria (as Klebsiella pneumoniae) were more in those with MC-HC. Cigarette and alternative tobacco smoking had a significant impact on the supragingival microbiome. Indeed, further studies are required to unravel the consequences of oral dysbiosis triggered by smoking. This could pave the way for microbiota-based interventional measures for restoring a healthy oral microbiome which could be a promising strategy to prevent dental caries.

Benzene emissions from gas station clusters: a new framework for estimating lifetime cancer risk.

PURPOSE: During gas station operation, unburned fuel can be released to the environment through distribution, delivery, and storage. Due to the toxicity of fuel compounds, setback distances have been implemented to protect the general population. However, these distances treat gasoline sales volume as a categorical variable and only account for the presence of a single gas station and not clusters, which frequently occur. This paper introduces a framework for recommending setback distances for gas station clusters based on estimated lifetime cancer risk from benzene exposure. METHODS: Using the air quality dispersion model AERMOD, we simulated levels of benzene released to the atmosphere from single and clusters of generic gas stations and the associated lifetime cancer risk under meteorological conditions representative of Albany, New York. RESULTS: Cancer risk as a function of distance from gas station(s) and as a continuous function of total sales volume can be estimated from an equation we developed. We found that clusters of gas stations have increased cancer risk compared to a single station because of cumulative emissions from the individual gas stations. For instance, the cancer risk at 40 m for four gas stations each dispensing 1 million gal/year is 9.84 × 10-6 compared to 2.45 × 10-6 for one gas station. CONCLUSION: The framework we developed for estimating cancer risk from gas station(s) could be adopted by regulatory agencies to make setback distances a function of sales volume and the number of gas stations in a cluster, rather than on a sales volume category. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40201-020-00601-w.

Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data.

INTRODUCTION: To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and results in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies. METHODS: We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions. RESULTS: Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels. CONCLUSION: Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

Social-distancing fatigue: Evidence from real-time crowd-sourced traffic data.

INTRODUCTION: To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies. METHODS: We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions. RESULTS: Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels. CONCLUSION: Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

The impact of smoking different tobacco types on the subgingival microbiome and periodontal health: a pilot study.

Smoking is a risk factor for periodontal disease, and a cause of oral microbiome dysbiosis. While this has been evaluated for traditional cigarette smoking, there is limited research on the effect of other tobacco types on the oral microbiome. This study investigates subgingival microbiome composition in smokers of different tobacco types and their effect on periodontal health. Subgingival plaques were collected from 40 individuals, including smokers of either cigarettes, medwakh, or shisha, and non-smokers seeking dental treatment at the University Dental Hospital in Sharjah, United Arab Emirates. The entire (~ 1500 bp) 16S rRNA bacterial gene was fully amplified and sequenced using Oxford Nanopore technology. Subjects were compared for the relative abundance and diversity of subgingival microbiota, considering smoking and periodontal condition. The relative abundances of several pathogens were significantly higher among smokers, such as Prevotella denticola and Treponema sp. OMZ 838 in medwakh smokers, Streptococcus mutans and Veillonella dispar in cigarette smokers, Streptococcus sanguinis and Tannerella forsythia in shisha smokers. Subgingival microbiome of smokers was altered even in subjects with no or mild periodontitis, probably making them more prone to severe periodontal diseases. Microbiome profiling can be a useful tool for periodontal risk assessment. Further studies are recommended to investigate the impact of tobacco cessation on periodontal disease progression and oral microbiome.