E-cigarette use and respiratory illnesses among U.S. adults: An analysis of the Population Assessment of Tobacco and Health Study.

INTRODUCTION: Electronic cigarettes (e-cigarette) entered the United States marketplace in 2007. Because of the chemical composition of e-cigarette liquid, there are concerns related to its effects on respiratory illnesses. METHODS: Using Wave 4 (2016-2018) of the adult (≥18 years of age) Population Assessment of Tobacco and Health study, logistic regression was used to quantify the association between e-cigarette use and any respiratory illness (asthma, chronic bronchitis, COPD, emphysema, or other lung or respiratory conditions). Age, gender, and the use of other tobacco products were assessed as modifiers of this relationship via interactions on the additive and multiplicative scales. RESULTS: After adjustment, ever e-cigarette use was associated with a 1.32 (95 % confidence intervals [CI]:1.03, 1.69) greater odds of any respiratory illness compared with never e-cigarette users among U.S. adults. When comparing ever and never e-cigarette users, older adults (Odds Ratio [OR] = 1.65, 95 %CI:1.20, 2.26), and ever users of other tobacco products (OR = 1.77, 95 %CI:1.37, 2.29) had greater odds of reporting any respiratory illness than younger adults and those who never used other tobacco products. Among never e-cigarette users, men (OR = 1.51, 95 %CI:1.12, 2.04) were less likely to report any respiratory illness than women. Only the joint effect of age and e-cigarette use on any respiratory illness was statistically significant, suggesting this effect was greater than expected on the additive and multiplicative scales. CONCLUSION: These findings suggest that the relationship between e-cigarette use and any respiratory illness varies with age. Interventions and policies to reduce e-cigarette use should target high-risk groups for any respiratory illness.

Ecologic study of influenza vaccination uptake and COVID-19 death rate in New York City.

BACKGROUND: The aim of this ecological study was to assess the area-level relationship between cumulative death rate for COVID-19 and historic influenza vaccination uptake in the New York City population. METHODS: Predictors of COVID-19 death included self-reported influenza vaccination in 2017, as well as four CDC-defined risk factors of severe COVID-19 infection available at the ecological level, which were diabetes, asthma, BMI 30-100 (2 kg/m2) and hypertension, in addition to race and age (65 + years). RESULTS: After adjusting for potential confounders, for every one-unit increase in influenza vaccination uptake for each zip code area, the rate of COVID-19 deaths decreased by 5.17 per 100,000 residents (p < 0.0001). CONCLUSIONS: Zip codes with a higher prevalence of influenza vaccination had lower rates of COVID-19 mortality, inciting the need to further explore the relationship between influenza vaccination uptake and COVID-19 mortality at the individual level.

Using NYC 311 Call Center Data to Assess Short- and Long-Term Needs Following Hurricane Sandy.

OBJECTIVES: Hurricane Sandy made landfall across New York City (NYC) in October 2012, but the long-term consequences of the storm are still not fully understood. We analyzed NYC data to quantify the extent of Hurricane Sandy-related concerns over time. METHODS: Data on NYC 311 Call Center inquiries were downloaded from the NYC Open Data website (October 29, 2012 to May 26, 2020) to provide information about Sandy-related calls using the keywords "Hurricane" and "Sandy". RESULTS: In the first 2 wk after Hurricane Sandy, 15.6% of 311 calls were related to the storm. From 2012 to 2020, the volume of inquiries decreased from 87,209 to 25. The majority of calls in 2012 (49,181; 56%) was requesting general Hurricane Sandy information, and in 2020 assistance with property restoration (20; 79%). CONCLUSIONS: The long-term consequences of Hurricane Sandy in NYC persist into 2020, almost 8 y after the initial event. The needs of Hurricane Sandy victims have changed over time from requiring general information regarding closures, property destruction and immediate disaster relief to aid with legal, financial, and mental health consequences. Disaster response policy-makers must understand the changing needs of NYC residents to provide resources and prepare for future disasters.

A Neighborhood Analysis of Time Trends in COVID-19 Infection in NYC.

To understand how observed COVID-19 diagnostic testing disparities across New York City (NYC) have impacted infection rates and COVID-19 spread, we examined neighborhood-level factors associated with, and the spatial distribution of, antibody test and infection rates, and compared changes over time by NYC ZIP code tabulation area (ZCTA). Data were obtained from 2019 American Community Survey 5-year estimates to create an SES index by ZCTA. Other predictors obtained from 2018 census data were the proportions of white residents, Hispanic residents and residents ≥ 65 years old. Multivariable Poisson regressions were performed to assess the rate of change for antibody testing and positivity, and to assess the independent associations with SES, race and age. Results: There was a significant association between the rate of antibody tests and SES quartiles (Q1: ßadj = 0.04, Q2: ßadj = 0.03 and Q3: ßadj = - 0.03, compared to Q4), and the proportion of residents who are white (ßadj = 0.004, p < .0001), Hispanic   (ßadj = 0.001, p < .0001), and ≥ 65 years (ßadj = 0.01, p < .0001). Total number of positive antibody tests was significantly inversely associated with SES quartile (Q1: ßadj = 0.50, Q2: ßadj = 0.48 and Q3: ßadj = 0.29, compared to Q4), and proportion of white residents (ß = - 0.001, p < .0001) and ≥ 65 years (ß = - 0.02, p < .0001), and significantly positively associated with proportion of Hispanic residents (ß = 0.003, p < .0001). There are disparities in antibody testing and positivity, reflecting disproportionate impacts and undercounts of COVID-19 infection across NYC ZCTAs. Future public health response should increase testing in these vulnerable areas to diminish infection spread.

Trends in COVID-19 School Related Inquiries Using 311 New York City Open Data.

This study aimed to assess the coronavirus disease (COVID-19) school-related information New York City residents sought through the 311 Call Center. July to November inquiries were downloaded from the NYC Open Data website for 2018-2020. Calls were categorized as related to "Schools", "Access", "Food", "Hospitals", "Transportation", and "Unemployment". Overall call types, and among school-related calls, detailed call types, were compared over the years, using chi-squared tests. School-related inquiries increased by 71% from 2018 to 2020. During 2020, the most common (49%, n = 22,471) call description was "Coronavirus and Schools", encompassing calls about learning options, safety, and resources. Spikes in these calls corresponded to official announcements, including those about Fall reopening plans (August 31: n = 678; September 1: n = 624) and schedules and staffing (September 16th: n = 1043; September 17th: n = 713), and after the start of in-person learning (September 21: n = 680). This study demonstrates that as government officials updated NYC schooling plans for Fall 2020, there were increased concerns among NYC residents. Future COVID-19 schooling changes need to be conveyed clearly and disseminated effectively in order to avoid confusion about NYC's pandemic learning strategy and to address health and safety concerns.

Using Rapid Research Implementation and Collaborations to Assess the Mental Health Impact of the COVID-19 Pandemic Among Community and Clinical Cohorts.

A research initiative was launched during the initial coronavirus disease (COVID-19) outbreak by 3 New York metropolitan area institutions. Collaborators recruited community members and patients from previous research studies to examine COVID-19 experiences and mental health symptoms through self-report surveys. The current report descriptively presents findings from the initial survey characterized by both community and clinical cohorts, and discusses challenges encountered with rapid implementation. The clinical cohort exhibited higher rates of symptoms of mental health difficulties (depression, anxiety, and posttraumatic stress disorder [PTSD]) as compared to the community cohort. COVID-19 positivity rates were similar among both groups and lower than the national average. While both groups reported low rates of job loss, community members reported higher rates of financial difficulty resulting from the pandemic. Findings indicate the need for further collaborative research on the mental health impact of COVID-19.