Faculty Bibliography

TikTok is a social network launched in 2016, which is used to create and share short videos (≤60 seconds). TikTok was the most downloaded app in the U.S. in 2018 and 2019 and is currently available in >55 countries. Similar to other social networks, TikTok users can follow other content creators and view a feed of videos. Users may associate their videos with captions and hashtags, and comment on others' videos. TikTok has 800 million total active users with >1 billion videos viewed daily.[1].

INTRODUCTION/BACKGROUND:Cardiovascular disease is the leading cause of death worldwide, and cardiovascular disease burden is increasing in low-resource settings and for lower socioeconomic groups. Machine learning algorithms are being developed rapidly and incorporated into clinical practice for cardiovascular disease prediction and treatment decisions. Significant opportunities for reducing death and disability from cardiovascular disease worldwide lie with accounting for the social determinants of cardiovascular outcomes. This study reviews how social determinants of health are being included in machine learning algorithms to inform best practices for the development of algorithms that account for social determinants. METHODS:A systematic review using 5 databases was conducted in 2020. English language articles from any location published from inception to April 10, 2020, which reported on the use of machine learning for cardiovascular disease prediction that incorporated social determinants of health, were included. RESULTS:Most studies that compared machine learning algorithms and regression showed increased performance of machine learning, and most studies that compared performance with or without social determinants of health showed increased performance with them. The most frequently included social determinants of health variables were gender, race/ethnicity, marital status, occupation, and income. Studies were largely from North America, Europe, and China, limiting the diversity of the included populations and variance in social determinants of health. DISCUSSION/CONCLUSIONS:Given their flexibility, machine learning approaches may provide an opportunity to incorporate the complex nature of social determinants of health. The limited variety of sources and data in the reviewed studies emphasize that there is an opportunity to include more social determinants of health variables, especially environmental ones, that are known to impact cardiovascular disease risk and that recording such data in electronic databases will enable their use.

BACKGROUND/UNASSIGNED:The SUSIT significantly increased delivery of BI for drug use by PCPs during routine primary care encounters.

BACKGROUND/UNASSIGNED:Emergency department (ED) patients commonly experience both substance use and homelessness, and social relationships impact each in varied ways not fully captured by existing quantitative research. This qualitative study examines how social relationships can precipitate or ameliorate homelessness and the connection (if any) between substance use and social relationships among ED patients experiencing homelessness. METHODS/UNASSIGNED:As part of a broader study to develop ED-based homelessness prevention interventions, we conducted in-depth interviews with 25 ED patients who used alcohol or drugs and had recently become homeless. We asked patients about the relationship between their substance use and homelessness. Interviews were recorded, transcribed, and coded line-by-line by investigators. Final codes formed the basis for thematic analysis through consensus discussions. RESULTS/UNASSIGNED:Social relationships emerged as focal points for understanding the four major themes related to the intersection of homelessness and substance use: (1) Substance use can create strain in relationships; (2) Help is there until it's not; (3) Social relationships can create challenges contributing to substance use; and (4) Reciprocal relationship of substance use and isolation. Sub-themes were also identified and described. CONCLUSIONS/UNASSIGNED:The association between substance use and homelessness is multifaceted and social relationships are a complex factor linking the two. Social relationships are often critical for homelessness prevention, but they are impacted by and reciprocally affect substance use. ED-based substance use interventions should consider the high prevalence of homelessness and the impact of social relationships on the interaction between homelessness and substance use.

BACKGROUND:Harnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. OBJECTIVE:This study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States. METHODS:We used natural language processing (NLP) algorithms to identify symptom- and medical condition-related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics. RESULTS:Within a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition-related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05). CONCLUSIONS:COVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease.

We identified family risk profiles at 6 months using socioeconomic status (SES) and maternal mental health indicators with data from the Family Life Project (N = 1,292). We related profiles to executive function (EF) at 36 months (intercept) and growth in EF between 36 and 60 months. Latent profile analysis revealed five distinct profiles, characterized by different combinations of SES and maternal mental health symptoms. Maternal sensitivity predicted faster growth in EF among children in the profile characterized by deep poverty and the absence of maternal mental health symptoms. Maternal sensitivity also predicted higher EF intercept but slower EF growth among children in the profile characterized by deep poverty and maternal mental health symptoms, and children in the near poor (low SES), mentally healthy profile. Maternal sensitivity also predicted higher EF intercept but had no effect on growth in EF in the near poor, mentally distressed profile. In contrast, maternal sensitivity did not predict the intercept or growth of EF in the privileged SES/mentally healthy profile. Our findings using a person-centered approach provide a more nuanced understanding of the role of maternal sensitivity in the growth of EF, such that maternal sensitivity may differentially affect the growth of EF in various contexts.

BACKGROUND:Limited research has addressed the obesity-COVID-19 severity association in paediatric patients. OBJECTIVE:To determine whether obesity is an independent risk factor for COVID-19 severity in paediatric patients and whether age modifies this association. METHODS:SARS-CoV-2-positive patients at NYU Langone Health from 1 March 2020 to 3 January 2021 aged 0-21 years with available anthropometric measurements: weight, length/height and/or body mass index (BMI). Modified log-Poisson models were utilized for the analysis. Main outcomes were 1) hospitalization and 2) critical illness (intensive care unit [ICU] admission). RESULTS:One hundred and fifteen of four hundred and ninety-four (23.3%) patients had obesity. Obesity was an independent risk factor for critical illness (adjusted risk ratio [ARR] 2.02, 95% CI 1.17 to 3.48). This association was modified by age, with obesity related to a greater risk for critical illness in adolescents (13-21 years) [ARR 3.09, 95% CI 1.48 to 6.47], but not in children (0-12 years). Obesity was not an independent risk factor for hospitalization for any age. CONCLUSION/CONCLUSIONS:Obesity was an independent risk factor for critical illness in paediatric patients, and this association was modified by age, with obesity related to a greater risk for critical illness in adolescents, but not in children. These findings are crucial for patient risk stratification and care.

BACKGROUND:Evidence suggests that individuals with history of substance use disorder (SUD) are at increased risk of COVID-19, but little is known about relationships between SUDs, overdose and COVID-19 severity and mortality. This study investigated risks of severe COVID-19 among patients with SUDs. METHODS:We conducted a retrospective review of data from a hospital system in New York City. Patient records from 1 January to 26 October 2020 were included. We assessed positive COVID-19 tests, hospitalizations, intensive care unit (ICU) admissions and death. Descriptive statistics and bivariable analyses compared the prevalence of COVID-19 by baseline characteristics. Logistic regression estimated unadjusted and sex-, age-, race- and comorbidity-adjusted odds ratios (AORs) for associations between SUD history, overdose history and outcomes. RESULTS:Of patients tested for COVID-19 (n = 188 653), 2.7% (n = 5107) had any history of SUD. Associations with hospitalization [AORs (95% confidence interval)] ranged from 1.78 (0.85-3.74) for cocaine use disorder (COUD) to 6.68 (4.33-10.33) for alcohol use disorder. Associations with ICU admission ranged from 0.57 (0.17-1.93) for COUD to 5.00 (3.02-8.30) for overdose. Associations with death ranged from 0.64 (0.14-2.84) for COUD to 3.03 (1.70-5.43) for overdose. DISCUSSION/CONCLUSIONS:Patients with histories of SUD and drug overdose may be at elevated risk of adverse COVID-19 outcomes.

BACKGROUND:The growth in e-cigarette use may be driven by the perception that they are a safer, healthier alternative to conventional cigarettes. However, their long-term health implications are not well known and use is discouraged by most cancer societies. It is currently unclear how cancer survivors perceive the risks associated with e-cigarette and how this may influence use in this population. METHODS:A cross-sectional analysis was conducted using the Health Information National Trends Survey (HINTS) (Years 2017-2019). Our primary study outcome was the perception of harm associated with e-cigarettes compared to traditional cigarettes among adults with and without a self-reported history of cancer. We used logistic regression analyses assessing the association of a cancer history with the perception that e-cigarettes are as much or more harmful than cigarettes. RESULTS:A total of 11,846 respondents (weighted population estimate 243,728,483) were included. Of these, 26.6% reported a history of cancer. The proportion of cancer survivors who perceived e-cigarettes to be as much or more harmful than conventional cigarettes was similar to non-cancer respondents (70.6% vs 68.3%, P = 0.35). There was no difference in perception of harm among cancer and non-cancer respondents, adjusted for sociodemographic factors (OR 0.82, 95% CI 0.6-1.1). Past (OR 9.06, 95% Cl 5.06-16.20) and never e-cigarette use (OR 23.40, 95% Cl 13.56-40.38) as well as having a history of cardiopulmonary disease (OR 1.28, 95% Cl 1.05-1.56) was associated with higher odds of perceiving e-cigarettes to be as much or more harmful. CONCLUSION/CONCLUSIONS:Cancer survivors commonly perceive e-cigarettes to be as much or more harmful than traditional cigarettes though these findings are similar to perceptions among adults without a history of cancer. There is a strong association with avoidance of e-cigarette products among those who perceive them to be harmful.