Risk Factors for E-Cigarette, or Vaping, Product Use-Associated Lung Injury (EVALI) Among Adults Who Use E-Cigarette, or Vaping, Products - Illinois, July-October 2019.

The United States is experiencing an unprecedented outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI) (1). All EVALI patients have used e-cigarette, or vaping, products, and most (≥85%) have reported using products containing tetrahydrocannabinol (THC) (2,3), the principal psychoactive component of cannabis. To examine whether e-cigarette, or vaping, product use behaviors differed between adult EVALI patients and adults who use these products but have not developed lung injury, the Illinois Department of Public Health (IDPH) conducted an online public survey during September-October 2019 targeting e-cigarette, or vaping, product users in Illinois. Among 4,631 survey respondents, 94% reported using any nicotine-containing e-cigarette, or vaping, products in the past 3 months; 21% used any THC-containing products; and 11% used both THC-containing products and nicotine-containing products. Prevalence of THC-containing product use was highest among survey respondents aged 18-24 years (36%) and decreased with increasing age. E-cigarette, or vaping, product use behaviors of 66 EVALI patients aged 18-44 years who were interviewed as part of the ongoing outbreak investigation were compared with a subset of 519 survey respondents aged 18-44 years who reported use of THC-containing e-cigarette, or vaping, products. Compared with these survey respondents, EVALI patients had higher odds of reporting exclusive use of THC-containing products (adjusted odds ratio [aOR] = 2.0, 95% confidence interval [CI] = 1.1-3.6); frequent use (more than five times per day) of these products (aOR = 3.1, 95% CI = 1.6-6.0), and obtaining these products from informal sources, such as a dealer, off the street, or from a friend (aOR = 9.2, 95% CI = 2.2-39.4). The odds of using Dank Vapes, a class of largely counterfeit THC-containing products, was also higher among EVALI patients (aOR = 8.5, 95% CI = 3.8-19.0). These findings reinforce current recommendations not to use e-cigarette, or vaping, products that contain THC and not to use any e-cigarette, or vaping, products obtained from informal sources. In addition, because the specific compound or ingredient causing lung injury is not yet known, CDC continues to recommend that persons consider refraining from use of all e-cigarette, or vaping, products while the outbreak investigation continues (1).

Severe Pulmonary Disease Associated with Electronic-Cigarette-Product Use - Interim Guidance.

On September 6, 2019, this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr). As of August 27, 2019, 215 possible cases of severe pulmonary disease associated with the use of electronic cigarette (e-cigarette) products (e.g., devices, liquids, refill pods, and cartridges) had been reported to CDC by 25 state health departments. E-cigarettes are devices that produce an aerosol by heating a liquid containing various chemicals, including nicotine, flavorings, and other additives (e.g., propellants, solvents, and oils). Users inhale the aerosol, including any additives, into their lungs. Aerosols produced by e-cigarettes can contain harmful or potentially harmful substances, including heavy metals such as lead, volatile organic compounds, ultrafine particles, cancer-causing chemicals, or other agents such as chemicals used for cleaning the device (1). E-cigarettes also can be used to deliver tetrahydrocannabinol (THC), the principal psychoactive component of cannabis, or other drugs; for example, "dabbing" involves superheating substances that contain high concentrations of THC and other plant compounds (e.g., cannabidiol) with the intent of inhaling the aerosol. E-cigarette users could potentially add other substances to the devices. This report summarizes available information and provides interim case definitions and guidance for reporting possible cases of severe pulmonary disease. The guidance in this report reflects data available as of September 6, 2019; guidance will be updated as additional information becomes available.

Mental and behavioral health screening at preventive visits: opportunities for follow-up of patients who are nonadherent with the next preventive visit.

OBJECTIVES: To determine the type of subsequent care received by children nonadherent with their next preventive visit and whether behavioral factors predict use of emergency or acute care in this population. STUDY DESIGN: Data on 1703 children (4-16 years) screened at a preventive visit with the Pediatric Symptom Checklist (PSC)/Youth-PSC were examined to determine subsequent preventive care adherence (10-18 months later). Then, nonadherent children were monitored to determine whether they returned to their medical home for acute care, delayed preventive care, or visited the emergency department (ED). Multivariate analyses were conducted to determine whether demographic and behavioral factors predicted return to either acute care or ED care site. RESULTS: Of the 461 children who were nonadherent with a second preventive care visit, most (85%) subsequently returned for acute, emergency, or delayed preventive care in the same medical system. Predictors of acute care or ED use included behavioral health risk characteristics (positive PSC, counseling, referral, parental concern), as well as adolescent age, self-pay and public insurance status, and living in lower socioeconomic communities. CONCLUSIONS: Pediatricians should consider acute care or ED visits as opportunities for mental health screening follow-up, and intervention in populations at high risk who miss preventive care.

Adolescents With Attention-Deficit/Hyperactivity Disorder: Adverse Behaviors and Comorbidity.

PURPOSE AND OBJECTIVE: There is growing evidence that adolescents with ADHD develop long-term impairments and adverse outcomes, yet less is known about their adverse behaviors. To quantify rates of mental health comorbidities in adolescents with ADHD and compare the risks of adverse behaviors among adolescents with and without ADHD. METHODS: We performed a cohort analysis of 6,483 youth from the National Comorbidity Survey Adolescent Supplement (NCS-A), a nationally representative in-person structured diagnostic interview of adolescents aged 14-18 years focusing on mental, emotional, and behavioral disorders. MAIN OUTCOMES: (1) Percentages with comorbid anxiety, mood, disruptive behavior, and substance use disorders. (2) Strength of associations of ADHD with several adverse behaviors, including suicidal symptoms, aggression, behavior regulation, life events, education, and substance use. Odds ratios were adjusted for age, sex, and race. RESULTS: Among the sample of 6,483 adolescents, overall, 9.5% met the criteria for ADHD. Most (69.5%) of adolescents with ADHD had at least one comorbid mental health condition. As compared to adolescents without ADHD, those with ADHD were significantly more likely to have had a suicide attempt (aOR 2.9, 95% CI = 1.3-6.6) and to have had perpetrated physical aggression (aOR 2.3, 95% CI = 1.7-3.2). Adolescents with ADHD were also more likely to have been expelled from school or fired from a job (aOR 3.3, 95% CI = 1.7-6.5) and to have had problems related to drinking alcohol (95% CI = 1.2-2.9). CONCLUSIONS: ADHD in adolescents is a complicated disorder with elevated risks for a wide range of adverse behaviors.

Characteristics of e-Cigarette Use Behaviors Among US Youth, 2020.

Importance: Comprehensive surveillance of e-cigarette use behaviors among youth is important for informing strategies to address this public health epidemic. Objective: To characterize e-cigarette use behaviors among US youth in 2020. Design, Setting, and Participants: The 2020 National Youth Tobacco Survey, a nationally representative, cross-sectional, school-based survey of middle school (grades 6-8) and high school (grades 9-12) students, was conducted from January 16, 2020, to March 16, 2020. A total of 14 531 students from 180 schools participated in the 2020 survey, yielding a corresponding student-level participation rate of 87.4% and school-level participation rate of 49.9%. The overall response rate, a product of the school-level and student-level participation rates, was 43.6%. Exposures: Current (past 30-day) e-cigarette use. Main Outcomes and Measures: Self-reported current e-cigarette use behaviors (frequency of use, usual e-cigarette brand, and access source) by school level and flavored e-cigarette use and flavor types among current e-cigarette users by school level and device type. Prevalence estimates were weighted to account for the complex survey design. Results: Overall, 14 531 students completed the survey, including 7330 female students and 7133 male students with self-reported grade level and sex. In 2020, 19.6% (95% CI, 17.2%-22.2%) of high school students and 4.7% (95% CI, 3.6%-6.0%) of middle school students reported current e-cigarette use. Among them, 38.9% (95% CI, 35.2%-42.6%) of high school users and 20.0% (95% CI, 16.0%-24.8%) of middle school users reported e-cigarette use on 20 to 30 days within the past 30 days. Among current users, JUUL was the most commonly reported usual brand (high school: 25.4%; 95% CI, 18.8%-33.4%; middle school: 35.1%; 95% CI, 27.9%-43.1%). Among current users, the most common source of obtaining e-cigarettes was from a friend (high school: 57.1%; 95% CI, 52.6%-61.4%; middle school: 58.9%; 95% CI, 51.4%-66.1%). Among current users, 84.7% (95% CI, 82.2%-86.9%) of high school students and 73.9% (95% CI, 66.9%-79.8%) of middle school students reported flavored e-cigarette use. Fruit-flavored e-cigarettes were the most commonly reported flavor among current exclusive e-cigarette users of prefilled pods or cartridges (67.3%; 95% CI, 60.9%-73.0%), disposable e-cigarettes (85.8%; 95% CI, 79.8%-90.3%), and tank-based devices (82.7%; 95% CI, 68.9%-91.1%), followed by mint-flavored e-cigarettes. Conclusions and Relevance: These results suggest that although current e-cigarette use decreased during 2019 to 2020, overall prevalence, frequent use, and flavored e-cigarette use remained high. Continued actions are warranted to prevent and reduce e-cigarette use among US youth.

Development of a Synthetic Population Model for Assessing Excess Risk for Cardiovascular Disease Death.

Importance: Evaluating the association of social determinants of health with chronic diseases at the population level requires access to individual-level factors associated with disease, which are rarely available for large populations. Synthetic populations are a possible alternative for this purpose. Objective: To construct and validate a synthetic population that statistically mimics the characteristics and spatial disease distribution of a real population, using real and synthetic data. Design, Setting, and Participants: This population-based decision analytical model used data for Allegheny County, Pennsylvania, collected from January 2015 to December 2016, to build a semisynthetic population based on the synthetic population used by the modeling and simulation platform FRED (A Framework for Reconstructing Epidemiological Dynamics). Disease status was assigned to this population using health insurer claims data from the 3 major insurance providers in the county or from the National Health and Nutrition Examination Survey. Biological, social, and other variables were also obtained from the National Health Interview Survey, Allegheny County, and public databases. Data analysis was performed from November 2016 to February 2020. Exposures: Risk of cardiovascular disease (CVD) death. Main Outcomes and Measures: Difference between expected and observed CVD death risk. A validated risk equation was used to estimate CVD death risk. Results: The synthetic population comprised 1 188 112 individuals with demographic characteristics similar to those of the 2010 census population in the same county. In the synthetic population, the mean (SD) age was 40.6 (23.3) years, and 622 997 were female individuals (52.4%). Mean (SD) observed 4-year rate of excess CVD death risk at the census tract level was -40 (523) per 100 000 persons. The correlation of social determinant data with difference between expected and observed CVD death risk indicated that income- and education-based social determinants were associated with risk. Estimating improved social determinants of health and biological factors associated with disease did not entirely remove the excess in CVD death rates. That is, a 20% improvement in the most significant determinants still resulted in 105 census tracts with excess CVD death risk, which represented 24% of the county population. Conclusions and Relevance: The results of this study suggest that creating a geographically explicit synthetic population from real and synthetic data is feasible and that synthetic populations are useful for modeling disease in large populations and for estimating the outcome of interventions.

Developmental differences in risk factors for suicide attempts between ninth and eleventh graders.

In order to identify differences in risk factors for suicide attempts throughout adolescence, this study utilized a school-based survey of ninth (n = 1,192) and eleventh graders (N = 1,055). Suicide attempts were associated with cigarette and alcohol use, family violence, and depression for ninth graders and with illicit drug use, school violence, and sexual abuse for eleventh graders, while having friends was protective for both groups. Additionally, having more than one risk factor imparted an exponential risk for suicide attempts (ninth > eleventh graders). The differences detected are consistent with developmental changes of adolescence and represent important information for identification of at-risk youth.

Effect of Pediatric Behavioral Health Screening and Colocated Services on Ambulatory and Inpatient Utilization.

OBJECTIVE: The study sought to determine the impact of a pediatric behavioral health screening and colocation model on utilization of behavioral health care. METHODS: In 2003, Cambridge Health Alliance, a Massachusetts public health system, introduced behavioral health screening and colocation of social workers sequentially within its pediatric practices. An interrupted time-series study was conducted to determine the impact on behavioral health care utilization in the 30 months after model implementation compared with the 18 months prior. Specifically, the change in trends of ambulatory, emergency, and inpatient behavioral health utilization was examined. Utilization data for 11,223 children ages ≥4 years 9 months to <18 years 3 months seen from 2003 to 2008 contributed to the study. RESULTS: In the 30 months after implementation of pediatric behavioral health screening and colocation, there was a 20.4% cumulative increase in specialty behavioral health visit rates (trend of .013% per month, p=.049) and a 67.7% cumulative increase in behavioral health primary care visit rates (trend of .019% per month, p<.001) compared with the expected rates predicted by the 18-month preintervention trend. In addition, behavioral health emergency department visit rates increased 245% compared with the expected rate (trend .01% per month, p=.002). CONCLUSIONS: After the implementation of a behavioral health screening and colocation model, more children received behavioral health treatment. Contrary to expectations, behavioral health emergency department visits also increased. Further study is needed to determine whether this is an effect of how care was organized for children newly engaged in behavioral health care or a reflection of secular trends in behavioral health utilization or both.

A planned care approach and patient registry to improve adherence to clinical guidelines for the diagnosis and management of attention-deficit/hyperactivity disorder.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) affects almost 2.4 million US children. Because American Academy of Pediatrics guidelines for ADHD recommend use of standardized diagnostic instruments, regular follow-up and the chronic care model, this pilot project sought to implement and assess an electronic registry of patients with ADHD combined with care coordination by a planned care team. METHODS: This quality improvement project was structured with 2 intervention and 2 control clinics to facilitate evaluation of the use of a planned care system for management of ADHD. Care teams included a pediatrician, nurse, medical assistant, and care coordinator and tracked patients using an electronic registry with data drawn from the EMR. Clinical work flows were pilot tested to facilitate use of the Vanderbilt scales and their incorporation into the EMR at intervention sites. Outcome measures included 2 recommended clinical follow-ups based on HEDIS measures as well as use of the Vanderbilt rating scales. Initiation phase measure was for follow-up after initiating medication, while the continuation phase measure was for subsequent follow-up during the first year of treatment. Measures were monitored during the project year and then also in the ensuing period of spread of the intervention to other sites. RESULTS: Although the modified HEDIS initiation phase measure for patients newly on medication remained static at approximately 50% throughout the project period, the continuation phase measure showed improvement from 35% at baseline to 45% at the end of the project assessment year, a 29% increase. Follow-up for patients stable on medications also remained unchanged during the project period, but during subsequent spreading of the intervention to nonproject sites, follow-up of these patients improved to over 90%. In adjusted analyses, patients with ADHD at intervention sites were over 2 times more likely than patients at control sites to have had a Vanderbilt score documented in their records. CONCLUSIONS: The project achieved modest improvements in the diagnostic and treatment process for patients with ADHD. The use of a planned care system and electronic patient registry shows promise for improving the diagnosis and treatment process for patients with ADHD.

Defining and understanding healthy lifestyles choices for adolescents.

PURPOSE: To: (a) establish criteria for defining positive health behaviors and lifestyle; and (b) identify characteristics of adolescents who practice a healthy lifestyle. METHODS: Responses from a 1998 survey via questionnaire, of 1487 students, from a public high school, Cambridge, Massachusetts, were used to assess correlates of healthy lifestyle choices. Strict and broad assessments of healthy behaviors were defined for students: use of alcohol, tobacco, and illegal drugs; sexual behavior; attempted suicide. Whereas the "strict" criteria included only those adolescents who did not practice any of the behaviors in question, the broad criteria reflected experimentation and moderate risk-taking. The prevalence of positive behaviors was assessed by demographic and student characteristics. In addition, logistic regression models were created to predict determinants of teenagers' healthy lifestyles using both strict and broad definitions. RESULTS: Using strict criteria of healthy lifestyle, significant predictors were being female, born outside the United States, higher academic performance, and fewer stressful life events. Using a broad definition of a healthy lifestyle, significant predictors were being non-Caucasian, in the lower grade levels at the school, higher academic performance, and fewer stressful life events. In both models, peers' approval of risky behaviors negatively influenced teens' lifestyles, whereas parents' disapproval of risky behaviors was a positive influence. CONCLUSIONS: These results reinforce the importance of school, peer, and parent support of positive behaviors. It is important for public health workers and families to understand and define healthy lifestyles choices for adolescents.

Parents' obesity-related behavior and confidence to support behavioral change in their obese child: data from the STAR study.

OBJECTIVE: Successful childhood obesity interventions frequently focus on behavioral modification and involve parents or family members. Parental confidence in supporting behavior change may be an element of successful family-based prevention efforts. We aimed to determine whether parents' own obesity-related behaviors were related to their confidence in supporting their child's achievement of obesity-related behavioral goals. METHODS: Cross-sectional analyses of data collected at baseline of a randomized control trial testing a treatment intervention for obese children (n = 787) in primary care settings (n = 14). Five obesity-related behaviors (physical activity, screen time, sugar-sweetened beverage, sleep duration, fast food) were self-reported by parents for themselves and their child. Behaviors were dichotomized on the basis of achievement of behavioral goals. Five confidence questions asked how confident the parent was in helping their child achieve each goal. Logistic regression modeling high confidence was conducted with goal achievement and demographics as independent variables. RESULTS: Parents achieving physical activity or sleep duration goals were significantly more likely to be highly confident in supporting their child's achievement of those goals (physical activity, odds ratio 1.76; 95% confidence interval 1.19-2.60; sleep, odds ratio 1.74; 95% confidence interval 1.09-2.79) independent of sociodemographic variables and child's current behavior. Parental achievements of TV watching and fast food goals were also associated with confidence, but significance was attenuated after child's behavior was included in models. CONCLUSIONS: Parents' own obesity-related behaviors are factors that may affect their confidence to support their child's behavior change. Providers seeking to prevent childhood obesity should address parent/family behaviors as part of their obesity prevention strategies.

Screening for behavioral health issues in children enrolled in Massachusetts Medicaid.

OBJECTIVES: To understand mandated behavioral health (BH) screening in Massachusetts Medicaid including characteristics of screened children, predictors of positive screens, and whether screening identifies children without a previous BH history. METHODS: Massachusetts mandated BH screening in particularly among underidentified groups. 2008. Providers used a billing code and modifier to indicate a completed screen and whether a BH need was identified. Using MassHealth claims data, children with ≥ 300 days of eligibility in fiscal year (FY) 2009 were identified and categorized into groups based on first use of the modifier, screening code, or claim. Bivariate analyses were conducted to determine differences among groups. BH history was examined by limiting the sample to those continuously enrolled in FY 2008 and 2009. Multivariate logistic regression was used to determine predictors of positive screens. RESULTS: Of 355,490 eligible children, 46% had evidence of screening. Of those with modifiers, 12% were positive. Among continuously enrolled children (FY 2008 and FY 2009) with evidence of screening, 43% with positive modifiers had no BH history. This "newly identified" group were more likely to be female, younger, minority, and from rural residences (P < .0001). Among children with modifiers; gender (male), age (5-7), being in foster care, recent BH history, and Hispanic ethnicity predicted having a positive modifier. CONCLUSIONS: The high rate of newly identified Medicaid children with a BH need suggests that screening is performing well, particularly among underidentified groups. To better assess screening value, future work on cost-effectiveness and the impact on subsequent mental health treatment is needed.

Behavioral health services following implementation of screening in Massachusetts Medicaid children.

OBJECTIVES: To determine the relationship of child behavioral health (BH) screening results to receipt of BH services in Massachusetts Medicaid (MassHealth) children. METHODS: After a court decision, Massachusetts primary care providers were mandated to conduct BH screening at well-child visits and use a Current Procedural Terminology code along with a modifier indicating whether a BH need was identified. Using MassHealth claims data, a cohort of continuously enrolled (July 2007-June 2010) children was constructed. The salient visit (first use of the modifier, screening code, or claim in fiscal year 2009) was considered a reference point to examine BH history and postscreening BH services. Bivariate and multivariate logistic regression analyses were performed to determine predictors of postscreening BH services. RESULTS: Of 261,160 children in the cohort, 45% (118,464) were screened and 37% had modifiers. Fifty-seven percent of children screening positive received postscreening BH services compared with 22% of children screening negative. However, only 30% of newly identified children received BH services. The strongest predictors of postscreening BH services for children without a BH history were being in foster care (odds ratio, 10.38; 95% confidence interval, 9.22-11.68) and having a positive modifier (odds ratio, 3.79; 95% confidence interval, 3.53-4.06). CONCLUSIONS: Previous BH history, a positive modifier, and foster care predicted postscreening BH services. Only one-third of newly identified children received services. Thus although screening is associated with an increase in BH recognition, it may be insufficient to improve care. Additional strategies may be needed to enhance engagement in BH services.

Evaluation results from an active living intervention in Somerville, Massachusetts.

BACKGROUND: Community policies and programs can encourage active living and promote physical activity among residents. Somerville MA implemented an Active Living by Design project in 2003-2008 that promoted partnerships and advocacy to encourage physical activity. PURPOSE: To evaluate the Active Living by Design project implemented in Somerville. METHODS: A retrospective design assessed relative differences in the rates of meeting moderate or vigorous physical activity recommendations among middle- and high-school students and adults at baseline and follow-up within Somerville and at follow-up only in Everett MA, a comparison community. The middle- and high-school Youth Risk Behavior Surveys and the adult Behavioral Risk Factor Surveillance Survey were supplemented with Active Living by Design evaluation-specific questions at follow-up. Analyses included chi-square and logistic regression modeling to assess relationships. RESULTS: Approximately 1000 youth completed surveys at baseline and follow-up in Somerville and at follow-up in Everett. Similarly, adult residents completed surveys at baseline (n=1081) and follow-up in Somerville (n=644) and follow-up in Everett (n=608). Within Somerville, high school-aged students and adults were more likely to meet physical activity recommendations at follow-up after adjusting for demographic, health, and behavioral variables (OR=1.6 [95% CI=1.34, 1.92] and 2.36 [95% CI=2.29, 2.43], respectively). Between cities, Somerville adults were 1.47 (95% CI=1.37, 1.56) times more likely than Everett adults to meet physical activity recommendations. CONCLUSIONS: Community-based active living interventions may help residents meet physical activity recommendations. To improve community health, public health surveillance data can identify predictors of meeting physical activity recommendations that can be used to inform city policy and planning.