Depression screening outcomes among adolescents, young adults, and adults reporting past 30-day tobacco and cannabis use.

BACKGROUND: Few studies examine the relationship between depression and use of specific tobacco and/or cannabis products among adolescents, young adults, and adults. We determined whether the odds of depression are greater among those who used specific tobacco and/or cannabis products and among co-users of tobacco and cannabis. METHOD: Cross-sectional online survey of a national convenience sample of 13-40-year-olds (N = 6,038). The survey included depression screening and past 30-day use of specific tobacco and cannabis products (cigarettes; e-cigarettes, vaped cannabis, little cigars, cigarillos, cigars, hookah, chewing tobacco, smoked cannabis, edible cannabis, blunts). Analyses correspond to the total sample, and 13-17-, 18-24-, and 25-40-year-olds. RESULTS: Among 5,281 individuals who responded to the depression screener and nine product use questions, 1,803 (34.1 %) reported co-use of at least one tobacco product and one cannabis product in the past 30 days. Past 30-day co-use was associated with higher likelihood of screening positive for depression compared to past 30-day use of tobacco-only (aOR = 1.32, 1.06-1.65; 0.006) or cannabis-only (aOR = 1.94, 1.28-2.94; <0.001). Screening positive for depression was more likely among those who reported past 30-day use of e-cigarettes (aOR = 1.56; 1.35-1.80; <0.001), cigarettes (aOR = 1.24, 1.04-1.48; 0.016), chewed tobacco (aOR = 1.91, 1.51-2.42; <0.001), and blunts (aOR = 1.22, 1.00-1.48; 0.053) compared to those who did not report past 30-day use of these products. Among the 2,223 individuals who screened positive for depression, the most used two-product combination was nicotine e-cigarettes and smoked cannabis (614 individuals, 27.6 %). CONCLUSIONS: Screening positive for depression was more likely among past 30-day co-users versus past 30-day users of tobacco-only or cannabis-only. Findings suggest that prevention programs for depression and substance use address tobacco and cannabis co-use.

Acceptability and feasibility of implementing the Enhanced Assess, Acknowledge, Act (EAAA) sexual assault prevention intervention on a U.S. university campus: Themes from qualitative interviews and written reflections.

Objectives: Assess the acceptability of and self-reported behavioral change from participation in a sexual assault prevention intervention on a U.S. university campus. Participants: Thirty-one undergraduate students who identified as women and opted-in to participating in both the intervention and research. Methods: In-depth interviews and written reflections were collected. Analysis was thematic with three investigators coding and reaching consensus. Results: The EAAA program was well-liked by most participants, with positive behavior changes reported. Five key themes reflecting strengths of EAAA were identified, including improved verbal communication, reduced sexual assault myths, empowerment, recognizing and responding to danger cues, and learning about healthy sexuality. Three key themes reflecting challenges were identified, including time commitment, use of outdated program materials, and a single gender focus. Conclusions: EAAA translates well, with a few adaptations, to a residential campus environment in the U.S. Research is needed to assess program effectiveness in reducing sexual assault.

Addressing food insecurity and chronic conditions in community health centres: protocol of a quasi-experimental evaluation of Recipe4Health.

INTRODUCTION: Chronic conditions, such as diabetes, obesity, heart disease and depression, are highly prevalent and frequently co-occur with food insecurity in communities served by community health centres in the USA. Community health centres are increasingly implementing 'Food as Medicine' programmes to address the dual challenge of chronic conditions and food insecurity, yet they have been infrequently evaluated. METHODS AND ANALYSIS: The goal of this quasi-experimental study was to evaluate the effectiveness of Recipe4Health, a 'Food as Medicine' programme. Recipe4Health includes two components: (1) a 'Food Farmacy' that includes 16 weekly deliveries of produce and (2) a 'Behavioural Pharmacy' which is a group medical visit. We will use mixed models to compare pre/post changes among participants who receive the Food Farmacy alone (n=250) and those who receive the Food Farmacy and Behavioural Pharmacy (n=140). The primary outcome, fruit and vegetable consumption, and secondary outcomes (eg, food security status, physical activity, depressive symptoms) will be collected via survey. We will also use electronic health record (EHR) data on laboratory values, prescriptions and healthcare usage. Propensity score matching will be used to compare Recipe4Health participants to a control group of patients in clinics where Recipe4Health has not been implemented for EHR-derived outcomes. Data from surveys, EHR, group visit attendance and produce delivery is linked with a common identifier (medical record number) and then deidentified for analysis with use of an assigned unique study ID. This study will provide important preliminary evidence on the effectiveness of primary care-based strategies to address food insecurity and chronic conditions. ETHICS AND DISSEMINATION: This study was approved by the Stanford University Institutional Review Board (reference protocol ID 57239). Appropriate study result dissemination will be determined in partnership with the Community Advisory Board.

Robust Designs for Prospective Randomized Trials Surveying Sensitive Topics.

We consider the problem of designing a prospective randomized trial in which the outcome data will be self-reported and will involve sensitive topics. Our interest is in how a researcher can adequately power her study when some respondents misreport the binary outcome of interest. To correct the power calculations, we first obtain expressions for the bias and variance induced by misreporting. We model the problem by assuming each individual in our study is a member of one "reporting class": a true-reporter, false-reporter, never-reporter, or always-reporter. We show that the joint distribution of reporting classes and "response classes" (characterizing individuals' response to the treatment) will exactly define the error terms for our causal estimate. We propose a novel procedure for determining adequate sample sizes under the worst-case power corresponding to a given level of misreporting. Our problem is motivated by prior experience implementing a randomized controlled trial of a sexual-violence prevention program among adolescent girls in Kenya.

Combining observational and experimental datasets using shrinkage estimators.

We consider the problem of combining data from observational and experimental sources to draw causal conclusions. To derive combined estimators with desirable properties, we extend results from the Stein shrinkage literature. Our contributions are threefold. First, we propose a generic procedure for deriving shrinkage estimators in this setting, making use of a generalized unbiased risk estimate. Second, we develop two new estimators, prove finite sample conditions under which they have lower risk than an estimator using only experimental data, and show that each achieves a notion of asymptotic optimality. Third, we draw connections between our approach and results in sensitivity analysis, including proposing a method for evaluating the feasibility of our estimators.

Propensity score methods for merging observational and experimental datasets.

We consider how to merge a limited amount of data from a randomized controlled trial (RCT) into a much larger set of data from an observational data base (ODB), to estimate an average causal treatment effect. Our methods are based on stratification. The strata are defined in terms of effect moderators as well as propensity scores estimated in the ODB. Data from the RCT are placed into the strata they would have occupied, had they been in the ODB instead. We assume that treatment differences are comparable in the two data sources. Our first "spiked-in" method simply inserts the RCT data into their corresponding ODB strata. We also consider a data-driven convex combination of the ODB and RCT treatment effect estimates within each stratum. Using the delta method and simulations, we identify a bias problem with the spiked-in estimator that is ameliorated by the convex combination estimator. We apply our methods to data from the Women's Health Initiative, a study of thousands of postmenopausal women which has both observational and experimental data on hormone therapy (HT). Using half of the RCT to define a gold standard, we find that a version of the spiked-in estimator yields lower-MSE estimates of the causal impact of HT on coronary heart disease than would be achieved using either a small RCT or the observational component on its own.

Evaluation of Data Sharing After Implementation of the International Committee of Medical Journal Editors Data Sharing Statement Requirement.

Importance: The benefits of responsible sharing of individual-participant data (IPD) from clinical studies are well recognized, but stakeholders often disagree on how to align those benefits with privacy risks, costs, and incentives for clinical trialists and sponsors. The International Committee of Medical Journal Editors (ICMJE) required a data sharing statement (DSS) from submissions reporting clinical trials effective July 1, 2018. The required DSSs provide a window into current data sharing rates, practices, and norms among trialists and sponsors. Objective: To evaluate the implementation of the ICMJE DSS requirement in 3 leading medical journals: JAMA, Lancet, and New England Journal of Medicine (NEJM). Design, Setting, and Participants: This is a cross-sectional study of clinical trial reports published as articles in JAMA, Lancet, and NEJM between July 1, 2018, and April 4, 2020. Articles not eligible for DSS, including observational studies and letters or correspondence, were excluded. A MEDLINE/PubMed search identified 487 eligible clinical trials in JAMA (112 trials), Lancet (147 trials), and NEJM (228 trials). Two reviewers evaluated each of the 487 articles independently. Exposure: Publication of clinical trial reports in an ICMJE medical journal requiring a DSS. Main Outcomes and Measures: The primary outcomes of the study were declared data availability and actual data availability in repositories. Other captured outcomes were data type, access, and conditions and reasons for data availability or unavailability. Associations with funding sources were examined. Results: A total of 334 of 487 articles (68.6%; 95% CI, 64%-73%) declared data sharing, with nonindustry NIH-funded trials exhibiting the highest rates of declared data sharing (89%; 95% CI, 80%-98%) and industry-funded trials the lowest (61%; 95% CI, 54%-68%). However, only 2 IPD sets (0.6%; 95% CI, 0.0%-1.5%) were actually deidentified and publicly available as of April 10, 2020. The remaining were supposedly accessible via request to authors (143 of 334 articles [42.8%]), repository (89 of 334 articles [26.6%]), and company (78 of 334 articles [23.4%]). Among the 89 articles declaring that IPD would be stored in repositories, only 17 (19.1%) deposited data, mostly because of embargo and regulatory approval. Embargo was set in 47.3% of data-sharing articles (158 of 334), and in half of them the period exceeded 1 year or was unspecified. Conclusions and Relevance: Most trials published in JAMA, Lancet, and NEJM after the implementation of the ICMJE policy declared their intent to make clinical data available. However, a wide gap between declared and actual data sharing exists. To improve transparency and data reuse, journals should promote the use of unique pointers to data set location and standardized choices for embargo periods and access requirements.

Predicting preventable hospital readmissions with causal machine learning.

OBJECTIVE: To assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program). DATA SOURCES: Electronic health records maintained by Kaiser Permanente Northern California (KPNC). STUDY DESIGN: Retrospective causal forest analysis of postdischarge outcomes among KPNC inpatients. Using data from both before and after implementation, we apply causal forests to estimate individual-level treatment effects of the Transitions Program intervention on 30-day readmission. These estimates are used to characterize treatment effect heterogeneity and to assess the notional impacts of alternative targeting strategies in terms of the number of readmissions prevented. DATA COLLECTION: 1 539 285 index hospitalizations meeting the inclusion criteria and occurring between June 2010 and December 2018 at 21 KPNC hospitals. PRINCIPAL FINDINGS: There appears to be substantial heterogeneity in patients' responses to the intervention (omnibus test for heterogeneity p = 2.23 × 10-7 ), particularly across levels of predicted risk. Notably, predicted treatment effects become more positive as predicted risk increases; patients at somewhat lower risk appear to have the largest predicted effects. Moreover, these estimates appear to be well calibrated, yielding the same estimate of annual readmissions prevented in the actual treatment subgroup (1246, 95% confidence interval [CI] 1110-1381) as did a formal evaluation of the Transitions Program (1210, 95% CI 990-1430). Estimates of the impacts of alternative targeting strategies suggest that as many as 4458 (95% CI 3925-4990) readmissions could be prevented annually, while decreasing the number needed to treat from 33 to 23, by targeting patients with the largest predicted effects rather than those at highest risk. CONCLUSIONS: Causal machine learning can be used to identify preventable hospital readmissions, if the requisite interventional data are available. Moreover, our results suggest a mismatch between risk and treatment effects.

Longitudinal trends in e-cigarette devices used by Californian youth, 2014-2018.

The rate of adolescent and young adult (AYA) e-cigarette usage has increased in recent years, possibly due to the introduction of sleek new e-cigarette devices such as JUUL. This study analyzed data from 400 California AYA to examine trends in e-cigarette usage by device type (disposables, large-size rechargeables, vape/hookah pens, JUUL/pod-based). Participants were asked about their ever, past 30-day, and past 7-day use of e-cigarettes; their usual e-cigarette device used; and co-use of devices in seven surveys administered approximately biannually from 2014 to 2018. During this time period, total e-cigarette ever-usage in our cohort increased linearly from 14.1% to 46.2% (ptrend < 0.001). JUUL/pod-based e-cigarette ever-usage increased from 14.9% to 22.5% in just six months in 2018. Furthermore, a majority of new e-cigarette users at the time of the survey endorsed using JUUL/pod-based devices (58.3% in Wave 6, 73.0% in Wave 7). With newer device options, AYA were also increasingly less likely to endorse older models such as disposables (19.1% to 6.9% from 2014 to 2018, ptrend < 0.01) and rechargeables (69.1% to 26.2% from 2014 to 2018, ptrend < 0.001) as their usual e-cigarette device. Participants who used JUUL/pod-based only as their usual device were more likely to endorse using only JUUL/pod-based devices during follow-up survey (70%), and none switched to a new device completely. Overall, this study provides a snapshot of how AYA's e-cigarette preferences appear to respond to new devices entering the market.

Did internal displacement from the 2010 earthquake in Haiti lead to long-term violence against children? A matched pairs study design.

BACKGROUND: Empirical evidence is limited and contradictory on violence against children after internal displacement from natural disasters. Understanding how internal displacement affects violence is key in structuring effective prevention and response. OBJECTIVE: We examined the effect of internal displacement from the 2010 Haitian earthquake on long-term physical, emotional, and sexual violence against children and outlined a methodological framework to improve future evidence quality. PARTICIPANTS AND SETTING: We analyzed violence against adolescent girls and boys within the nationally representative, Haiti Violence Against Children Survey. METHODS: We pre-processed data by matching on pre-earthquake characteristics for displaced and non-displaced children and applied 95 % confidence intervals from McNemar's exact test, with sensitivity analyses, to evaluate differences in violence outcomes between matched pairs after the earthquake. RESULTS: Internal displacement was not associated with past 12-month physical, emotional, and sexual violence two years after the earthquake for girls and boys. Most violence outcomes were robust to potential unmeasured confounding. Odds ratios for any form of violence against girls were 0.84 (95 % CI: 0.52-1.33, p = 0.500) and against boys were 1.03 (95 % CI: 0.61-1.73, p = 1.000). CONCLUSIONS: Internal displacement was not a driver of long-term violence against children in Haiti. Current global protocols in disaster settings may initiate services after the optimal window of time to protect children from violence, and the post-displacement setting may be central in determining violence outcomes. The combination of specific data structures and matching methodologies is promising to increase evidence quality after rapid-onset natural disasters, especially in low-resource settings.

Sex-specific association between gut microbiome and fat distribution.

The gut microbiome has been linked to host obesity; however, sex-specific associations between microbiome and fat distribution are not well understood. Here we show sex-specific microbiome signatures contributing to obesity despite both sexes having similar gut microbiome characteristics, including overall abundance and diversity. Our comparisons of the taxa associated with the android fat ratio in men and women found that there is no widespread species-level overlap. We did observe overlap between the sexes at the genus and family levels in the gut microbiome, such as Holdemanella and Gemmiger; however, they had opposite correlations with fat distribution in men and women. Our findings support a role for fat distribution in sex-specific relationships with the composition of the microbiome. Our results suggest that studies of the gut microbiome and abdominal obesity-related disease outcomes should account for sex-specific differences.

A cigarette pack by any other color: Youth perceptions mostly align with tobacco industry-ascribed meanings.

•Youth interpret cigarette pack-colors in line with industry-intended associations.•Product-packaging restrictions may be circumvented by use of colors that misrepresent product harms.•43.2% of participants attributed extra strong to the black cigarette pack.•35.6% of participants ascribed rich to gold.•31.1% of participants ascribed menthol to green.

Youth say ads for flavored e-liquids are for them.

INTRODUCTION: E-cigarettes are the most popular tobacco product among adolescents and young adults ("AYA") and are available in many flavors. The e-cigarette industry argues that flavors are not meant to appeal to youth, yet no study has asked youth what age group they think ads for flavored e-liquids are targeting. We asked AYA which age group they thought ads for flavored e-liquids targeted. METHODS: In 2016 as part of a larger survey, a random sample of 255 youth from across California (62.4% female, mean age = 17.5, SD = 1.7) viewed eight ads, presented in randomized order, for fruit-, dessert-, alcohol-, and coffee-flavored e-liquids and indicated the age group they thought the ads targeted: younger, same age, a little older, or much older than them. Population means and 95% confidence intervals were estimated using bootstrapping (100,000 replicate samples). RESULTS: Most participants (93.7%) indicated the cupcake man flavor ad targeted an audience of people younger than they. Over half felt ads for smoothy (68.2%), cherry (63.9%), vanilla cupcake (58%), and caramel cappuccino (50.4%) targeted their age and for no flavor ad did most feel the primary target age group was much older. CONCLUSIONS: Youth believe ads for flavored e-liquids target individuals about their age, not older adults. Findings support the need to regulate flavored e-liquids and associated ads to reduce youth appeal, which ultimately could reduce youth use of e-cigarettes.

Adolescents' and Young Adults' Use and Perceptions of Pod-Based Electronic Cigarettes.

Importance: Electronic cigarettes (e-cigarettes) are the most commonly used tobacco product among adolescents and young adults, and the new pod-based e-cigarette devices may put adolescents and young adults at increased risk for polytobacco use and nicotine dependence. Objective: To build an evidence base for perceptions of risk from and use of pod-based e-cigarettes among adolescents and young adults. Design, Setting, and Participants: In a survey study, a cross-sectional analysis was performed of data collected from April 6 to June 20, 2018, from 445 California adolescents and young adults as part of an ongoing prospective cohort study designed to measure the use and perceptions of tobacco products. Exposures: Use of pod-based e-cigarettes, e-cigarettes, and cigarettes. Main Outcomes and Measures: Ever use, past 7-day use, and past 30-day use and co-use of pod-based e-cigarettes, e-cigarettes, and cigarettes; use of flavors and nicotine in pod-based e-cigarettes and e-cigarettes; and associated perceptions of risks, benefits, and nicotine dependence. Results: Among 445 adolescents and young adults (280 females, 140 males, 6 transgender individuals, and 19 missing data; mean [SD] age, 19.3 [1.7] years) who completed wave 6 of the ongoing prospective cohort study, ever use information was provided by 437 respondents, of which 68 (15.6%) reported use of pod-based e-cigarettes, 133 (30.4%) reported use of e-cigarettes, and 106 (24.3%) reported use of cigarettes. The mean (SD) number of days that pod-based e-cigarettes were used in the past 7 days was 1.5 (2.4) and in the past 30 days was 6.7 (10.0). The mean (SD) number of days that other e-cigarettes were used in the past 7 days was 0.8 (1.8) and in the past 30 days was 3.2 (7.4). The mean (SD) number of days that cigarettes were used in the past 7 days was 0.7 (1.8) and in the past 30 days was 3.0 (7.6). Among ever users of pod-based e-cigarettes, 18 (26.5%) reported their first e-liquid was flavored menthol or mint and 19 (27.9%) reported fruit (vs 13 [9.8%] and 50 [37.6%] for other e-cigarettes). The mean perceived chance of experiencing social risks and short-term and long-term health risks from the use of either pod-based e-cigarettes or other e-cigarettes was 40% and did not differ statistically by e-cigarette type. Among 34 adolescents and young adults reporting any loss of autonomy from nicotine, there was no difference in mean (SD) Hooked On Nicotine Checklist scores between those using pod-based e-cigarettes (2.59 [3.14]) and other e-cigarettes (2.32 [2.55]). Conclusions and Relevance: Use by adolescents and young adults of newer types of e-cigarettes such as pod-based systems is increasing rapidly, and adolescents and young adults report corresponding misperceptions and lack of knowledge about these products. Rapid innovation by e-cigarette manufacturers suggests that public health and prevention efforts appear to be needed to include messages targeting components common to all current and emerging e-cigarette products to increase knowledge and decrease misperceptions, with the goal to try to ultimately reduce e-cigarette use among adolescents and young adults.

Near/far matching: a study design approach to instrumental variables.

Classic instrumental variable techniques involve the use of structural equation modeling or other forms of parameterized modeling. In this paper we use a nonparametric, matching-based instrumental variable methodology that is based on a study design approach. Similar to propensity score matching, though unlike classic instrumental variable approaches, near/far matching is capable of estimating causal effects when the outcome is not continuous. Unlike propensity score matching, though similar to instrumental variable techniques, near/far matching is also capable of estimating causal effects even when unmeasured covariates produce selection bias. We illustrate near/far matching by using Medicare data to compare the effectiveness of carotid arterial stents with cerebral protection versus carotid endarterectomy for the treatment of carotid stenosis.