Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial.

Importance: The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population. Objective: To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults. Design, Setting, and Participants: This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023. Interventions: Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years. Main Outcomes and Measures: For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality. Results: The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%). Conclusions and Relevance: Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.

Impact of High Deductible Health Plans on U.S. Adults With Chronic Pain.

INTRODUCTION: Chronic pain affects an estimated 20% of U.S. adults. Because high-deductible health plans have captured a growing share of the commercial insurance market, it is unknown how high-deductible health plans impact care for chronic pain. METHODS: Using 2007-2017 claims data from a large national commercial insurer, statistical analyses conducted in 2022-2023 estimated changes in enrollee outcomes before and after their firm began offering a high-deductible health plan compared with changes in outcomes in a comparison group of enrollees at firms never offering a high-deductible health plan. The sample included 757,530 commercially insured adults aged 18-64 years with headache, low back pain, arthritis, neuropathic pain, or fibromyalgia. Outcomes, measured at the enrollee year level, included the probability of receiving any chronic pain treatment, nonpharmacologic pain treatment, and opioid and nonopioid prescriptions; the number of nonpharmacologic pain treatment days; number and days' supply of opioid and nonopioid prescriptions; and total annual spending and out-of-pocket spending. RESULTS: High-deductible health plan offer was associated with a 1.2 percentage point reduction (95% CI= -1.8, -0.5) in the probability of any chronic pain treatment and an $11 increase (95% CI=$6, $15) in annual out-of-pocket spending on chronic pain treatments among those with any use, representing a 16% increase in average annual out-of-pocket spending over the pre-high deductible health plan offer annual average. Results were driven by changes in nonpharmacologic treatment use. CONCLUSIONS: By reducing the use of nonpharmacologic chronic pain treatments and marginally increasing out-of-pocket costs among those using these services, high-deductible health plans may discourage more holistic, integrated approaches to caring for patients with chronic pain conditions.

Effects of Opioid Prescribing Cap Laws on Opioid and Other Pain Treatments Among Persons with Chronic Pain.

BACKGROUND: Many states have adopted laws that limit the amount or duration of opioid prescriptions. These limits often focus on prescriptions for acute pain, but there may be unintended consequences for those diagnosed with chronic pain, including reduced opioid prescribing without substitution of appropriate non-opioid treatments. OBJECTIVE: To evaluate the effects of state opioid prescribing cap laws on opioid and non-opioid treatment among those diagnosed with chronic pain. DESIGN: We used a difference-in-differences approach that accounts for staggered policy adoption. Treated states included 32 states that implemented a prescribing cap law between 2017 and 2019. POPULATION: A total of 480,856 adults in the USA who were continuously enrolled in medical and pharmacy coverage from 2013 to 2019 and diagnosed with a chronic pain condition between 2013 and 2016. MAIN MEASURES: Among individuals with chronic pain in each state: proportion with at least one opioid prescription and with prescriptions of a specific duration or dose, average number of opioid prescriptions, average opioid prescription duration and dose, proportion with at least one non-opioid chronic pain prescription, average number of such prescriptions, proportion with at least one chronic pain procedure, and average number of such procedures. KEY RESULTS: State laws limiting opioid prescriptions were not associated with changes in opioid prescribing, non-opioid medication prescribing, or non-opioid chronic pain procedures among patients with chronic pain diagnoses. CONCLUSIONS: These findings do not support an association between state opioid prescribing cap laws and changes in the treatment of chronic non-cancer pain.

Association Between State Opioid Prescribing Cap Laws and Receipt of Opioid Prescriptions Among Children and Adolescents.

Importance: High-dose and long-duration opioid prescriptions remain relatively common among children and adolescents, but there is insufficient research on the association of state laws limiting the dose and/or duration of opioid prescriptions (referred to as opioid prescribing cap laws) with opioid prescribing for this group. Objective: To examine the association between state opioid prescribing cap laws and the receipt of opioid prescriptions among children and adolescents. Design Setting and Participants: This repeated cross-sectional study used a difference-in-differences approach accounting for staggered policy adoption to assess the association of state opioid prescribing cap laws in the US from January 1, 2013, to December 31, 2019, with receipt of opioid prescriptions among children and adolescents. Analyses were conducted between March 22 and December 15, 2021. Data were obtained from the OptumLabs Data Warehouse, a national commercial insurance claims database. The analysis included 482 118 commercially insured children and adolescents aged 0 to 17 years with full calendar-year continuous insurance enrollment between 2013 and 2019. Individuals were included for every year in which they were continuously enrolled; they did not need to be enrolled for the entire 7-year study period. Those with any cancer diagnosis were excluded from analysis. Exposure: Implementation of a state opioid prescribing cap law between January 1, 2017, and July 1, 2019. This date range allowed analysis of the same number years for both pre-cap and post-cap data. Main Outcomes and Measures: Outcomes of interest included receipt of any opioid prescription and, among those with at least 1 opioid prescription, the mean number of opioid prescriptions, mean morphine milligram equivalents (MMEs) per day, and mean days' supply. Results: Among 482 118 children and adolescents (754 368 person-years of data aggregated to the state-year level), 245 178 (50.9%) were male, with a mean (SD) age of 9.8 (4.8) years at the first year included in the sample (data on race and ethnicity were not collected as part of this data set, which was obtained from insurance billing claims). Overall, 10 659 children and adolescents (2.2%) received at least 1 opioid prescription during the study period. Among those with at least 1 prescription, the mean (SD) number of filled opioid prescriptions was 1.2 (0.8) per person per year. No statistically significant association was found between state opioid prescribing cap laws and any outcome. After opioid prescribing cap laws were implemented, a -0.001 (95% CI, -0.005 to 0.002) percentage point decrease in the proportion of youths receiving any opioid prescription was observed. In addition, percentage point decreases of -0.01 (95% CI, -0.10 to 0.09) in high-dose opioid prescriptions (>50 MMEs per day) and -0.02 (95% CI, -0.12 to 0.08) in long-duration opioid prescriptions (>7 days' supply) were found after cap laws were implemented. Conclusions and Relevance: In this cross-sectional study, no association was observed between state opioid prescribing cap laws and the receipt of opioid prescriptions among children and adolescents. Alternative strategies, such as opioid prescribing guidelines tailored to youths, are needed.

Effects of state opioid prescribing cap laws on opioid prescribing after surgery.

OBJECTIVE: To evaluate the effects of state opioid prescribing cap laws on opioid prescribing after surgery. DATA SOURCES: OptumLabs Data Warehouse administrative claims data covering all 50 states from July 2012 through June 2019. STUDY DESIGN: We included individuals from 20 states that had implemented prescribing cap laws without exemptions for postsurgical pain by June 2019 and individuals from 16 control states plus the District of Columbia. We used a difference-in-differences approach accounting for differential timing in law implementation across states to estimate the effects of state prescribing cap laws on postsurgical prescribing of opioids. Outcome measures included filling an opioid prescription within 30 days after surgery; filling opioid prescriptions of specific doses or durations; and the number, days' supply, daily dose, and pill quantity of opioid prescriptions. To assess the validity of the parallel counterfactual trends assumption, we examined differences in outcome trends between law-implementing and control states in the years preceding law implementation using an equivalence testing framework. DATA COLLECTION/EXTRACTION METHODS: We included the first surgery in the study period for opioid-naïve individuals undergoing one of eight common surgical procedures. PRINCIPAL FINDINGS: State prescribing cap laws were associated with 0.109 lower days' supply of postsurgical opioids on the log scale (95% Confidence Interval [CI]: -0.139, -0.080) but were not associated with the number (Average treatment effect on the treated [ATT]: -0.011; 95% CI: -0.043, 0.021) or daily dose of postsurgical opioid prescriptions (ATT: -0.013; 95% CI: -0.030, 0.005). The negative association observed between prescribing cap laws and the probability of filling a postsurgical opioid prescription (ATT: -0.041; 95% CI: -0.054, -0.028) was likely spurious, given differences between law-implementing and control states in the pre-law period. CONCLUSIONS: Prescribing cap laws appear to have minimal effects on postsurgical opioid prescribing.

Risk and Protective Factors Associated with Suicidal Thoughts and Behaviors Among Maryland Middle School Students.

The aim of this study was to identify independent risk and protective factors associated with self-reported suicidal thoughts and behaviors (STB) among young adolescents by examining self-report data on mental health, substance abuse, violence involvement, social and economic challenges and supports, physical health and demographics in relation to STB. Data from nearly 27,000 students who completed the 2018-19 Maryland Middle School Youth Risk Behavior Survey/Youth Tobacco Survey (YRBS/YTS) were used to identify independent risk and protective factors associated with STB among middle school students (grades 6-8; ages 11-14). Twenty-three percent of students reported lifetime suicidal ideation and nine percent reported lifetime attempt(s). Independent risk factors associated with STB include depression, substance abuse or misuse, violence involvement, bullying victimization at school or electronically, sexual activity, and sleep deprivation. Protective factors include having an adult outside of school to confide in and feeling that teachers care and provide encouragement. Interactive effects by gender and/or race/ethnicity were observed for some factors in relation to STB. These results suggest that evidence-based programs and policies at the universal and selective/indicated levels in school settings are needed and should be introduced earlier on to address the widespread prevalence of STB in young adolescents. Program planners should take into consideration social, cultural and language needs when implementing and developing intervention strategies. Supplementary Information: The online version contains supplementary material available at 10.1007/s12310-022-09521-6.

Effects of State Opioid Prescribing Laws on Use of Opioid and Other Pain Treatments Among Commercially Insured U.S. Adults.

BACKGROUND: There is concern that state laws to curb opioid prescribing may adversely affect patients with chronic noncancer pain, but the laws' effects are unclear because of challenges in disentangling multiple laws implemented around the same time. OBJECTIVE: To study the association between state opioid prescribing cap laws, pill mill laws, and mandatory prescription drug monitoring program query or enrollment laws and trends in opioid and guideline-concordant nonopioid pain treatment among commercially insured adults, including a subgroup with chronic noncancer pain conditions. DESIGN: Thirteen treatment states that implemented a single law of interest in a 4-year period and unique groups of control states for each treatment state were identified. Augmented synthetic control analyses were used to estimate the association between each state law and outcomes. SETTING: United States, 2008 to 2019. PATIENTS: 7 694 514 commercially insured adults aged 18 years or older, including 1 976 355 diagnosed with arthritis, low back pain, headache, fibromyalgia, and/or neuropathic pain. MEASUREMENTS: Proportion of patients receiving any opioid prescription or guideline-concordant nonopioid pain treatment per month, and mean days' supply and morphine milligram equivalents (MME) of prescribed opioids per day, per patient, per month. RESULTS: Laws were associated with small-in-magnitude and non-statistically significant changes in outcomes, although CIs around some estimates were wide. For adults overall and those with chronic noncancer pain, the 13 state laws were each associated with a change of less than 1 percentage point in the proportion of patients receiving any opioid prescription and a change of less than 2 percentage points in the proportion receiving any guideline-concordant nonopioid treatment, per month. The laws were associated with a change of less than 1 in days' supply of opioid prescriptions and a change of less than 4 in average monthly MME per day per patient prescribed opioids. LIMITATIONS: Results may not be generalizable to non-commercially insured populations and were imprecise for some estimates. Use of claims data precluded assessment of the clinical appropriateness of pain treatments. CONCLUSION: This study did not identify changes in opioid prescribing or nonopioid pain treatment attributable to state laws. PRIMARY FUNDING SOURCE: National Institute on Drug Abuse.

Estimating subgroup effects in generalizability and transportability analyses.

Methods for extending - generalizing or transporting - inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is sufficient for rendering the randomized and non-randomized groups exchangeable. Yet, decision-makers are often interested in examining treatment effects in subgroups of the target population defined in terms of only a few discrete covariates. Here, we propose methods for estimating subgroup-specific potential outcome means and average treatment effects in generalizability and transportability analyses, using outcome model-based (g-formula), weighting, and augmented weighting estimators. We consider estimating subgroup-specific average treatment effects in the target population and its non-randomized subset, and provide methods that are appropriate both for nested and non-nested trial designs. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study to compare the effect of surgery plus medical therapy versus medical therapy alone for chronic coronary artery disease in subgroups defined by history of myocardial infarction.

Exploring the Potential of External Control Arms created from Patient Level Data: A case study in non-small cell lung cancer.

Randomized controlled trials (RCTs) are the gold standard for evaluation of new medical products. However, RCTs may not always be ethical or feasible. In cases where the investigational product is available outside the trial (e.g., through accelerated approval), patients may fail to enroll in clinical trials or drop out early to take the investigational product. These challenges to enrolling or maintaining a concurrent control arm may compromise timely recruitment, retention, or compliance. This can threaten the study's integrity, including the validity of results. External control arms (ECAs) may be a promising augmentation to RCTs when encountered with challenges that threaten the feasibility and reliability of a randomized controlled clinical trial. Here, we propose the use of ECAs created from patient-level data from previously conducted clinical trials or real-world data in the same indication. Propensity score methods are used to balance observed disease characteristics and demographics in the previous clinical trial or real-world data with those of present-day trial participants assigned to receive the investigational product. These methods are explored in a case study in non-small cell lung cancer (NSCLC) derived from multiple previously conducted open label or blinded phase 2 and 3 multinational clinical trials initiated between 2004 and 2013. The case study indicated that when balanced for baseline characteristics, the overall survival estimates from the ECA were very similar to those of the target randomized control, based on Kaplan-Meier curves and hazard ratio and confidence interval estimates. This suggests that in the future, a randomized control may be able to be augmented by an ECA without compromising the understanding of the treatment effect, assuming sufficient knowledge, measurement, and availability of all or most of the important prognostic variables.

Diagnoses and Treatment After Depression Screening in Primary Care Among Youth.

INTRODUCTION: Depression screening is universally recommended for adolescents presenting in primary care settings in the U.S. However, little is known about how depression screening affects the likelihood of being diagnosed with a mental disorder or accessing mental health care over time. METHODS: This longitudinal cohort study used insurance claims data from adolescents who attended a well-visit between 2014 and 2017. Propensity score matching was used to compare adolescents who were screened for depression with similar unscreened adolescents. Diagnoses and treatment uptake were examined over a 6-month follow-up and included depression diagnoses, mood-related diagnoses, antidepressant medications, any mental health medication, and psychotherapy. Heterogeneity of associations by sex was also examined. Analyses were conducted from December 2020 to June 2021. RESULTS: The sample included 57,732 adolescents (mean age, 14.26 years; 48.9% female). Compared with adolescents who were not screened for depression, adolescents screened for depression were 30% more likely to be diagnosed with depression (risk ratio=1.30, 95% CI=1.11, 1.52) and 17% more likely to receive a mood-related diagnosis (risk ratio=1.17, 95% CI=1.08, 1.27) but were not more likely to be treated with an antidepressant medication (risk ratio=1.11, 95% CI=0.82, 1.51), any mental health medication (risk ratio=1.15, 95% CI=0.87, 1.53), or psychotherapy (risk ratio=1.13, 95% CI=0.98, 1.31). Associations were generally stronger among female adolescents. CONCLUSIONS: Adolescents who were screened for depression during a well-visit were more likely to receive a diagnosis of depression or a mood-related disorder in the 6 months after screening. Future research should explore methods for increasing treatment uptake after screening.

A Comparison of Hypofractionated and Twice-Daily Thoracic Irradiation in Limited-Stage Small-Cell Lung Cancer: An Overlap-Weighted Analysis.

Despite evidence for the superiority of twice-daily (BID) radiotherapy schedules, their utilization in practice remains logistically challenging. Hypofractionation (HFRT) is a commonly implemented alternative. We aim to compare the outcomes and toxicities in limited-stage small-cell lung cancer (LS-SCLC) patients treated with hypofractionated versus BID schedules. A bi-institutional retrospective cohort review was conducted of LS-SCLC patients treated with BID (45 Gy/30 fractions) or HFRT (40 Gy/15 fractions) schedules from 2007 to 2019. Overlap weighting using propensity scores was performed to balance observed covariates between the two radiotherapy schedule groups. Effect estimates of radiotherapy schedule on overall survival (OS), locoregional recurrence (LRR) risk, thoracic response, any ≥grade 3 (including lung, and esophageal) toxicity were determined using multivariable regression modelling. A total of 173 patients were included in the overlap-weighted analysis, with 110 patients having received BID treatment, and 63 treated by HFRT. The median follow-up was 20.4 months. Multivariable regression modelling did not reveal any significant differences in OS (hazard ratio [HR] 1.67, p = 0.38), LRR risk (HR 1.48, p = 0.38), thoracic response (odds ratio [OR] 0.23, p = 0.21), any ≥grade 3+ toxicity (OR 1.67, p = 0.33), ≥grade 3 pneumonitis (OR 1.14, p = 0.84), or ≥grade 3 esophagitis (OR 1.41, p = 0.62). HFRT, in comparison to BID radiotherapy schedules, does not appear to result in significantly different survival, locoregional control, or toxicity outcomes.

Agent-Based Modeling for Implementation Research: An Application to Tobacco Smoking Cessation for Persons with Serious Mental Illness.

BACKGROUND: Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this paper is to explain how agent-based modeling could fulfill this role. METHODS: We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics. RESULTS: For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. CONCLUSION: In this paper we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components.

Heterogeneity in prenatal substance use screening despite universal screening recommendations: findings from the Pregnancy Risk Assessment Monitoring System, 2016-2018.

BACKGROUND: The American College of Obstetricians and Gynecologists recommends universal screening for tobacco, alcohol, and drug use as a part of routine prenatal care. However, little is known about the prevalence of prenatal substance use screening or factors that may contribute to differential rates of screening during prenatal care. OBJECTIVE: This study aimed to describe the prevalence of prenatal substance use screening by substance, year, state, and state-level prenatal substance use policies and to examine individual-level factors associated with receipt of screening. STUDY DESIGN: We analyzed 2016 to 2018 data from 103,608 women participating in the Pregnancy Risk Assessment Monitoring System, a population-based survey among women with recent live births. The Pregnancy Risk Assessment Monitoring System survey sampling weights were applied to all analyses. We described the percentage of individuals asked by a healthcare worker about substance use during a prenatal care appointment by substance, year, and state. Using chi-squared tests, we examined differences in the prevalence of screening by state-level prenatal substance use policies, including policies regarding classification of prenatal substance use as child abuse or neglect, mandatory testing or reporting of prenatal substance use, and targeted treatment funding and access for pregnant individuals with substance use disorders. Finally, we estimated the association between individual-level characteristics and receipt of prenatal substance use screening using logistic regression, controlling for year and state fixed effects and accounting for missingness using multiple imputation. RESULTS: In 2018, approximately 95% individuals reported being asked about cigarette or alcohol use during a prenatal care appointment, whereas only 80% reported being asked about drug use. The percentage of individuals who were asked about substance use during a prenatal care appointment increased overall between 2016 and 2018, with variability across states. For all substances, states with laws designating prenatal drug use as child abuse or neglect had lower prevalence of screening, whereas states with laws mandating providers to test for substance use in pregnancy had higher prevalence of screening. Several individual-level characteristics were associated with increased odds of reported prenatal substance use screening for one or more substances, including being younger, less educated, unmarried, Black (vs White), non-Hispanic, or publicly insured (vs privately insured), receiving adequate prenatal care, and having a history of prepregnancy cigarette use. CONCLUSION: Our study finds that despite recommendations for universal prenatal substance use screening, there are differences in who is actually asked about substance use during prenatal care appointments. This may be influenced by state-level prenatal substance use policies and selective screening approaches in which certain individuals are more likely to be asked about substance use during their prenatal care appointment. A better understanding of the repercussions of selective screening approaches on outcomes and the roles that policies, systems, and provider biases play in perpetuating these approaches is needed to advance guideline implementation efforts in prenatal care settings.

Adapting the Comprehensive Unit Safety Program (CUSP) implementation strategy to increase delivery of evidence-based cardiovascular risk factor care in community mental health organizations: protocol for a pilot study.

BACKGROUND: People with serious mental illnesses (SMI) such as schizophrenia and bipolar disorder experience excess mortality driven in large part by high rates of poorly controlled and under-treated cardiovascular risk factors. In the USA, integrated "behavioral health home" models in which specialty mental health organizations coordinate and manage physical health care for people with SMI are designed to improve guideline-concordant cardiovascular care for this group. Such models have been shown to improve cardiovascular care for clients with SMI in randomized clinical trials, but real-world implementation has fallen short. Key implementation barriers include lack of alignment of specialty mental health program culture and physical health care coordination and management for clients with SMI and lack of structured protocols for conducting effective physical health care coordination and management in the specialty mental health program context. This protocol describes a pilot study of an implementation intervention designed to overcome these barriers. METHODS: This pilot study uses a single-group, pre/post-study design to examine the effects of an adapted Comprehensive Unit Safety Program (CUSP) implementation strategy designed to support behavioral health home programs in conducting effective cardiovascular care coordination and management for clients with SMI. The CUSP strategy, which was originally designed to improve inpatient safety, includes provider training, expert facilitation, and implementation of a five-step quality improvement process. We will examine the acceptability, appropriateness, and feasibility of the implementation strategy and how this strategy influences mental health organization culture; specialty mental health providers' self-efficacy to conduct evidence-based cardiovascular care coordination and management; and receipt of guideline-concordant care for hypertension, dyslipidemia, and diabetes mellitus among people with SMI. DISCUSSION: While we apply CUSP to the implementation of evidence-based hypertension, dyslipidemia, and diabetes care, this implementation strategy could be used in the future to support the delivery of other types of evidence-based care, such as smoking cessation treatment, in behavioral health home programs. CUSP is designed to be fully integrated into organizations, sustained indefinitely, and used to continually improve evidence-based practice delivery. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04696653 . Registered on January 6, 2021.

Protocol: mixed-methods study of how implementation of US state medical cannabis laws affects treatment of chronic non-cancer pain and adverse opioid outcomes.

BACKGROUND: Thirty-three US states and Washington, D.C., have enacted medical cannabis laws allowing patients with chronic non-cancer pain to use cannabis, when recommended by a physician, to manage their condition. However, clinical guidelines do not recommend cannabis for treatment of chronic non-cancer pain due to limited and mixed evidence of effectiveness. How state medical cannabis laws affect delivery of evidence-based treatment for chronic non-cancer pain is unclear. These laws could lead to substitution of cannabis in place of clinical guideline-discordant opioid prescribing, reducing risk of opioid use disorder and overdose. Conversely, state medical cannabis laws could lead to substitution of cannabis in place of guideline-concordant treatments such as topical analgesics or physical therapy. This protocol describes a mixed-methods study examining the implementation and effects of state medical cannabis laws on treatment of chronic non-cancer pain. A key contribution of the study is the examination of how variation in state medical cannabis laws' policy implementation rules affects receipt of chronic non-cancer pain treatments. METHODS: The study uses a concurrent-embedded design. The primary quantitative component of the study employs a difference-in-differences design using a policy trial emulation approach. Quantitative analyses will evaluate state medical cannabis laws' effects on treatment for chronic non-cancer pain as well as on receipt of treatment for opioid use disorder, opioid overdose, cannabis use disorder, and cannabis poisoning among people with chronic non-cancer pain. Secondary qualitative and survey methods will be used to characterize implementation of state medical cannabis laws through interviews with state leaders and representative surveys of physicians who treat, and patients who experience, chronic non-cancer pain in states with medical cannabis laws. DISCUSSION: This study will examine the effects of medical cannabis laws on patients' receipt of guideline-concordant non-opioid, non-cannabis treatments for chronic non-cancer pain and generate new evidence on the effects of state medical cannabis laws on adverse opioid outcomes. Results will inform the dynamic policy environment in which numerous states consider, enact, and/or amend medical cannabis laws each year.

Induction immunosuppression and the risk of incident malignancies among older and younger kidney transplant recipients: A prospective cohort study.

BACKGROUND: Older (≥65) KT recipients differ from their younger counterparts in their immune response to immunosuppression (IS) and may have a different risk of malignancy after receiving induction. METHODS: We identified 66 700 adult KT recipients treated with anti-thymocyte globulin (ATG) (n = 40 443) or interleukin-2 receptor antagonist (IL-2RA) (n = 26 327) induction (1/1/1999-12/31/2014) using USRDS/Medicare data. We estimated the risk of first-diagnosed post-KT malignancy associated with induction (ATG vs. IL-2RA) using Cox proportional hazard models. We then tested whether these risks differed between older and younger recipients (Wald test for interaction). Models incorporated inverse probability of treatment weights to adjust for confounders. RESULTS: The 3-year cumulative incidences of any diagnosed malignancy were 11.5%. ATG was associated with a higher malignancy risk (HR = 1.12, 95%CI:1.06-1.18). This association differed (pinteraction  = 0.04) between younger (HR = 1.12, 95%CI:1.06-1.18) and older recipients (HR = 1.03, 95%CI:0.96-1.09). ATG was also associated with higher risk of skin (HR = 1.18, 95%CI:1.08-1.29), lung (HR = 1.24, 95%CI:1.05-1.47), and ovary malignancies (HR = 1.94, 95%CI:1.08-3.48). However, only the association of ATG with post-KT skin malignancy differed (pinteraction  = 0.01) between younger (HR = 1.18; 95%CI:1.08-1.29) and older (HR = 1.01; 95%CI:0.93-1.09) recipients. CONCLUSIONS: Compared with IL-2RA induction, ATG was associated with elevated post-KT malignancy risk but only among younger recipients. Transplant centers may need to tailor induction IS for younger recipients to mitigate malignancy risk.

Mental Distress in the United States at the Beginning of the COVID-19 Pandemic.

Objectives. To assess the impact of the COVID-19 pandemic on mental distress in US adults.Methods. Participants were 5065 adults from the Understanding America Study, a probability-based Internet panel representative of the US adult population. The main exposure was survey completion date (March 10-16, 2020). The outcome was mental distress measured via the 4-item version of the Patient Health Questionnaire.Results. Among states with 50 or more COVID-19 cases as of March 10, each additional day was significantly associated with an 11% increase in the odds of moving up a category of distress (odds ratio = 1.11; 95% confidence interval = 1.01, 1.21; P = .02). Perceptions about the likelihood of getting infected, death from the virus, and steps taken to avoid infecting others were associated with increased mental distress in the model that included all states. Individuals with higher consumption of alcohol or cannabis or with history of depressive symptoms were at significantly higher risk for mental distress.Conclusions. These data suggest that as the COVID-19 pandemic continues, mental distress may continue to increase and should be regularly monitored. Specific populations are at high risk for mental distress, particularly those with preexisting depressive symptoms.

Extending inferences from a randomized trial to a new target population.

When treatment effect modifiers influence the decision to participate in a randomized trial, the average treatment effect in the population represented by the randomized individuals will differ from the effect in other populations. In this tutorial, we consider methods for extending causal inferences about time-fixed treatments from a trial to a new target population of nonparticipants, using data from a completed randomized trial and baseline covariate data from a sample from the target population. We examine methods based on modeling the expectation of the outcome, the probability of participation, or both (doubly robust). We compare the methods in a simulation study and show how they can be implemented in software. We apply the methods to a randomized trial nested within a cohort of trial-eligible patients to compare coronary artery surgery plus medical therapy versus medical therapy alone for patients with chronic coronary artery disease. We conclude by discussing issues that arise when using the methods in applied analyses.

Association Between the Maryland Medicaid Behavioral Health Home Program and Cancer Screening in People With Serious Mental Illness.

OBJECTIVE: This study evaluated the association of the Maryland Medicaid behavioral health home (BHH) integrated care program with cancer screening. METHODS: Using administrative claims data from October 2012 to September 2016, the authors measured cancer screening among 12,176 adults in Maryland's psychiatric rehabilitation program who were eligible for cervical (N=6,811), breast (N=1,658), and colorectal (N=3,430) cancer screening. Marginal structural modeling was used to examine the association between receipt of annual cancer screening and whether participants had ever enrolled in a BHH (enrolled: N=3,298, 27%; not enrolled: N=8,878, 73%). RESULTS: Relative to nonenrollment, BHH enrollment was associated with increased screening for cervical and breast cancer but not for colorectal cancer. Predicted annual rates remained low, even in BHHs. CONCLUSIONS: Despite estimates of improvements in cervical and breast cancer screening after BHH implementation, cancer screening rates remained suboptimal. Broader cancer screening interventions are needed to improve cancer screening for people with mental illness.

The Role of Screening in Depression Diagnosis and Treatment in a Representative Sample of US Primary Care Visits.

BACKGROUND: Primary care providers encounter a large proportion of the population with depression. Yet, many primary care patients with depression remain undiagnosed and untreated. OBJECTIVE: This study aims to examine depression screening patterns and the role of screening in depression diagnosis and treatment in the outpatient primary care setting. DESIGN: This is a cross-sectional analysis of nationally representative survey data of visits to outpatient physician offices from the 2005 to 2015 National Ambulatory Medical Care Surveys. PARTICIPANTS: The sample included the first visit in the past year to a primary care provider by patients 12 years and older (N = 16,887). METHODS: The associations of visit characteristics with depression screening and of depression screening with depression diagnosis and treatment during the visit were assessed using logistic regression. Logistic regression with propensity score weighting was used to estimate the odds of depression diagnosis and treatment under the counterfactual scenario in which patients who visited providers with lower depression screening rates had visited providers with higher screening rates instead. All models were adjusted for patient and visit characteristics. KEY RESULTS: A small proportion of sample visits involved depression screening (3.0%). Visits by patients with depressive symptom complaints were associated with higher odds of depression screening than other visits. When visits were weighted to have similar demographic and clinical characteristics, visits to providers with higher screening rates had higher odds of diagnosis (OR = 1.99, p < 0.001) and treatment (OR = 1.61, p = 0.001) compared to visits to providers with lower screening rates. CONCLUSIONS: Physicians appear to use depression screening selectively based on patients' presenting symptoms. Higher screening rates were associated with higher odds of depression diagnosis and treatment, and even modest increases in screening rates could meaningfully increase population-level rates of depression identification and treatment in primary care. Future research is needed to identify barriers to depression care and implement systematic interventions to improve services and patient outcomes.