Developing the Blueprint for Youth Suicide Prevention.

Suicide and suicidal behavior among youth and young adults are a major public health crisis, exacerbated by the COVID-19 pandemic and demonstrated by increases in suicidal ideation and attempts among youth. Supports are needed to identify youth at risk and intervene in safe and effective ways. To address this need, the American Academy of Pediatrics and the American Foundation for Suicide Prevention, in collaboration with experts from the National Institute of Mental Health, developed the Blueprint for Youth Suicide Prevention ( Blueprint ) to translate research into strategies that are feasible, pragmatic, and actionable across all contexts in which youth live, learn, work, and play. In this piece, we describe the process of developing and disseminating the Blueprint. Through a summit and focus meetings, cross-sectoral partners convened to discuss the context of suicide risk among youth; explore the landscape of science, practice, and policy; build partnerships; and identify strategies for clinics, communities, and schools-all with a focus on health disparities and equity. These meetings resulted in 5 major takeaways: (1) suicide is often preventable; (2) health equity is critical to suicide prevention; (3) individual and systems changes are needed; (4) resilience should be a key focus; and (5) cross-sectoral partnerships are critical. These meetings and takeaways then informed the content of the Blueprint , which discusses the epidemiology of youth and young adult suicide and suicide risk, including health disparities; the importance of a public health framework; risk factors, protective factors, and warning signs; strategies for clinical settings, strategies for community and school settings; and policy priorities. Following the process description, lessons learned are also discussed, followed by a call to action for the public health community and all who serve and support youth. Finally, key steps to establishing and sustaining partnerships and implications for policy and practice are discussed.

A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases.

Treatment effects are often anticipated to vary across groups of patients with different baseline risk. The Predictive Approaches to Treatment Effect Heterogeneity (PATH) statement focused on baseline risk as a robust predictor of treatment effect and provided guidance on risk-based assessment of treatment effect heterogeneity in a randomized controlled trial. The aim of this study is to extend this approach to the observational setting using a standardized scalable framework. The proposed framework consists of five steps: (1) definition of the research aim, i.e., the population, the treatment, the comparator and the outcome(s) of interest; (2) identification of relevant databases; (3) development of a prediction model for the outcome(s) of interest; (4) estimation of relative and absolute treatment effect within strata of predicted risk, after adjusting for observed confounding; (5) presentation of the results. We demonstrate our framework by evaluating heterogeneity of the effect of thiazide or thiazide-like diuretics versus angiotensin-converting enzyme inhibitors on three efficacy and nine safety outcomes across three observational databases. We provide a publicly available R software package for applying this framework to any database mapped to the Observational Medical Outcomes Partnership Common Data Model. In our demonstration, patients at low risk of acute myocardial infarction receive negligible absolute benefits for all three efficacy outcomes, though they are more pronounced in the highest risk group, especially for acute myocardial infarction. Our framework allows for the evaluation of differential treatment effects across risk strata, which offers the opportunity to consider the benefit-harm trade-off between alternative treatments.

Assessment of the Oxidative Potential and Oxidative Burden from Occupational Exposures to Particulate Matter.

Oxidative potential (OP) is a toxicologically relevant metric that integrates features like mass concentration and chemical composition of particulate matter (PM). Although it has been extensively explored as a metric for the characterization of environmental particles, this is still an underexplored application in the occupational field. This study aimed to estimate the OP of particles in two occupational settings from a construction trades school. This characterization also includes the comparison between activities, sampling strategies, and size fractions. Particulate mass concentrations (PM4-Personal, PM4-Area, and PM2.5-Area) and number concentrations were measured during three weeks of welding and construction/bricklaying activities. The OP was assessed by the ascorbate assay (OPAA) using a synthetic respiratory tract lining fluid (RTLF), while the oxidative burden (OBAA) was determined by multiplying the OPAA values with PM concentrations. Median (25th-75th percentiles) of PM mass and number concentrations were 900 (672-1730) µg m-3 and 128 000 (78 000-169 000) particles cm-3 for welding, and 432 (345-530) µg m-3 and 2800 (1700-4400) particles cm-3 for construction. Welding particles, especially from the first week of activities, were also associated with higher redox activity (OPAA: 3.3 (2.3-4.6) ρmol min-1 µg-1; OBAA: 1750 (893-4560) ρmol min-1 m-3) compared to the construction site (OPAA: 1.4 (1.0-1.8) ρmol min-1 µg-1; OBAA: 486 (341-695) ρmol min-1 m-3). The OPAA was independent of the sampling strategy or size fraction. However, driven by the higher PM concentrations, the OBAA from personal samples was higher compared to area samples in the welding shop, suggesting an influence of the sampling strategy on PM concentrations and OBAA. These results demonstrate that important levels of OPAA can be found in occupational settings, especially during welding activities. Furthermore, the OBAA found in both workplaces largely exceeded the levels found in environmental studies. Therefore, measures of OP and OB could be further explored as metrics for exposure assessment to occupational PM, as well as for associations with cardiorespiratory outcomes in future occupational epidemiological studies.

Assessing and Augmenting Predictive Models for Hospital Readmissions With Novel Variables in an Urban Safety-net Population.

BACKGROUND: The performance of existing predictive models of readmissions, such as the LACE, LACE+, and Epic models, is not established in urban safety-net populations. We assessed previously validated predictive models of readmission performance in a socially complex, urban safety-net population, and if augmentation with additional variables such as the Area Deprivation Index, mental health diagnoses, and housing access improves prediction. Through the addition of new variables, we introduce the LACE-social determinants of health (SDH) model. METHODS: This retrospective cohort study included adult admissions from July 1, 2016, to June 30, 2018, at a single urban safety-net health system, assessing the performance of the LACE, LACE+, and Epic models in predicting 30-day, unplanned rehospitalization. The LACE-SDH development is presented through logistic regression. Predictive model performance was compared using C-statistics. RESULTS: A total of 16,540 patients met the inclusion criteria. Within the validation cohort (n=8314), the Epic model performed the best (C-statistic=0.71, P<0.05), compared with LACE-SDH (0.67), LACE (0.65), and LACE+ (0.61). The variables most associated with readmissions were (odds ratio, 95% confidence interval) against medical advice discharge (3.19, 2.28-4.45), mental health diagnosis (2.06, 1.72-2.47), and health care utilization (1.94, 1.47-2.55). CONCLUSIONS: The Epic model performed the best in our sample but requires the use of the Epic Electronic Health Record. The LACE-SDH performed significantly better than the LACE and LACE+ models when applied to a safety-net population, demonstrating the importance of accounting for socioeconomic stressors, mental health, and health care utilization in assessing readmission risk in urban safety-net patients.

A systematic assessment of the epidemiologic literature regarding an association between acetaminophen exposure and cancer.

Introduced in the 1950s, acetaminophen is one of the most widely used antipyretics and analgesics worldwide. In 1999, the International Agency for Research on Cancer (IARC) reviewed the epidemiologic studies of acetaminophen and the data were judged to be "inadequate" to conclude that it is carcinogenic. In 2019 the California Office of Environmental Health Hazard Assessment initiated a review process on the carcinogenic hazard potential of acetaminophen. To inform this review process, the authors performed a comprehensive literature search and identified 136 epidemiologic studies, which for most cancer types suggest no alteration in risk associated with acetaminophen use. For 3 cancer types, renal cell, liver, and some forms of lymphohematopoietic, some studies suggest an increased risk; however, multiple factors unique to acetaminophen need to be considered to determine if these results are real and clinically meaningful. The objective of this publication is to analyze the results of these epidemiologic studies using a framework that accounts for the inherent challenge of evaluating acetaminophen, including, broad population-wide use in multiple disease states, challenges with exposure measurement, protopathic bias, channeling bias, and recall bias. When evaluated using this framework, the data do not support a causal association between acetaminophen use and cancer.

Pediatric asthma incidence rates in the United States from 1980 to 2017.

BACKGROUND: Few studies have examined longitudinal asthma incidence rates (IRs) from a public health surveillance perspective. OBJECTIVE: Our aim was to calculate descriptive asthma IRs in children over time with consideration for demographics and parental asthma history. METHODS: Data from 9 US birth cohorts were pooled into 1 population covering the period from 1980 to 2017. The outcome was earliest parental report of a doctor diagnosis of asthma. IRs per 1,000 person-years were calculated. RESULTS: The racial/ethnic backgrounds of the 6,283 children studied were as follows: 55% European American (EA), 25.5% African American (AA), 9.5% Mexican-Hispanic American (MA) and 8.5% Caribbean-Hispanic American (CA). The average follow-up was 10.4 years (SD = 8.5 years; median = 8.4 years), totaling 65,291 person-years, with 1789 asthma diagnoses yielding a crude IR of 27.5 per 1,000 person-years (95% CI = 26.3-28.8). Age-specific rates were highest among children aged 0 to 4 years, notably from 1995 to 1999, with a decline in EA and MA children in 2000 to 2004 followed by a decline in AA and CA children in 2010 to 2014. Parental asthma history was associated with statistically significantly increased rates. IRs were similar and higher in AA and CA children versus lower but similar in EA and MA children. The differential rates by sex from birth through adolescence principally resulted from a decline in rates among males but relatively stable rates among females. CONCLUSIONS: US childhood asthma IRs varied dramatically by age, sex, parental asthma history, race/ethnicity, and calendar year. Higher rates in the 0- to 4-year-olds group, particularly among AA/CA males with a parental history of asthma, as well as changes in rates over time and by demographic factors, suggest that asthma is driven by complex interactions between genetic susceptibility and variation in time-dependent environmental and social factors.

The Society for Vascular Surgery Alternative Payment Model Task Force report on opportunities for value-based reimbursement in care for patients with peripheral artery disease.

The Society for Vascular Surgery Alternative Payment Model (APM) Taskforce document explores the drivers and implications for developing objective value-based reimbursement plans for the care of patients with peripheral arterial disease (PAD). The APM is a payment approach that highlights high-quality and cost-efficient care and is a financially incentivized pathway for participation in the Quality Payment Program, which aims to replace the traditional fee-for-service payment method. At present, the participation of vascular specialists in APMs is hampered owing to the absence of dedicated models. The increasing prevalence of PAD diagnosis, technological advances in therapeutic devices, and the increasing cost of care of the affected patients have financial consequences on care delivery models and population health. The document summarizes the existing measurement methods of cost, care processes, and outcomes using payor data, patient-reported outcomes, and registry participation. The document also evaluates the existing challenges in the evaluation of PAD care, including intervention overuse, treatment disparities, varied clinical presentations, and the effects of multiple comorbid conditions on the cost potentially attributable to the vascular interventionalist. Medicare reimbursement data analysis also confirmed the prolonged need for additional healthcare services after vascular interventions. The Society for Vascular Surgery proposes that a PAD APM should provide patients with comprehensive care using a longitudinal approach with integration of multiple key medical and surgical services. It should maintain appropriate access to diagnostic and therapeutic advancements and eliminate unnecessary interventions. It should also decrease the variability in care but must also consider the varying complexity of the presenting PAD conditions. Enhanced quality of care and physician innovation should be rewarded. In addition, provisions should be present within an APM for high-risk patients who carry the risk of exclusion from care because of the naturally associated high costs. Although the document demonstrates clear opportunities for quality improvement and cost savings in PAD care, continued PAD APM development requires the assessment of more granular data for accurate risk adjustment, in addition to largescale testing before public release. Collaboration between payors and physician specialty societies remains key.

Defining the 90-day cost structure of lower extremity revascularization for alternative payment model assessment.

BACKGROUND: The U.S. healthcare system is undergoing a broad transformation from the traditional fee-for-service model to value-based payments. The changes introduced by the Medicare Quality Payment Program, including the establishment of Alternative Payment Models, ensure that the practice of vascular surgery is likely to face significant reimbursement changes as payments transition to favor these models. The Society for Vascular Surgery Alternative Payment Model taskforce was formed to explore the opportunities to develop a physician-focused payment model that will allow vascular surgeons to continue to deliver the complex care required for peripheral arterial disease (PAD). METHODS: A financial analysis was performed based on Medicare beneficiaries who had undergone qualifying index procedures during fiscal year 2016 through the third quarter of 2017. Index procedures were defined using a list of Healthcare Common Procedural Coding (HCPC) procedure codes that represent open and endovascular PAD interventions. Inpatient procedures were mapped to three diagnosis-related group (DRG) families consistent with PAD conditions: other vascular procedures (codes, 252-254), aortic and heart assist procedures (codes, 268, 269), and other major vascular procedures (codes, 270-272). Patients undergoing outpatient or office-based procedures were included if the claims data were inclusive of the HCPC procedure codes. Emergent procedures, patients with end-stage renal disease, and patients undergoing interventions within the 30 days preceding the index procedure were excluded. The analysis included usage of postacute care services (PACS) and 90-day postdischarge events (PDEs). PACS are defined as rehabilitation, skilled nursing facility, and home health services. PDEs included emergency department visits, observation stays, inpatient readmissions, and reinterventions. RESULTS: A total of 123,180 cases were included. Of these 123,180 cases, 82% had been performed in the outpatient setting. The Medicare expenditures for all periprocedural services provided at the index procedure (ie, professional, technical, and facility fees) were higher in the inpatient setting, with an average reimbursement per index case of $18,755, $34,600, and $25,245 for DRG codes 252 to 254, DRG codes 268 and 269, and DRG codes 270 to 272, respectively. Outpatient facility interventions had an average reimbursement of $11,458, and office-based index procedures had costs of $11,533. PACS were more commonly used after inpatient index procedures. In the inpatient setting, PACS usage and reimbursement were 58.6% ($5338), 57.2% ($4192), and 55.9% ($5275) for DRG codes 252 to 254, DRG codes 268 and 269, and DRG codes 270 to 272, respectively. Outpatient facility cases required PACS for 13.7% of cases (average cost, $1352), and office-based procedures required PACS in 15% of cases (average cost, $1467). The 90-day PDEs were frequent across all sites of service (range, 38.9%-50.2%) and carried significant costs. Readmission was associated with the highest average PDE expenditure (range, $13,950-$18.934). The average readmission Medicare reimbursement exceeded that of the index procedures performed in the outpatient setting. CONCLUSIONS: The cost of PAD interventions extends beyond the index procedure and includes relevant spending during the long postoperative period. Despite the analysis challenges related to the breadth of vascular procedures and the site of service variability, the data identified potential cost-saving opportunities in the management of costly PDEs. Because of the vulnerability of the PAD patient population, alternative payment modeling using a bundled value-based approach will require reallocation of resources to provide longitudinal patient care extending beyond the initial intervention.

Wisdom of the CROUD: Development and validation of a patient-level prediction model for opioid use disorder using population-level claims data.

OBJECTIVE: Some patients who are given opioids for pain could develop opioid use disorder. If it was possible to identify patients who are at a higher risk of opioid use disorder, then clinicians could spend more time educating these patients about the risks. We develop and validate a model to predict a person's future risk of opioid use disorder at the point before being dispensed their first opioid. METHODS: A cohort study patient-level prediction using four US claims databases with target populations ranging between 343,552 and 384,424 patients. The outcome was recorded diagnosis of opioid abuse, dependency or unspecified drug abuse as a proxy for opioid use disorder from 1 day until 365 days after the first opioid is dispensed. We trained a regularized logistic regression using candidate predictors consisting of demographics and any conditions, drugs, procedures or visits prior to the first opioid. We then selected the top predictors and created a simple 8 variable score model. RESULTS: We estimated the percentage of new users of opioids with reported opioid use disorder within a year to range between 0.04%-0.26% across US claims data. We developed an 8 variable Calculator of Risk for Opioid Use Disorder (CROUD) score, derived from the prediction models to stratify patients into higher and lower risk groups. The 8 baseline variables were age 15-29, medical history of substance abuse, mood disorder, anxiety disorder, low back pain, renal impairment, painful neuropathy and recent ER visit. 1.8% of people were in the high risk group for opioid use disorder and had a score > = 23 with the model obtaining a sensitivity of 13%, specificity of 98% and PPV of 1.14% for predicting opioid use disorder. CONCLUSIONS: CROUD could be used by clinicians to obtain personalized risk scores. CROUD could be used to further educate those at higher risk and to personalize new opioid dispensing guidelines such as urine testing. Due to the high false positive rate, it should not be used for contraindication or to restrict utilization.

Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases.

BACKGROUND: Confounding by disease severity is an issue in pharmacoepidemiology studies of rheumatoid arthritis (RA), due to channeling of sicker patients to certain therapies. To address the issue of limited clinical data for confounder adjustment, a patient-level prediction model to differentiate between patients prescribed and not prescribed advanced therapies was developed as a surrogate for disease severity, using all available data from a US claims database. METHODS: Data from adult RA patients were used to build regularized logistic regression models to predict current and future disease severity using a biologic or tofacitinib prescription claim as a surrogate for moderate-to-severe disease. Model discrimination was assessed using the area under the receiver (AUC) operating characteristic curve, tested and trained in Optum Clinformatics® Extended DataMart (Optum) and additionally validated in three external IBM MarketScan® databases. The model was further validated in the Optum database across a range of patient cohorts. RESULTS: In the Optum database (n = 68,608), the AUC for discriminating RA patients with a prescription claim for a biologic or tofacitinib versus those without in the 90 days following index diagnosis was 0.80. Model AUCs were 0.77 in IBM CCAE (n = 75,579) and IBM MDCD (n = 7,537) and 0.75 in IBM MDCR (n = 36,090). There was little change in the prediction model assessing discrimination 730 days following index diagnosis (prediction model AUC in Optum was 0.79). CONCLUSIONS: A prediction model demonstrated good discrimination across multiple claims databases to identify RA patients with a prescription claim for advanced therapies during different time-at-risk periods as proxy for current and future moderate-to-severe disease. This work provides a robust model-derived risk score that can be used as a potential covariate and proxy measure to adjust for confounding by severity in multivariable models in the RA population. An R package to develop the prediction model and risk score are available in an open source platform for researchers.

Feasibility of Using Real-World Data to Replicate Clinical Trial Evidence.

Importance: Although randomized clinical trials are considered to be the criterion standard for generating clinical evidence, the use of real-world evidence to evaluate the efficacy and safety of medical interventions is gaining interest. Whether observational data can be used to address the same clinical questions being answered by traditional clinical trials is still unclear. Objective: To identify the number of clinical trials published in high-impact journals in 2017 that could be feasibly replicated using observational data from insurance claims and/or electronic health records (EHRs). Design, Setting, and Participants: In this cross-sectional analysis, PubMed was searched to identify all US-based clinical trials, regardless of randomization, published between January 1, 2017, and December 31, 2017, in the top 7 highest-impact general medical journals of 2017. Trials were excluded if they did not involve human participants, did not use end points that represented clinical outcomes among patients, were not characterized as clinical trials, and had no recruitment sites in the United States. Main Outcomes and Measures: The primary outcomes were the number and percentage of trials for which the intervention, indication, trial inclusion and exclusion criteria, and primary end points could be ascertained from insurance claims and/or EHR data. Results: Of the 220 US-based trials analyzed, 33 (15.0%) could be replicated using observational data because their intervention, indication, inclusion and exclusion criteria, and primary end points could be routinely ascertained from insurance claims and/or EHR data. Of the 220 trials, 86 (39.1%) had an intervention that could be ascertained from insurance claims and/or EHR data. Among the 86 trials, 62 (72.1%) had an indication that could be ascertained. Forty-five (72.6%) of 62 trials had at least 80% of inclusion and exclusion criteria data that could be ascertained. Of these 45 studies, 33 (73.3%) had at least 1 primary end point that could be ascertained. Conclusions and Relevance: This study found that only 15% of the US-based clinical trials published in high-impact journals in 2017 could be feasibly replicated through analysis of administrative claims or EHR data. This finding suggests the potential for real-world evidence to complement clinical trials, both by examining the concordance between randomized experiments and observational studies and by comparing the generalizability of the trial population with the real-world population of interest.

Challenges with quality of race and ethnicity data in observational databases.

OBJECTIVE: We sought to assess the quality of race and ethnicity information in observational health databases, including electronic health records (EHRs), and to propose patient self-recording as an improvement strategy. MATERIALS AND METHODS: We assessed completeness of race and ethnicity information in large observational health databases in the United States (Healthcare Cost and Utilization Project and Optum Labs), and at a single healthcare system in New York City serving a racially and ethnically diverse population. We compared race and ethnicity data collected via administrative processes with data recorded directly by respondents via paper surveys (National Health and Nutrition Examination Survey and Hospital Consumer Assessment of Healthcare Providers and Systems). Respondent-recorded data were considered the gold standard for the collection of race and ethnicity information. RESULTS: Among the 160 million patients from the Healthcare Cost and Utilization Project and Optum Labs datasets, race or ethnicity was unknown for 25%. Among the 2.4 million patients in the single New York City healthcare system's EHR, race or ethnicity was unknown for 57%. However, when patients directly recorded their race and ethnicity, 86% provided clinically meaningful information, and 66% of patients reported information that was discrepant with the EHR. DISCUSSION: Race and ethnicity data are critical to support precision medicine initiatives and to determine healthcare disparities; however, the quality of this information in observational databases is concerning. Patient self-recording through the use of patient-facing tools can substantially increase the quality of the information while engaging patients in their health. CONCLUSIONS: Patient self-recording may improve the completeness of race and ethnicity information.

Supplementing claims data analysis using self-reported data to develop a probabilistic phenotype model for current smoking status.

OBJECTIVES: Smoking status is poorly record in US claims data. IBM MarketScan Commercial is a claims database that can be linked to an additional health risk assessment with self-reported smoking status for a subset of 1,966,174 patients. We investigate whether this subset could be used to learn a smoking status phenotype model generalizable to all US claims data that calculates the probability of being a current smoker. METHODS: 251,643 (12.8%) had self-reported their smoking status as 'current smoker'. A regularized logistic regression model, the Current Risk of Smoking Status (CROSS), was trained using the subset of patients with self-reported smoking status. CROSS considered 53,027 candidate covariates including demographics and conditions/drugs/measurements/procedures/observations recorded in the prior 365 days, The CROSS phenotype model was validated across multiple other claims data. RESULTS: The internal validation showed the CROSS model achieved an area under the receiver operating characteristic curve (AUC) of 0.76 and the calibration plots indicated it was well calibrated. The external validation across three US claims databases obtained AUCs ranging between 0.82 and 0.87 showing the model appears to be transportable across Claims data. CONCLUSION: CROSS predicts current smoking status based on the claims records in the prior year. CROSS can be readily implemented to any US insurance claims mapped to the OMOP common data model and will be a useful way to impute smoking status when conducting epidemiology studies where smoking is a known confounder but smoking status is not recorded. CROSS is available from https://github.com/OHDSI/StudyProtocolSandbox/tree/master/SmokingModel.