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The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are not known to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004-2009, 41.5% occurred among 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100 000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded.

OBJECTIVE: To describe a new resource for quality of care research based on a linkage between the Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS®) patient surveys and the NCI’s Surveillance, Epidemiology and End Results (SEER) data.

The use of many services is lower in Medicare Advantage (MA) compared with traditional Medicare, generating cost savings for insurers, whereas the quality of ambulatory services is higher. This study examined the role of selective contracting with providers in achieving these outcomes, focusing on primary care physicians. Assessing primary care physician costliness based on the gap between observed and predicted costs for their traditional Medicare patients, we found that the average primary care physician in MA networks was $433 less costly per patient (2.9 percent of baseline) compared with the regional mean, with less costly primary care physicians included in more networks than more costly ones. Favorable selection of patients by MA primary care physicians contributed partially to this result. The quality measures of MA primary care physicians were similar to the regional mean. In contrast, primary care physicians excluded from all MA networks were $1,617 (13.8 percent) costlier than the regional mean, with lower quality. Primary care physicians in narrow networks were $212 (1.4 percent) less costly than those in wide networks, but their quality was slightly lower. These findings highlight the potential role of selective contracting in reducing costs in the MA program.

Importance: Dementia is a life-altering diagnosis that may affect medication safety and goals for chronic disease management., Objective: To examine changes in medication use following an incident dementia diagnosis among community-dwelling older adults., Design, Setting, and Participants: In this cohort study of adults aged 67 years or older enrolled in traditional Medicare and Medicare Part D, patients with incident dementia diagnosed between January 2012 and December 2018 were matched to control patients based on demographics, geographic location, and baseline medication count. The index date was defined as the date of first dementia diagnosis or, for controls, the date of the closest office visit. Data were analyzed from August 2021 to June 2023., Exposure: Incident dementia diagnosis., Main Outcomes and Measures: The main outcomes were overall medication counts and use of cardiometabolic, central nervous system (CNS)-active, and anticholinergic medications. A comparative time-series analysis was conducted to examine quarterly changes in medication use in the year before through the year following the index date., Results: The study included 266 675 adults with incident dementia and 266 675 control adults; in both groups, 65.1% were aged 80 years or older (mean [SD] age, 82.2 [7.1] years) and 67.8% were female. At baseline, patients with incident dementia were more likely than controls to use CNS-active medications (54.32% vs 48.39%) and anticholinergic medications (17.79% vs 15.96%) and less likely to use most cardiometabolic medications (eg, diabetes medications, 31.19% vs 36.45%). Immediately following the index date, the cohort with dementia had a greater increase in mean number of medications used (0.41 vs -0.06; difference, 0.46 [95% CI, 0.27-0.66]) and in the proportion of patients using CNS-active medications (absolute change, 3.44% vs 0.79%; difference, 2.65% [95% CI, 0.85%-4.45%]) owing to an increased use of antipsychotics, antidepressants, and antiepileptics. The cohort with dementia also had a modestly greater decline in use of anticholinergic medications (quarterly change in use, -0.53% vs -0.21%; difference, -0.32% [95% CI, -0.55% to -0.08%]) and most cardiometabolic medications (eg, quarterly change in antihypertensive use: -0.84% vs -0.40%; difference, -0.44% [95% CI, -0.64% to -0.25%]). One year after diagnosis, 75.2% of the cohort with dementia were using 5 or more medications (2.8% increase)., Conclusions and Relevance: In this cohort study of Medicare Part D beneficiaries, following an incident dementia diagnosis, patients were more likely to initiate CNS-active medications and modestly more likely to discontinue cardiometabolic and anticholinergic medications compared with the control group. These findings suggest missed opportunities to reduce burdensome polypharmacy by deprescribing long-term medications with high safety risks or limited likelihood of benefit or that may be associated with impaired cognition.

Background: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA)., Methods: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample., Results: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors., Conclusions: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Background: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan., Methods: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample., Results: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables., Conclusions: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Background: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA)., Methods: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample., Results: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics., Limitations: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results., Conclusions: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection., Competing Interests: Conflict of interest In the past 3 years, Dr. Kessler was a consultant for Cambridge Health Alliance, Canandaigua VA Medical Center, Holmusk, Partners Healthcare, Inc., RallyPoint Networks, Inc., and Sage Therapeutics. He has stock options in Cerebral Inc., Mirah, PYM, and Roga Sciences. Dr. Pigeon consulted for CurAegis Technologies and received clinical trial support from Pfizer, Inc. and Abbvie, Inc. Dr. Zubizarreta consulted for Johnson & Johnson Real World Data Analytics. Dr. Cipriani is supported by the National Institute for Health Research (NIHR) Oxford Cognitive Health Clinical Research Facility, by an NIHR Research Professorship (grant RP-2017-08-ST2-006), by the NIHR Oxford and Thames Valley Applied Research Collaboration and by the NIHR Oxford Health Biomedical Research Centre (grant BRC-1215-20005); he has also received research, educational and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO Foundation and Angelini Pharma. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, or the UK Department of Health. The remaining authors report no conflicts of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Medicare Advantage (MA) enrollment growth could make it difficult for MA plans to maintain their track record of limiting discretionary utilization while delivering higher-quality care than traditional Medicare. We compared quality and utilization measures in Medicare Advantage and traditional Medicare in 2010 and 2017. Clinical quality performance was higher in MA health maintenance organizations (HMOs) and preferred provider organizations (PPOs) than in traditional Medicare for almost all measures in both years. MA HMOs outperformed traditional Medicare on all measures in 2017. MA HMOs' performance on nearly all seven patient-reported quality measures improved, and MA HMOs outperformed traditional Medicare on five of those measures in 2017. MA PPOs performed the same as or better than traditional Medicare on all but one patient-reported quality measure in 2010 and 2017. The number of emergency department visits was 30 percent lower, the number of elective hip and knee replacements was approximately 10 percent lower, and the number of back surgeries was almost 30 percent lower in MA HMOs than in traditional Medicare in 2017. Utilization trends were similar in MA PPOs, but differences from traditional Medicare were narrower. Despite increased enrollment, overall utilization remains lower in Medicare Advantage than in traditional Medicare, whereas quality performance is the same or higher.

Background: The most common treatment for major depressive disorder (MDD) is antidepressant medication (ADM). Results are reported on frequency of ADM use, reasons for use, and perceived effectiveness of use in general population surveys across 20 countries., Methods: Face-to-face interviews with community samples totaling n = 49 919 respondents in the World Health Organization (WHO) World Mental Health (WMH) Surveys asked about ADM use anytime in the prior 12 months in conjunction with validated fully structured diagnostic interviews. Treatment questions were administered independently of diagnoses and asked of all respondents., Results: 3.1% of respondents reported ADM use within the past 12 months. In high-income countries (HICs), depression (49.2%) and anxiety (36.4%) were the most common reasons for use. In low- and middle-income countries (LMICs), depression (38.4%) and sleep problems (31.9%) were the most common reasons for use. Prevalence of use was 2-4 times as high in HICs as LMICs across all examined diagnoses. Newer ADMs were proportionally used more often in HICs than LMICs. Across all conditions, ADMs were reported as very effective by 58.8% of users and somewhat effective by an additional 28.3% of users, with both proportions higher in LMICs than HICs. Neither ADM class nor reason for use was a significant predictor of perceived effectiveness., Conclusion: ADMs are in widespread use and for a variety of conditions including but going beyond depression and anxiety. In a general population sample from multiple LMICs and HICs, ADMs were widely perceived to be either very or somewhat effective by the people who use them.

Importance: Medicare Advantage health plans covered 37% of beneficiaries in 2018, and coverage increased to 48% in 2022. Whether Medicare Advantage plans provide similar care for patients presenting with specific clinical conditions is unknown., Objective: To compare 30-day mortality and treatment for Medicare Advantage and traditional Medicare patients presenting with acute myocardial infarction (MI) from 2009 to 2018., Design, Setting, and Participants: Retrospective cohort study that included 557 309 participants with ST-segment elevation [acute] MI (STEMI) and 1 670 193 with non-ST-segment elevation [acute] MI (NSTEMI) presenting to US hospitals from 2009-2018 (date of final follow up, December 31, 2019)., Exposures: Enrollment in Medicare Advantage vs traditional Medicare., Main Outcomes and Measures: The primary outcome was adjusted 30-day mortality. Secondary outcomes included age- and sex-adjusted rates of procedure use (catheterization, revascularization), postdischarge medication prescriptions and adherence, and measures of health system performance (intensive care unit [ICU] admission and 30-day readmissions)., Results: The study included a total of 2 227 502 participants, and the mean age in 2018 ranged from 76.9 years (Medicare Advantage STEMI) to 79.3 years (traditional Medicare NSTEMI), with similar proportions of female patients in Medicare Advantage and traditional Medicare (41.4% vs 41.9% for STEMI in 2018). Enrollment in Medicare Advantage vs traditional Medicare was associated with significantly lower adjusted 30-day mortality rates in 2009 (19.1% vs 20.6% for STEMI; difference, -1.5 percentage points [95% CI, -2.2 to -0.7] and 12.0% vs 12.5% for NSTEMI; difference, -0.5 percentage points [95% CI, -0.9% to -0.1%]). By 2018, mortality had declined in all groups, and there were no longer statically significant differences between Medicare Advantage (17.7%) and traditional Medicare (17.8%) for STEMI (difference, 0.0 percentage points [95% CI, -0.7 to 0.6]) or between Medicare Advantage (10.9%) and traditional Medicare (11.1%) for NSTEMI (difference, -0.2 percentage points [95% CI, -0.4 to 0.1]). By 2018, there was no statistically significant difference in standardized 90-day revascularization rates between Medicare Advantage and traditional Medicare. Rates of guideline-recommended medication prescriptions were significantly higher in Medicare Advantage (91.7%) vs traditional Medicare patients (89.0%) who received a statin prescription (difference, 2.7 percentage points [95% CI, 1.2 to 4.2] for 2018 STEMI). Medicare Advantage patients were significantly less likely to be admitted to an ICU than traditional Medicare patients (for 2018 STEMI, 50.3% vs 51.2%; difference, -0.9 percentage points [95% CI, -1.8 to 0.0]) and significantly more likely to be discharged to home rather than to a postacute facility (for 2018 STEMI, 71.5% vs 70.2%; difference, 1.3 percentage points [95% CI, 0.5 to 2.1]). Adjusted 30-day readmission rates were consistently lower in Medicare Advantage than in traditional Medicare (for 2009 STEMI, 13.8% vs 15.2%; difference, -1.3 percentage points [95% CI, -2.0 to -0.6]; and for 2018 STEMI, 11.2% vs 11.9%; difference, 0.6 percentage points [95% CI, -1.5 to 0.0])., Conclusions and Relevance: Among Medicare beneficiaries with acute MI, enrollment in Medicare Advantage, compared with traditional Medicare, was significantly associated with modestly lower rates of 30-day mortality in 2009, and the difference was no longer statistically significant by 2018. These findings, considered with other outcomes, may provide insight into differences in treatment and outcomes by Medicare insurance type.

Background: In 2014, hypertension guidelines for older adults endorsed increased use of fixed-dose combinations, prioritized thiazide diuretics and calcium channel blockers (CCBs) for Black patients, and no longer recommend beta-blockers as first-line therapy., Objective: To evaluate older adults' antihypertensive use following guideline changes., Design: Time series analysis., Patients: Twenty percent national sample of Medicare Part D beneficiaries aged 66 years and older with hypertension., Intervention: Eighth Joint National Committee (JNC8) guidelines MAIN MEASURES: Quarterly trends in prevalent and initial antihypertensive use were examined before (2008 to 2013) and after (2014 to 2017) JNC8. Analyses were conducted among all beneficiaries with hypertension, beneficiaries without chronic conditions that might influence antihypertensive selection (hypertension-only cohort), and among Black patients, given race-based guideline recommendations., Key Results: The number of beneficiaries with hypertension increased from 1,978,494 in 2008 to 2,809,680 in 2017, the proportions using antihypertensives increased from 80.3 to 81.2%, and the proportion using multiple classes and fixed-dose combinations declined (60.8 to 58.1% and 20.7 to 15.1%, respectively, all P<.01). Prior to JNC8, the use of angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and CCBs was increasing. Use of CCBs as initial therapy increased more rapidly following JNC8 (relative change in quarterly trend 0.15% [95% CI, 0.13-0.18%), especially among Black beneficiaries (relative change 0.44% [95% CI, 0.21-0.68%]). Contrary to guidelines, the use of thiazides and combinations as initial therapy consistently decreased in the hypertension-only cohort (13.8 to 8.3% and 25.1 to 15.7% respectively). By 2017, 65.9% of Black patients in the hypertension-only cohort were initiated on recommended first-line or combination therapy compared to 80.3% of non-Black patients., Conclusions: Many older adults, particularly Black patients, continue to be initiated on antihypertensive classes not recommended as first-line, indicating opportunities to improve the effectiveness and equity of hypertension care and potentially reduce antihypertensive regimen complexity., (© 2022. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Background: Physician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes., Objective: To examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment., Design: Patient self-report surveys at treatment initiation linked to medical records., Setting: Veterans Health Administration (VA) clinics nationally, 2018-2020., Participants: A total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White) MAIN MEASURES: Patient self-reported preferences for medication and psychotherapy on 0-10 self-anchoring visual analog scales (0="completely unwilling"; 10="completely willing"). Treatment receipt and adherence (refilling medications; attending 3+ psychotherapy sessions) over 3 months. Logistic regression models controlled for socio-demographics and geographic variables., Key Results: More patients reported strong preferences (10/10) for psychotherapy than medication (51.2% versus 36.7%, McNemar χ 2 1 =175.3, p<0.001). A total of 32.1% of patients who preferred (7-10/10) medication and 21.8% who preferred psychotherapy did not receive these treatments. Patients who strongly preferred medication were substantially more likely to receive medication than those who had strong negative preferences (odds ratios [OR]=17.5; 95% confidence interval [CI]=12.5-24.5). Compared with patients who had strong negative psychotherapy preferences, those with strong psychotherapy preferences were about twice as likely to receive psychotherapy (OR=1.9; 95% CI=1.0-3.5). Patients who strongly preferred psychotherapy were more likely to adhere to psychotherapy than those with strong negative preferences (OR=3.3; 95% CI=1.4-7.4). Treatment preferences were not associated with medication or combined treatment adherence. Patients in primary care settings had lower odds of receiving (but not adhering to) psychotherapy than patients in specialty mental health settings. Depression severity was not associated with treatment receipt or adherence., Conclusions: Mismatches between treatment preferences and treatment type received were common and associated with worse treatment adherence for psychotherapy. Future research could examine ways to decrease mismatch between patient preferences and treatments received and potential effects on patient outcomes., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Objectives: To characterize the proportion of Medicare Advantage (MA) enrollees who switched insurers or disenrolled to traditional Medicare (TM) in the years immediately after first choosing to join an MA health plan., Study Design: Retrospective analysis using 2012-2017 Medicare enrollment data., Methods: We studied enrollees who joined MA between 2012 and 2016 and identified all enrollees who changed insurers (switched insurance or disenrolled to TM) at least once between the start of enrollment and the end of the study period. We categorized each change as switching insurers or disenrollment to TM, and by whether the previous insurer had exited the market., Results: Among 6,520,169 new MA enrollees, 15.6% had changed insurance within 1 year after enrollment in MA and 49.2% had changed insurance by 5 years. More enrollees switched insurers rather than disenrolled, and most enrollees who changed insurers did not do so as a result of insurer exits., Conclusions: New MA enrollees change insurers at a substantial rate when followed across multiple years. These changes may disincentivize insurers from investing in preventive care and chronic disease management and, as shown in several non-MA populations, may lead to discontinuities in care, increased expenditures, and inferior health outcomes.

Purpose: To investigate the associations of childhood adversities (CAs) with lifetime onset and transitions across suicidal thoughts and behaviors (STB) among incoming college students., Methods: Web-based self-report surveys administered to 20,842 incoming college students from nine countries (response rate 45.6%) assessed lifetime suicidal ideation, plans and attempts along with seven CAs: parental psychopathology, three types of abuse (emotional, physical, sexual), neglect, bully victimization, and dating violence. Logistic regression estimated individual- and population-level associations using CA operationalizations for type, number, severity, and frequency., Results: Associations of CAs with lifetime ideation and the transition from ideation to plan were best explained by the exact number of CA types (OR range 1.32-52.30 for exactly two to seven CAs). Associations of CAs with a transition to attempts were best explained by the frequency of specific CA types (scaled 0-4). Attempts among ideators with a plan were significantly associated with all seven CAs (OR range 1.16-1.59) and associations remained significant in adjusted analyses with the frequency of sexual abuse (OR = 1.42), dating violence (OR = 1.29), physical abuse (OR = 1.17) and bully victimization (OR = 1.17). Attempts among ideators without plan were significantly associated with frequency of emotional abuse (OR = 1.29) and bully victimization (OR = 1.36), in both unadjusted and adjusted analyses. Population attributable risk simulations found 63% of ideation and 30-47% of STB transitions associated with CAs., Conclusion: Early-life adversities represent a potentially important driver in explaining lifetime STB among incoming college students. Comprehensive intervention strategies that prevent or reduce the negative effects of CAs may reduce subsequent onset of STB., (© 2021. Springer-Verlag GmbH Germany, part of Springer Nature.)

Importance: Claims of dramatic increases in clinically significant anxiety and depression early in the COVID-19 pandemic came from online surveys with extremely low or unreported response rates., Objective: To examine trend data in a calibrated screening for clinically significant anxiety and depression among adults in the only US government benchmark probability trend survey not disrupted by the COVID-19 pandemic., Design, Setting, and Participants: This survey study used the US Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System (BRFSS), a monthly state-based trend survey conducted over the telephone. Participants were adult respondents in the 50 US states and District of Columbia who were surveyed March to December 2020 compared with the same months in 2017 to 2019., Exposures: Monthly state COVID-19 death rates., Main Outcomes and Measures: Estimated 30-day prevalence of clinically significant anxiety and depression based on responses to a single BRFSS item calibrated to a score of 6 or greater on the 4-item Patient Health Questionnaire (area under the receiver operating characteristic curve, 0.84). All percentages are weighted based on BRFSS calibration weights., Results: Overall, there were 1 429 354 respondents, with 1 093 663 in 2017 to 2019 (600 416 [51.1%] women; 87 153 [11.8%] non-Hispanic Black; 826 334 [61.5%] non-Hispanic White; 411 254 [27.8%] with college education; and 543 619 [56.8] employed) and 335 691 in 2020 (182 351 [51.3%] women; 25 517 [11.7%] non-Hispanic Black; 250 333 [60.5%] non-Hispanic White; 130 642 [29.3%] with college education; and 168 921 [54.9%] employed). Median within-state response rates were 45.9% to 49.4% in 2017 to 2019 and 47.9% in 2020. Estimated 30-day prevalence of clinically significant anxiety and depression was 0.4 (95% CI, 0.0 to 0.7) percentage points higher in March to December 2020 (12.4%) than March to December 2017 to 2019 (12.1%). This estimated increase was limited, however, to students (2.4 [95% CI, 0.8 to 3.9] percentage points) and the employed (0.9 [95% CI, 0.5 to 1.4] percentage points). Estimated prevalence decreased among the short-term unemployed (-1.8 [95% CI, -3.1 to -0.5] percentage points) and those unable to work (-4.2 [95% CI, -5.3 to -3.2] percentage points), but did not change significantly among the long-term unemployed (-2.1 [95% CI, -4.5 to 0.5] percentage points), homemakers (0.8 [95% CI, -0.3 to 1.9] percentage points), or the retired (0.1 [95% CI, -0.6 to 0.8] percentage points). The increase in anxiety and depression prevalence among employed people was positively associated with the state-month COVID-19 death rate (1.8 [95% CI, 1.2 to 2.5] percentage points when high and 0.0 [95% CI, -0.7 to 0.6] percentage points when low) and was elevated among women compared with men (2.0 [95% CI, 1.4 to 2.5] percentage points vs 0.2 [95% CI, -0.1 to 0.6] percentage points), Non-Hispanic White individuals compared with Hispanic and non-Hispanic Black individuals (1.3 [95% CI, 0.6 to 1.9] percentage points vs 1.1 [95% CI, -0.2 to 2.5] percentage points and 0.7 [95% CI, -0.1 to 1.5] percentage points), and those with college educations compared with less than high school educations (2.5 [95% CI, 1.9 to 3.1] percentage points vs -0.6 [95% CI, -2.7 to 1.4] percentage points)., Conclusions and Relevance: In this survey study, clinically significant US adult anxiety and depression increased less during 2020 than suggested by online surveys. However, this modest aggregate increase could mask more substantial increases in key population segments (eg, first responders) and might have become larger in 2021 and 2022.

Introduction: Emotion reactivity (ER) and distress intolerance (DI) may be associated with increased suicide attempt (SA) risk among U.S. Army soldiers., Method: In this case-control study, 74 soldiers recently hospitalized for SA (cases) were compared with 133 control soldiers from the same Army installations selected based on either propensity score matching (n = 103) or reported 12-month suicide ideation (SI) (n = 30). Controls were weighted to represent the total Army population at the study sites and the subpopulation of 12-month ideators. Participants completed questionnaires assessing ER, DI, and other psychosocial variables. Logistic regression analyses examined whether ER and DI differentiated SA cases from the general population and from 12-month ideators before and after controlling for additional important risk factors (sociodemographic characteristics, stressors, mental disorders)., Results: In univariate analyses, ER differentiated SA cases from both the general population (OR = 2.5[95%CI = 1.7-3.6]) and soldiers with 12-month SI (OR = 2.5[95%CI = 1.3-4.6]). DI also differentiated cases from the general population (OR = 2.9[95%CI = 2.0-4.1]) and 12-month ideators (OR = 1.9[95%CI = 1.1-3.5]). These associations persisted after controlling for sociodemographic variables, stressors, and mental disorders., Conclusion: Findings provide evidence that higher ER and DI are associated with increased risk of SA among soldiers, even after adjusting for known risk factors. Prospective research with larger samples is needed., (© 2021 American Association of Suicidology. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

Racial inequities in clinical performance diminish overall health care system performance; however, quality assessments have rarely incorporated reliable measures of racial inequities. We studied care for more than one million Medicare fee-for-service beneficiaries with cancer to assess the feasibility of calculating reliable practice-level measures of racial inequities in chemotherapy-associated emergency department (ED) visits and hospitalizations. Specifically, we used hierarchical models to estimate adjusted practice-level Black-White differences in these events and described differences across practices. We calculated reliable inequity measures for 426 and 322 practices, depending on the measure. These practices reflected fewer than 10 percent of practices treating Medicare beneficiaries with chemotherapy, but they treated approximately half of all White and Black Medicare beneficiaries receiving chemotherapy and two-thirds of Black Medicare beneficiaries receiving chemotherapy. Black patients experienced chemotherapy-associated ED visits and hospitalizations at higher rates (54.2 percent and 35.8 percent, respectively) than White patients (45.7 percent and 31.9 percent, respectively). The median within-practice Black-White difference was 8.1 percentage points for chemotherapy-associated ED visits and 2.7 percentage points for chemotherapy-associated hospitalizations. Additional research is needed to identify other reliable measures of racial inequities in health care quality, measure care inequities in smaller practices, and assess whether providing practice-level feedback could improve equity.

The authors review trend and cohort surveys and administrative data comparing prevalence of mental disorders during, versus, and before the COVID-19 pandemic and changes in mental health disparities. Best evidence suggests clinically significant anxiety-depression point prevalence increased by relative-risk (RR) = 1.3 to 1.5 during the pandemic compared with before. This level of increase is much less than the implausibly high RR = 5.0 to 8.0 estimates reported in trend studies early in the pandemic based on less-appropriate comparisons. Changes in prevalence also occurred during the pandemic, but relative prevalence appears not to have changed substantially over this time., Competing Interests: Disclosure In the past 3 years, Dr R.C. Kessler was a consultant for Datastat, Inc, Holmusk, RallyPoint Networks, Inc, and Sage Therapeutics. He has stock options in Mirah, PYM, and Roga Sciences. The other authors report no conflicts., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objective: The authors sought to identify predictors of imminent suicide attempt (within 30 days) among U.S. Army soldiers following their first documented suicidal ideation., Methods: Using administrative data from the Army Study to Assess Risk and Resilience in Servicemembers, the authors identified 11,178 active-duty Regular Army enlisted soldiers (2006-2009) with medically documented suicidal ideation and no prior medically documented suicide attempts. The authors examined risk factors for suicide attempt within 30 days of first suicidal ideation using logistic regression analyses, including sociodemographic and service-related characteristics, psychiatric diagnoses, physical health care visits, injuries, and history of family violence or crime perpetration or victimization., Results: Among soldiers with first documented suicidal ideation, 830 (7.4%) attempted suicide, 46.3% of whom (N=387) attempted suicide within 30 days (rate, 35.4 per 1,000 soldiers). Following a series of multivariate analyses, the final model identified females (odds ratio=1.3, 95% CI=1.0, 1.8), combat medics (odds ratio=1.6, 95% CI=1.1, 2.2), individuals with an anxiety disorder diagnosis prior to suicidal ideation (odds ratio=1.3, 95% CI=1.0, 1.6), and those who received a sleep disorder diagnosis on the same day as the recorded suicidal ideation (odds ratio=2.3, 95% CI=1.1, 4.6) as being more likely to attempt suicide within 30 days. Black soldiers (odds ratio=0.6, 95% CI=0.4, 0.9) and those who received an anxiety disorder diagnosis on the same day as suicidal ideation (odds ratio=0.7, 95% CI=0.5, 0.9) were less likely., Conclusions: Suicide attempt risk is highest in the first 30 days following ideation diagnosis and is more likely among women, combat medics, and soldiers with an anxiety disorder diagnosis before suicidal ideation and a same-day sleep disorder diagnosis. Black soldiers and those with a same-day anxiety disorder diagnosis were at decreased risk. These factors may help identify soldiers at imminent risk of suicide attempt.

Importance: Patient reviews of health care experiences are increasingly used for public reporting and alternative payment models. Critics have argued that this incentivizes physicians to provide more care, including low-value care, undermining efforts to reduce wasteful practices., Objective: To assess associations between rates of low-value service provision to a primary care professional (PCP) patient panel and patients' ratings of their health care experiences., Design, Setting, and Participants: This quality improvement study used Medicare fee-for-service claims from January 1, 2007, to December 31, 2014, for a random 20% sample of beneficiaries to identify beneficiaries for whom each of 8 low-value services could be ordered but would be considered unnecessary. The study also used health care experience reports from independently sampled beneficiaries who responded to the 2010-2015 Consumer Assessment of Healthcare Providers and Systems (CAHPS) Medicare fee-for-service survey. Statistical analysis was performed from January 1, 2019, to December 9, 2020., Main Outcomes and Measures: The main outcomes were health care experience ratings from Medicare beneficiaries who responded to the CAHPS survey from 2 domains, namely "Your Health Care in the Last 6 Months" (overall health care, office wait time, timely access to nonurgent care, and timely access to urgent care) and "Your Personal Doctor" (overall personal physician and a composite score for interactions with personal physician). Beneficiaries in both samples were attributed to the PCP with whom they had the most spending. For each PCP, a composite score of low-value service exposure was constructed using the 20% sample; this score represented the adjusted relative propensity of the PCP patient panel to receive low-value care. The association between low-value service exposure and health care experience ratings reported by the CAHPS respondents in the PCP patient panel was estimated using regression analysis., Results: The final sample had 100 743 PCPs, with a mean of approximately 258 patients per PCP. Only 1 notable association was found; more low-value care exposure was associated with more frequent reports of having to wait more than 15 minutes after the scheduled time of an appointment (a mean of 0.448 points lower CAHPS score on a 10-point scale for PCP patient panels who received the most low-value care vs the least low-value care). Although some other associations were statistically significant, their magnitudes were substantially smaller than those typically considered meaningful in other CAHPS literature and were inconsistent in direction across levels of low-value service exposure., Conclusions and Relevance: This quality improvement study found that more low-value care exposure for a PCP patient panel was not associated with more favorable patient ratings of their health care experiences.

Background: Psychiatric comorbidities may complicate depression treatment by being associated with increased role impairments. However, depression symptom severity might account for these associations. Understanding the independent associations of depression severity and comorbidity with impairments could help in treatment planning. This is especially true for depressed Veterans, who have high psychiatric comorbidity rates., Methods: 2,610 Veterans beginning major depression treatment at the Veterans Health Administration (VHA) were administered a baseline self-report survey that screened for diverse psychiatric comorbidities and assessed depression severity and role impairments. Logistic and generalized linear regression models estimated univariable and multivariable associations of depression severity and comorbidities with impairments. Population attributable risk proportions (PARPs) estimated the relative importance of depression severity and comorbidities in accounting for role impairments., Results: Nearly all patients (97.8%) screened positive for at least one comorbidity and half (49.8%) for 4+ comorbidities. The most common positive screens were for generalized anxiety disorder (80.2%), posttraumatic stress disorder (77.9%), and panic/phobia (77.4%). Depression severity and comorbidities were significantly and additively associated with impairments in multivariable models. Associations were attenuated much less for depression severity than for comorbidities in multivariable versus univariable models. PARPs indicated that 15-60% of role impairments were attributable to depression severity and 5-32% to comorbidities., Limitations: The screening scales could have over-estimated comorbidity prevalence. The cross-sectional observational design cannot determine either temporal or causal priorities., Conclusions: Although positive screens for psychiatric comorbidity are pervasive among depressed VHA patients, depression severity accounts for most of the associations of these comorbidities with role impairments., (Copyright © 2021. Published by Elsevier B.V.)

Objective: Dual Eligible Special Needs Plans (D-SNPs) were intended to provide better care for beneficiaries eligible for both Medicare and Medicaid through better coordination of these two programs., Data Sources: 671 913 dual eligible (DE) respondents to the 2009-2019 Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey., Study Design: We compared the 2015-2019 experiences of DE beneficiaries in D-SNPs relative to fee-for-service Medicare (FFS) and non-SNP Medicare Advantage (MA) using propensity-score weighted linear regression. Comparisons were made to 2009-2014. 12 patient experience measures were considered., Data Collection Methods: Annual mail survey with telephone follow-up of non-respondents., Principal Findings: More than 65% of DE beneficiaries enrolled in FFS. Of 12 measures, D-SNP performance was higher than non-SNP MA on two (P < .05), lower than non-SNP MA on two (P < .05), and higher than FFS on four (P < .01). DE beneficiaries did not report better coordination of care in D-SNPs. D-SNP performance was often worse than other coverage types in prior periods., Conclusions: Relative to FFS Medicare, DE beneficiaries report higher immunization rates in D-SNPs, but slight or no better performance on other dimensions of patient experience. New requirements in 2021 may help D-SNPs attain their goal of better care coordination., (© 2021 Health Research and Educational Trust.)

Objective: To illustrate a method that accounts for sampling variation in identifying suppliers and counties with outlying rates of a particular pattern of inconsistent billing for ambulance services to Medicare., Data Sources: US Medicare claims for a 20% simple random sample of 2010-2014 fee-for-service beneficiaries., Study Design: We identified instances in which ambulance suppliers billed Medicare for transporting a patient to a hospital, but no corresponding hospital visit appeared in billing claims. We estimated the distributions of outlier supplier and county rates of such "ghost rides" by fitting a nonparametric empirical Bayes model with flexible distributional assumptions to account for sampling variation., Data Collection: We included Basic and advanced life support ground emergency ambulance claims with a hospital destination., Principal Findings: "Ghost ride" rates varied considerably across both ambulance suppliers and counties. We estimated 6.1% of suppliers and 5.0% of counties had rates that exceeded 3.6%, which was twice the national average of "ghost rides" (1.8% of all ambulance transports)., Conclusions: Health care fraud and abuse are frequently asserted but can be difficult to detect. Our data-driven approach may be a useful starting point for further investigation., (© 2021 Health Research and Educational Trust.)

Objectives: To compare use of diabetes medications between beneficiaries enrolled in Medicare Advantage (MA) and traditional Medicare (TM)., Study Design: Retrospective cohort analysis of Medicare enrollment and Part D event claims during 2015-2016., Methods: Data came from 1,027,884 TM and 838,420 MA beneficiaries who received at least 1 prescription for an oral or injectable diabetes medication. After matching MA and TM enrollees by demographic characteristics and geography, we analyzed use of medication overall, choices of first diabetes medication for those new to medication, and patterns of adding medications., Results: Overall and for patients on 1, 2, or 3 diabetes medications, use of metformin was higher in MA by about 3 percentage points, but use of newer medication classes was 5.1 percentage points higher in TM overall (21.3% vs 16.2%). Use of guideline-recommended first-line agents was higher in MA. For those who started metformin first, use of a sulfonylurea as a second medication was 7.8 percentage points higher in MA than TM (61.5% vs 53.7%), whereas use of medications from newer classes was 7.7 percentage points lower (22.0% vs 29.7%). Mean total spending was $149 higher in TM for those taking 1 medication and $298 higher for those taking 2 medications. Differences in spending among MA plans were of similar magnitude to the MA-TM differences., Conclusions: MA enrollees are more likely to be treated with metformin and sulfonylureas and less likely to receive costly newer medications than those in TM, but there also is substantial variation within MA. A limitation of the study is that we could not assess glucose control using glycated hemoglobin levels.

Purpose: Schools provide access to mental health services for traditionally underserved youth. However, there is variability in the types of school-based services students receive (e.g., school counseling, services in separate classrooms, or schools serving students with psychiatric disorders). Prior research has typically not distinguished among these different types of school-based services. The present study examines sociodemographic characteristics and disorders associated with the types of services received in schools., Methods: Data were analyzed from a sample of adolescent-parent pairs in the U.S. National Comorbidity Survey Adolescent Supplement who received school mental health services (N = 1,204). DSM-IV diagnoses were based on the Composite International Diagnostic Interview administered to adolescents and questionnaires self-administered to parents. Adolescents (aged 13-18 years) and parents also responded to questions about lifetime school-based mental health service receipt., Results: Among those receiving school-based mental health services, almost one-third (29.7%) received services in a separate classroom and almost one-fourth (22.3%) in a separate school. Increased likelihood of lifetime placement in a separate classroom or school was detected among older youth, males, blacks, Latinos, youth with learning disabilities, those whose parents had fewer years of education, and those who received community-based mental health services. Oppositional defiant disorder was associated with increased lifetime placement in a separate school., Conclusions: The results advance the evidence base by indicating that racial/ethnic minority youth and those whose parents have fewer years of education were more likely to receive school-based mental health services in separate settings. These results provide more context to studies of school-based mental health service receipt., (Copyright © 2020 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Risk for suicide attempt (SA) versus suicide ideation (SI) is clinically important and difficult to differentiate. We examined whether a history of self-injurious thoughts and behaviors (SITBs) differentiates soldiers with a recent SA from nonattempting soldiers with current/recent SI., Methods: Using a unique case-control design, we administered the same questionnaire (assessing the history of SITBs and psychosocial variables) to representative U.S. Army soldiers recently hospitalized for SA (n = 132) and soldiers from the same Army installations who reported 30-day SI but did not make an attempt (n = 125). Logistic regression analyses examined whether SITBs differentiated attempters and ideators after controlling for previously identified covariates., Results: In separate models that weighted for systematic nonresponse and controlled for gender, education, posttraumatic stress disorder, and intermittent explosive disorder, SA was positively and significantly associated with the history of suicide plan and/or intention to act (odds ratio [OR] = 12.1 [95% confidence interval {CI} = 3.6-40.4]), difficulty controlling suicidal thoughts during the worst week of ideation (OR = 3.5 [95% CI = 1.1-11.3]), and nonsuicidal self-injury (NSSI) (OR = 4.9 [95% CI = 1.3-18.0]). Area under the curve was 0.87 in a full model that combined these SITBs and covariates. The top ventile based on predicted risk had a sensitivity of 24.7%, specificity of 99.8%, and positive predictive value of 97.5%., Conclusions: History of suicide plan/intention, difficult to control ideation, and NSSI differentiate soldiers with recent SA from those with current/recent SI independent of sociodemographic characteristics and mental disorders. Longitudinal research is needed to determine whether these factors are prospectively associated with the short-term transition from SI to SA., (Published 2020. This article is a U.S. Government work and is in the public domain in the USA.)

Background: Hospitals are rapidly increasing efforts to improve the pediatric inpatient experience. However, hospitals often do not know what to target for improvement. To determine what matters most to families, we assessed which aspects of experience have the strongest relationships with parents' willingness to recommend a hospital., Methods: Cross-sectional study of 17 727 surveys completed from November 2012 to January 2014 by parents of children hospitalized at 69 hospitals in 34 states using the Child Hospital Consumer Assessment of Healthcare Providers and Systems Survey. Hierarchical logistic regressions predicted the "top box" for willingness to recommend from measures of specific care dimensions (nurse-parent communication, doctor-parent communication, communication about medicines, keeping parents informed about the child's care, privacy with providers, preparing to leave the hospital, mistakes and concerns, child comfort, cleanliness, and quietness), adjusting for parent-child characteristics. Relative importance was assessed by using partially standardized adjusted odds ratios (aORs)., Results: Child comfort (aOR 1.50; 95% confidence interval 1.41-1.60) and nurse-parent communication (aOR 1.50; 95% confidence interval 1.42-1.58) showed the strongest relationships with willingness to recommend, followed by preparing to leave the hospital, doctor-parent communication, and keeping parents informed. Privacy and quietness were not significantly associated with willingness to recommend in multivariate analysis., Conclusions: Our study uncovered highly valued dimensions that are distinct to pediatric care. Hospitals that care for children should consider using dedicated pediatric instruments to measure and track their performance. Improvement efforts should focus on creating an age-appropriate environment, improving the effectiveness of provider interactions, and engaging parents to share their values and concerns., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2020 by the American Academy of Pediatrics.)

Background: Deployment-related experiences might be risk factors for soldier suicides, in which case identification of vulnerable soldiers before deployment could inform preventive efforts. We investigated this possibility by using pre-deployment survey and administrative data in a sample of US Army soldiers to develop a risk model for suicide attempt (SA) during and shortly after deployment., Methods: Data came from the Army Study to Assess Risk and Resilience in Servicemembers Pre-Post Deployment Survey (PPDS). Soldiers completed a baseline survey shortly before deploying to Afghanistan in 2011-2012. Survey measures were used to predict SAs, defined using administrative and subsequent survey data, through 30 months after deployment. Models were built using penalized regression and ensemble machine learning methods., Results: Significant pre-deployment risk factors were history of traumatic brain injury, 9 + mental health treatment visits in the 12 months before deployment, young age, female, previously married, and low relationship quality. Cross-validated AUC of the best penalized and ensemble models were .75-.77. 21.3-40.4% of SAs occurred among the 5-10% of soldiers with highest predicted risk and positive predictive value (PPV) among these high-risk soldiers was 4.4-5.7%., Conclusions: SA can be predicted significantly from pre-deployment data, but intervention planning needs to take PPV into consideration., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among civilian trauma survivors and military veterans. These APNS, as traditionally classified, include posttraumatic stress, postconcussion syndrome, depression, and regional or widespread pain. Traditional classifications have come to hamper scientific progress because they artificially fragment APNS into siloed, syndromic diagnoses unmoored to discrete components of brain functioning and studied in isolation. These limitations in classification and ontology slow the discovery of pathophysiologic mechanisms, biobehavioral markers, risk prediction tools, and preventive/treatment interventions. Progress in overcoming these limitations has been challenging because such progress would require studies that both evaluate a broad spectrum of posttraumatic sequelae (to overcome fragmentation) and also perform in-depth biobehavioral evaluation (to index sequelae to domains of brain function). This article summarizes the methods of the Advancing Understanding of RecOvery afteR traumA (AURORA) Study. AURORA conducts a large-scale (n = 5000 target sample) in-depth assessment of APNS development using a state-of-the-art battery of self-report, neurocognitive, physiologic, digital phenotyping, psychophysical, neuroimaging, and genomic assessments, beginning in the early aftermath of trauma and continuing for 1 year. The goals of AURORA are to achieve improved phenotypes, prediction tools, and understanding of molecular mechanisms to inform the future development and testing of preventive and treatment interventions.

Importance: Understanding suicide ideation (SI) during combat deployment can inform prevention and treatment during and after deployment., Objective: To examine associations of sociodemographic characteristics, lifetime and past-year stressors, and mental disorders with 30-day SI among a representative sample of US Army soldiers deployed in Afghanistan., Design, Setting, and Participants: In this survey study, soldiers deployed to Afghanistan completed self-administered questionnaires in July 2012. The sample was weighted to represent all 87 032 soldiers serving in Afghanistan. Prevalence of lifetime, past-year, and 30-day SI and mental disorders was determined. Logistic regression analyses examined risk factors associated with SI. Data analyses for this study were conducted between August 2018 and August 2019., Main Outcomes and Measures: Suicide ideation, lifetime and 12-month stressors, and mental disorders were assessed with questionnaires. Administrative records identified sociodemographic characteristics and suicide attempts., Results: A total of 3957 soldiers (3473 [weighted 87.5%] male; 2135 [weighted 52.6%] aged ≤29 years) completed self-administered questionnaires during their deployment in Afghanistan. Lifetime, past-year, and 30-day SI prevalence estimates were 11.7%, 3.0%, and 1.9%, respectively. Among soldiers with SI, 44.2% had major depressive disorder (MDD) and 19.3% had posttraumatic stress disorder in the past 30-day period. A series of analyses of the 23 grouped variables potentially associated with SI resulted in a final model of sex; race/ethnicity; lifetime noncombat trauma; past 12-month relationship problems, legal problems, and death or illness of a friend or family member; and MDD. In this final multivariable model, white race/ethnicity (odds ratio [OR], 3.1 [95% CI, 1.8-5.1]), lifetime noncombat trauma (OR, 2.1 [95% CI, 1.1-4.0]), and MDD (past 30 days: OR, 31.8 [95% CI, 15.0-67.7]; before past 30 days: OR, 4.9 [95% CI, 2.5-9.6]) were associated with SI. Among the 85 soldiers with past 30-day SI, from survey administration through 12 months after returning from deployment, 6% (5 participants) had a documented suicide attempt vs 0.14% (6 participants) of the 3872 soldiers without SI., Conclusions and Relevance: This study suggests that major depressive disorder and noncombat trauma are important factors in identifying SI risk during combat deployment.

While the majority of U.S. Christians opposed the Affordable Care Act (ACA) in 2010, opinion was deeply divided by race/ethnicity and denominational group. Using national data from the Cooperative Congressional Election Study, we analyzed variation in ACA support across Christian denominational groups and offered explanations for these differences. We found overwhelming support among African Americans, less unified but majority support from Hispanics, and majority opposition from non-Hispanic White Christians. Among White Catholics and Protestants, ACA support varied considerably, and views toward abortion policy statistically explained much of the difference in likelihood to support the ACA across denominational categories (comparing White mainline versus Evangelical Protestants, and white moderate versus devout Catholics). Differences in anti-Black racial resentment were substantial between White Evangelical and mainline Protestants, and helped explain some of the gaps in ACA support between these groups. We conclude that differences in ACA views by denomination can be explained by firmly held group beliefs that may be difficult to sway, even if the program is a technical success.

Suicide is a leading cause of death. A substantial proportion of the people who die by suicide come into contact with the health care system in the year before their death. This observation has resulted in the development of numerous suicide prediction tools to help target patients for preventive interventions. However, low sensitivity and low positive predictive value have led critics to argue that these tools have no clinical value. We review these tools and critiques here. We conclude that existing tools are suboptimal and that improvements, if they can be made, will require developers to work with more comprehensive predictor sets, staged screening designs, and advanced statistical analysis methods. We also conclude that although existing suicide prediction tools currently have little clinical value, and in some cases might do more harm than good, an even-handed assessment of the potential value of refined tools of this sort cannot currently be made because such an assessment would depend on evidence that currently does not exist about the effectiveness of preventive interventions. We argue that the only way to resolve this uncertainty is to link future efforts to develop or evaluate suicide prediction tools with concrete questions about specific clinical decisions aimed at reducing suicides and to evaluate the clinical value of these tools in terms of net benefit rather than sensitivity or positive predictive value. We also argue for a focus on the development of individualized treatment rules to help select the right suicide-focused treatments for the right patients at the right times. Challenges will exist in doing this because of the rarity of suicide even among patients considered high-risk, but we offer practical suggestions for how these challenges can be addressed.

Study Objectives: Short sleep duration is associated with increased cardiovascular disease (CVD) risk. However, it is uncertain whether sleep debt, a measure of sleep deficiency during the week compared to the weekend, confers increased cardiovascular risk. Because sleep disturbances increase with age particularly in women, we examined the relationship between sleep debt and ideal cardiovascular health (ICH) in older women., Methods: Sleep debt is defined as the difference between self-reported total weekday and weekend sleep hours of at least 2 hours among women without apparent CVD and cancer participating in the Women's Health Stress Study follow-up cohort of female health professionals (N = 22 082). The ICH consisted of seven health factors and behaviors as defined by the American Heart Association Strategic 2020 goals including body mass index, smoking, physical activity, diet, blood pressure, total cholesterol, and glucose., Results: Mean age was 72.1 ± 6.0 years. Compared to women with no sleep debt, women with sleep debt were more likely to be obese and have hypertension (pall < .05). Linear regression models adjusted for age and race/ethnicity revealed that sleep debt was significantly associated with poorer ICH (B = -0.13 [95% CI = -0.18 to -0.08]). The relationship was attenuated but remained significant after adjustment for education, income, depression/anxiety, cumulative stress, and snoring., Conclusion: Sleep debt was associated with poorer ICH, despite taking into account socioeconomic status and psychosocial factors. These results suggest that weekly sleep duration variation, possibly leading to circadian misalignment, may be associated with cardiovascular risk in older women., (© Sleep Research Society 2019. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)

Background: The Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey includes items about chronic conditions, health history, and self-rated health. Questions remain about the concordance between patient reports and administrative sources on questions related to health history., Objective: To validate CAHPS measures of chronic conditions against claims-based measures from the Medicare Chronic Conditions Warehouse (CCW)., Methods: We linked CAHPS responses from 301,050 fee-for-service Medicare beneficiaries in 2010-2012 with summaries of their claims in the CCW and identified nearest equivalent measures of conditions across sources. We calculated sensitivities and specificities for conditions and estimated regression models to assess the effects of patient characteristics on the sensitivity., Results: The sensitivity of CAHPS measures differed across conditions, ranging from 0.513 for history of stroke to 0.773 for history of cancer. Sensitivity was generally lower for older beneficiaries, those reporting good self-rated health, and those with fair or poor mental health. Specificity was 0.904 or greater for all conditions, up to 0.961 for stroke., Conclusions: Despite difference in timeframes and definitions of conditions, the measured sensitivities demonstrated reasonable validity. Variation in sensitivity is consistent with covariates that either directly measure health severity within a diagnosis or can be construed as a proxy for severity of illness.

Most nonfatal suicide attempts and suicide deaths occur among patients who deny suicidal ideation (SI) during suicide risk screenings. Little is known about risk factors for suicidal behaviors among such patients. We investigated this in a representative sample of U.S. Army soldiers who denied lifetime SI in a survey and were then followed through administrative records for up to 45 months to learn of administratively-recorded suicide attempts (SA). A novel two-stage risk assessment approach was used that combined first-stage prediction from administrative records to find the subsample of SI deniers with highest subsequent SA risk and then used survey reports to estimate a second-stage model identifying the subset of individuals in the high-risk subsample at highest SA risk. 70% of survey respondents denied lifetime SI. Administrative data identified 30% of this 70% who accounted for 81.2% of subsequent administratively-recorded SAs. A relatively small number of self-report survey variables were then used to create a prediction model that identified 10% of the first-stage high-risk sample (i.e., 3% of all soldiers) at highest SA risk (accounting for 45% of SAs in the total sample). We close by discussing potential applications of this approach for identifying future SI deniers at highest SA risk., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Clinical trials have identified a variety of predictor variables for use in precision treatment protocols, ranging from clinical biomarkers and symptom profiles to self-report measures of various sorts. Although such variables are informative collectively, none has proven sufficiently powerful to guide optimal treatment selection individually. This has prompted growing interest in the development of composite precision treatment rules (PTRs) that are constructed by combining information across a range of predictors. But this work has been hampered by the generally small samples in randomized clinical trials and the use of suboptimal analysis methods to analyze the resulting data. In this paper, we propose to address the sample size problem by: working with large observational electronic medical record databases rather than controlled clinical trials to develop preliminary PTRs; validating these preliminary PTRs in subsequent pragmatic trials; and using ensemble machine learning methods rather than individual algorithms to carry out statistical analyses to develop the PTRs. The major challenges in this proposed approach are that treatment are not randomly assigned in observational databases and that these databases often lack measures of key prescriptive predictors and mental disorder treatment outcomes. We proposed a tiered case-cohort design approach that uses innovative methods for measuring and balancing baseline covariates and estimating PTRs to address these challenges., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Aims: To examine cross-national patterns of 12-month substance use disorder (SUD) treatment and minimally adequate treatment (MAT), and associations with mental disorder comorbidity., Design: Cross-sectional, representative household surveys., Setting: Twenty-seven surveys from 25 countries of the WHO World Mental Health Survey Initiative., Participants: A total of 2446 people with past-year DSM-IV SUD diagnoses (alcohol or illicit drug abuse and dependence)., Measurements: Outcomes were SUD treatment, defined as having either received professional treatment or attended a self-help group for substance-related problems in the past 12 months, and MAT, defined as having either four or more SUD treatment visits to a health-care professional, six or more visits to a non-health-care professional or being in ongoing treatment at the time of interview. Covariates were mental disorder comorbidity and several socio-economic characteristics. Pooled estimates reflect country sample sizes rather than population sizes., Findings: Of respondents with past-year SUD, 11.0% [standard error (SE) = 0.8] received past 12-month SUD treatment. SUD treatment was more common among people with comorbid mental disorders than with pure SUDs (18.1%, SE = 1.6 versus 6.8%, SE = 0.7), as was MAT (84.0%, SE = 2.5 versus 68.3%, SE = 3.8) and treatment by health-care professionals (88.9%, SE = 1.9 versus 78.8%, SE = 3.0) among treated SUD cases. Adjusting for socio-economic characteristics, mental disorder comorbidity doubled the odds of SUD treatment [odds ratio (OR) = 2.34; 95% confidence interval (CI) = 1.71-3.20], MAT among SUD cases (OR = 2.75; 95% CI = 1.90-3.97) and MAT among treated cases (OR = 2.48; 95% CI = 1.23-5.02). Patterns were similar within country income groups, although the proportions receiving SUD treatment and MAT were higher in high- than low-/middle-income countries., Conclusions: Few people with past-year substance use disorders receive adequate 12-month substance use disorder treatment, even when comorbid with a mental disorder. This is largely due to the low proportion of people receiving any substance use disorder treatment, as the proportion of patients whose treatment is at least minimally adequate is high., (© 2019 Society for the Study of Addiction.)

Objectives: To develop and assess the reliability and validity of composite measures of reasons for disenrollment from Medicare Advantage (MA) and prescription drug plans (PDPs)., Data Source: Medicare beneficiaries who responded to the Medicare Advantage and Prescription Drug Plan Disenrollment Reasons Survey., Study Design: Separate multilevel factor analyses of MA and PDP data suggested groupings of survey items to form composite measures, for which internal consistency and interunit reliability were estimated. The association of each composite with an overall plan rating was examined to evaluate criterion validity., Principal Findings: Five composites were identified: financial reasons for disenrollment; problems with prescription drug benefits and coverage; problems getting information and help from the plan; problems getting needed care, coverage, and cost information; and problems with coverage of doctors and hospitals. Beneficiary-level internal consistency reliability exceeded 0.70 for all but one composite (financial reasons); plan-level internal consistency reliability exceeded 0.80 for all composites; average interunit reliability for plans with ≥ 30 survey completes exceeded 0.75 for 3 of 5 composites. As expected, greater endorsement of reasons for disenrollment was associated with lower overall plan ratings., Conclusions: The Disenrollment Reasons Survey provides a reliable and valid assessment of beneficiaries' reasons for leaving their plans. Multiple reasons for disenrollment may indicate especially poor experiences., (© Health Research and Educational Trust.)

Objective: Comparison of readmission rates requires adjustment for case-mix (ie, differences in patient populations), but previously only claims data were available for this purpose. We examined whether incorporation of relatively readily available clinical data improves prediction of pediatric readmissions and thus might enhance case-mix adjustment., Methods: We examined 30-day readmissions using claims and electronic health record data for patients ≤18 years and 29 days of age who were admitted to 3 children's hospitals from February 2011 to February 2014. Using the Pediatric All-Condition Readmission Measure and starting with a model including age, gender, chronic conditions, and primary diagnosis, we examined whether the addition of initial vital sign and laboratory data improved model performance. We employed machine learning to evaluate the same variables, using the L2-regularized logistic regression with cost-sensitive learning and convolutional neural network., Results: Controlling for the core model variables, low red blood cell count and mean corpuscular hemoglobin concentration and high red cell distribution width were associated with greater readmission risk, as were certain interactions between laboratory and chronic condition variables. However, the C-statistic (0.722 vs 0.713) and McFadden's pseudo R 2 (0.085 vs 0.076) for this and the core model were similar, suggesting minimal improvement in performance. In machine learning analyses, the F-measure (harmonic mean of sensitivity and positive predictive value) was similar for the best-performing model (containing all variables) and core model (0.250 vs 0.243)., Conclusions: Readily available clinical variables do not meaningfully improve the prediction of pediatric readmissions and would be unlikely to enhance case-mix adjustment unless their distributions varied widely across hospitals., (Copyright © 2018 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.)

Objective: The objective of this study is to assess the contribution of mental comorbidity to role impairment among college students., Methods: Web-based self-report surveys from 14,348 first-year college students (Response Rate [RR] = 45.5%): 19 universities, eight countries of the World Mental Health International College Student Initiative. We assessed impairment (Sheehan Disability Scales and number of days out of role [DOR] in the past 30 days) and seven 12-month DSM-IV disorders. We defined six multivariate mental disorder classes using latent class analysis (LCA). We simulated population attributable risk proportions (PARPs) of impairment., Results: Highest prevalence of role impairment was highest among the 1.9% of students in the LCA class with very high comorbidity and bipolar disorder (C1): 78.3% of them had severe role impairment (vs. 20.8%, total sample). Impairment was lower in two other comorbid classes (C2 and C3) and successively lower in the rest. A similar monotonic pattern was found for DOR. Both LCA classes and some mental disorders (major depression and panic, in particular) were significant predictors of role impairment. PARP analyses suggest that eliminating all mental disorders might reduce severe role impairment by 64.6% and DOR by 44.3%., Conclusions: Comorbid mental disorders account for a substantial part of role impairment in college students., (© 2018 John Wiley & Sons, Ltd.)