Development of a model to predict psychotherapy response for depression among Veterans.

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.

Development of a model to predict antidepressant treatment response for depression among Veterans.

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.

Antidepressant use in low- middle- and high-income countries: a World Mental Health Surveys report.

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.

National Trends in Antihypertensive Treatment Among Older Adults by Race and Presence of Comorbidity, 2008 to 2017.

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.

Are Veterans Getting Their Preferred Depression Treatment? A National Observational Study in the Veterans Health Administration.

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 χ21=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.

Childhood adversities and suicidal thoughts and behaviors among first-year college students: results from the WMH-ICS initiative.

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.

Association of Medicare Advantage vs Traditional Medicare With 30-Day Mortality Among Patients With Acute Myocardial Infarction.

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.

Suicide prediction models: a critical review of recent research with recommendations for the way forward.

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.

Self-injurious thoughts and behaviors that differentiate soldiers who attempt suicide from those with recent suicide ideation.

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.

Validating Reports of Chronic Conditions in the Medicare CAHPS Survey.

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.

Association of Federal Mental Health Parity Legislation With Health Care Use and Spending Among High Utilizers of Services.

BACKGROUND: Decades-long efforts to require parity between behavioral and physical health insurance coverage culminated in the comprehensive federal Mental Health Parity and Addiction Equity Act. OBJECTIVES: To determine the association between federal parity and changes in mental health care utilization and spending, particularly among high utilizers. RESEARCH DESIGN: Difference-in-differences analyses compared changes before and after exposure to federal parity versus a comparison group. SUBJECTS: Commercially insured enrollees aged 18-64 with a mental health disorder drawn from 24 states where self-insured employers were newly subject to federal parity in 2010 (exposure group), but small employers were exempt before-and-after parity (comparison group). A total of 11,226 exposure group members were propensity score matched (1:1) to comparison group members, all of whom were continuously enrolled from 1 year prepolicy to 1-2 years postpolicy. MEASURES: Mental health outpatient visits, out-of-pocket spending for these visits, emergency department visits, and hospitalizations. RESULTS: Relative to comparison group members, mean out-of-pocket spending per outpatient mental health visit declined among exposure enrollees by $0.74 (1.40, 0.07) and $2.03 (3.17, 0.89) in years 1 and 2 after the policy, respectively. Corresponding annual mental health visits increased by 0.31 (0.12, 0.51) and 0.59 (0.37, 0.81) per enrollee. Difference-in-difference changes were larger for the highest baseline quartile mental health care utilizers [year 2: 0.76 visits per enrollee (0.14, 1.38); relative increase 10.07%] and spenders [year 2: $-2.28 (-3.76, -0.79); relative reduction 5.91%]. There were no significant difference-in-differences changes in emergency department visits or hospitalizations. CONCLUSIONS: In 24 states, commercially insured high utilizers of mental health services experienced modest increases in outpatient mental health visits 2 years postparity.

Methodological Approaches to Understanding Causes of Health Disparities.

Understanding health disparity causes is an important first step toward developing policies or interventions to eliminate disparities, but their nature makes identifying and addressing their causes challenging. Potential causal factors are often correlated, making it difficult to distinguish their effects. These factors may exist at different organizational levels (e.g., individual, family, neighborhood), each of which needs to be appropriately conceptualized and measured. The processes that generate health disparities may include complex relationships with feedback loops and dynamic properties that traditional statistical models represent poorly. Because of this complexity, identifying disparities' causes and remedies requires integrating findings from multiple methodologies. We highlight analytic methods and designs, multilevel approaches, complex systems modeling techniques, and qualitative methods that should be more broadly employed and adapted to advance health disparities research and identify approaches to mitigate them.

Bayesian hierarchical modeling of substate area estimates from the Medicare CAHPS survey.

Each year, surveys are conducted to assess the quality of care for Medicare beneficiaries, using instruments from the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) program. Currently, survey measures presented for Fee-for-Service beneficiaries are either pooled at the state level or unpooled for smaller substate areas nested within the state; the choice in each state is based on statistical tests of measure heterogeneity across areas within state. We fit spatial-temporal Bayesian random-effects models using a flexible parameterization to estimate mean scores for each of the domains formed by 94 areas in 32 states measured over 5 years. A Bayesian hat matrix provides a heuristic interpretation of the way the model combines information for estimates in these domains. The model can be used to choose between reporting of state- or substate-level direct estimates in each state, or as a source of alternative small-area estimates superior to either direct estimate. We compare several candidate models using log pseudomarginal likelihood and posterior predictive checks. Results from the best-performing model for 8 measures surveyed from 2012 to 2016 show substantial reductions in mean squared error (MSE) over direct estimates.

Transition to suicide attempt from recent suicide ideation in U.S. Army soldiers: Results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

BACKGROUND: Most people with suicide ideation (SI) do not attempt suicide (SA). Understanding the transition from current/recent SI to SA is important for mental health care. Our objective was to identify characteristics that differentiate SA from 30-day SI among representative U.S. Army soldiers. METHODS: Using a unique case-control design, soldiers recently hospitalized for SA (n = 132) and representative soldiers from the same four communities (n = 10,193) were administered the same questionnaire. We systematically identified variables that differentiated suicide attempters from the total population, then examined whether those same variables differentiated all 30-day ideators (n = 257) from the total population and attempters from nonattempting 30-day ideators. RESULTS: In univariable analyses, 20 of 23 predictors were associated with SA in the total population (0.05 level). The best multivariable model included eight significant predictors: interpersonal violence, relationship problems, major depressive disorder, posttraumatic stress disorder (PTSD), and substance use disorder (all having positive associations), as well as past 12-month combat trauma, intermittent explosive disorder (IED), and any college education (all having negative associations). Six of these differentiated 30-day ideators from the population. Three differentiated attempters from ideators: past 30-day PTSD (OR = 6.7 [95% CI = 1.1-39.4]), past 30-day IED (OR = 0.2 [95% CI = 0.1-0.5]), and any college education (OR = 0.1 [95% CI = 0.0-0.6]). The 5% of ideators with highest predicted risk in this final model included 20.9% of attempters, a four-fold concentration of risk. CONCLUSIONS: Prospective army research examining transition from SI to SA should consider PTSD, IED, and education. Combat exposure did not differentiate attempters from ideators. Many SA risk factors in the Army population are actually risk factors for SI.

The relationship between neighborhood-level socioeconomic characteristics and individual mental disorders in five cities in Latin America: multilevel models from the World Mental Health Surveys.

PURPOSE: Our understanding of community-level predictors of individual mental disorders in large urban areas of lower income countries is limited. In particular, the proportion of migrant, unemployed, and poorly educated residents in neighborhoods of these urban areas may characterize group contexts and shape residents' health. METHODS: Cross-sectional household interviews of 7251 adults were completed across 83 neighborhoods of Buenos Aires, Argentina; Medellín, Colombia; São Paulo, Brazil; Lima, Peru; and Mexico City, Mexico as part of the World Mental Health Survey Initiative. Past-year internalizing and externalizing mental disorders were assessed, and multilevel models were used. RESULTS: Living in neighborhoods with either an above-average or below-average proportion of migrants and highly educated residents was associated with lower odds of any internalizing disorder (for proportion migrants: OR 0.75, 95% CI 0.62-0.91 for the bottom tertile and OR 0.79, 95% CI 0.67-0.94 for the top tertile compared to the middle tertile; for proportion highly educated: OR 0.76, 95% CI 0.64-0.90 for the bottom tertile and OR 0.58, 95% CI 0.37-0.90 for the top tertile compared to the middle tertile). Living in neighborhoods with an above-average proportion of unemployed individuals was associated with higher odds of having any internalizing disorder (OR 1.49, 95% CI 1.14-1.95 for the top tertile compared to the middle tertile). The proportion of highly educated residents was associated with lower odds of externalizing disorder (OR 0.54, 95% CI 0.31-0.93 for the top tertile compared to the middle tertile). CONCLUSIONS: The associations of neighborhood-level migration, unemployment, and education with individual-level odds of mental disorders highlight the importance of community context for understanding the burden of mental disorders among residents of rapidly urbanizing global settings.

The Value-Based Payment Modifier: Program Outcomes and Implications for Disparities.

Background: When risk adjustment is inadequate and incentives are weak, pay-for-performance programs, such as the Value-Based Payment Modifier (Value Modifier [VM]) implemented by the Centers for Medicare & Medicaid Services, may contribute to health care disparities without improving performance on average. Objective: To estimate the association between VM exposure and performance on quality and spending measures and to assess the effects of adjusting for additional patient characteristics on performance differences between practices serving higher-risk and those serving lower-risk patients. Design: Exploiting the phase-in of the VM on the basis of practice size, regression discontinuity analysis and 2014 Medicare claims were used to estimate differences in practice performance associated with exposure of practices with 100 or more clinicians to full VM incentives (bonuses and penalties) and exposure of practices with 10 or more clinicians to partial incentives (bonuses only). Analyses were repeated with 2015 claims to estimate performance differences associated with a second year of exposure above the threshold of 100 or more clinicians. Performance differences were assessed between practices serving higher- and those serving lower-risk patients after standard Medicare adjustments versus adjustment for additional patient characteristics. Setting: Fee-for-service Medicare. Patients: Random 20% sample of beneficiaries. Measurements: Hospitalization for ambulatory care-sensitive conditions, all-cause 30-day readmissions, Medicare spending, and mortality. Results: No statistically significant discontinuities were found at the threshold of 10 or more or 100 or more clinicians in the relationship between practice size and performance on quality or spending measures in either year. Adjustment for additional patient characteristics narrowed performance differences by 9.2% to 67.9% between practices in the highest and those in the lowest quartile of Medicaid patients and Hierarchical Condition Category scores. Limitation: Observational design and administrative data. Conclusion: The VM was not associated with differences in performance on program measures. Performance differences between practices serving higher- and those serving lower-risk patients were affected considerably by additional adjustments, suggesting a potential for Medicare's pay-for-performance programs to exacerbate health care disparities. Primary Funding Source: The Laura and John Arnold Foundation and National Institute on Aging.

Trends in Diabetes Treatment and Monitoring among Medicare Beneficiaries.

BACKGROUND: Diabetes is a costly and common condition, but little is known about recent trends in diabetes management among Medicare beneficiaries. OBJECTIVE: To evaluate the use of diabetes medications and testing supplies among Medicare beneficiaries. DESIGN/SETTING: Retrospective cohort analysis of Medicare claims from 2007 to 2014. PARTICIPANTS: Traditional Medicare beneficiaries with a diagnosis of diabetes in the current or any prior year. MAIN MEASURES: We analyzed choices of first diabetes medication for those new to medication and patterns of adding medications. We also examined the use of testing supplies, use of statins and ACE inhibitors/angiotensin receptor blockers, and spending. KEY RESULTS: Diagnosed diabetes increased from 28.7% to 30.2% of beneficiaries from 2007 to 2014. The use of metformin as the most commonly prescribed first medication increased from 50.2% in 2007 to 70.2% in 2014, whereas long-acting sulfonylureas decreased from 16.6% to 8.2%. The use of thiazolidinediones fell considerably, while the use of new diabetes medication classes increased. Among patients prescribed insulin, long-acting insulin as the first choice increased substantially, from 38.9% to 56.8%, but short-acting or combination regimens remained common, particularly among older or sicker beneficiaries. Prescriptions of testing supplies for more than once-daily testing were also common. The mean total cost of diabetes medications per patient increased over the period due to the increasing use of high-cost drugs, particularly by those patients with costs above the 90th percentile of spending, although the median costs decreased for both medications and testing supplies. CONCLUSIONS: The use of metformin and long-acting insulin have increased substantially among elderly Medicare patients with diabetes, but a substantial subgroup continues to receive costly and complex treatment regimens.

Separable covariance models for health care quality measures across years and topics.

Public quality reports for Medicare Advantage health plans include 11 measures of patient experiences reported in the annual Consumer Assessment of Healthcare Providers and Systems surveys. Computing summaries at the health plan level (of multiple measures in multiple years) yields an array-structured random variable. To summarize associations among measures and years, we model the variance-covariance matrix governing the plan-level vectors of yearly quality measures as a Kronecker product of an across-measure matrix and an across-year matrix, or a sum of such Kronecker products. This approach extends separable covariance structure to Fay-Herriot models. In addition, we develop linear combinations of Kronecker products similar to principal components for array random variables. To each Kronecker-product term, we apply post hoc analyses suited to the corresponding dimension of the cross-classification: 1-way factor analysis for the across-measure factor and time-series analysis to the across-year factor. These methods draw out key patterns of variation in the quality measures over time and suggest new strategies for reporting quality information to consumers.

Predeployment predictors of psychiatric disorder-symptoms and interpersonal violence during combat deployment.

BACKGROUND: Preventing suicides, mental disorders, and noncombat-related interpersonal violence during deployment are priorities of the US Army. We used predeployment survey and administrative data to develop actuarial models to identify soldiers at high risk of these outcomes during combat deployment. METHODS: The models were developed in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) Pre-Post Deployment Study, a panel study of soldiers deployed to Afghanistan in 2012-2013. Soldiers completed self-administered questionnaires before deployment and one (T1), three (T2), and nine months (T3) after deployment, and consented to administrative data linkage. Seven during-deployment outcomes were operationalized using the postdeployment surveys. Two overlapping samples were used because some outcomes were assessed at T1 (n = 7,048) and others at T2-T3 (n = 7,081). Ensemble machine learning was used to develop a model for each outcome from 273 predeployment predictors, which were compared to simple logistic regression models. RESULTS: The relative improvement in area under the receiver operating characteristic curve (AUC) obtained by machine learning compared to the logistic models ranged from 1.11 (major depression) to 1.83 (suicidality).The best-performing machine learning models were for major depression (AUC = 0.88), suicidality (0.86), and generalized anxiety disorder (0.85). Roughly 40% of these outcomes occurred among the 5% of soldiers with highest predicted risk. CONCLUSIONS: Actuarial models could be used to identify high risk soldiers either for exclusion from deployment or preventive interventions. However, the ultimate value of this approach depends on the associated costs, competing risks (e.g. stigma), and the effectiveness to-be-determined interventions.

Severe role impairment associated with mental disorders: Results of the WHO World Mental Health Surveys International College Student Project.

BACKGROUND: College entrance is a stressful period with a high prevalence of mental disorders. AIMS: To assess the role impairment associated with 12-month mental disorders among incoming first-year college students within a large cross-national sample. METHODS: Web-based self-report surveys assessing the prevalence of DSM-IV mental disorders and health-related role impairment (Sheehan Disability Scale) were obtained and analyzed from 13,984 incoming first-year college students (Response = 45.5%), across 19 universities in eight countries. Impairment was assessed in the following domains: home management, work (e.g., college-related problems), close personal relationships, and social life. RESULTS: Mean age of the sample was 19.3 (SD = 0.59) and 54.4% were female. Findings showed that 20.4% of students reported any severe role impairment (10% of those without a mental disorder vs. 42.9% of those with at least one disorder, P < 0.01). In bivariate analyses, panic disorder, and mania were associated most frequently with severe impairment (60.6% and 57.5%, respectively). Students reporting three or more mental disorders had almost fivefold more frequently severe impairment relative to those without mental disorders. Multiple logistic regression showed that major depression (OR = 4.0; 95%CI = 3.3, 4.8), generalized anxiety (OR = 3.9; 95%CI = 3.1, 4.8), and panic disorder (OR = 2.9; 95%CI 2.4, 4.2) were associated with the highest odds of severe impairment. Only minimal deviations from these overall associations were found across countries. CONCLUSION: Mental disorders among first-year college students are associated with substantial role impairment. Providing preventative interventions targeting mental disorders and associated impairments is a critical need for institutions to address.