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OBJECTIVE: Geography may influence the relationships of predictors for suicidal ideation (SI) and suicide attempts (SA) in children and youth. METHOD: This is a nationwide retrospective cohort study of 124,424 individuals less than 25 years of age using commercial claims data (2011-2015) from the Health Care Cost Institute. Outcomes were time to SI or SA within 3 months after the indexed mental health or substance use disorder (MH/SUD) outpatient visit. Predictors included sociodemographic and clinical characteristics up to 3 years before the index event. RESULTS: At each follow-up time period, rates of SI and SA varied by the US geographic division (p < .001), and the Mountain Division consistently had the highest rates for both SI and SA (5.44%-10.26% for SI; 0.70%-2.82% for SA). Having MH emergency department (ED) visits in the past year increased the risk of SI by 28% to 65% for individuals residing in the New England, Mid-Atlantic, East North Central, West North Central, and East South Central Divisions. The main effects of geographic divisions were significant for SA (p

ObjectivePrimary care is the de facto mental health system in the United States where physicians treat large numbers of depressed older adults with antidepressant medication. This study aimed to examine whether antidepressant dosage adequacy and patient adherence are associated with depression response among middle-aged and older adults prescribed with antidepressants by their primary care provider.DesignA secondary analysis was conducted on a sample drawn from a randomized controlled trial comparing Treatment as Usual to Treatment Initiation Program, an adherence intervention. Treatment Initiation Program improved adherence but not depression compared to Treatment as Usual (Sirey et al., 2017). For this analysis, we examined dosing adequacy and adherence at 6 and 12 weeks as predictors of depression response in both groups at 12 and 24 weeks.SettingPrimary care practices.ParticipantsOne hundred eighty-seven older adults with depression prescribed an antidepressant for depression by their primary care provider.MeasurementsDepression response was defined as 50% reduction on the Hamilton Rating Scale for Depression. Adherence was defined as taking 80% of doses at follow-up interviews (6 and 12 weeks). Patient-reported dosage and duration of antidepressant therapy was collected using the Composite Antidepressant Score (adequacy score of >3) at follow-up.ResultsGreater adherence, but not receipt of adequate dosage, was associated with higher likelihood of treatment response at both 12 (Odds ratio (OR) = 2.63; 95% Confidence Interval (CI), 1.19-5.84) and 24 weeks (OR = 3.09; 95% CI, 1.46-6.55).ConclusionAs physicians prescribe antidepressants to the diverse group of adults seen in primary care, special attention to patients' views and approach to adherence may improve depression outcomes.

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated. [ABSTRACT FROM AUTHOR]

The article discusses the use of hierarchical endpoints analyzed using the win-ratio method in deprescribing trials for older adults with multimorbidity. Deprescribing, the process of discontinuing drugs when harms outweigh benefits, has shown potential to improve quality of life and reduce medication burden. The win-ratio method integrates patient priorities into composite outcomes, enhancing statistical power and accounting for competing risks like mortality. This approach can advance deprescribing research by focusing on patient-centered outcomes and improving study design for clinical trials. [Extracted from the article]

Background: One‐third of people living with dementia (PLWD) have highly fragmented care (i.e., care spread across many ambulatory providers without a dominant provider). It is unclear whether PLWD with fragmented care and their caregivers perceive gaps in communication among the providers involved and whether any such gaps are perceived as benign inconveniences or as clinically meaningful, leading to adverse events. We sought to determine the frequency of perceived gaps in communication (coordination) among providers and the frequency of self‐reported adverse events attributed to poor coordination. Methods: We conducted a cross‐sectional study in the context of a Medicare accountable care organization (ACO) in New York in 2022–2023. We included PLWD who were attributed to the ACO, had fragmented care in the past year by claims (reversed Bice‐Boxerman Index ≥0.86), and were in a pragmatic clinical trial on care management. We used an existing survey instrument to determine perceptions of care coordination and perceptions of four adverse events (repeat tests, drug–drug interactions, emergency department visits, and hospital admissions). ACO care managers collected data by telephone, using clinical judgment to determine whether each survey respondent was the patient or a caregiver. We used descriptive statistics to summarize results. Results: Of 167 eligible PLWD, surveys were completed for 97 (58.1%). Of those, 88 (90.7%) reported having >1 ambulatory visit and >1 ambulatory provider and were thus at risk for gaps in care coordination and included in the analysis. Of those, 23 respondents were patients (26.1%) and 64 were caregivers (72.7%), with one respondent's role missing. Overall, 57% of respondents reported a problem (or "gap") in the coordination of care and, separately, 18% reported an adverse event that they attributed to poor care coordination. Conclusion: Gaps in coordination of care for PLWD are reported to be very common and often perceived as hazardous. [ABSTRACT FROM AUTHOR]

ObjectiveTo test the hypotheses that (1) clinical case management integrated with problem-solving therapy (CM-PST) is more effective than clinical case management alone (CM) in reducing depressive symptoms of depressed, disabled, impoverished patients and that (2) development of problem-solving skills mediates improvement of depression.MethodsThis randomized clinical trial with a parallel design allocated participants to CM or CM-PST at 1:1 ratio. Raters were blind to patients' assignments. Two hundred seventy-one individuals were screened and 171 were randomized to 12 weekly sessions of either CM or CM-PST. Participants were at least 60 years old with major depression measured with the 24-item Hamilton Depression Rating Scale (HAM-D), had at least one disability, were eligible for home-based meals services, and had income no more than 30% of their counties' median.ResultsCM and CM-PST led to similar declines in HAM-D over 12 weeks (t = 0.37, df = 547, p = 0.71); CM was noninferior to CM-PST. The entire study group (CM plus CM-PST) had a 9.6-point decline in HAM-D (t = 18.7, df = 547, p

ObjectiveTo test the following hypotheses: (1) Clinical case management integrated with problem-solving therapy (CM-PST) is more effective than clinical case management alone (CM) in improving functional outcomes in disabled, impoverished patients and (2) improvement in depression, self-efficacy, and problem-solving skills mediates improvement of disability.MethodsUsing a randomized controlled trial with a parallel design, 271 individuals were screened and 171 were randomized to 12 weekly sessions of either CM or CM-PST at 1:1 ratio. Raters were blind to patients' assignments. Participants were at least age 60 years with major depression, had at least one disability, were eligible for home-based meals services, and had income no more than 30% of their counties' median. The WHO Disability Assessment Scale was used.ResultsBoth interventions resulted in improved functioning by 12 weeks (t = 4.28, df = 554, p = 0.001), which was maintained until 24 weeks. Contrary to hypothesis, CM was noninferior to CM-PST (one-sided p = 0.0003, t = -3.5, df = 558). Change in disability was not affected by baseline depression severity, cognitive function, or number of unmet social service needs. Improvements in self efficacy (t = -2.45, df = 672, p = 0.021), problem-solving skill (t = -2.44, df = 546, p = 0.015), and depression symptoms (t = 2.25, df = 672, p = .025) by week 9 predicted improvement in function across groups by week 12.ConclusionCM is noninferior to CM-PST for late-life depression in low-income populations. The effect of these interventions occur early, with benefits in functional status maintained as long as 24 weeks after treatment initiation (clinicaltrials.gov; NCT00540865).

Background and Objectives: Use of algorithms to identify patients with high data‐continuity in electronic health records (EHRs) may increase study validity. Practical experience with this approach remains limited. Methods: We developed and validated four algorithms to identify patients with high data continuity in an EHR‐based data source. Selected algorithms were then applied to a pharmacoepidemiologic study comparing rates of COVID‐19 hospitalization in patients exposed to insulin versus noninsulin antidiabetic drugs. Results: A model using a short list of five EHR‐derived variables performed as well as more complex models to distinguish high‐ from low‐data continuity patients. Higher data continuity was associated with more accurate ascertainment of key variables. In the pharmacoepidemiologic study, patients with higher data continuity had higher observed rates of the COVID‐19 outcome and a large unadjusted association between insulin use and the outcome, but no association after propensity score adjustment. Discussion: We found that a simple, portable algorithm to predict data continuity gave comparable performance to more complex methods. Use of the algorithm significantly impacted the results of an empirical study, with evidence of more valid results at higher levels of data continuity. Plain Language Summary: When doing research using electronic health records, one big problem is that sometimes information is missing or not consistently recorded. There are methods (algorithms) that can help identify which patients' records are more complete and reliable. However, it's not clear how much these methods actually improve the results of studies about how drugs affect populations (pharmacoepidemiologic studies). This manuscript demonstrates that using a straightforward and easy‐to‐use method seems to make the results of such a drug study more accurate. [ABSTRACT FROM AUTHOR]

Background: Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory. Method: One hundred and twenty-six persons aged 18–85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics. Results: Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model. Conclusions: Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms. [ABSTRACT FROM AUTHOR]