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Background: Given the low usage of virtual health care prior to the COVID‐19 pandemic, it was unclear whether those living in rural locations would benefit from increased availability of virtual mental health care. The rapid transition to virtual services during the COVID‐19 pandemic allowed for a unique opportunity to examine how the transition to virtual mental health care impacted psychotherapy disruption (i.e., 45+ days between appointments) among individuals living in rural locations compared with those living in nonrural locations. Methods: Electronic health record and insurance claims data were collected from three health care systems in the United States including rurality status and psychotherapy disruption. Psychotherapy disruption was measured before and after the COVID‐19 pandemic onset. Results: Both the nonrural and rural cohorts had significant decreases in the rates of psychotherapy disruption from pre‐ to post‐COVID‐19 onset (32.5–16.0% and 44.7–24.8%, respectively, p < 0.001). The nonrural cohort had a greater reduction of in‐person visits compared with the rural cohort (96.6–45.0 vs. 98.0–66.2%, respectively, p < 0.001). Among the rural cohort, those who were younger and those with lower education had greater reductions in psychotherapy disruption rates from pre‐ to post‐COVID‐19 onset. Several mental health disorders were associated with experiencing psychotherapy disruption. Conclusions: Though the rapid transition to virtual mental health care decreased the rate of psychotherapy disruption for those living in rural locations, the reduction was less compared with nonrural locations. Other strategies are needed to improve psychotherapy disruption, especially among rural locations (i.e., telephone visits). [ABSTRACT FROM AUTHOR]

Purpose: Observational studies assessing effects of medical products on suicidal behavior often rely on health record data to account for pre‐existing risk. We assess whether high‐dimensional models predicting suicide risk using data derived from insurance claims and electronic health records (EHRs) are superior to models using data from insurance claims alone. Methods: Data were from seven large health systems identified outpatient mental health visits by patients aged 11 or older between 1/1/2009 and 9/30/2017. Data for the 5 years prior to each visit identified potential predictors of suicidal behavior typically available from insurance claims (e.g., mental health diagnoses, procedure codes, medication dispensings) and additional potential predictors available from EHRs (self‐reported race and ethnicity, responses to Patient Health Questionnaire or PHQ‐9 depression questionnaires). Nonfatal self‐harm events following each visit were identified from insurance claims data and fatal self‐harm events were identified by linkage to state mortality records. Random forest models predicting nonfatal or fatal self‐harm over 90 days following each visit were developed in a 70% random sample of visits and validated in a held‐out sample of 30%. Performance of models using linked claims and EHR data was compared to models using claims data only. Results: Among 15 845 047 encounters by 1 574 612 patients, 99 098 (0.6%) were followed by a self‐harm event within 90 days. Overall classification performance did not differ between the best‐fitting model using all data (area under the receiver operating curve or AUC = 0.846, 95% CI 0.839–0.854) and the best‐fitting model limited to data available from insurance claims (AUC = 0.846, 95% CI 0.838–0.853). Competing models showed similar classification performance across a range of cut‐points and similar calibration performance across a range of risk strata. Results were similar when the sample was limited to health systems and time periods where PHQ‐9 depression questionnaires were recorded more frequently. Conclusion: Investigators using health record data to account for pre‐existing risk in observational studies of suicidal behavior need not limit that research to databases including linked EHR data. [ABSTRACT FROM AUTHOR]

Introduction: Previous studies report that item 9 of the Patient Health Questionnaire (PHQ9) is useful for stratifying risk of suicide attempt in adults. This study re-produced the utility of item 9 of PHQ9 in assessing risk of suicide attempt in adolescents.Materials and Methods: Individuals aged 13 to 17 years in 4 health systems with a diagnosis of depression and history of treatment were included. We estimated time to first observed fatal or non-fatal suicide attempt in the 2 years following completion of a PHQ9, stratified by response to item 9.Results: There were 51,807 PHQ9 questionnaires for 20,363 youth and 861 instances of suicide attempt. Cumulative probability of suicide attempt ranged from approximately 3.3% (95% CI, 3.0 to 3.5%) for those responding "not at all" on item 9 to 10.8% (95% CI, 9.2 to 12.4%) for those responding "nearly every day". These probabilities are more than 3 times higher than previously reported in adults.Conclusion: PHQ item 9 is useful for stratifying risk of suicide attempt in the 2 years following completion of the questionnaire. Monitoring PHQ item 9 over time for patients in treatment for depression can be useful for population health management of adolescents with depression. [ABSTRACT FROM AUTHOR]

Objective: To identify space-time clusters of changes in prescribing aripiprazole for bipolar disorder among providers in the VA.Data Sources: VA administrative data from 2002 to 2010 were used to identify prescriptions of aripiprazole for bipolar disorder. Prescriber characteristics were obtained using the Personnel and Accounting Integrated Database.Study Design: We conducted a retrospective space-time cluster analysis using the space-time permutation statistic.Data Extraction Methods: All VA service users with a diagnosis of bipolar disorder were included in the patient population. Individuals with any schizophrenia spectrum diagnoses were excluded. We also identified all clinicians who wrote a prescription for any bipolar disorder medication.Principal Findings: The study population included 32,630 prescribers. Of these, 8,643 wrote qualifying prescriptions. We identified three clusters of aripiprazole prescribing centered in Massachusetts, Ohio, and the Pacific Northwest. Clusters were associated with prescribing by VA-employed (vs. contracted) prescribers. Nurses with prescribing privileges were more likely to make a prescription for aripiprazole in cluster locations compared with psychiatrists. Primary care physicians were less likely.Conclusions: Early prescribing of aripiprazole for bipolar disorder clustered geographically and was associated with prescriber subgroups. These methods support prospective surveillance of practice changes and identification of associated health system characteristics. [ABSTRACT FROM AUTHOR]

Objective: To learn if a quality of care Medicaid child psychiatric consultation service implemented in three different steps was linked to changes in statewide child antipsychotic utilization.Data Sources/study Setting: Washington State child psychiatry consultation program primary data and Medicaid pharmacy division antipsychotic utilization secondary data from July 1, 2006, through December 31, 2013.Study Design: Observational study in which consult program data were analyzed with a time series analysis of statewide antipsychotic utilization.Data Collection/extraction Methods: All consultation program database information involving antipsychotics was compared to Medicaid pharmacy division database information involving antipsychotic utilization.Principal Findings: Washington State's total child Medicaid antipsychotic utilization fell from 0.51 to 0.25 percent. The monthly prevalence of use fell by a mean of 0.022 per thousand per month following the initiation of elective consults (p = .004), by 0.065 following the initiation of age/dose triggered mandatory reviews (p < .001), then by another 0.022 following the initiation of two or more concurrent antipsychotic mandatory reviews (p = .001). High-dose antipsychotic use fell by 57.8 percent in children 6- to 12-year old and fell by 52.1 percent in teens.Conclusions: Statewide antipsychotic prescribing for Medicaid clients fell significantly at different rates following each implementation step of a multilevel consultation and best-practice education service. [ABSTRACT FROM AUTHOR]

Background Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) carry a high mortality risk. While identifying clinical and genetic risk factors for these conditions has been hindered by their rarity, large electronic health databases hold promise for identifying large numbers of cases for study, especially with the introduction in 2008 of ICD-9 codes more specific for these conditions. Objective The objective of this study is to estimate the validity of ICD-9 codes for ascertaining SJS/TEN in 12 collaborating research units in the USA, covering almost 60 million lives. Methods From the electronic databases at each site, we ascertained potential cases of SJS/TEN using ICD-9 codes. At five sites, a subset of medical records was abstracted and standardized criteria applied by board-certified dermatologists to adjudicate diagnoses. Multivariate logistic regression was used to identify factors independently associated with validated SJS/TEN cases. Results A total of 56 591 potential cases of SJS/TEN were identified. A subset of 276 charts was selected for adjudication and 39 (of the 276) were confirmed as SJS/TEN. Patients with the ICD-9 codes introduced after 2008 were more likely to be confirmed as cases (OR 3.32; 95%CI 0.82, 13.47) than those identified in earlier years. Likelihood of case status increased with length of hospitalization. Applying the probability of case status to the 56 591 potential cases, we estimated 475-875 to be valid SJS/TEN cases. Conclusion Newer ICD-9 codes, along with length of hospitalization, identified patients with a high likelihood of SJS/TEN. This is important for identification of subjects for future pharmacogenomics studies. [ABSTRACT FROM AUTHOR]

Purpose The prescription of psychotropic medications to children and adolescents has increased dramatically over the last decade. However, the development of disparities in prescribing is poorly understood. We examined whether clustering of utilization is a common phenomenon among early adopters of medications described the characteristics of clusters. Methods We obtained the complete Medicaid Analytic Extract (MAX) files for the State of Michigan between 1 January 2000 and 31 December 2003. We tracked the adoption of: aripiprazole, atomoxetine, escitalopram, methylphenidate OROS, and ziprasidone. We conducted retrospective, space-time analyses, scanning for clusters with high rates of prescribing. χ 2 analyses were used to compare the attributes of patients living within clusters to patients living in the rest of the state for each medication. Clusters of utilization were identified via the spatial scan statistic. Analysis of variance (ANOVA) was then used to compare the numbers of mental health professionals per capita in geographic areas that did and did not demonstrate clustering of prescriptions for new psychotropic medications. Results All five medications exhibited space-time clustering within the first 90 days following the US Food and Drug Administration (FDA) approval. The Medicaid population surrounding Kalamazoo was more likely to receive a prescription on multiple occasions. Excluding ziprasidone, clusters were not associated with greater geographic access to mental health care professionals. Conclusions Clustering of new prescriptions for psychotropic medications was a common phenomenon in this population. Surveillance and cluster identification allow the development of disparities to be studied. This information permits interventions to be targeted to locations prospectively. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The Capital Asset Pricing Model is used to examine the relationship between covariance risk in employment and growth in employment for Canadian census metropolitan areas. A new version of location quotient (LQ) is presented that is based on covariance risk. This risk quotient is shown to be a better predictor of employment growth than the simple LQ. The portfolio theoretic model and covariance risk are shown to be useful in predicting growth in addition to studying the regional stability of employment. [ABSTRACT FROM AUTHOR]