Psychotherapy Disruption Before and After the Transition to Virtual Mental Health Care Induced by the COVID-19 Pandemic.

OBJECTIVE: This study aimed to examine population-level disruption in psychotherapy before and after the rapid shift to virtual mental health care induced by the onset of the COVID-19 pandemic in the United States. METHODS: This retrospective study used electronic health record and insurance claims data from three U.S. health systems. The sample included 110,089 patients with mental health conditions who were members of the health systems' affiliated health plans and attended at least two psychotherapy visits from June 14, 2019, through December 15, 2020. Data were subdivided into two 9-month periods (before vs. after COVID-19 onset, defined in this study as March 14, 2020). Psychotherapy visits were measured via health records and categorized as in person or virtual. Disruption was defined as a gap of >45 days between visits. RESULTS: Visits in the preonset period were almost exclusively in person (97%), whereas over half of visits in the postonset period were virtual (52%). Approximately 35% of psychotherapy visits were followed by a disruption in the preonset period, compared with 18% in the postonset period. Disruption continued to be less common (adjusted OR=0.45) during the postonset period after adjustment for visit, mental health, and sociodemographic factors. The magnitude of the difference in disruption between periods was homogeneous across sociodemographic characteristics but heterogeneous across psychiatric diagnoses. CONCLUSIONS: This study found fewer population-level disruptions in psychotherapy receipt after rapid transition to virtual mental health care following COVID-19 onset. These data support the continued availability of virtual psychotherapy.

Diagnosed Posttraumatic Stress Disorder and Other Trauma-Associated Stress Disorders and Risk for Suicide Mortality.

OBJECTIVE: Strong evidence exists for posttraumatic stress disorder (PTSD) as a risk factor for suicidal thoughts and behaviors across diverse populations. However, few empirical studies have examined PTSD and other trauma-associated stress disorders as risk factors for suicide mortality among health system populations. This study aimed to assess trauma-associated stress diagnoses as risk factors for suicide mortality in a U.S. health system population. METHODS: This case-control, matched-design study examined individuals who died by suicide between 2000 and 2015 and had received care from nine U.S. health systems affiliated with the Mental Health Research Network (N=3,330). Individuals who died by suicide were matched with individuals from the general health system population (N=333,000): 120 individuals with PTSD who died by suicide were matched with 1,592 control group members, 84 with acute reaction to stress were matched with 2,218 control individuals, and 331 with other stress reactions were matched with 8,174 control individuals. RESULTS: After analyses were adjusted for age and sex, individuals with any trauma-associated stress condition were more likely to have died by suicide. Risk was highest among individuals with PTSD (adjusted OR [AOR]=10.10, 95% CI=8.31-12.27), followed by those with other stress reactions (AOR=5.38, 95% CI=4.78-6.06) and those with acute reaction to stress (AOR=4.49, 95% CI=3.58-5.62). Patterns of risk remained the same when the analyses were adjusted for any comorbid psychiatric condition. CONCLUSIONS: All trauma-associated stress disorders are risk factors for suicide mortality, highlighting the importance of health system suicide prevention protocols that consider the full spectrum of traumatic stress diagnoses.

Detecting and distinguishing indicators of risk for suicide using clinical records.

Health systems are essential for suicide risk detection. Most efforts target people with mental health (MH) diagnoses, but this only represents half of the people who die by suicide. This study seeks to discover and validate health indicators of suicide death among those with, and without, MH diagnoses. This case-control study used statistical modeling with health record data on diagnoses, procedures, and encounters. The study included 3,195 individuals who died by suicide from 2000 to 2015 and 249,092 randomly selected matched controls, who were age 18+ and affiliated with nine Mental Health Research Network affiliated health systems. Of the 202 indicators studied, 170 (84%) were associated with suicide in the discovery cohort, with 148 (86%) of those in the validation cohort. Malignant cancer diagnoses were risk factors for suicide in those without MH diagnoses, and multiple individual psychiatric-related indicators were unique to the MH subgroup. Protective effects across MH-stratified models included diagnoses of benign neoplasms, respiratory infections, and utilization of reproductive services. MH-stratified latent class models validated five subgroups with distinct patterns of indicators in both those with and without MH. The highest risk groups were characterized via high utilization with multiple healthcare concerns in both groups. The lowest risk groups were characterized as predominantly young, female, and high utilizers of preventive services. Healthcare data include many indicators of suicide risk for those with and without MH diagnoses, which may be used to support the identification and understanding of risk as well as targeting of prevention in health systems.

Enhancement and validation of an Arab surname database.

OBJECTIVES: Arab Americans constitute a large, heterogeneous, and quickly growing subpopulation in the United States. Health statistics for this group are difficult to find because US governmental offices do not recognize Arab as separate from white. The development and validation of an Arab- and Chaldean-American name database will enhance research efforts in this population subgroup. METHODS: A previously validated name database was supplemented with newly identified names gathered primarily from vital statistic records and then evaluated using a multistep process. This process included 1) review by 4 Arabic- and Chaldean-speaking reviewers, 2) ethnicity assessment by social media searches, and 3) self-report of ancestry obtained from a telephone survey. RESULTS: Our Arab- and Chaldean-American name algorithm has a positive predictive value of 91 percent and a negative predictive value of 100 percent. CONCLUSIONS: This enhanced name database and algorithm can be used to identify Arab Americans in health statistics data, such as cancer and hospital registries, where they are often coded as white, to determine the extent of health disparities in this population.