Impact of geographic access to primary care providers on pediatric behavioral health screening.

Behavioral Health (BH) screening is critical for early diagnosis and treatment of pediatric mental disorders. The objective of this study was to assess the impact of geographic access to primary care providers (PCP) on pediatric BH screening in children with different race/ethnicity. A retrospective cohort study was conducted using the 2013-2016 administrative claims data from a large pediatric Medicaid Managed Care Plan that have been linked to 2010 US Census data and the 2017 National Provider Identifier (NPI) Registry. Geographic access was defined as the actual travel distance to nearest PCP and the PCP density within 10-mile travel radius from each individual's residence. Stratified multivariate logistic regression was conducted to examine the association between the geographic access to PCP and the likelihood of receiving screening for behavioral disorders within each racial/ethnic group. BH screening rate was 12.6% among 402,655 children and adolescents who met the inclusion criteria. Multivariable analysis stratified by individual race/ethnicity revealed that Hispanic and Black children were more vulnerable to the geographic access barriers than their non-Hispanic White counterparts. The increase in travel distance to the nearest PCP was negatively associated with screening uptake only among Hispanics (10-20 miles vs. 0-10 miles: OR = 0.78, 95% CI [0.71-0.86]; 20-30 miles vs. 0-10 miles: OR = 0.35, 95% CI [0.23-0.54]). In a subgroup that had access to at least one PCP within 10 miles of travel distance, the variation in PCP density had a greater impact on the screening uptake among Hispanics and Blacks than that in non-Hispanic Whites.

High test-retest reliability of the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire completed by primary care patients in routine care.

BACKGROUND: The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) is a three-item screening measure of unhealthy alcohol use that is widely used in healthcare settings. Evidence shows high test-retest reliability of the AUDIT-C in research samples, but most studies had limited external validity and used small samples that could not be used to evaluate reliability across demographic subgroups and/or screening modalities. This study evaluates the test-retest reliability of the AUDIT-C completed in routine care in a large primary care sample, including across demographic subgroups defined by age, sex, race, ethnicity, and screening modality (i.e., completed in-clinic or online). METHODS: We used electronic health record (EHR) data from Kaiser Permanente Washington. The sample included 18,491 adult primary care patients who completed two AUDIT-C screens 1-21 days apart as part of routine care in 2021. Test-retest reliability was evaluated for AUDIT-C total scores (0-12) and for a binary measure indicating unhealthy alcohol use (scores ≥3 women, ≥4 men). Using previously established cutoffs, we interpreted reliability coefficients >0.75 as indicating "excellent" reliability. RESULTS: AUDIT-C screens completed in routine care and documented in EHRs had excellent test-retest reliability for total scores (ICC = 0.87, 95% CI: 0.87-0.87) and the binary indicator of unhealthy alcohol use (κ = 0.79, 95% CI: 0.78-0.80). Reliability coefficients were good to excellent across all demographic groups and for in-clinic and online modalities. Higher reliability was seen when both screens were completed through online patient portals (ICC = 0.93, 95% CI: 0.93-0.93) versus in-clinic (ICC = 0.81, 95% CI: 0.79-0.82) or when one screen was completed using each modality (ICC = 0.83, 95% CI: 0.82-0.83). Lower reliability was seen in American Indian/Alaska Native (ICC = 0.82, 95% CI: 0.75-0.87) and multiracial individuals (ICC = 0.82, 95% 0.80-0.84). CONCLUSIONS: In real-world routine care conditions, AUDIT-C screens have excellent test-retest reliability across demographic subgroups and modalities (online and in-clinic). Future research should examine why reliability varies slightly across modalities and demographic subgroups.

Measuring reliability and validity of "Stressometer®": A computer-based mass screening and assessment tool for evaluation of stress level and sources of stressors.

INTRODUCTION: It is essential to develop tools that can identify stress manifestation, source of stressors, and suffering in an effort to bridge the treatment gap and enhance behavioral health in the developing world. To that aim, the Stressometer® (SOM) was developed as a comprehensive scale of stress and behavioral health for use around the world. MATERIALS AND METHODS: A validation study of the Stressometer® (SOM) was undertaken with a sample in India that included a nonclinical group and a group of patients at a clinic in New Delhi. For validation purposes, participants were also administered three currently validated scales, including Perceived Stress Scale, Stress Overload Scale (SOS), and Depression Anxiety Stress Scale (DASS). RESULTS: The Stressometer® (SOM) was found to be reliable and had high correlations with established scales. CONCLUSION: Stressometer® (SOM) is a valid and reliable, computer based mass screening tool for evaluation of stress level and sources of stress. Overall, Stressometer® (SOM) creates a robust measurement of stress and behavioral health that is likely culturally neutral and thus has universal applicability. A scale such as this one is ideal for use in the developing world to help bridge the treatment gap created and enhance behavioral health, especially in those suffering.

The Impact of the Massachusetts Behavioral Health Child Screening Policy on Service Utilization.

OBJECTIVE: In 2008, Massachusetts Medicaid implemented a pediatric behavioral health (BH) screening mandate. This study conducted a population-level, longitudinal policy analysis to determine the impact of the policy on ambulatory, emergency, and inpatient BH care in comparison with use of these services in California, where no similar policy exists. METHODS: With Medicaid Analytic Extract (MAX) data, an interrupted time-series analysis with control series design was performed to assess changes in service utilization in the 18 months (January 2008-June 2009) after a BH screening policy was implemented in Massachusetts and to compare service utilization with California's. Outcomes included population rates of BH screening, BH-related outpatient visits, BH-related emergency department visits, BH-related hospitalizations, and psychotropic drug use. Medicaid-eligible children from January 1, 2006, to December 31, 2009, with at least ten months of Medicaid eligibility who were older than 4.5 years and younger than 18 years were included. RESULTS: Compared with rates in California, Massachusetts rates of BH screening and BH-related outpatient visits rose significantly after Massachusetts implemented its screening policy. BH screening rose about 13 per 1,000 youths per month during the first nine months, and BH-related outpatient visits rose to about 4.5 per 1,000 youths per month (p<.001). Although BH-related emergency department visits, hospitalization and psychotropic drug use increased, there was no difference between the states in rate of increase. CONCLUSIONS: The goal of BH screening is to identify previously unidentified children with BH issues and provide earlier treatment options. The short-term outcomes of the Massachusetts policy suggest that screening at preventive care visits led to more BH-related outpatient visits among vulnerable children.

Behavioral health referrals in pediatric epilepsy.

The purpose of this study was to examine the feasibility of a behavioral health referral protocol and barriers to behavioral health care in a pediatric epilepsy clinic. A sample of 93 youth with epilepsy ages 10-17 and caregivers completed behavioral health and seizure severity measures during a routine epilepsy clinic visit. Key findings are that 47 (50.5%) of the youth screened positive for a behavioral health referral, and 35 of these youth were referred for behavioral health services. However, only 20% made and presented for the behavioral health appointment. The most commonly cited barrier for accessing and utilizing behavioral health care was stigma related- a mental health label for the child. The significance of this study lies in the revelation that solely screening for and educating caregivers about behavioral health symptoms and providing behavioral health referral information is not an ideal model. Instead, stigma related barriers point to the necessity of continued integrated physical and behavioral health care within the pediatric epilepsy visit.

Referral and follow-up after mental health screening in commercially insured adolescents.

PURPOSE: Although mental health screening is recommended for adolescents, little is known about the predictors of referral to mental health services or engagement in treatment. We examined predictors of mental health referral from primary care and service use for commercially insured youth who had been screened using the Pediatric Symptom Checklist or Youth-Pediatric Symptom Checklist. METHODS: A retrospective chart review was conducted of commercially insured patients 14-17 years of age who were newly identified by the Pediatric Symptom Checklist or Youth-Pediatric Symptom Checklist at a well-child visit. Comparisons were made with propensity-matched negative adolescents meeting the same criteria. Bivariate analyses were conducted to examine differences between positives and negatives and between referred and nonreferred positives. Logistic regression analyses were performed to assess predictors of mental health referral for positive youth. RESULTS: Medical records of 117 positive and 110 negative youth were examined. Compared with negative youth, positive youth were significantly more likely to be referred for mental health treatment (p < .0001) and receive specialty mental health services (p < .0001). Of the positives, 54% were referred for mental health care and 67% of them accepted. However, only 18% completed a face-to-face mental health visit in the next 180 days. Pediatric Symptom Checklist score (odds ratio, 1.21; confidence interval, 1.03-1.42), parental or personal concern (odds ratio, 10.87; confidence interval, 2.70-43.76), and having depressive symptoms (odds ratio, 9.18; confidence interval, 1.49-56.60) were predictive of referral. CONCLUSIONS: Despite identification after behavioral health screening, limited treatment engagement by referred patients persists. Primary care physicians and mental health specialists must enhance their efforts to engage and monitor identified patients.

Baseline screening tools as indicators for symptom outcomes and health services utilization in a collaborative care model for depression in primary care: a practice-based observational study.

OBJECTIVE: Within a practice-based collaborative care program for depression, we examined associations between positive baseline screens for comorbid mental and behavioral health problems, depression remission and utilization after 1 year. METHODS: This observational study of 1507 depressed adults examined baseline screens for hazardous drinking (Alcohol Use Disorders Identification Test score ≥ 8), severe anxiety (Generalized Anxiety Disorder 7-item score ≥ 15) and bipolar disorder [Mood Disorders Questionnaire (MDQ) positive screen]; 6-month depression remission; primary care, psychiatric, emergency department (ED) and inpatient visits 1 year postbaseline; and multiple covariates. Analyses included logistic and zero-inflated negative binomial regression. RESULTS: At unadjusted baseline, 60.7% had no positive screens beyond depression, 31.5% had one (mostly severe anxiety), 6.6% had two and 1.2% had all three. In multivariate models, positive screens reduced odds of remission versus no positive screens [e.g., one screen odds ratio (OR) = 0.608, p = .000; all three OR = 0.152, p = .018]. Screening positive for severe anxiety predicted more postbaseline visits of all types; severe anxiety plus hazardous drinking predicted greater primary care, ED and inpatient; severe anxiety plus MDQ and the combination of all three positive screens both predicted greater psychiatric visits (all p < .05). Regression-adjusted utilization patterns varied across combinations of positive screens. CONCLUSIONS: Positive screens predicted lower remission. Severe anxiety and its combinations with other positive screens were common and generally predicted greater utilization. Practices may benefit from assessing collaborative care patients presenting with these screening patterns to determine resource allocation.