The Effects of Integrating Behavioral Health into Pediatric Primary Care at Federally Qualified Health Centers: An All Payer Analysis
Notice bibliographique
Résumé
Research Objective Approximately 1 in 5 US children have a mental health (MH) disorder. Children with MH disorders, particularly those that are under‐diagnosed or under‐treated, have higher rates of avoidable utilization and health care costs. Despite the availability of evidence‐based treatments for child MH conditions, there are many systemic barriers to receiving adequate MH care, especially for low‐income and racial/ethnic minority populations. There is also substantial unmet need. As such, starting in mid‐2016, three Boston‐based community health centers (CHC) began implementing TEAM UP—a complete behavioral health integration model for low‐income children. Our objective was to examine the impact of TEAM UP on rates of health care utilization in children. Study Design Our primary data source was the 2014–2017 Massachusetts All Payer Claims Data (APCD). Our primary utilization outcomes included inpatient admissions, emergency department visits, primary care visits, other professional and outpatient visits, and use of behavioral health services (any services, intake/evaluation, psychotherapy, group therapy, psychiatric medication management, family consultation, screening, testing, other therapeutic services, family training and counseling, other outpatient services). Our unit of analysis was the person‐quarter. A difference‐in‐differences approach was used to estimate the effect of the intervention on intervention‐site patients, relative to a comparison group of similar non‐intervention site patients. Utilization outcomes were estimated using generalized estimating equations (GEE) with a negative binomial distribution and log link. For all models, outcome variable Y iq was indexed to patient i in quarter q. Independent variables included a dummy for whether a patient was attributed to an intervention site, a dummy for the pre‐ (2014q1‐2016q2) versus post‐period (2016q3‐2017q4), an interaction term between intervention status and post‐period, quarter , number of eligible member months in quarter q for patient i, a vector member‐level covariates (age, sex, payer type, clinical indicators, zip code‐level covariates), and site fixed effects, with errors clustered at the site‐level and using robust standard errors to account for repeated patient measures. All results are reported as marginal effects. Population Studied Children ages 3–21 who were attributed to one of three intervention site CHCs or to one of six geographically proximal non‐intervention site CHCs. This included a final sample of 325,675 person‐quarters representing 31,626 unique children, after exclusions; we excluded the first 6 months before and after implementation due to differential ramp‐up. Principal Findings After 1.5 years of implementation time, TEAM UP was associated with increases in behavioral health service utilization, especially for other therapeutic MH services (difference‐in‐difference: 108.6 visits/1000 patients/quarter, 95% CI: 95.2, 122.0) and family training and counseling (difference‐in‐difference: 78.5 visits/1000 patients/quarter, 95% CI: 69.0, 88.1). Effects were greatest in Medicaid‐enrolled children. We did not observe any short‐term effects on other utilization measures. Conclusions TEAM UP was associated with increased utilization of pediatric behavioral health services. Additional implementation time is necessary to determine if this will translate into reductions in avoidable utilization. Implications for Policy or Practice The TEAM UP model may hold promise in linking low‐income children to behavioral health services. Primary Funding Source Smith Family Foundation; Klarman Family Foundation.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,012 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».