Using claims data to generate clinical flags predicting short-term risk of continued psychiatric hospitalizations

Most Individuals Are Seen in Outpatient Medical Settings Prior to Intentional Self-Harm and Suicide Attempts Treated in a Hospital Setting

The objective of this quantitative retrospective study is to understand healthcare patterns prior to self-harm, which may inform prevention efforts and identify intervention sites. Medicaid claims within 30, 60, 90, and 365 days prior to hospital-treated self-harm between 11/1/2015 and 11/1/2016 in New York State (N = 7492) were examined. Numbers and proportions were calculated for all service types. Participants were predominately 15-34, female, and White. Most (97%) had prior-year services, 94% outpatient care (73% behavioral health, 90% medical), 69% emergency department (37% behavioral health, 59% medical), and 42% inpatient services (34% behavioral health, 20% medical). About 86% received services within 90 days and 80% within 60 days. Utilization was high within 30 days prior, with 69% having one or more services. Medical services were more common than behavioral health (94% versus 79% in prior year); outpatient (94%) was more common than emergency (69%) and inpatient (42%) care. Given that most patients received health services within 30 days and almost all saw providers within the year prior, the findings indicate that improved prevention efforts within the healthcare system can reduce the incidence of self-harm.

Predictors of Receipt of Physical Health Services in Mental Health Clinics

To inform efforts to improve physical health care for adults with serious mental illness, this study examines predictors of provision and receipt of physical health services in freestanding mental health clinics in New York state. The number of services provided over the initial 12-months of implementation varied across clinics from 0 to 1407. Receipt of services was associated with a diagnosis of schizophrenia, frequent mental and physical health visits in the prior year, and prescription of antipsychotic medications. Additional support may also be needed to enable clinics to target patients without established patterns of frequent mental health or medical visits.

Hospitalization risk factors in antipsychotic-treated schizophrenia, bipolar I disorder or major depressive disorder

Aim: To examine hospitalization risk factors in antipsychotic-treated patients with schizophrenia, bipolar I disorder (BD-I) or major depressive disorder (MDD). Patients & methods: Using Truven Health MarketScan® Commercial, Medicaid and Medicare Supplemental data (01/01/2012–06/30/2016), logistic regression models were performed to identify risk factors for both psychiatric and all-cause hospitalization in three separate analyses. Results: Significant risk factors included prior hospitalization (schizophrenia: odds ratio [95% CI]: 2.83 [2.50–3.21; psychiatric]; 2.58 [2.31–2.87; all-cause]; BD-I: 2.42 [2.23–2.63]; 2.09 [1.96–2.23]; MDD: 2.81 [2.49–3.16]; 2.21 [2.03–2.40]), previous antipsychotic treatment (schizophrenia: 1.71 [1.52–1.93]; 1.31 [1.18–1.46]; BD-I: 1.33 [1.23–1.44]; 1.22 [1.14–1.30]; MDD: 1.31 (1.11–1.54); 1.17 (1.04–1.32) and substance abuse (schizophrenia: 1.42 [1.27–1.60]; 1.37 [1.23–1.53]; BD-I: 1.72 [1.58–1.86]; 1.61 [1.50–1.72]; MDD: 1.90 [1.68–2.15] and 1.55 [1.41–1.71]). Conclusion: Prior hospitalization, previous antipsychotic treatment and substance abuse were associated with increased hospitalization risk in schizophrenia, BD-I or MDD.

Capture-recapture methodology to study rare conditions using surveillance data for fragile X syndrome and muscular dystrophy

BACKGROUND

Rare conditions can be catastrophic for families and the implications for public health can be substantial. Our study compared basic surveillance through active medical record review with a linked administrative data file to assess the number of cases of two rare conditions, fragile X syndrome (FXS) and muscular dystrophy (MD) in a population.

METHODS

Two methods of data collection were used to collect information from five counties comprising two standard metropolitan statistical areas of South Carolina. The passive system relied mostly on health claims data using ICD-9 CM diagnostic codes. The active system relied on a nurse abstracting records from a list of all licensed physicians with specialties in neurology, orthopedics, and genetics.

RESULTS

There were 141 FXS cases and 348 MD cases that met the case definitions using active surveillance. Additional cases were found for both conditions but they were determined to not be true cases. After linking the actively collected MD and FXS cases to passive datasets, we found that the estimated total numbers of cases were similar to using capture-recapture analysis; the positive predictive values for cases identified in the passive system were 56.6% for MD and 75.7% for FXS.

CONCLUSIONS

Applying capture-recapture methods to passively collected surveillance data for rare health conditions produced an estimate of the number of true cases that was similar to that obtained through active data collection.

Use of Pooled State Administrative Data for Mental Health Services Research

State systems are a rich, albeit challenging, laboratory for policy-relevant services research studies. State mental health authorities routinely devote resources to collect data for state planning and reporting purposes. However, these data are rarely used in cross-state comparisons to inform state or federal policy development. In 2008, in response to key recommendations from the National Institute of Mental Health (NIMH) Advisory Council's "The Road Ahead: Research Partnership to Transform Services," (http://www.nimh.nih.gov/about/advisory-boards-and-groups/namhc/reports/road-ahead.pdf), NIMH issued a request for applications (RFA) to support studies on the impact of state policy changes on access, cost, quality and outcomes of care for individuals with mental disorders. The purpose of the RFA was to bridge the divide between research and policy by encouraging research that used state administrative data across states, and to address significant state-defined health policy initiatives. Five projects involving eight states were selected through peer review for funding. Projects began in 2009 and were funded for 3 years. This report provides a brief description of the five projects, followed by an analysis of the impact, challenges, and lessons learned from these policy-partnered studies. We conclude by offering suggestions on ways to use state administrative data for informing state health policies, which is especially timely given national and state changes in the structure and financing of healthcare.