Alcoholism treatment episodes validly defined using mental health care utilization records

Wall, Melanie M.; Stromberg, Kurt; Pothoff, Sandra; Kane, Robert L

doi:10.1016/j.jclinepi.2003.08.008

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…An episode of care methodology based on intensity of alcoholism treatment visits per month was used to define the alcoholism treatment period [17] which provides a clearly defined beginning and ending month of treatment. Alcoholism treatment typically lasts several months and the length and exact calendar months vary by patient.…”

Section: Data and Model Motivationmentioning

confidence: 99%

Multiple indicator hidden Markov model with an application to medical utilization data

Wall

2008

Statistics in Medicine

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Monthly counts of medical visits across several years for persons identified to have alcoholism problems are modeled using two-state hidden Markov models (HMM) in order to describe the effect of alcoholism treatment on the likelihood of persons to be in a “healthy” or “unhealthy” state. The medical visits can be classified into different types leading to multivariate counts of medical visits each month. A multiple indicator hidden Markov model is introduced that simultaneously fits the multivariate Poisson counts by assuming a shared hidden state underlying all of them. The multiple indicator hidden Markov model borrows information across different types of medical encounters. A univariate HMMs based on the total count across types of medical visits each month is also considered. Comparisons between the multiple indicator HMM and the total count HMM are made, as well as comparisons with more traditional longitudinal models that directly model the counts. A Bayesian framework is used for estimation of the HMM and implementation is in Winbugs.

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Section: Data and Model Motivationmentioning

confidence: 99%

Multiple indicator hidden Markov model with an application to medical utilization data

Wall

2008

Statistics in Medicine

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“…A patient was deÿned to have engaged in treatment if they had at least three alcoholism treatment visits separated by no more than 3 months without contact. This episode of care methodology [9] provides a clearly deÿned beginning and ending month of treatment which can be di erent for every patient. The remaining 12 948 patients who did not meet the criteria for being engaged in treatment will be used as the control group throughout the analyses.…”

Section: Medical Utilization Data For Alcoholism Patientsmentioning

confidence: 99%

GEE estimation of a misspecified time‐varying covariate: an example with the effect of alcoholism treatment on medical utilization

Wall

Dai

Eberly

2004

Statistics in Medicine

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The generalized estimation equation (GEE) method is widely used in longitudinal data analysis, particularly when the outcome variable is non-Gaussian distributed. Under mild regulatory conditions, the parameter estimates are consistent and their asymptotic variances are efficient. In an observational study focusing on alcoholism patients, we applied the GEE method to longitudinal count data from medical utilization records from a large national managed care organization. The health services research question was whether there was a change in medical utilization for patients after engaging in alcoholism treatment as compared to before treatment. Thus, the main effect of interest was a time-varying covariate indicating whether the patient had undergone treatment yet or not. GEE under five different working correlations was employed and mixed results regarding the significance of the treatment effect were found. Because of the large sample size, i.e. 8485 patients with an average of 46 repeated measurements per patient, differences across the estimates produced by the different working correlation structures was suspicious. It is shown that these differences are maybe caused by the fact that the time-varying covariate in the marginal mean model is misspecified. A simulation study is performed to demonstrate that misspecification of the time-varying covariate in the marginal mean structure can cause differences in GEE results across various choices of working correlation structure.

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Mental health and substance abuse treatment utilization among individuals served by multiple public agencies in 3 states

Bray

Davis

Graver

et al. 2005

The Journal of Behavioral Health Services & Research

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Patterns of mental health (MH) and substance abuse (SA) treatment utilization among populations receiving services through multiple public programs are not well known. This study examines to what extent populations with MH and/or SA conditions utilize treatment services through Medicaid and State MH/SA Agencies. Data are from the Substance Abuse and Mental Health Services Administration Integrated Database, a multiyear file for 3 states combining Medicaid and State MH/SA Agency administrative data into a uniform database. Although populations with co-occurring conditions and those served by both Medicaid and State MH/SA Agencies have substantial contact with the public treatment system, a majority of the MH/SA populations examined here utilize few services over brief periods of time. Utilization is most limited among individuals with MH-only conditions and those served exclusively by Medicaid. While a lack of data on clinical outcomes prevents us from drawing conclusions about the effectiveness of MH/SA services, results of this analysis indicate that public programs in the states examined here do not provide services that are primarily utilized on a frequent or chronic basis.

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Alcoholism treatment episodes validly defined using mental health care utilization records

Cited by 8 publications

References 18 publications

Multiple indicator hidden Markov model with an application to medical utilization data

Multiple indicator hidden Markov model with an application to medical utilization data

GEE estimation of a misspecified time‐varying covariate: an example with the effect of alcoholism treatment on medical utilization

Mental health and substance abuse treatment utilization among individuals served by multiple public agencies in 3 states

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