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A Bayesian two-part latent class model for longitudinal medical expenditure data: assessing the impact of mental health and substance abuse parity. (English) Zbl 1216.62040

Summary: In 2001, the U.S. Office of Personnel Management required all health plans participating in the Federal Employees Health Benefits Program to offer mental health and substance abuse benefits on par with general medical benefits. The initial evaluation found that, on average, parity did not result in either large spending increases or increased service use over the four-year observational period. However, some groups of enrollees may have benefited from parity more than others. To address this question, we propose a Bayesian two-part latent class model to characterize the effect of parity on mental health use and expenditures. Within each class, we fit a two-part random effects model to separately model the probability of mental health or substance abuse use and mean spending trajectories among those having used services. The regression coefficients and random effect covariances vary across classes, thus permitting class-varying correlation structures between the two components of the model. Our analysis identified three classes of subjects: a group of low spenders that tended to be male, had relatively rare use of services, and decreased their spending pattern over time; a group of moderate spenders, primarily female, that had an increase in both use and mean spending after the introduction of parity; and a group of high spenders that tended to have chronic service use and constant spending patterns. By examining the joint 95% highest probability density regions of expected changes in use and spending for each class, we confirmed that parity had an impact only on the moderate spender class.

MSC:

62F15 Bayesian inference
62P10 Applications of statistics to biology and medical sciences; meta analysis
65C60 Computational problems in statistics (MSC2010)
90B99 Operations research and management science

Software:

boa; R
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References:

[1] Beunckens, A latent-class mixture model for incomplete longitudinal Gaussian data, Biometrics 64 pp 96– (2008) · Zbl 1274.62721 · doi:10.1111/j.1541-0420.2007.00837.x
[2] Celeux, Deviance information criteria for missing data models, Bayesian Analysis 1 pp 651– (2006) · Zbl 1331.62329 · doi:10.1214/06-BA122
[3] Congdon, Bayesian Models for Categorical Data (2005) · Zbl 1079.62036 · doi:10.1002/0470092394
[4] Elliott, Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction, Biostatistics 6 pp 119– (2005) · Zbl 1069.62095 · doi:10.1093/biostatistics/kxh022
[5] Ferguson, A Bayesian analysis of some nonparametric problems, Annals of Statistics 1 pp 209– (1973) · Zbl 0255.62037 · doi:10.1214/aos/1176342360
[6] Frühwirth-Schnatter, Finite Mixture and Markov Switching Models (2006)
[7] Garrett, Latent class model diagnosis, Biometrics 56 pp 1055– (2000) · Zbl 1116.62428 · doi:10.1111/j.0006-341X.2000.01055.x
[8] Gelman, Bayesian Data Analysis (2004) · Zbl 1117.62343
[9] Gelman, Posterior predictive assessment of model fitness via realized discrepancies (with discussion), Statistica Sinica 6 pp 733– (1996) · Zbl 0859.62028
[10] Geweke, Bayesian Statistics pp 169– (1992)
[11] Ghosh, A Bayesian analysis for longitudinal semi-continuous data with an application to an acupuncture clinical trial, Computational Statistics & Data Analysis 53 pp 699– (2009) · Zbl 1452.62808 · doi:10.1016/j.csda.2008.09.011
[12] Goldman, Behavioral health insurance parity for federal employees, The New England Journal of Medicine 354 pp 1378– (2006) · doi:10.1056/NEJMsa053737
[13] Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika 82 pp 711– (1995) · Zbl 0861.62023 · doi:10.1093/biomet/82.4.711
[14] Leiby, Identification of multivariate responders and non-responders by using Bayesian growth curve latent class models, Journal of the Royal Statistical Society, Series C 58 pp 505– (2009) · doi:10.1111/j.1467-9876.2009.00663.x
[15] Lenk, Bayesian inference for finite mixtures of generalized linear models with random effects, Psychometrika 65 pp 93– (2000) · Zbl 1291.62225 · doi:10.1007/BF02294188
[16] Lin, A latent class mixed model for analyzing biomarker trajectories with irregularly scheduled observations, Statistics in Medicine 19 pp 1303– (2000) · doi:10.1002/(SICI)1097-0258(20000530)19:10<1303::AID-SIM424>3.0.CO;2-E
[17] Lin, Latent class models for joint analysis of longitudinal biomarker and event process data: Application to longitudinal prostate-specific antigen readings and prostate cancer, Journal of the American Statistical Association 97 pp 53– (2002) · Zbl 1073.62582 · doi:10.1198/016214502753479220
[18] Muthén, Finite mixture modeling with mixture outcomes using the EM algorithm, Biometrics 55 pp 463– (1999) · Zbl 1059.62599 · doi:10.1111/j.0006-341X.1999.00463.x
[19] Muthén, General growth mixture modeling for randomized preventive interventions, Biostatistics 3 pp 459– (2002) · Zbl 1138.62365 · doi:10.1093/biostatistics/3.4.459
[20] Neelon, A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use., Statistical Modelling (2010) · doi:10.1177/1471082X0901000404
[21] Olsen, A two-part random-effects model for semi-continuous longitudinal data, Journal of the American Statistical Association 96 pp 730– (2001) · Zbl 1017.62064 · doi:10.1198/016214501753168389
[22] Proust-Lima, C., Letenneur L., and Jacqmin-Gaddam, A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome, Statistics in Medicine 26 pp 2229– (2007) · doi:10.1002/sim.2659
[23] Proust-Lima, Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach, Computational Statistics & Data Analysis 53 pp 1142– (2009) · Zbl 1452.62841 · doi:10.1016/j.csda.2008.10.017
[24] R Development Core Team., R: A language and environment for statistical computing (2008)
[25] Richardson, Discussion of Spiegelhalter TTTTT, Journal of the Royal Statistical Society, Series B 64 pp 626– (2002)
[26] Smith, boa: An R package for MCMC output convergence assessment and posterior inference, Journal of Statistical Software 21 pp 1– (2007) · doi:10.18637/jss.v021.i11
[27] Spiegelhalter, Bayesian measures of model complexity and fit (with discussion), Journal of the Royal Statistical Society, Series B 64 pp 583– (2002) · Zbl 1067.62010 · doi:10.1111/1467-9868.00353
[28] Spiegelhalter , D. J. Thomas , A. Best , N. Lunn , D. 2003 http://www.mrc-bsu.cam.ac.uk/bugs
[29] Stephens, Dealing with label switching in mixture models, Journal of the Royal Statistical Society, Series B 62 pp 795– (2000) · Zbl 0957.62020 · doi:10.1111/1467-9868.00265
[30] Su, Bias in 2-part mixed models for longitudinal semicontinuous data, Biostatistics 10 pp 374– (2009) · doi:10.1093/biostatistics/kxn044
[31] Tooze, Analysis of repeated measures data with clumping at zero, Statistical Methods in Medical Research 11 pp 341– (2002) · Zbl 1121.62674 · doi:10.1191/0962280202sm291ra
[32] U.S. Office of Personnel Management., Call letter for contract year 2001: Policy guidance (2000)
[33] Verbeke, A linear mixed-effects model with heterogeneity in the random-effects population, Journal of the American Statistical Association 91 pp 217– (1996) · Zbl 0870.62057 · doi:10.2307/2291398
[34] White, Markov chain Monte Carlo methods for assigning larvae natal sites using natural geochemical tags, Ecological Applications 18 pp 1901– (2008) · doi:10.1890/07-1792.1
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