tracking protocol is included in the attached Supporting Materials. Our history of longitudinal research in this population suggests we can expect a 65% response rate. Analysis. Analysis of survey data will the effects associated with CCOs, and will describe any differential effects for different groups over time. Multivariate regression models will be used to assess the net effects of CCO membership, along with other potential covariates, on each dependent variable. To take into account the longitudinal nature of the dataset, Generalized Estimating Equation (GEE) models will be employed, which are appropriate when individuals are observed at several points in time, work for both continuous and categorical outcomes, and accommodate curvilinear trends, which is important given that the study period includes baseline data and staged implementation. For all models, propensity scores will be used to adjust for sociodemographic differences using the method described in Aim 2, below. The output from each regression model will be an adjusted risk ratio with its 95% confidence interval for each predictor; and each model will be fully interpreted in the final report. Aim 2: Evaluate the impact of CCOs on cost & utilization patterns. We will use APAC, Oregon’s comprehensive utilization and claims database, to construct a detailed encounter and cost history for each participant, covering 18 months pre-CCO kickoff and 18 months after. We will assess changes in utilization and costs over time, comparing those who enroll in a CCO to those who do not (Exhibit 4). Exhibit 4. Plan for Claims & Utilization Data to the CCO Evaluation Data Sources. We will use the Medicaid component of Oregon’s APAC database, a comprehensive claims dataset that contains all claims, encounter, and Medicaid
tracking protocol is included in the attached Supporting Materials. Our history of longitudinal research in this population suggests we can expect a 65% response rate. Analysis. Analysis of survey data will the effects associated with CCOs, and will describe any differential effects for different groups over time. Multivariate regression models will be used to assess the net effects of CCO membership, along with other potential covariates, on each dependent variable. To take into account the longitudinal nature of the dataset, Generalized Estimating Equation (GEE) models will be employed, which are appropriate when individuals are observed at several points in time, work for both continuous and categorical outcomes, and accommodate curvilinear trends, which is important given that the study period includes baseline data and staged implementation. For all models, propensity scores will be used to adjust for sociodemographic differences using the method described in Aim 2, below. The output from each regression model will be an adjusted risk ratio with its 95% confidence interval for each predictor; and each model will be fully interpreted in the final report. Aim 2: Evaluate the impact of CCOs on cost & utilization patterns. We will use APAC, Oregon’s comprehensive utilization and claims database, to construct a detailed encounter and cost history for each participant, covering 18 months pre-CCO kickoff and 18 months after. We will assess changes in utilization and costs over time, comparing those who enroll in a CCO to those who do not (Exhibit 4). Exhibit 4. Plan for Claims & Utilization Data to the CCO Evaluation Data Sources. We will use the Medicaid component of Oregon’s APAC database, a comprehensive claims dataset that contains all claims, encounter, and Medicaid
SHARECCOProposal.docx