Comparison of the Inpatient Complications and Health Care Costs of Anterior versus Posterior Cervical Decompression and Fusion in Patients with Multilevel Degenerative Cervical Myelopathy: A Retrospective Propensity Score–Matched Analysis

Background: Although conventional form of propensity score matching (PSM) is widely used in outcomes research field, its application on multilevel treatment is limited.</p> Objectives: This article reviews PSM and illustrates their use when there are more than two treatment choices, which is very common in health services research. Methods: Generalized PSM technique was applied to commercial claims data to estimate the treatment effect of reliever only, controller only and combination therapy of patients with asthma. The propensity score is estimated using multinomial logistic regression. The outcome variable was total annual health care costs. Inverse probability weighting was applied to calculate risk adjusted costs. Results are compared with multivariate regression analysis, where the generalized linear model is used with gamma family and log link function. Results: Based on the study’s definitions of an asthma episode, we obtained a sample that included 25,124 patients in fee-for-service (FFS) plans and 6,603 patients in non-FFS plans. Under each plan type, patients who were prescribed three different treatment options were significantly different in terms of their demographic and clinical characteristics. Compared to combination therapy under FFS group, the difference of the total health care costs among reliever therapy and controller only group was significant ($728 and $1,216, respectively). Under non-FFS group, reliever only therapy totaled $1,266; controller only therapy was $1,959, and combination therapy totaled $1,933. Although the cost difference between reliever only and combination therapy was significant, there was no evidence that combination therapy cost more than controller only therapy. There were no significant differences in the multi-level propensity score adjusted results and multivariate regression results. Conclusion: This analysis presents the potential value of generalized PSM methods in health services when there are multilevel treatment options.

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Comparison of Approaches to Select a Propensity Score Matched Control Group in the Absence of an Obvious Start of Follow Up for this Group: An Example Study on the Economic Impact of the DMP Bronchial Asthma

Das Gesundheitswesen ◽

10.1055/a-0948-5356 ◽

2019 ◽

Vol 82 (S 02) ◽

pp. S151-S157

Author(s):

Josephine Jacob ◽

Niklas Schmedt ◽

Lennart Hickstein ◽

Wolfgang Galetzka ◽

Jochen Walker ◽

...

Keyword(s):

Health Care ◽

Propensity Score ◽

Bronchial Asthma ◽

Health Care Costs ◽

Index Date ◽

Control Group ◽

Random Index ◽

One Year ◽

Care Costs

Abstract Background Claims data are a valuable data source to investigate the economic impact of new health care services. While the date of enrollment into the new service is an obvious start of follow-up for participants, the strategy to select potential controls is not straightforward due to a missing start of follow-up to ascertain possible confounders. The aim of this study was to compare different approaches to select controls via Propensity Score Matching (PSM) using the disease management program (DMP) bronchial asthma (BA) as an example. Methods We conducted a retrospective cohort study of BA patients between 2013 and 2016 to examine total one-year health care costs and all-cause mortality. We implemented different scenarios regarding the selection of potential controls: I) allotment of a random index date with subsequent PSM, II) calendar year-based PSM (landmark analysis) and III) calendar quarter-based PSM. In scenario I, we applied 2 approaches to assign a random index date: a) assign random index date among all quarters with a BA diagnosis and b) assign random index date and thereafter examine if a BA diagnosis was documented in that quarter. Results No significant differences in total one-year health care costs between DMP BA participants and non-participants were observed in any of the scenarios. This could to some extent be explained by the higher mortality in the control groups in all scenarios. Conclusion If the loss of potential controls can be compensated, scenario Ib is a pragmatic option to select a control group. If that is not the case, scenario III is the more sophisticated approach, with the limitation that baseline characteristics prior PSM cannot be depicted and computational time or memory size needed to conduct the analysis need to be sufficient.

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