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Using linear regression and mixed models to predict health care costs after an inpatient event

Freyder, Christopher (2016) Using linear regression and mixed models to predict health care costs after an inpatient event. Master's Thesis, University of Pittsburgh. (Unpublished)

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Gateway Health Plan® wanted to compare the before and after costs of a member who had an inpatient stay in a hospital which will allow them to evaluate costs in comparison trials. As part of my internship with Gateway Health Plan®, I was able to estimate a formula to evaluate difference in costs.
Using Gateway Health Plan’s® internal data from the past three years, I used regression to evaluate the difference in cost for members before and after an inpatient event. I ran a simple linear regression model as well as a mixed effects model in order to look at the comparison of the before and after costs. Age and gender were also considered at as possible covariates in the prediction process because both of those factors are known to be associated with healthcare costs. The results showed that average cost before an inpatient event as well as gender were significant in estimating the average cost after an inpatient event. I found that females tend to cost less than males, and female patients cost less after the inpatient event compared to before the inpatient event, while men cost more after the event.
Public health significance: This research will help Gateway Health Plan to evaluate interventions to assess whether they lower health care costs. Being able to evaluate if interventions are cost efficient will improve healthcare leading to an improvement in population health.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Freyder, Christopherchf53@pitt.eduCHF53
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairFeingold, Eleanorfeingold@pitt.eduFEINGOLD
Committee MemberBuchanich, Jeanine Mjeanine@pitt.eduJEANINE
Committee MemberYouk, Adaayouk@pitt.eduAYOUK
Date: 9 September 2016
Date Type: Publication
Defense Date: 1 June 2016
Approval Date: 9 September 2016
Submission Date: 26 May 2016
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 43
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Mixed Models, Health Care, Patient as their own Control
Date Deposited: 09 Sep 2016 17:34
Last Modified: 15 Nov 2016 14:33


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