PhD Defense by Melike Yildirim

Event Details
  • Date/Time:
    • Tuesday January 12, 2021 - Wednesday January 13, 2021
      11:30 am - 12:59 pm
  • Location: Atlanta, GA
  • Phone:
  • URL: Bluejeans
  • Email:
  • Fee(s):
    N/A
  • Extras:
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Summaries

Summary Sentence: : Cost-Effective Management of Diseases: Early Detection and Interventions for Improved Health Outcomes

Full Summary: No summary paragraph submitted.

Thesis Title: Cost-Effective Management of Diseases: Early Detection and Interventions for Improved Health Outcomes  

  

Advisor: Dr. Pinar Keskinocak, School of Industrial and Systems Engineering 

  

Committee members

Dr. David Goldsman, School of Industrial and Systems Engineering 

Dr. Paul Griffin, Department of Industrial and Manufacturing Engineering, Penn State University (Adjunct Professor at Georgia Tech ISyE) 

Dr. Julie Swann, Department of Industrial and Systems Engineering, North Carolina State University (Adjunct Professor at Georgia Tech ISyE) 

Dr. Jean O’Connor, School of Public Health, Emory University  

  

Date and Time: 11:30 am, Tuesday, Jan 12th, 2021 

  

Meeting URL:  https://bluejeans.com/531438339 

Meeting ID:  531 438 339 (bluejeans) 

  

Abstract

Mental health and chronic physical conditions significantly impact the patient’s daily life. If those conditions are not detected, treated, and controlled, it can cause functional impairment and contribute to poor health outcomes. This thesis contributes to the decision-making process of preventive intervention programs for major public health problems such as asthma and depression. 

In Chapter 2, we estimate the return on investment (ROI) of AS-ME and Home Visit for Medicaid-enrolled children with asthma. We model the progression of pediatric asthma patients by utilizing the Markov chain model. Discrete event simulation is used to estimate the healthcare utilization and costs for no-intervention and intervention scenarios. The main effects of intervention programs, transition probabilities after the intervention are estimated from the literature. ROI calculation is performed for different sub-populations based on characteristics, including utilization of services (Emergency Department (ED) and/or Inpatient (IP) visits), age, Asthma Medication Ratio (AMR), and whether they lived in geographic regions with higher rates of ED visits for asthma. 

In Chapter 3, we quantify the effect of a set of interventions including AS-ME, influenza vaccine, spacers, and nebulizers on health utilization and expenditures for Medicaid-enrolled children with asthma in New York and Michigan. We evaluate children aged 0-17 with persistent asthma in 2010 and 2011. Difference-in-difference regression is used to quantify the effect of the intervention on the probability of asthma-related healthcare utilization, asthma medication, and utilization costs. We estimate the average change in outcome measures from pre-intervention/intervention (2010) to post-intervention (2011) periods for the intervention group by comparing this with the average change in the control group over the same time horizon. This study also addresses issues that caused the under/overestimation of intervention effects, considering population differences and multiple interventions at a time. 

In chapter 4, we performed a systematic investigation of parameters and calibrations to adapt the natural history model of major depression to the current US adult population. We used differential equations to show the discordance between incidence and prevalence. A natural history model can be utilized to make informed decisions about interventions and treatments of major depression, validated with recall bias that increases with age. 

Finally, in Chapter 5, our primary goal is to understand the potential benefits of routine depression for the general US population. We develop a discrete-time nonstationary Markov model with annual transitions dependent on patient histories, such as the number of previous episodes, treatment status, and time spent without treatment state based on the available data. Monte Carlo simulation model is used to evaluate the cost-effectiveness of screening strategies that allows screening for disease to be personalized to different population cohorts (e.g., based on age).  

Additional Information

In Campus Calendar
No
Groups

Graduate Studies

Invited Audience
Faculty/Staff, Public, Undergraduate students
Categories
Other/Miscellaneous
Keywords
Phd Defense
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Dec 30, 2020 - 1:07pm
  • Last Updated: Dec 30, 2020 - 1:07pm