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Yildirim, Fatma Melike Melike
Swann, Julie
Griffin, Paul
Goldsman, David
O'Connor, Jean
Keskinocak, Pinar
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Physical and mental health conditions have an impact on a person’s daily life. If those conditions are not properly treated and managed, it may affect patients’ overall health. This thesis contributes to the decision-making process of preventive intervention programs for major public health problems such as asthma and depression. Asthma is a lifelong condition, and many variables are making it one of the most common and severe chronic diseases for children. Asthma may change over time, with varying severity levels, and cause profound adverse effects financially, physically, and mentally. However, patients can sustain their life longer periods without symptoms or attacks by choosing proper treatment. The 6—18 Initiative by the Center for Disease Control (CDC) has developed intervention strategies to improve patients’ health outcomes. These intervention strategies include: (i) self-management education (AS-ME) for individuals whose asthma is not well-controlled, (ii) home visit to improve self-management education and reduce home asthma triggers for individuals whose asthma is not well-controlled, and (iii) strategies that enhance access and adherence to asthma medications and devices. In Chapter 2, we estimated the return on investment (ROI) of AS-ME and Home Visit for Medicaid-enrolled children with asthma. The cost and utilization measures were quantified using claims data from the Medicaid Analytic eXtract (MAX) files. We modeled the progression of pediatric asthma patients by utilizing the Markov chain model. Discrete event simulation was 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 were obtained from the literature. The ROI calculation was performed for different sub-populations based on characteristics, including utilization of services (Emergency Department (ED) 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 quantified the effect of a set of interventions including AS-ME, influenza vaccine, and asthma devices (spacers and nebulizers) on health utilization and expenditures for Medicaid-enrolled children with asthma in New York and Michigan. We evaluated the children aged 0-17 with persistent asthma in 2010 and 2011. Difference-indifference regression was used to quantify the interventions’ effect on the probability of asthma-related healthcare utilization, asthma medication, and utilization costs. We estimated 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. We utilized patients’ data, asthma-related expenditures, utilizations, and interventions in 2010 and 2011 from MAX files. In Chapters 4 and 5, we focused on one of the significant mental health conditions, depression. Depression is a common mental disorder, and it affects a substantial percentage of people in the US. Major depressive disorder (MDD) is a severe form of depression that may lead to increased health services use, functional impairment, disability, and suicide. It is a treatable disease; the combination of psychotherapy and pharmacotherapy is an effective treatment. Minor depression (mD) is another form of depression with fewer symptoms. Improvement of depressive conditions may be achieved with specific treatments (antidepressants, psychotherapy, etc.) or watchful waiting. Current studies show that mental disorder is underdiagnosed and undertreated in the US population. Untreated mental illnesses may cause serious individual and societal consequences. 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 utilized secondary data that was collected from laptop computer-assisted personal interviews and a national telephone survey of adults in the US. We derived data for the US adult population (18 and over) from nationally representative samples of cohorts from the National Comorbidity Survey Replication and from the Baltimore Epidemiologic Catchment Area study. The model is feasible if incidence is low and lifetime prevalence is 30.2% (females) or 17.6% (males). 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. 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 that were dependent on patient histories, such as the number of previous episodes, treatment status, and time spent without treatment state based on the available data. Markov model was simulated for the hypothetical cohort of 18-year-old and older adults. We evaluated the cost-effectiveness of screening scenarios with different frequencies. In the general population, all screening strategies were cost-effective compare to the baseline. However, there was a difference between age groups of male and female populations based on cost over quality-adjusted life years (QALY). We showed that routine screening is cost-effective for all age groups of females and young, middle-aged males. Male population results are sensitive to the higher costs of screening.
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