Title:
COST-EFFECTIVE MANAGEMENT OF DISEASES: EARLY DETECTION AND INTERVENTIONS FOR IMPROVED HEALTH OUTCOMES
COST-EFFECTIVE MANAGEMENT OF DISEASES: EARLY DETECTION AND INTERVENTIONS FOR IMPROVED HEALTH OUTCOMES
Author(s)
Yildirim, Fatma Melike Melike
Advisor(s)
Swann, Julie
Griffin, Paul
Goldsman, David
O'Connor, Jean
Keskinocak, Pinar
Griffin, Paul
Goldsman, David
O'Connor, Jean
Keskinocak, Pinar
Editor(s)
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Abstract
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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Date Issued
2021-01-26
Extent
Resource Type
Text
Resource Subtype
Dissertation