does health insurance matter? establishing insurance status as a risk factor for mortality rate...
TRANSCRIPT
Does health insurance matter? Establishing insurance status as a risk factor for
mortality rate
Hisham Talukder, Applied MathematicsHéctor Corrada Bravo, Computer
ScienceZachary Dezman, Emergency MedicineBruce Golden, Smith School of Business
Shawn Mankad, Smith School of Business
University of Maryland
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National Trauma Data Bank
The National Trauma Data Bank (NTDB) is a repository of patient data compiled from trauma centers across the United States. • 1,926,245 individual patient cases in
over 900 trauma centers from 2002-2006
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Why is Trauma Important?
Trauma is the most common cause of death in persons between ages 1 and 44 in the US
The fifth most common cause of death overall (CDC)
Approximately 37.9 million Americans are treated for traumatic injuries annually
4Age group 19-64 selected for further investigation.
Distribution of insurance types by age
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Research Questions
Do self-pay and insured patients differ in mortality rates?
How does arrival time affect mortality rates?
Can we find new factors through data exploration?
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Q1: Insured vs. Self Pay
Well established in previous works
Still of interest to medical communities, like emergency medicine and trauma
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Q2: Time of Arrival
Why would arrival time matter?
Resources available during late nights are much less than at peak hours of the day
If we find that self-pay patients are more likely to arrive during late nights, this may help explain their lower chances of survival (see Anderson, Gao, Golden, forthcoming POM)
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Q3: Other (new) risk factors
Data contains categorical variables like approximate type or cause of injury
Typically ignored in previous works, but are they of value?
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METHODOLOGY
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Insurance as a binary variable
Insured patients:– Private insurance–Medicare–Medicaid–Worker’s compensation– Others
Self pay patients: – No insurance– Out of pocket cost
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All analyses done defines insurance types with either Insured or Self pay.
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Injury Severity Score (ISS)
Risk of incoming patient measured with ISS– Score of 0-75– Score of 0 corresponds to 100% chance of
survival– Score of 75 corresponds to 0% chance of
survival
Risk partitioned into four categories: – Minor (ISS 0-8) – Moderate (ISS 9-15)– Major (ISS 16-25)– Critical (ISS 25-75)
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Mortality rate by payment source and type of injury
Across all levels of risk there is a higher percentage of patients dying under self pay vs. insured.
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Likelihood of Survival
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Likelihood of Survival
For less risky injuries (Minor, moderate) the survival likelihood between insured and self pay are similar across both facility levels
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Likelihood of Survival
For major injuries the survival likelihood for self pay patients are 5% and 17% lower in level I and II, respectively
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Likelihood of Survival
For critical injuries the survival likelihood for self pay patients are 27% and 28% lower
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Q2: Arrival Times
From 6 pm to 6 am, 47% of all insured patients admit to trauma centers
Same time slot accounts for 55% of self pay patients
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Developing a Risk Model
Variables of interest– Insurance type (Q1)– Time of admit (Q2)– Injury type (Q3)
Control variables– Age– Race – Gender– Hospital size– Region– Facility level
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Logistic Regression Model
Controlvariables
Variables ofinterest
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MAIN RESULTS
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Q1: Insured vs Self Pay
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Q1: Insured vs Self Pay
Two patientsSimilar ageSimilar raceSimilar injuries
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Q1: Insured vs Self Pay
Two patientsSimilar ageSimilar raceSimilar injuries
HEALTH INSURANCE
NO INSURANCE
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Q1: Insured vs Self Pay
Two patientsSimilar ageSimilar raceSimilar injuries
HEALTH INSURANCE
NO INSURANCE
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Q1: Insured vs Self Pay
Two patientsSimilar ageSimilar raceSimilar injuries
HEALTH INSURANCE
NO INSURANCE
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Q1: Insured vs Self Pay
Two patientsSimilar ageSimilar raceSimilar injuries
HEALTH INSURANCE
NO INSURANCE
5%-28% drop
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Q2: Arrival Times
Arriving off-hours (12am – 6am) has a statistically significant negative affect on survival rates
Lowers survival odds by almost 20%
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Q3: New Risk Factors
The regression analysis shows risk is significantly higher in penetrating trauma than for blunt trauma, even if the ISS and other control variables are the same
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Implications and Future Work
Operation Questions: Should/can hospitals staff more specialists off-hours?
Clinical Questions: Can we develop an Injury type corrected severity score?
Methodological Question: What kind of graphics are useful with medical databases?
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How accurate is our survival likelihoods?
Model 1
Model 2
Model 3
AUC
Model 1 .6970
Model 2 .7364
Model 3 .7971