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