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Does health insurance matter? Establishing insurance status as a risk factor for mortality rate Hisham Talukder, Applied Mathematics Héctor Corrada Bravo,

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Presentation on theme: "Does health insurance matter? Establishing insurance status as a risk factor for mortality rate Hisham Talukder, Applied Mathematics Héctor Corrada Bravo,"— Presentation transcript:

1 Does health insurance matter? Establishing insurance status as a risk factor for mortality rate Hisham Talukder, Applied Mathematics Héctor Corrada Bravo, Computer Science Zachary Dezman, Emergency Medicine Bruce Golden, Smith School of Business Shawn Mankad, Smith School of Business University of Maryland

2 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

3 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 3

4 Age group selected for further investigation. 4 Distribution of insurance types by age

5 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? 5

6 Q1: Insured vs. Self Pay Well established in previous works Still of interest to medical communities, like emergency medicine and trauma 6

7 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) 7

8 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? 8

9 METHODOLOGY 9

10 Insurance as a binary variable Insured patients: – Private insurance – Medicare – Medicaid – Worker’s compensation – Others Self pay patients: – No insurance – Out of pocket cost 10

11 11 All analyses done defines insurance types with either Insured or Self pay.

12 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) 12

13 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. 13

14 Likelihood of Survival 14

15 Likelihood of Survival 15 For less risky injuries (Minor, moderate) the survival likelihood between insured and self pay are similar across both facility levels

16 Likelihood of Survival 16 For major injuries the survival likelihood for self pay patients are 5% and 17% lower in level I and II, respectively

17 Likelihood of Survival 17 For critical injuries the survival likelihood for self pay patients are 27% and 28% lower

18 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 18

19 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 19

20 Logistic Regression Model 20 Control variables Variables of interest

21 MAIN RESULTS 21

22 Q1: Insured vs Self Pay 22

23 Q1: Insured vs Self Pay 23 Two patients Similar age Similar race Similar injuries

24 Q1: Insured vs Self Pay 24 Two patients Similar age Similar race Similar injuries HEALTH INSURANCE NO INSURANCE

25 Q1: Insured vs Self Pay 25 Two patients Similar age Similar race Similar injuries HEALTH INSURANCE NO INSURANCE

26 Q1: Insured vs Self Pay 26 Two patients Similar age Similar race Similar injuries HEALTH INSURANCE NO INSURANCE

27 Q1: Insured vs Self Pay 27 Two patients Similar age Similar race Similar injuries HEALTH INSURANCE NO INSURANCE 5%-28% drop

28 Q2: Arrival Times Arriving off-hours (12am – 6am) has a statistically significant negative affect on survival rates Lowers survival odds by almost 20% 28

29 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 29

30 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? 30

31 How accurate is our survival likelihoods? 31 Model 1 Model 2 Model 3 AUC Model Model Model


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