1. Quality of Life Lost due to Road Crashes Patricia Cubí-Mollá University of Alicante XXXIII SIMPOSIO DE ANÁLISIS ECONÓMICO ZARAGOZA 2008 Job Market Paper

2. General setting Summary Motivation Data Methodology Results Conclusions Evaluation of health effects in quality-of-life terms Chronic illnessessRoad CrashesDisabilities General setting

3. General setting Summary Motivation Data Methodology Results Conclusions Evaluation of health effects in quality-of-life terms Chronic illnessessRoad CrashesDisabilities Cost-Utility Analysis Cost-effectiveness Analysis Burden of Diseases General setting

4. General setting Summary Motivation Data Methodology Results Conclusions Evaluation of health effects in quality-of-life terms Chronic illnessessRoad CrashesDisabilities Cost-Utility Analysis Cost-effectiveness Analysis Burden of Diseases How to measure health in quality-of-life terms Categorical measures Continuous measures Individual preferences over chronic health states Scaling methods General setting

5. Summary Objectives Estimate the chronic loss of health following a non-fatal road crash - Reduction in quality of life, one year after the road crash? - Equal for men/women? Independent on age? - Average health losses on the affected Quality of life time chronic recover Potential health state Actual health state +1 year loss of health Road crash General setting Summary Motivation Data Methodology Results Conclusions

6. Summary Evidence to the existence of a reduction in quality of life for those injured by a road crash - decreases on a rate of 6.23% (quality of life lost with respect to his/her potential quality of life) Main results The loss of health is, on average, more significative for men General setting Summary Motivation Data Methodology Results Conclusions Meaning of a reduction of 6.23%, with respect to the average:

7. Summary General setting Summary Motivation Data Methodology Results Conclusions Best imaginable health state Worst imaginable health state Evidence to the existence of a reduction in quality of life for those injured by a road crash - decreases on a rate of 6.23% (quality of life lost with respect to his/her potential quality of life) Main results The loss of health is, on average, more significative for men Meaning of a reduction of 6.23%, with respect to the average:

8. Summary Monetary evaluation We chose this arbitrary cut-off point of $100,000 per QALY because this ratio has been reported as an upper bound in other evaluations of medical and injury-related interventions (Graham et al. 1998, 2002). General setting Summary Motivation Data Methodology Results Conclusions Spain, 2007: - Seriously injured by road crashes: 19,295 individuals  1,158 QALYs lost  $ 115,800,000 lost

9. Motivation Why “road crashes”? Deaths by road crash: 40% over total of deaths for those aged 15-24 Potential years of life lost, by causes. Men. Spain, 2005 (Ratios PYLL / 100.000) General setting Summary Motivation Data Methodology Results Conclusions

10. Motivation Why “non-fatal road crashes”? 1 death  7 seriously injured General setting Summary Motivation Data Methodology Results Conclusions Applications: effectiveness of policies aimed at reducing the seriousness of traffic injuries (e.g. improvements in emergency transport, trauma care, passenger protection devices, etc.) Observed patterns in rich countries show only a decline in fatalities, but no decline of crashes or seriousness of injuries (Bishai et al., 2006)

11. Motivation Why “in terms of quality of life”? Few literature: - Pérez et al. (2007). Topic: effectiveness of speed cameras Outcome: number of crashes, number of people injured. - Seguí-Gómez et. al (2006). Topic: traffic injuries Outcome: number of deaths, injuried, ISS-scaling General setting Summary Motivation Data Methodology Results Conclusions The most common approach to estimate health losses: metrics based on the severity of the injury (FCI, AIS, ISS, etc.), rather than metrics based on the general health state of the injured (SAH, VAS, EQ-5D, HUI, etc.)

12. Data General setting Summary Motivation Data Methodology Results Conclusions Encuesta de Discapacidades, Deficiencias y Estados de Salud (survey about diseases, disabilities and health states), INE 1999 (MS) Sample: –population aged 15 or higher –final size: 53,303 individuals To target those seriously injured due to a road crash: –"During the last 12 months, have you suffered from a traffic accident that has prevented you from performing any usual activity?" (Yes/No)  900 say “yes” –"How has this traffic accident influenced in your daily life?“ (Seriously/ Quite a lot /Slightly).  149 say “seriously”, + 178 say “quite a lot” Affected group: 327 individuals

13. Express health valuations as “utility weights” Methodology: measurement of health General setting Summary Motivation Data Methodology Results Conclusions Death Full health 0 1 x

14. Express health valuations as “utility weights” Preference tariffs validated in Spain: VAS tariff and TTO tariff Methodology: measurement of health General setting Summary Motivation Data Methodology Results Conclusions - Obtained by using a representative sample of the general population - Not directly reported in the survey - Scaling method: Interval/Grouped Data Regression for ill-health van Doorslaer and Jones, 2003 + Wagstaff and van Doorslaer, 1994 Death Full health 0 1 x

15. ESCA 02 MS 99 Catalonia N = 7.081 Spain N = 52.802 (327) SAH EQ-5D ESCA 06 Catalonia N = 15. 875 General setting Summary Motivation Data Methodology Results Conclusions SAH Methodology: measurement of health

16. ESCA 02 MS 99 Catalonia N = 7.081 Spain N = 52.802 (327) SAH EQ-5D VAS tariffTTO tariff zero rescaled VASzVASrTTOzTTOr SAH ESCA 06 Catalonia N = 15. 875 General setting Summary Motivation Data Methodology Results Conclusions Methodology: measurement of health

17. ESCA 02 MS 99 Catalonia N = 7.081 Spain N = 52.802 (327) SAH EQ-5D VAS tariffTTO tariff zero rescaled VASzVASrTTOzTTOr SAH ESCA 06 Catalonia N = 15. 875 Interval regression (eight utility health measures) General setting Summary Motivation Data Methodology Results Conclusions Methodology: measurement of health

18. Methodology: evaluation of health losses Health losses = Quality of life of the affected individuals, had the road crash not happened Actual quality of life of the affected individuals, after the road crash - Idea (the estimation is performed on average terms) Potential health status: Unknown! General setting Summary Motivation Data Methodology Results Conclusions

19. Methodology: evaluation of health losses Idea (the estimation is performed on average terms) Health losses = Quality of life of other individuals, who did not suffer a road crash Actual quality of life of the affected individuals, after the road crash - Health status of a comparison group: Known! Is it a reasonable comparation? General setting Summary Motivation Data Methodology Results Conclusions

20. Methodology: evaluation of health losses Descriptive statistics for injured and non-injured (tested differences in means) Injured by a road traffic crash (D=1) Non-injured by a road traffic crash (D=0) N 32752,802 male5246 age44 (20.7)50 (20.1) 16 - 2522.012.2 26 - 3521.715.4 36 - 4513.514.3 46 - 559.212.9 56 - 658.914.0 66 - 7515.617.9 75 + 9.213.3 income 101,184 (63,759)102,881 (64,087) smoker44.028.4 alcohol labor days5.84.7 alcohol weekends 25.121.6 studies no studies19.323.3 primary studies30.933.6 secondary studies39.829.1 superior studies10.114.1

21. Methodology: evaluation of health losses Idea (the estimation is performed on average terms) Health losses = Quality of life of other individuals, who did not suffer a road crash Actual quality of life of the affected individuals, after the road crash - Health status of a comparison group: Known! Is it a reasonable comparation? General setting Summary Motivation Data Methodology Results Conclusions NO

22. Methodology: evaluation of health losses Idea (the estimation is performed on average terms) Health losses = Quality of life of other individuals, who did not suffer a road crash Actual quality of life of the affected individuals, after the road crash - Is it a reasonable comparation? General setting Summary Motivation Data Methodology Results Conclusions Provided that unobserved individual characteristics do not affect the causal analysis, or its overall average impact is equal for both treatment and comparison group w w depends on every variable that could affect the probability of having a road crash: gender, age, region, marital status, educational level, income, if hard smoker, if drinks alcohol, if is afraid of going out, if pregnant, household size. Health losses = E C [H·w] - E A [H]

23. X E T [X]E C [X] E C [w·X] male524652 age44 (20.7)50 (20.1)44 (27.9) 16 - 2522.012.223.3 26 - 3521.715.418.7 36 - 4513.514.313.3 46 - 559.212.910.7 56 - 658.914.010.9 66 - 7515.617.913.7 75 + 9.213.39.3 income 101,184 (63,759) 102,881 (64,087) 101,011 (101,777) smoker44.028.443.8 alcohol labor days5.84.75.7 alcohol weekends 25.121.624.9 studies no studies19.323.319.1 primary studies30.933.630.6 secondary studies39.829.140.0 superior studies10.114.110.3 Results Descriptive statistics for those injured, comparison group and comparison group with adjustment General setting Summary Motivation Data Methodology Results Conclusions

24. Results Evaluating the average health effect: ESCA02ESCA06 (robust to different utility measures). General setting Summary Motivation Data Methodology Results Conclusions

25. Results ESCA02ESCA06ESCA02 ESCA06 MEN WOMEN Evaluating the average health effect, by gender: General setting Summary Motivation Data Methodology Results Conclusions

26. tariffsHealth losses E T [H] - E C [H] ΔHΔHCI (ΔH) ESCA02 VAS zero0.0560.0377.10%[3.12%, 10.09%] VAS resc0.0520.0356.55%[2.87%, 9.35%] TTO zero0.0560.0386.61%[3.53%, 9.35%] TTO resc0.0370.0264.15%[2.16%, 6.09%] ESCA06 VAS zero0.0530.0356.97%[2.84%, 7.89%] VAS resc0.0500.0336.41%[2.55%, 9.82%] TTO zero0.0610.0417.37%[1.21%, 10.42%] TTO resc0.0410.0284.63%[3.48%, 9.36%] Results ΔH : proportion of health lost, with respect to the potential health state,estimated by using adjusted comparison groups. General setting Summary Motivation Data Methodology Results Conclusions E C [H·w] - E A [H] EC [H·w] ΔHΔH =

27. Conclusions evidence to the existence of a reduction in quality of life for those injured by a RC (decreases on a rate of 6.23%) the loss of health is, on average, more significative for men  It is plausible to talk about chronic effects in QoL produced by a RC what should be taking into account at evaluating the impact on the injured individuals. General setting Summary Motivation Data Methodology Results Conclusions

28. Thank you!

29. EQ-5D descriptive system Mobility 1 = No problems Desirability of the health state: Self-care Usual activities Pain / Discomfort Anxiety / Depression 2 = Some problems 3 = Severe problems Levels: 1 1 1 2 2 EQ VAS Dimensions: rate also states: “unconscious” “death” VAS tariff +

30. 1. Order: own health state (11122) 3. Include: “death”, “unconscious” For health states ranked “better than death” : (11111, x years) ~ (targeted state, 10 years) For health states ranked “worse than death” : (die, 0 years) ~ (targeted state, 10 - x years) (Following “death”) (Following x years in “full health”) EQ-5D descriptive systemDesirability of the health state: TTO TTO tariff Mobility 1 = No problems Self-care Usual activities Pain / Discomfort Anxiety / Depression 2 = Some problems 3 = Severe problems Levels: 1 1 1 2 2 Dimensions: 2. Order: 13 new targeted health states (and then “death”)

31. ESCA02ESCA06 Aged 15-35 Aged 35-55 Aged 55 + ESCA02ESCA06ESCA02ESCA06 Results ATET by different health measures and age-groups.

32. Potential years of life lost, by causes and gender. Spain, 2005 Ratios PYLL / 100.000 Men Women Introduction Source: INE

33. Deaths or chronic injuries 313.293 DeathsInjuries 48.683264.610 Men 37.339 Women 11.344 Aged from 0 to 14 1.444 Aged from 15 to 34 Aged from 35 to 54 Aged 55+ Men 188.694 Women 75.916 10.763 20.273143.829 11.94463. 316 15.022 46.702 Introduction Source: INE and DGT Spain, 1996-2004 Road Crashes

34. Model h * True ill-health (latent)VAS_ihSAH_ih Interval/Grouped Data Regression  η j = F −1 (G j ) G j = cum.freq. for SAH_ih = j. F −1 (·) = inverse EDF of VAS_ih x = (age-gender category; region; chronic illness; life style; studies; marital status; labour; income; h.size)

35. Abadie (2005) develops a simple two-step procedure to identify the ATET, using the difference-in-differences estimator. This procedure is now adapted to the case in which we only have data for the post- tratment period. –Assumption 2: P (D = 1) > 0 and with probability one P (D = 1|Z) < 1. –P (D = 1|Z) “propensity score” where Methodology: evaluation of health losses

36. T H 0 |D=1 H 1 |D=1 H 0 |D=0 t-1t QoL Health loss (evaluated at time t) If H 0 |Z,D=1 ~ H 0 |Z,D=0 Road crash ATET time Methodology: evaluation of health losses Health losses under assumption 1

38. Propensity score coeficients