Measuring equity in utilization of health care in OECD countries

1. Measuring equity in utilization of health care in OECD countries Eddy van Doorslaer, Cristina Masseria Erasmus University Rotterdam & the OECD Equity Research Group Paris, 4-5 Sept 2003

2. OECD Equity Research Group • Australia: Philip Clarke • Finland:Unto Häkkinen • France:Agnès Couffinhal, Sandy Tubeuf, Paul Dourgnon • Germany: Martin Schellhorn • Hungary:Agota Szende • Mexico: Gustavo Nigenda, Hector Arreola • Norway: Astrid Grasdal • Sweden: Ulf Gerdtham • Switzerland: Robert Leu • USA: Frank Puffer, Elizabeth Seidler • All other countries:Eddy van Doorslaer, Cristina Masseria, Xander Koolman

3. Aims • Present new international comparative evidence on income-related inequality and inequity in use of 5 types of health care: • Physician visits (GP and specialist) • Hospital care • Dental care • Explore determinants of income-related inequalities and inequity in health care use of GPs in 21 OECD member states • Extension and update of methods and results of Wagstaff, Koolman, Puffer (2002)

4. Extensions • Update from 1996 to 2000 for 13 countries: Austria, Belgium, Canada, Denmark, Greece, Germany, Ireland, Italy, Portugal, Spain, United Kingdom, United States • Extended coverage to 8 new countries: Australia, Finland, France, Hungary, Mexico, Norway, Switzerland and Sweden • More services: physician and hospital and dental care utilization • New method for need standardization • Decomposition by probability of use and total use • Decomposition analysis into sources of inequ(al)ity

5. Background • Most OECD countries have achieved close to universal public coverage • Most EU countries subscribe to egalitarian goal of “equal treatment for equal need”, at least for public sector • But pressure on public sector and growth of private insurance and delivery ‘complements’or ‘supplements’ may affect equity performance • Standard methodology now available for ‘broad-brush’ assessments and cross-country comparisons of equity

6. Defining and describing horizontal equity • Care is unequally distributed by income • But also need is distributed unequally by income • To assess whether care is equitably distributed: • Either: compare actual distribution of care (by income) with distribution of need for such care • Or: assess (in)equality in need-standardized distribution of care • Income quintile distribution of: • Actual use describes inequality • Need-standardized use describes inequity

7. Need standardization - 1 • Let medical care use (yi) be explained by • where the vector of explanatory variables consits of (log) income, a set of k need predictor variables (xk) and a set of p other, non-need variables (zp). • The parameters are to be estimated for the sample.

8. Need standardization - 2 • Then the need-expected use of medical care yX can be generated using (i) the estimated parameters, (ii) the actual values of xk and (iii) the sample means of lninc and zp from: • Estimates of (indirectly) need-standardized use are obtained as the difference between actual and x-expected utilization, expressed as deviation from the sample mean

9. Measurement of inequality by C • Convert relative (eg quintile) distributions into cumulative distributions • Plot concentration curve of actual • Concentration curve L(s) lies above diagonal when use is concentrated among the poor • Concentration index C based on area between conc curve and diagonal • C>0 if inequality “favours” rich, C<0 if it “favours” poor

10. Measurement of inequity by C* = HI • Convert relative (eg quintile) into cumulative distributions of need-standardized use • Concentration curve L*(s) lies above diagonal when use is concentrated among the poor • HI=C* • Concentration index C* based on area between conc curve and diagonal • HI=C*>0 if inequity “favours” rich, HI=C*<0 if it “favours” poor • Equity only if HI=C*=0

11. Decomposing inequality - 1 In general, Wagstaff, Van Doorslaer and Watanabe (2003) have shown thatfor any linear additive explanatory model such as : where y is medical care demand, X is a vector of determinants, and e is a disturbance term, one can write:

12. Decomposing inequality - 3 Using the decomposition method we can decompose total inequality in observed use of care into: • “acceptable” or need-induced inequality (Cx) • “unacceptable”or non-need related inequality due to • direct contribution of income itself (Clninc), (c) contribution of other, non-need variables (zp: education, activity status, region) (d) contribution of residuals (unexplained inequality) HI = C - (b) = (a) + (c) + (d)

13. Decomposing inequality - 4 Clearly, since a contribution is defined as the product Any variable xk will have a greater contribution if • it is more unequally distributed by income (Cx,k) • or if it has a greater use elasticity (i.e. it has a stronger effect on use ( ) in relation to its mean xkm)

14. Equity-relevant system characteristics vary across countries • Income-related variation in: • Provider remuneration • Degree and type of insurance coverage • Degree of cost sharing and exemptions • Regional variation in: • Supply of medical care (both quality and quantity) • Coverage levels • Access costs

15. Survey data • Data from 2000 wave of European Community Household Panel on reported utilization over past 12 months for 10 countries • Data from nationally representative surveys for 11 other countries (Table 1) • Adults (16+) only • Varying sample sizes

16. Data availability: medical care utilization Utilization variables not complete: • No GP/specialist split in Australia, Germany, Mexico, Sweden, US • No number of visits in Australia, Mexico, (UK) • Shorter recall period (3 months) for Germany and Sweden • No hospital care utilization for Norway • No dental care for Australia, Germany, Mexico, and no number of visits for Sweden and UK

17. Data availability: explanatory variables Disposable income per adult equivalent Need indicators: • Self-reported health • Health problems and degree of limitation Other variables: • Health insurance coverage only for Australia, France, Germany, Ireland, Switzerland, UK and US • No region of residence for Denmark, Finland, Netherlands, Sweden and very limited for most other countries

18. Results: inequality in actual, observed use Cf Tables 6-10 • Substantial cross-country differences in mean levels of use But in all countries: • Pro-poor distributions for GP and hospital care (CI negative) • Mostly pro-rich distributions for specialist care (CI positive) • Very pro-rich distributions for dental care (CI positive)

19. Inequity indices for number of physician visits (with 95% confid intervals)

20. Inequity indices for probability of a physician visit (with 95% confid intervals)

21. Inequity indices for number of GP visits (with 95% confid intervals)

22. Inequity indices for probabilityof any GP visit (with 95% confid intervals)

23. Inequity indices for number of specialist visits (with 95% confid intervals)

24. Inequity indices for probabilityof any specialist visit (with 95% confid intervals)

25. Inequity indices for number of hospital nights (with 95% confid intervals)

26. Inequity indices for probabilityof hospital admission (with 95% confid intervals)

27. Inequity indices for number of dentist visits (with 95% confid intervals)

28. Inequity indices for probabilityof any dentist visit (with 95% confid intervals)

29. Detailed decomposition of inequality in totalspecialist visits, Spain, 1999 (Table 11)

30. Detailed decomposition of inequality in totalspecialist visits, Spain, 1999 (Table 11 cont’d)

31. Decomposition of inequality in totalnumber of physician visits

32. Decomposition of inequity in probability of a physician visit

33. Decomposition of inequity in totalnumber of GP visits

34. Decomposition of inequity in probability of a specialist visit

35. Decomposition of inequity in probability of a hospital admission

36. Decomposition of inequity in totalnumber of dentist visits

37. Conclusions - 1 Feasibility • Increased coverage of OECD countries at expense of comparability • Country-specific survey data had to be used for 11 countries • Not all countries represented in all comparisons Methods • Need standardization crucial for horizontal inequity concept • Decomposition ‘by parts’ into initial and subsequent use • Decomposition ‘by sources’ of inequity

38. Conclusions - 2 Health care use differences across OECD countries • Tremendous variation in mean rates of doctor, hospital and dentist utilization Inequity comparisons all physician visits • Observed use: slightly pro-poor inequality • Significant pro-rich inequity in half the countries, but not very high • Highest in US, Portugal and Finland for total number • Highest in US, Sweden and Portugal for probability Inequity comparisons GP visits • Pro-poor use • But little or no significant inequity, and very small • Therefore: GP visits are equitably distributed

39. Conclusions - 3 Inequity comparisons medical specialist visits • Fairly equal observed use • But significant pro-rich inequity in all, and fairly high degrees in some • Particularly high in Portugal, Ireland and Finland • Surprisingly low in UK • Not clearly associated with one particular determinant, though clearly private insurance and private delivery play some role Inequity comparisons hospital care use • Pro-poor in some with large samples (Canada, Australia, US, Mexico) • Pro-rich in some others (Italy, Portugal, Spain) • Regional disparities play some role here

40. Conclusions - 5 • Is this inequitable? Only to the extent that the principle of equal (public?) treatment for equal need is violated • Quality differences make this very likely