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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. OECD Equity Research Group. Australia: Philip Clarke Finland: Unto Häkkinen

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measuring equity in utilization of health care in oecd countries

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

oecd equity research group
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
slide3
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)
extensions
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
background
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
defining and describing horizontal equity
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
need standardization 1
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.
need standardization 2
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
measurement of inequality by c
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
measurement of inequity by c hi
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
decomposing inequality 1
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:

decomposing inequality 3
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)

decomposing inequality 4
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)

equity relevant system characteristics vary across countries
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
survey data
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
data availability medical care utilization
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
data availability explanatory variables
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
results inequality in actual observed use
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)
detailed decomposition of inequality in total specialist visits spain 1999 table 11 cont d
Detailed decomposition of inequality in totalspecialist visits, Spain, 1999 (Table 11 cont’d)
conclusions 1
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
conclusions 2
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
conclusions 3
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
conclusions 5
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