Analyzing Health Equity Using Household Survey Data

1. Analyzing Health Equity Using Household Survey Data Lecture 15 Measuring and Explaining Inequity in Health Service Delivery “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity

2. Equitable distribution of health care is a widely supported principle • Distinguish between • Horizontal equity – equal treatment of equals • Vertically equity – appropriate unequal treatment of unequals • In health care, most attention given to “equal treament for equal medical need irrespective of income, race, etc” • Methods available to measure and explain horizontal inequity in health care utilisation

3. Identifying horizontal inequity through the need-standardized distribution of health care • Care is unequally distributed by income • But so is need • To assess whether care is equitably distributed: • Either compare actual distribution of care (by income) with distribution of need • Or assess (in)equality in need-standardized distribution care • Income-related distribution of: • Actual use describes inequality • Need-standardized use describes inequity

4. What is need? • Not easily answered – illness, capacity to benefit from health care,… • With available data, rely on age, gender and health indicators to proxy for need • That is, define need as expected utilisation given these variables • It is not possible to measure horizontal inequity without specifying a vertical equity norm • Is generally assumed that, on average, differential utilisation across different levels of need is appropriate • Given this, assess whether there is equal utilisation for the same level of need “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity

5. Analogous to (indirect) demographic standardisation Let medical care use (yi) be explained linearly by where ln inc is log income, xj are the need-proxies and zk are the non-need control variables Need-expected utilisation: where overscore indicates mean values and ^ indicates OLS coefficients (Indirectly) need standarised utilisation is: Need standardization by linear regression

6. Assessment and measurement of inequity based on the need-standardised concentration curve • Need-standardised concentration curve L*(y) lies above diagonal when, for given need, use is concentrated among the poor • Concentration index C* derived from the L*(y) is a measure of horizontal inequity (HI) • HI=C*>0 if inequity “favours” rich, HI=C*<0 if it “favours” poor • Equity only if HI=C*=0 “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity

7. Need-standardization with nonlinear models • Since health care utilisation if often a binary, categorical • or count variable, nonlinear models are preferable • Let y be a nonlinear function of the need and control • variables • where G() takes particular forms for probit, logit, negbin,.. • One could define standardized use by: • Then but depends on the values to • which z are set in the prediction. • Hence the concentration index depends on arbitrary z values

8. Distributions of Actual, Need-Predicted and Need-Standardized Preventive Visits to Doctor, Nurse, or Other HealthPractitioner, Jamaica 1989 “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity

9. Measuring Inequity through Decomposition As seen in lecture 13, for any linear model: the concentration index for y can be written as: The index of horizontal inequity (HI) can be obtained directly from this decomposition: That is, inequity is total inequality minus need-related inequality

10. Explaining Inequity through Decomposition Can decompose total income-related inequality in observed use into: • “acceptable”, or need-induced, inequality • inequity, or non-need related inequality, due to • direct contribution of income • contribution of other, non-need variables (e.g. education, health insurance, location) • contribution of residuals (unexplained inequality) HI = C - (a) = (i) + (ii) + (iii)

11. Decomposition for nonlinear models • Decomposition of the concentration index holds only if the regression model is linear • To apply to a nonlinear model, a linear approximation must be made, e.g. where and are partial effects (evaluated at, e.g., means). • Decomposition applied to this gives: “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity

12. Decomposition of Concentration Index for Access to Preventive Health Care in Jamaica, 1989 “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity

13. OECD Study of Equity in Health Care Utilisation (van Doorslaer et al 2004) • Data from 2000 wave of European Community Household Panel for 10 countries • Data from nationally representative surveys for 11 other OECD countries • Adults (16+) only • Reported utilization over past 12 months • Disposable income per equivalent adult “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity

14. Utilisation and explanatory variables • Utilisation – GP visits, specialist visits, hospital admissions, dental care visits • 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 “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity

15. Horizontal Inequity indices (95% confidence intervals) – Number of GP visits “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity

16. Horizontal Inequity indices - probability of specialist visit

17. Decomposition of inequality in totalnumber of physician visits

18. Horizontal Inequity indices - probability of hospital admission “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity

19. Decomposition of inequity in probability of hospital admission “Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity