Measuring equity in the health system scorecard Report on the development of a “health equity coefficient” June 19, 2009 Prepared by Brenda Tipper & Carey Levinton 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION Background The Ontario Health System Scorecard reports the results of performance measures linked to nine strategic dimensions One of the dimensions focuses on improving equity in health. Results for many of the performance measures are stratified and reported by geographic, demographic and SES variables of interest. We can examine differences - are they becoming larger or smaller over time? However, depending on groups identified and characteristics of interest, results can be difficult to interpret and to convey in a simple manner Unlike other performance dimensions, we have not been able to assess change in performance on the equity dimension over time, or to compare results across health regions. 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

“Equity” as currently reported in the health system scorecard What these charts and graphs tell us about equity: Reported unmet need for all groups decreased between 2000 and 2003, but increased between 2003 and 2005. The ratio between the highest (worst rate) and lowest (best rate) group was 2.3 in 2000, 3.2 in 2003 and 2.9 in 2005 What these charts and graphs don’t tell us about equity: Has the disparity in reported unmet health care needs among the groups identified increased or decreased over time? Have the right groups been identified? What would the picture look like if we throw education and/or income levels into the mix of groups? What does the picture look like at the LHIN level? 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

Traditional approach to reporting on health equity Stratify results for health access and outcome indicators by SES variables of interest – i.e., sex, income, education, geography (urban/rural), language, immigration status, etc. U.K.? (Check Carolyn’s write-up for details) POWER study AHRQ National Healthcare Disparities What this approach doesn’t always handle well Consider multiple factors and their interaction that may be of interest Education, income, geography (urban/rural), sex, age, neighbourhood/community, immigration status, language, etc. Consider the relative size of groups Provide a result that we can easily trend over time or across jurisdictions (i.e., LHINs) Often use ratios of highest(best) to lowest(worst) to get a single number to represent results 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION Other approaches Mackenbach* - European country comparisons using regression-based measures Relative index of inequality Slope index of inequality Allin** - Comparing equity in healthcare use across Canadian provinces Horizontal inequity index *Mackenbach, et al, 2008. Socioeconomic Inequalities in health in 22 European Countries, NEJM. **Allin, 2008. Does equity in healthcare use vary across Canadian provinces? Healthcare Policy 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION Objectives We wanted a summary measure of health equity to: Track performance over time Compare regional performance Potentially compare to other jurisdictions Determine impact of strategies and policies designed to improve equity The summary measure should be simple to report and to understand, with results that can be explored further to improve understanding and develop strategies We decided to explore the application of index of equity in (access to) health (care) similar in concept to Lorenz curve and Gini coefficient 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION Methods 1. Survey base and descriptive statistics by SES and demographic characteristics Selected “unmet health care needs” as performance measure Respondents in Canadian Community Health Survey (CCHS)* cycles 2003 and 2005 asked if there was ever a time during the past 12 months when they felt that they needed health care but didn’t receive it. This is a composite rate where any positive answers (for any reason) is counted as an “unmet health care need.” Results show differences when stratified by region and by various SES and demographic characteristics. 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION Methods (Cont’d) 2. Apply Classification and Regression Tree (CART) Analysis Classification and regression tree (CART) analysis was used to determine which of the SES and demographic characteristics were most significant in segmenting the population group into relatively homogeneous sub-groups based on rate of unmet health care need. Based on the CART methodology, the following factors were most significant in segmenting the prevalence of unmet health care needs (listed in order): Age Sex Income level Supplemental insurance Education Work status 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION Methods (Cont’d) Example of CART Analysis 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

The method under the development provides a way of determining a “coefficient of equity” based on an associated health care access or outcome indicator Illustration of typical Lorenz curves for income distribution Measure “distribution of ‘health’ (or ill-health or access or outcomes)” across multiple groups Analogous to income distribution, e.g., The bottom 20% of the population has 5% of the income The top 5% of the population has 50% of the income The cumulative distribution is represented as a Lorenz curve. The area under the curve between the straight line representing perfect equity and the Lorenz curve is the GINI coefficient. The bigger the area, the higher the coefficient and the less the equity 0 = no difference = perfect equity 1 = maximum difference = absolute inequity GINI coefficient, e.g., 0.20 GINI coefficient, e.g., 0.60 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION A simple way of applying the concept of a Lorenz curve to equity on the measure of unmet health care needs Order the results by (in this case) decreasing performance and consider the gradient or change from one group to the next. The line representing change in 2005 is steeper than the line for 2000, implying that equity in unmet health care need has decreased. By plotting a cumulative distribution on the ordered results, we can get a representation of a Lorenz curve. Note that the plot in this example is an illustration that assumes equal proportions of the population in each of the age group/sex categories. The curves for 2003 and 2005 show a larger deviation from the diagonal of perfect equity than the 2000 curve, indicating that equity in unmet health care needs has decreased. 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

The simple example needs more work It does not consider the relative sizes of the different groups The division of the population results into groups was arbitrary. What would happen if we added other factors in, e.g., income, education, urban/rural? Determining an equity coefficient consists of the following steps: Use CART (classification and regression tree) to identify the most significant social factors for differentiating the result Order the results from worst to best Plot a Lorenz curve and calculate a GINI coefficient based on the relative results and sizes of the groups identified through CART. 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION Results Based on the CART methodology, the following factors were most significant in discriminating the prevalence of unmet health care needs among various groups (listed in order): Age Sex Income level Supplemental insurance Education Work status GINI coefficient results for 2003 and 2005 (Lorenz curves are shown on the following slides) GINI coefficient % of Population Reporting Unmet Health Care Needs 2003 0.167 11.0% 2005 0.185 12.0% 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

Illustration of an application* to “unmet health care needs” Lorenz Curve for distribution of unmet health need, Ontario, 2003 GINI coefficient = 0.167 Cumulative Proportion of Population The remaining 10% of the population has 22% of the unmet health need 80% of the population has 59% of the unmet health need 90% of the population has 78% of the unmet health need 50% of the population has 25% of the unmet health need Source: CCHS, Cycle 2.1 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION *Results still be verified

Illustration of an application* to “unmet health care needs” Lorenz Curve for distribution of unmet health need, Ontario, 2005 The GINI coefficient increased slightly (less equity) as a result of share of unmet need of the “worst off” 20% pf the population increasing from 39% to 41%. GINI coefficient = 0.185 Cumulative Proportion of Population The remaining 10% of the population has 23% of the unmet health need 80% of the population has 61% of the unmet health need 90% of the population has 77% of the unmet health need 50% of the population has 26% of the unmet health need Source: CCHS, Cycle 3.1 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION *Results still be verified

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION Results Results by regional health system In 2005, the percentage of respondents reporting unmet need varied across regions from 10.0% to 14.3%. The equity coefficients varied from 0.126 (most equitable) to 0.231 (least equitable). Results for both the equity coefficient and percent reporting unmet health care needs improved in three of the fourteen regions; results for both measures declined in seven regions. 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION *Results still be verified Plotting both GINI coefficient and overall unmet health care need results by LHIN provides a picture of performance that includes an equity dimension Less Unmet Need The squares show the results by LHIN for 2005, relative to the Ontario 2005 average for both the GINI coefficient and overall % of the population reporting unmet health care needs. The origin of the dotted line shows the result for 2003. Less Equity (higher GINI) More Equity (lower GINI) Improved performance for both unmet need and equity would result in a line moving towards the upper right quadrant. Only LHINs 1, 13 and 14 are moving in this direction. Six LHINs show both less equity and more unmet need between 2003 and 2005, as does Ontario as a whole. More Unmet Need 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION *Results still be verified

Strengths / Limitations The use of CART to segment the population into relatively homogeneous groups based on demographic and SES characteristics provides a way to explore the factors driving inequities. The Slope Index has been suggested as superior to the Gini coefficient in measuring the extent of health inequity as it is based on an assignment of “social order” to the population groups. However, some factors (such as sex or age) do not readily lend themselves to ordering. The use of CART to develop the groupings used in determining the Gini coefficient means that the value of the coefficient reflects the extent of the underlying inequity in the sub-groups identified. The equity index is not an intuitive number in the way that percent of population reporting unmet health care needs is The meaning of 12% of respondents saying they could not get care when they felt they needed it is relatively obvious The meaning of a Gini coefficient of 0.185 is not without explanation and background of Lorenz curve 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

Conclusions and Implications for Policy A coefficient of equity is a summary measure of disparities in access to health services or outcomes across population sub-groups. This measure can highlight differences in equity across health systems and trends in equity over time. The methodology was applied to the measure of unmet health care needs. A summary measure of health system equity such as the equity coefficient can assist policy-makers in formulating and measuring targets for health system equity. The impact of strategies and policies can be tracked from the perspective of equity over time and we can assess whether improvement in equity coincides with improvement on health system measures of access or outcomes. Application of CART allows investigation of how factors change over time. It aids our understanding which factors are most significant in driving equity The summary measures allows policy-makers and health system managers to focus on both improving performance results (reducing percent reporting unmet health care need) and improving equity across identified groups. 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION

CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION Going Further Investigate further use of CART to analyze patterns of inequity in order to assist in developing strategies and policies to reduce these or assist those population groups most affected. Apply method to other measures of access to health care services and health outcomes: Rate of ambulatory care sensitive hospitalizations (access to and quality of primary health care) Rates of angiography (access to diagnostic imaging services) Rates of breast, cervical and colorectal cancer screening 4/12/2019 CONFIDENTIAL DRAFT - NOT FOR DISTRIBUTION