In previous lessons we looked at measures of health coverage and financial protection. And we seen they can vary significantly between countries. This is inequity. We are going to discuss now how to measure equity and the importance of measuring it. So we recall the definition of universal health coverage, which is that it is a system which ensures that everyone who needs health services is able to access them without undue financial hardship. This means everyone. And that means that we need to be able to assess whether the system is actually providing that financial protection to everyone and not just some proportion of the population. So, there are different ways in which inequity and health can occur, and different ways in which we want to measure it. So inequity in health usually occurs in some strata, such us by household income, expenditure or wealth, by sex, or by place of residence. There may be differences in health coverage or health quality. Between, for example, poor and wealthy households, between men and women, or between rural and urban areas. There can be different aspects of inequity in terms of outcomes. So, it may be that the risk of a particular disease or illness differs between groups. For example, poor people will smoke more, or richer people smoke more. There may be differences in disease, so some parts of the community may be a higher risk of disease than others. This may apply to death as well. And it may also apply to the social consequences of illness. So it may be that for wealthy people blindness is not a particularly serious problem, but for poor people it can be catastrophic. So, we need to be able to measure all these different aspects of inequity in all of these different strata. And finally, there are different dimensions of inequity. It may be that there are differences in access to care, or in access to treatment, for different household incomes, or areas. But it may be that, although people have the same access, they experience different quality of care, or they face different costs of treatment. So we need to look at all of these different aspects of inequity. There are two ways of measuring health and equality. We have simple measures, which just basically do pairwise comparisons between two subgroups of the population. For example, the wealthy and the poor. We also have complex measures, which use all available data from different subgroups in the population assess inequality. And typically, these measures, that says the distribution of a health outcome rather than differences between two groups. And there are two types of inequality we can measure. Absolute, which is the basic measure of whether or not people are poor or not. And relative, which measures the differences between groups without consideration to their absolute poverty. So, in terms of absolute inequality, typically, we measure this in a way that reflects the magnitude of the difference in health between two subgroups rather than some relative measure of their difference. So, typically what that means is that we get a mean measure of health between the two subgroups. And we subtract the mean values from one another. The only way that a measure of absolute inequality can be large is if one of the two groups involved has got a very low level of the health outcome. In contrast, relative inequality doesn't care so much about the specific levels of the outcome of interest. It only cares about the difference between them. And it reflects proportional differences in health among subgroups. Typically to measure relative inequality, we get the main health value of a health indicator into subgroups. And we divide the value in one group by the value in another. So that could mean, for example, that a group with 10% outcome in some health measure and a group with 15% outcome in some health measure, have the same ratio as a group with 50 and a group with 75%. Because they're a ratio. Whereas in absolute inequality we would see very different numbers for those two comparisons. So we want to measure both ideally, but depending on the outcome and depending on the situation, one may be more appropriate than the other. Complex measures of inequality can include measures such as the slope index of inequality, which measures the direct relationship between two groups. Or the ratio index or concentration index, which measures relative inequality. These can be weighted for population size, or they can be absolute crude measures, which are not adjusted for population size. We'll look a little bit in detail only at one of these, which is the concentration index. The concentration index is a relative measure of inequality. It doesn't measure absolute inequality. It measures the extent to which a particular health indicator is concentrated amongst the poor or the rich. It ranges from -1 to +1. And it's kind of equivalent to a kind of a genie coefficient of health, in a way. If it's negative, then that indicates that the health indicator is concentrated amongst the poor. That is, the poor have got better outcomes in that health indicator than the rich. And if it's positive, that indicates that the indicator is concentrated amongst the rich. That is, the rich people have got better outcomes in that health indicator. And if it's 0, that indicates no inequality. This is an example of how's it's calculated. This shows the concentration index being calculated for Bangladesh in 2007, and for Egypt in 2008, in terms of birth attendance in these two countries. So on the x-axis we have the cumulative proportion of births, which are ranked by wealth. So the births of the poorest part of the populations are in the left-hand of the x-axis. And the riches part of the population in the right-hand. And on the y-axis we have the cumulative fraction of births that were attended by a skilled birth attendant or some other skilled health personnel in each of those countries. And so you can see that the 0 at the bottom of the y-axis indicates 0% of births were attended by a skilled birth attendant. And in the top at the 1, 100% of births were attended by a skilled birth attendant. And they ranked. So, what this means is that if every birth is equally likely in every wealth group to have a skilled birth attendant, any curve which drops below that green line indicates that wealthier people are more likely to receive a birth attendant than poor people. And any line which goes above that green line indicates that poor people are more likely to receive a birth attendant than wealthy people. You can see in Bangladesh the red line, there's a significance deviation from that green central line. Indicating that there's high level of inequality in the distribution of birth attendance in Bangladesh. Wheras Egypt is very close to the green line, indicating not much inequality. Now the difference between the green line and the red line, or the green line and the blue line, in terms of area, the total area of that difference, is used as the concentration index. So this table shows some of the strata in which health inequality can occur and different simple and complex measures of measuring them. So for example, the main stratifiers of wealth inequality that we're concerned about are wealth, education, area, sex and region. Area means urban or rural, so it's really only a binary division, as is sex. We can differences in health outcomes between areas and in between sexes. Education can be measured in terms of continuous measures, such as years of education or education outcomes through testing. And wealth can also be measured through continuous measures. So, it can be assessed with complex or simple measures of inequality. You can see that the slope index of inequality is used to measure inequality in an absolute level between wealth or education. And the concentration index for relative inequality. So, this gives us some idea of the different measures that we can use for the different forms of inequality. And here, we have some examples of simple measures of inequality. Both absolute and relative in Bangladesh from some research that I prepared using the Bangladesh Demographic and Health Survey along with my colleague. There are five key indicators in this table. Care seeking for pneumonia, at least four antenatal care visits with a skilled birth attendant. Pneumonia treatment, one form of immunization, and delivery in institution. For the poorest 20% of the population, that is, quintile 1, Q1, you can that is the coverage of these indicators is not so great. And for the richest, Q5, quintile 5, the richest 20% of the population, you can see generally these levels are much higher. The difference between them is the difference in absolute inequality. That's an absolute inequality measure. And the ratio is the relative inequality measure. So from the ratio we can see that people in the richest 20% of the population in Bangladesh are six times more likely to have at least four antenatal care visits with a skilled birth attendant. But they're only about 38% more likely to have immunization. So from this we can see the different levels of inequality between the groups. So as a review, there are three aspects to inequity that we want to measure. The strata which they occur, the outcomes that differ, and the dimensions of health coverage in which they occur. That is whether the're inequity in access, in treatment quality, or in cost. And there are different measures of health inequality. They can be simple or complex. And we introduce the example of the concentration index as a way of measuring the distribution of health between continuous measures of strata. So that's how we measure inequity in universal health coverage. For now, we're going to move to the definitions of different aspects of universal health coverage to talk about its specific issues in regard to aging.