The place of population change in explaining geographical inequalities in health in New Zealand

Abstract

We appreciate Harper’s comments on the role of population change in explaining rising geographical inequalities in health in New Zealand. 1 As we note in our original paper, we agree that selective migration patterns may well be an important explanation as to why regional health status in New Zealand has become more geographically polarized during the 1980s and 1990s. 2 Selective patterns of migration between the regions of New Zealand and the high levels of immigration into the country, and emigration from it, are likely to lead to a high level of population sorting between areas and to have strengthened the widening life expectancy gap during the 1980s and 1990s. Given that New Zealand experiences almost the highest rate of combined immigration and emigration (population turnover) in the world, a if selective migration were to have an explanatory role anywhere it would be in helping to understand changing health patterns within this country. Although we agree that selective migration is likely to be an important factor in explaining our results, there are five areas of interpretation where we disagree with Harper. First, Harper contends that growing geographical inequalities in health observed using the Slope Index of Inequality (SII) may be due to differential levels of population growth between regions of the country (i.e. if areas with the highest or lowest life expectancies grow at a faster rate then the gap will increase even if the life expectancy in those areas remains the same). Harper gives an example of where he thinks the problem lies: ‘‘Given the near-linear relationship between socioeconomic rank and life expectancy among health districts, even if regional life expectancies had stayed exactly the same, but over this time there was more rapid population growth among areas of higher and lower life expectancy, the SII would register an increase.’’ (Harper, 2006, p. 604) It is not hard to see how Harper came to assume this—it is an easy error to make and perhaps we could have explained the SII more fully—but it is important that researchers recognize that the results are not an artefact of using the SII as Harper suggests. Rather, an increase in the index means that inequality amongst people measured between areas has risen. Suppose you were measuring educational attainment and over time a greater number of people failed to gain any qualifications and more gained degrees—inequality would have increased between people in terms of educational attainment. It is not that the population sizes of these groups had simply altered. Places are not entities of themselves—they are collections of people. 3 The SII is the slope of the regression line from the hypothetically poorest individual to the hypothetically richest individual derived from the relative poverty ranks of life expectancy for each geographic area, weighted for population size. The SII takes into account all measures for all areas and not, say, simply the worst-off and best-off 10th or 5th of areas. The index is most effective as a summary measure when the two measures are linearly related, as is the case with the data we analysed. The index has a further advantage that it is, by definition, unaffected by general increases or decreases in life expectancy over time (in this case the constant—that is the intercept—changes but not the slope). The SII is the slope coefficient in a simple regression analysis of life expectancy in years against a ranking of each area, where ranking is expressed as the cumulative proportions of the total population. An example of use of the SII in comparisons of life expectancy over time between continents shows that in 1950–55 the hypothetical worst-off person in Africa had a