Financial Benefits for Child Health and Well‐Being in Low Income or Socially Disadvantaged Families in Developed World Countries

The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.

Whether or not the scale of a society's income inequality is a determinant of population health is still regarded as a controversial issue. We decided to review the evidence and see if we could find a consistent interpretation of both the positive and negative findings. We identified 168 analyses in 155 papers reporting research findings on the association between income distribution and population health, and classified them according to how far their findings supported the hypothesis that greater income differences are associated with lower standards of population health. Analyses in which all adjusted associations between greater income equality and higher standards of population health were statistically significant and positive were classified as "wholly supportive"; if none were significant and positive they were classified as "unsupportive"; and if some but not all were significant and supportive they were classified as "partially supportive". Of those classified as either wholly supportive or unsupportive, a large majority (70 per cent) suggest that health is less good in societies where income differences are bigger. There were substantial differences in the proportion of supportive findings according to whether inequality was measured in large or small areas. We suggest that the studies of income inequality are more supportive in large areas because in that context income inequality serves as a measure of the scale of social stratification, or how hierarchical a society is. We suggest three explanations for the unsupportive findings reported by a minority of studies. First, many studies measured inequality in areas too small to reflect the scale of social class differences in a society; second, a number of studies controlled for factors which, rather than being genuine confounders, are likely either to mediate between class and health or to be other reflections of the scale of social stratification; and third, the international relationship was temporarily lost (in all but the youngest age groups) during the decade from the mid-1980s when income differences were widening particularly rapidly in a number of countries. We finish by discussing possible objections to our interpretation of the findings.