Poor Health Reporting? Using Anchoring Vignettes to Uncover Health Disparities by Wealth and Race.

In spite of the wide disparities in wealth and in objective health measures like mortality, observed inequality by wealth in self-reported health appears to be nearly nonexistent in low- to middle-income settings. To determine the extent to which this is driven by reporting tendencies, we use anchoring vignettes to test and correct for reporting heterogeneity in health among elderly South Africans. Significant reporting differences across wealth groups are detected. Poorer individuals rate the same health state description more positively than richer individuals. Only after we correct for these differences does a significant and substantial health disadvantage of the poor emerge. We also find that health inequality and reporting heterogeneity are confounded by race. Within race groups-especially among black Africans and to a lesser degree among whites-heterogeneous reporting leads to an underestimation of health inequalities between richest and poorest. More surprisingly, we also show that the correction may go in the opposite direction: the apparent black African (vs. white) health disadvantage within the top wealth quintile almost disappears after we correct for reporting tendencies. Such large shifts and even reversals of health gradients have not been documented in previous studies on reporting bias in health inequalities. The evidence for South Africa, with its history of racial segregation and socioeconomic inequality, highlights that correction for reporting matters greatly when using self-reported health measures in countries with such wide disparities.

The gender health gap in China: A decomposition analysis.

Around the world, and in spite of their higher life expectancy, women tend to report worse health than men until old age. Explanations for this gender gap in self-perceived health may be different in China than in other countries due to the traditional phenomenon of son preference. We examine several possible reasons for the gap using the Chinese SAGE data. We first rule out differential reporting by gender as a possible explanation, exploiting information on anchoring vignettes in eight domains of health functioning. Decomposing the gap in general self-assessed health, we find that about 31% can be explained by socio-demographic factors, most of all by discrimination against women in education in the 20th century. A more complete specification including chronic conditions and health functioning fully explains the remainder of the gap (about 69%). Adding chronic conditions and health functioning also explains at least two thirds of the education contribution, suggesting how education may affect health. In particular, women's higher rates of arthritis, angina and eye diseases make the largest contributions to the gender health gap, by limiting mobility, increasing pain and discomfort, and causing sleep problems and a feeling of low energy.

Inequity in the Face of Death.

We apply the theory of inequality of opportunity to the measurement of inequity in mortality. Using a rich data set linking records of mortality and health events to survey data on lifestyles for the Netherlands (1998-2007), we test the sensitivity of estimated inequity to different normative choices and conclude that the location of the responsibility cut is of vital importance. Traditional measures of inequity (such as socioeconomic and regional inequalities) only capture part of more comprehensive notions of unfairness. We show that distinguishing between different routes via which variables might be associated to mortality is essential to the application of different normative positions. Using the fairness gap (direct unfairness), measured inequity according to our implementation of the 'control' and 'preference' approaches ranges between 0.0229 and 0.0239 (0.0102-0.0218), while regional and socioeconomic inequalities are smaller than 0.0020 (0.0001). The usual practice of standardizing for age and gender has large effects on measured inequity. Finally, we use our model to measure inequity in simulated counterfactual situations. While it is a big challenge to identify all causal relationships involved in this empirical context, this does not affect our main conclusions regarding the importance of normative choices in the measurement of inequity. Copyright © 2014 John Wiley & Sons, Ltd.

Can Reporting Heterogeneity Explain Differences in Depressive Symptoms Across Europe?

Depression is one of the leading causes of disability in the developed world. Previous studies have shown varying depression prevalence rates between European countries, and also within countries, between socioeconomic groups. However, it is unclear whether these differences reflect true variations in prevalence or whether they are attributable to systematic differences in reporting styles (reporting heterogeneity) between countries and socioeconomic groups. In this study, we examine the prevalence of three depressive symptoms (mood, sleeping and concentration problems) and their association with educational level in 10 European countries, and examine whether these differences can be explained by differences in reporting styles. We use data from the first and second waves of the COMPARE study, comprising a sub-sample of 9,409 adults aged 50 and over in 10 European countries covered by the Survey of Health, Ageing and Retirement in Europe. We first use ordered probit models to estimate differences in the prevalence of self-reported depressive symptoms by country and education. We then use hierarchical ordered probit models to assess differences controlling for reporting heterogeneity. We find that depressive symptoms are most prevalent in Mediterranean and Eastern European countries, whereas Sweden and Denmark have the lowest prevalence. Lower educational level is associated with higher prevalence of depressive symptoms in all European regions, but this association is weaker in Northern European countries, and strong in Eastern European countries. Reporting heterogeneity does not explain these cross-national differences. Likewise, differences in depressive symptoms by educational level remain and in some regions increase after controlling for reporting heterogeneity. Our findings suggest that variations in depressive symptoms in Europe are not attributable to differences in reporting styles, but are instead likely to result from variations in the causes of depressive symptoms between countries and educational groups.

Do self-perceived health changes predict longevity?

Researchers can rely either on retrospectively reported or on prospectively measured health changes to identify and quantify recent changes in respondents' health status. The two methods typically do not provide the same answers. We compare the validity of prospective versus retrospective measures of health changes by investigating their predictive power for subsequent mortality. Data from a cohort study conducted in the Netherlands are used to compare the ability of changes in self-assessed health (SAH) - either reported retrospectively or measured prospectively in three waves (1991, 1993 and 1995) - to predict survival until 2004. We examine the relationship between health changes and mortality with a proportional hazard models controlling for individual unobserved heterogeneity, with and without control for pre-existing chronic conditions and the onset of new chronic diseases. For a high proportion of reports (39.8%), prospectively measured health changes in SAH do not concur with retrospectively reported health changes. Our results show that both measures of health changes are predictive of mortality in the model controlling for levels of SAH and socioeconomic characteristics only. Controlling for SAH, prior presence of chronic conditions, the onset of new conditions and unobserved characteristics, we find that prospectively reported health changes still predict longevity, whereas retrospective changes do not. These results suggest that the collection of longitudinal information on health changes has advantages over the - easier and cheaper - option of retrospective collection of the same information.

Health care utilisation in Europe: new evidence from the ECHP.

The ECHP is used to analyse health care utilisation care in Europe. We estimate a new latent class hurdle model for panel data and compare it with the latent class NegBin model and the standard hurdle model. Latent class specifications outperform the standard hurdle model and the latent class hurdle model reveals income effects that are masked in the NegBin model. For specialist visits, low users are more income elastic than high users and the probability of using care is more income elastic than the conditional number of visits. The effects of income on total use of GPs are mostly negative or insignificant but positive elasticities are found for Austria, Greece and, to a greater extent, Portugal. On the whole, richer individuals tend to use more specialist care, especially in Portugal, Ireland, Finland, Greece and Austria. Features of the health care systems of these countries may contribute to the observed inequities.

Measurement of horizontal inequity in health care utilisation using European panel data.

Measurement of inequity in health care delivery has focused on the extent to which health care utilisation is or is not distributed according to need, irrespective of income. Studies using cross-sectional data have proposed various ways of measuring and standardizing for need, but inevitably much of the inter-individual variation in needs remains unobserved in cross-sections. This paper exploits panel data methods to improve the measurement by including the time-invariant part of unobserved heterogeneity into the need-standardization procedure. Using latent class hurdle models for GP and specialist visits estimated on 8 annual waves of the European Community Household Panel we compute indices of horizontal equity that partition total income-related variation in use into a need- and a non-need related part, not only for the observed but also for the unobserved but time-invariant component. We also propose and compare a more conservative index of horizontal inequity to the conventional statistic. We find that many of the cross-country comparative results appear fairly robust to the panel data test, although the panel-based methods lead to significantly higher estimates of horizontal inequity for most countries. This confirms that better estimation and control for need often reveals more pro-rich distributions of doctor utilisation.

Does reporting heterogeneity bias the measurement of health disparities?

Heterogeneity in reporting of health by socio-economic and demographic characteristics potentially biases the measurement of health disparities. We use anchoring vignettes to identify socio-demographic differences in the reporting of health in Indonesia, India and China. Homogeneous reporting by socio-demographic group is rejected and correcting for reporting heterogeneity tends to reduce slightly estimated disparities in health by education (not China) and to increase those by income. But the method does not reveal substantial reporting bias in measures of health disparities.

Latent class models for use of primary care: evidence from a British panel.

This paper models access to and utilisation of primary care using data from the British Household Panel Survey for the period 1991-2001. A latent class panel data framework is adopted to model individual unobserved heterogeneity in a flexible way. Accounting for the panel structure of the data leads to a substantial improvement in fit, and permits the identification of latent classes of users of health care. Analysis by gender shows that men and women respond differently to some factors, in particular, to age and income. There is evidence of a positive impact of income on the probability of seeking primary care. This effect is especially significant in the case of women. For both genders, the marginal effect of income on the propensity to visit a GP is greater for individuals who are less likely to seek primary care. A latent class aggregated count data model for the number of GP visits classifies individuals in three latent classes and shows a positive income effect particularly amongst those with lower levels of utilisation.