Literature DB >> 30255428

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

Laura Rossouw1, Teresa Bago d'Uva2,3, Eddy van Doorslaer4,5,6,7.   

Abstract

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.

Keywords:  Health measurement; Inequality; Self-assessed health; South Africa; Vignettes

Mesh:

Year:  2018        PMID: 30255428     DOI: 10.1007/s13524-018-0709-0

Source DB:  PubMed          Journal:  Demography        ISSN: 0070-3370


  33 in total

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Authors:  Teresa Bago d'Uva; Maarten Lindeboom; Owen O'Donnell; Eddy van Doorslaer
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2.  Self-rated health showed a graded association with frequently used biomarkers in a large population sample.

Authors:  Marja Jylhä; Stefano Volpato; Jack M Guralnik
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3.  Paying for and receiving benefits from health services in South Africa: is the health system equitable?

Authors:  John E Ataguba; Di McIntyre
Journal:  Health Policy Plan       Date:  2012-03       Impact factor: 3.344

4.  Acculturation and cardiovascular disease screening practices among Mexican Americans living in Chicago.

Authors:  Janine M Jurkowski; Timothy P Johnson
Journal:  Ethn Dis       Date:  2005       Impact factor: 1.847

5.  Does reporting heterogeneity bias the measurement of health disparities?

Authors:  Teresa Bago d'Uva; Eddy Van Doorslaer; Maarten Lindeboom; Owen O'Donnell
Journal:  Health Econ       Date:  2008-03       Impact factor: 3.046

6.  Differential health reporting by education level and its impact on the measurement of health inequalities among older Europeans.

Authors:  Teresa Bago d'Uva; Owen O'Donnell; Eddy van Doorslaer
Journal:  Int J Epidemiol       Date:  2008-08-02       Impact factor: 7.196

7.  Does self-reported health bias the measurement of health inequalities in U.S. adults? Evidence using anchoring vignettes from the Health and Retirement Study.

Authors:  Jennifer Beam Dowd; Megan Todd
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2011-06-10       Impact factor: 4.077

8.  Language of interview, self-rated health, and the other Latino health puzzle.

Authors:  Edna A Viruell-Fuentes; Jeffrey D Morenoff; David R Williams; James S House
Journal:  Am J Public Health       Date:  2010-12-16       Impact factor: 9.308

9.  Mortality in South Africa - socioeconomic profile and association with self-reported health.

Authors:  Cally Ardington; Boingotlo Gasealahwe
Journal:  Dev South Afr       Date:  2014-01-01

10.  Investigating the relationship between self-rated health and social capital in South Africa: a multilevel panel data analysis.

Authors:  Yan Kwan Lau; John E Ataguba
Journal:  BMC Public Health       Date:  2015-03-19       Impact factor: 3.295

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  1 in total

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Journal:  Front Hum Neurosci       Date:  2019-08-08       Impact factor: 3.169

  1 in total

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