Comparison between two race/skin color classifications in relation to health-related outcomes in Brazil

<p>Abstract</p> <p>Background</p> <p>This paper aims to compare the classification of race/skin color based on the discrete categories used by the Demographic Census of the Brazilian Institute of Geography and Statistics (IBGE) and a skin color scale with values ranging from 1 (lighter skin) to 10 (darker skin), examining whether choosing one alternative or the other can influence measures of self-evaluation of health status, health care service utilization and discrimination in the health services.</p> <p>Methods</p> <p>This is a cross-sectional study based on data from the World Health Survey carried out in Brazil in 2003 with a sample of 5000 individuals older than 18 years. Similarities between the two classifications were evaluated by means of correspondence analysis. The effect of the two classifications on health outcomes was tested through logistic regression models for each sex, using age, educational level and ownership of consumer goods as covariables.</p> <p>Results</p> <p>Both measures of race/skin color represent the same race/skin color construct. The results show a tendency among Brazilians to classify their skin color in shades closer to the center of the color gradient. Women tend to classify their race/skin color as a little lighter than men in the skin color scale, an effect not observed when IBGE categories are used. With regard to health and health care utilization, race/skin color was not relevant in explaining any of them, regardless of the race/skin color classification. Lack of money and social class were the most prevalent reasons for discrimination in healthcare reported in the survey, suggesting that in Brazil the discussion about discrimination in the health care must not be restricted to racial discrimination and should also consider class-based discrimination. The study shows that the differences of the two classifications of race/skin color are small. However, the interval scale measure appeared to increase the freedom of choice of the respondent.</p

A Problem with the Individual Approach in the WHO Health Inequality Measurement

BACKGROUND: In the World Health Report 2000, the World Health Organization made the controversial choice to measure inequality across individuals rather than across groups, the standard in the field. This choice has been widely discussed and criticized. DISCUSSION: We look at the three questions: (1) is the World Health Organization's health inequality measure value-free as it claims? (2) if it is not, what is the normative position implied by its approach when measuring health inequality? and (3) is the individual approach a logically consistent methodological choice for that normative position? SUMMARY: We argue that the World Health Organization's health inequality measure is not value-free. If it was, the health inequality information that the measurement collected could not reasonably be included in its ranking of how well national health systems performed. The World Health Organization's normative position can be interpreted as a quite expansive view of justice, in which health distributions that have causes amenable to human intervention are considered to be matters of justice. Our conclusion is that if the World Health Organization's health inequality measure is to be interpreted meaningfully in a policy context, its conceptual underpinning must be re-evaluated

Estimating Design Effect and Calculating Sample Size for Respondent-Driven Sampling Studies of Injection Drug Users in the United States

Respondent-driven sampling (RDS) has become increasingly popular for sampling hidden populations, including injecting drug users (IDU). However, RDS data are unique and require specialized analysis techniques, many of which remain underdeveloped. RDS sample size estimation requires knowing design effect (DE), which can only be calculated post hoc. Few studies have analyzed RDS DE using real world empirical data. We analyze estimated DE from 43 samples of IDU collected using a standardized protocol. We find the previous recommendation that sample size be at least doubled, consistent with DE = 2, underestimates true DE and recommend researchers use DE = 4 as an alternate estimate when calculating sample size. A formula for calculating sample size for RDS studies among IDU is presented. Researchers faced with limited resources may wish to accept slightly higher standard errors to keep sample size requirements low. Our results highlight dangers of ignoring sampling design in analysis