Methods and software for estimating health disparities: the case of children's oral health

Abstract

The National Center for Health Statistics recently issued a monograph with 11 guidelines for reporting health disparities. However, guidelines on confidence intervals (CIs) cannot be readily implemented with the complex sample surveys often used for disease surveillance. In the United States, dental caries (decay) is the most common chronic childhood disease-5 times more common than asthma. Racial/ethnic minorities, immigrants, and persons of lower socioeconomic position (SEP) have a greater prevalence of caries. The authors provide methods for applying National Center for Health Statistics guidelines to complex sample surveys (health disparity indices and absolute and relative difference measures assessing associations of race/ethnicity and SEP to health outcomes with CIs); illustrate the application of those methods to children's untreated caries; provide relevant software; and report results from a simulation varying prevalence. They use data on untreated caries from the California Oral Health Needs Assessment of Children 2004-2005 and school percentage of participation in free/reduced-price lunch programs to illustrate the methods. Absolute and relative measures, the Slope Index of Inequality, the Relative Index of Inequality (mean and ratio), and the Health Concentration Index were estimated. Taylor series linearization and rescaling bootstrap methods were used to estimate CIs. Oral health differed significantly between White children and all non-White children and was significantly related to SEP.

Figure 1.

Percentage of California kindergarteners and third-graders with untreated caries in 2004–2005, by school percentage of participation in free/reduced-price lunch programs (%FRL) (≥75%, 50–<75%, 25–<50%, or <25%). Linear regression equation: y = 41.2 − 25.8x.

Figure 2.

Health concentration curve for untreated caries in California kindergarteners and third-graders in 2004–2005, plotted according to school percentage of participation in free/reduced-price lunch programs (%FRL).

Figure 3.

Simulation illustrating the effect of disease prevalence (proportion) on absolute and relative health disparity measures for ≥75% school participation in free/reduced-price lunch programs (≥75% FRL). Bars, 95% confidence interval.

Figure 4.

Simulation illustrating the effect of disease prevalence (proportion) on 3 health disparity indices (SII, RII(mean), and RII(ratio)), according to school percentage of participation in free/reduced-price lunch programs (≥75%, 50–<75%, 25–<50%, or <25%). RII(ratio), Relative Index of Inequality for the ratio; RII(mean), Relative Index of Inequality for the mean; SII, Slope Index of Inequality. Bars, 95% confidence interval.

Figure 5.

Simulation illustrating the effect of disease prevalence (proportion) on the Health Concentration Index disparity measure with both rescaled bootstrap and Taylor series linearization (TSL) 95% confidence interval estimates for school percentage of participation in free/reduced-price lunch programs (≥75%, 50–<75%, 25–<50%, or <25%). Bars, 95% confidence interval.