EXAMINING SOCIOECONOMIC HEALTH DISPARITIES USING A RANK-DEPENDENT RÉNYI INDEX

HPV vaccine status and sexual behavior among young sexually-active women in the US: evidence from the National Health and Nutrition Examination Survey, 2007-2014

Concern has been expressed that human papillomavirus (HPV) vaccination programs might promote risky sexual behavior through mechanisms such as risk compensation, behavioral disinhibition, or perceived endorsement of sexual activity. This study assesses whether HPV vaccination status is associated with any differences in selected sexual behaviors among young sexually-active women in the US. Our dataset includes young, adult female respondents from questionnaire data collected in the National Center for Health Statistics' National Health and Nutrition Examination Survey from 2007 to 2014. The empirical approach implements a doubly robust estimation procedure, based on inverse probability of treatment weighting. For robustness, we implement several specifications for the propensity model and the outcomes model. We find no consistent association between HPV vaccination and condom usage or frequency of sex. Specifically, we find no evidence that HPV vaccination is associated with condom usage or with whether a person had sex more than 52 or more than 104 times per year. We find inconsistent evidence that HPV vaccination is associated with a person having sex more than 12 times per year. As in previous research, HPV vaccination does not appear to have a substantive effect on sexual behavior among young sexually-active women in the US.

Statistical inference of the relative concentration index for complex surveys

The relative concentration index is a widely used measure for assessing relative differences in health across all socioeconomic population groups. We extend its usage to individual-level data collected through complex surveys by deriving its variance using the Taylor linearization (TL) method. Two existing plug-in variance estimators that only require grouped data are also compared. We discuss sources of uncertainty that each variance estimator considers and present simulation studies to compare the performance of the three estimators under various sampling designs. The proposed TL variance estimator consistently produces valid results; however, it requires the access to individual-level data. Both plug-in variance estimators are biased because of failure to account for certain error sources. However, when only grouped data is available, one of the plug-in estimators can be valid as long as the socioeconomic groups are treated equally sized, a commonly used analytic strategy to emphasize group's instead of individual's burden of disease in health disparity assessment. We illustrate the three variance estimators by applying them to assessing socioeconomic disparities in child and adolescent obesity using complex survey sampled drawn from the National Health and Nutrition Examination Survey.

Statistical Inference on Health Disparity Indices for Complex Surveys

The National Cancer Institute developed the Health Disparities Calculator (HD*Calc) to facilitate research on health disparities. HD*Calc calculates multiple measures of health disparities using data collected from population-based disease surveillance systems, such as cancer registries. In this paper, we extend the use of HD*Calc to complex survey data by developing plug-in point estimators and Taylor linearization variance estimators that consider complex designs: stratification, multistage clustering, and differential weighting. Our simulation indicates that the plug-in estimators are approximately unbiased and the Taylor linearization variance estimators are accurate. Using 2011-2016 data from the National Health and Nutrition Examination Survey, we demonstrate the use of these estimators in evaluating socioeconomic disparities in the prevalence of child and adolescent (ages 2-18 years) obesity in the United States. Statistical software has been developed for ease of disparity analyses using complex survey data.

Measurement of Health Disparities, Health Inequities, and Social Determinants of Health to Support the Advancement of Health Equity

Reduction of health disparities and advancement of health equity in the United States require high-quality data indicative of where the nation stands vis-à-vis health equity, as well as proper analytic tools to facilitate accurate interpretation of these data. This article opens with an overview of health equity and social determinants of health. It then proposes a set of recommended practices in measurement of health disparities, health inequities, and social determinants of health at the national level to support the advancement of health equity, highlighting that (1) differences in health and its determinants that are associated with social position are important to assess; (2) social and structural determinants of health should be assessed and multiple levels of measurement should be considered; (3) the rationale for methodological choices made and measures chosen should be made explicit; (4) groups to be compared should be simultaneously classified by multiple social statuses; and (5) stakeholders and their communication needs can often be considered in the selection of analytic methods. Although much is understood about the role of social determinants of health in shaping the health of populations, researchers should continue to advance understanding of the pathways through which they operate on particular health outcomes. There is still much to learn and implement about how to measure health disparities, health inequities, and social determinants of health at the national level, and the challenges of health equity persist. We anticipate that the present discussion will contribute to the laying of a foundation for standard practice in the monitoring of national progress toward achievement of health equity.