Background: Respiratory diseases encompass a number of complex disorders that constitute a major cause of both morbidity and mortality worldwide with a major burden to the afflicted as well as the health care systems that care for them. Although the prevalence of chronic respiratory diseases (CRDs) has been decreasing in industrialized countries due to a decreasing number of smokers and stricter laws aimed at reducing exposure to secondhand smoke (SHS), the burden of CRDs in developing world populations is expected to worsen due to communicable disease prevention programs, aging populations, environmental air pollution, and continued tobacco smoke exposure. Although tobacco smoking has been shown to be significantly associated with many CRDs, evidence linking SHS exposure to different CRDs is mixed, especially with low levels of SHS exposure.
Methods: The National Health and Nutrition Examination Survey (NHANES) is a series of studies designed to assess the health and nutritional status of non-institutionalized adults and children in the United States (U.S.). In addition to being used to monitor the health of the U.S. population, NHANES data allow for research into prevalent health problems and their risk factors in the population, such with CRDs and SHS exposure. NHANES data can be utilized to explore a variety of issues related to the assessment of SHS exposure and its association to respiratory symptoms and illnesses.
Results: First, we provide a brief review of NHANES including its strengths and limitations. We then provide a summary of the variables and publically available population based data that can be used to study associations between SHS exposure and CRD symptoms, testing and diagnoses.
Conclusion: Rich and cost effective, NHANES data provide a unique opportunity for research into the risk factors for CRDs in the U.S. population, particularly into the possible health effects of low levels of SHS exposure.
Some investigators argue that controlling for self-reported race or ethnicity, either in statistical analysis or in study design, is sufficient to mitigate unwanted influence from population stratification. In this report, we evaluated the effectiveness of a study design involving matching on self-reported ethnicity and race in minimizing bias due to population stratification within an ethnically admixed population in California. We estimated individual genetic ancestry using structured association methods and a panel of ancestry informative markers, and observed no statistically significant difference in distribution of genetic ancestry between cases and controls (P=0.46). Stratification by Hispanic ethnicity showed similar results. We evaluated potential confounding by genetic ancestry after adjustment for race and ethnicity for 1260 candidate gene SNPs, and found no major impact (>10%) on risk estimates. In conclusion, we found no evidence of confounding of genetic risk estimates by population substructure using this matched design. Our study provides strong evidence supporting the race- and ethnicity-matched case-control study design as an effective approach to minimizing systematic bias due to differences in genetic ancestry between cases and controls.
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