Abstrak: Mendelian randomization (MR) is a method for estimating the causal relationship between an exposure and an outcome using a genetic factor as an instrumental variable (IV) for the exposure. In the traditional MR setting, data on the IV, exposure, and outcome are available for all participants. However, obtaining complete exposure data may be difficult in some settings, due to high measurement costs or lack of appropriate biospecimens. We used simulated data sets to assess statistical power and bias for MR when exposure data are available for a subset (or an independent set) of participants. We show that obtaining exposure data for a subset of participants is a cost-efficient strategy, often having negligible effects on power in comparison with a traditional complete-data analysis. The size of the subset needed to achieve maximum power depends on IV strength, and maximum power is approximately equal to the power of traditional IV estimators. Weak IVs are shown to lead to bias towards the null when the subsample is small and towards the confounded association when the subset is relatively large. Various approaches for confidence interval calculation are considered. These results have important implications for reducing the costs and increasing the feasibility of MR studies.

Abstrak: Because of difficulties in finding, recruiting, and diagnosing dementia in the oldest old (ages ≥90 years), most incidence studies include few very elderly persons, and little is known about the characteristics of those who refuse participation. In a California longitudinal study of dementia and aging (The 90+ Study, 2003-2011), we compared nonresponders with responders with regard to information collected 20 years earlier and the impression of dementia as determined during telephone recruitment. Of 1,815 eligible subjects, 1,514 (83%) joined the study, 182 refused, and 119 could not be contacted. Responders did not differ from nonresponders by sex or previously collected medical history or lifestyle behaviors. Recruiters' impressions of dementia were similar in responders and nonresponders who refused (35% and 38%), and among responders, impressions of dementia showed high positive predictive value (95%) but low sensitivity (51%) for a diagnosis of dementia made during the study. Although epidemiologic studies among the very old have the potential for significant nonresponse bias due to a high proportion of frail, ill, and cognitively impaired persons, strategies can improve response rates to over 80%. Classifying nonresponders on cognitive ability at recruitment, though crude, will give some idea of the selective bias in dementia prevalence and incidence estimates introduced by nonresponse due to cognitive status.

Abstrak: We employed the parametric G formula to analyze lung cancer mortality in a cohort of textile manufacturing workers who were occupationally exposed to asbestos in South Carolina. A total of 3,002 adults with a median age of 24 years at enrollment (58% male, 81% Caucasian) were followed for 117,471 person-years between 1940 and 2001, and 195 lung cancer deaths were observed. Chrysotile asbestos exposure was measured in fiber-years per milliliter of air, and annual occupational exposures were estimated on the basis of detailed work histories. Sixteen percent of person-years involved exposure to asbestos, with a median exposure of 3.30 fiber-years/mL among those exposed. Lung cancer mortality by age 90 years under the observed asbestos exposure was 9.44%. In comparison with observed asbestos exposure, if the facility had operated under the current Occupational Safety and Health Administration asbestos exposure standard of < 0.1 fibers/mL, we estimate that the cohort would have experienced 24% less lung cancer mortality by age 90 years (mortality ratio = 0.76, 95% confidence interval: 0.62, 0.94). A further reduction in asbestos exposure to a standard of < 0.05 fibers/mL was estimated to have resulted in a minimal additional reduction in lung cancer mortality by age 90 years (mortality ratio = 0.75, 95% confidence interval: 0.61, 0.92).

Abstrak: Although socioeconomic position is conceptualized by social epidemiologists as a multidimensional construct, most research on socioeconomic disparities in health uses a limited set of observable indicators (e.g., educational attainment, household income, or occupational class) and typically analyzes and reports gradients in relation to one measure at a time. Societal changes in economic structures over time, however, can lead to changes in distributions of and associations between socioeconomic indicators, as has occurred with income returns to education in the United States over the last 50 years. Consequently, temporal comparisons of socioeconomic disparities from repeated cross-sectional surveys can be affected, particularly when salient dimensions of socioeconomic position are unobserved. We discuss this phenomenon within the framework of measurement error and identify sources of variation that can make identification of socioeconomic change difficult. Using simulations, we explore the utility of the quantile, slope index of inequality, and relative distribution approaches to minimizing bias in temporal comparisons and find that these methods yield correct inferences about temporal change only under limited conditions. We contrast these approaches with the use of an imputation model when validation data for the unobserved socioeconomic indicator exist. We discuss implications for analyzing changing socioeconomic health disparities over time.