Regression calibration to correct correlated errors in outcome and exposure

Higher Neighborhood Population Density Is Associated with Lower Potassium Intake in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Current U.S. dietary guidelines recommend a daily potassium intake of 3400 mg/day for men and 2600 mg/day for women. Sub-optimal access to nutrient-rich foods may limit potassium intake and increase cardiometabolic risk. We examined the association of neighborhood characteristics related to food availability with potassium intake in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). 13,835 participants completed a 24-h dietary recall assessment and had complete covariates. Self-reported potassium intake was calibrated with an objective 24-h urinary potassium biomarker, using equations developed in the SOL Nutrition & Physical Activity Assessment Study (SOLNAS, N = 440). Neighborhood population density, median household income, Hispanic/Latino diversity, and a retail food environment index by census tract were obtained. Linear regression assessed associations with 24-h potassium intake, adjusting for individual-level and neighborhood confounders. Mean 24-h potassium was 2629 mg/day based on the SOLNAS biomarker and 2702 mg/day using multiple imputation and HCHS/SOL biomarker calibration. Compared with the lowest quartile of neighborhood population density, living in the highest quartile was associated with a 26% lower potassium intake in SOLNAS (adjusted fold-change 0.74, 95% CI 0.59–0.94) and a 39% lower intake in HCHS/SOL (adjusted fold-change 0.61 95% CI 0.45–0.84). Results were only partially explained by the retail food environment. The mechanisms by which population density affects potassium intake should be further studied.

Errors in multiple variables in human immunodeficiency virus (HIV) cohort and electronic health record data: statistical challenges and opportunities

Objectives: Observational data derived from patient electronic health records (EHR) data are increasingly used for human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) research. There are challenges to using these data, in particular with regards to data quality; some are recognized, some unrecognized, and some recognized but ignored. There are great opportunities for the statistical community to improve inference by incorporating validation subsampling into analyses of EHR data.Methods: Methods to address measurement error, misclassification, and missing data are relevant, as are sampling designs such as two-phase sampling. However, many of the existing statistical methods for measurement error, for example, only address relatively simple settings, whereas the errors seen in these datasets span multiple variables (both predictors and outcomes), are correlated, and even affect who is included in the study.Results/Conclusion: We will discuss some preliminary methods in this area with a particular focus on time-to-event outcomes and outline areas of future research.