Grantee Research Project Results

Combining individual-and group-level exposure information: child carbon monoxide in the Guatemala Woodstove Randomized Control Trial.

Citation:

McCracken JP, Schwartz J, Bruce N, Mittleman M, Ryan LM, Smith KR. Combining individual- and group-level exposure information: child carbon monoxide in the Guatemala Woodstove Randomized Control Trial. Epidemiology 2009;20(1):127-136.

Abstract:

BACKGROUND: Epidemiology frequently relies on surrogates of long-term exposures, often either individual-level short-term measurements or group-level based on long-term characteristics of subjects and their environment. Whereas individual-level measures are often imprecise due to within-subject variability, group-level measures tend to be inaccurate due to residual between-subject variability within groups. Rather than choose between these error-prone estimates, we borrow strength from each by use of mixed-model prediction and we compare the predictive validity. METHODS: We compared alternative measures of long-term exposure to carbon monoxide (CO) among children in the RESPIRE woodstove randomized control trial during years 2003 and 2004. The main study included 1932 repeated 48-hour-average personal CO measures among 509 children from 0-18 months of age. We used a validation study with additional CO measures among a random subsample of 70 of the children to compare the predictive validity of individual-level estimates (based on observed short-term exposures), group-level estimates (based on stove type and other residential characteristics), and mixed-model predictions that combine these 2 sources of information. RESULTS: The estimated error variance for mixed-model prediction was 63% lower than the individual-level measure based on the exposure data and 58% lower than the corresponding group-level measure. CONCLUSIONS: When both individual- and group-level estimates are available but imperfect, mixed-model prediction may provide substantially better measures of long-term exposure, potentially increasing the sensitivity of epidemiologic studies to underlying causal relations.