The impact of population mobility on estimates of environmental exposure effects in a case‐control study

Abstract

In many studies of environmental risk factors for disease, researchers use the location at diagnosis as a geographic reference for environmental exposures. However, many environmental pollutants change continuously over space and time. The dynamic characteristics of these pollutants coupled with population mobility in the United States suggest that for diseases with long latencies like cancer, historic exposures may be more relevant than exposure at the time of diagnosis. In this article, we evaluated to what extent the commonly used assumption of no population mobility results in increased bias in the estimates of the relationship between environmental exposures and long‐latency health outcomes disease in a case‐control study. We conducted a simulation study using the residential histories of a random sample from the National Institutes of Health‐AARP (formerly American Association of Retired Persons) Diet and Health Study. We simulated case‐control status based on subject exposure and true exposure effects that varied temporally. We compared estimates from models using only subject location at diagnosis to estimates where subjects were assumed to be mobile. Ignoring population mobility resulted in underestimates of subject exposure, with largest deviations observed at time points further away from study enrollment. In general, the effect of population mobility on the bias of the estimates of the relationship between the exposure and the outcome was more prominent with exposures that showed substantial spatial and temporal variability. Based on our results, we recommend using residential histories when environmental exposures and disease latencies span a long enough time period that mobility is important.

Utilizing residential histories to assess environmental exposure and socioeconomic status over the life course among mesothelioma patients

“…Our analysis showed that not all earlier residential times prior to the cancer diagnosis had statistically significant higher REcan than that at the cancer diagnosis year, and the magnitude of the difference in the yearly REcan at cancer diagnosis and proceeding years tended to be larger at time points further away from the cancer diagnosis year. Similar findings were reported by a simulation study based on data from the Health-AARP Diet and Health Study ( 58 ). Interestingly, among the subset of our study population with 30-year residential history records, we found that significantly elevated REcan occurred in the earliest decade, which was equivalent to an age group of 46–55 years (assuming a median age at diagnosis of 76 years).…”

“…One explanation of this discrepancy is likely their differences in the spatial and temporal variability, as we would expect exposures that have a larger variation across space and time would suffer a larger misclassification bias when a single address rather than multiple addresses was used to estimate exposures. Indeed, studies have found that different exposure measures (e.g., different pollutants, greenspace, or agriculture land) with different spatiotemporal characteristics exhibited varied magnitudes of exposure misclassification associated with residential mobility ( 56 , 58 , 59 ).…”

Urbanization, air pollution, and water pollution: Identification of potential environmental risk factors associated with amyotrophic lateral sclerosis using systematic reviews

“…While several of the included case-control studies had geospatial information which allowed taking consideration of historical exposures in the surrounding environment of individuals, particularly among environmental factors related to air pollution, few studies utilized lag models ( 23 , 58 , 67 ) and many urbanization and water pollution studies relied merely on residence at onset for ascertaining associations to ALS. Additional studies with longitudinal high-resolution geospatial measurements and residential histories of participants are recommended to strengthen confidence in associations documented and potentially identify new underlying risk factors in the surrounding environment of those who develop ALS ( 126 ).…”