Estimating mixture effects and cumulative spatial risk over time simultaneously using a Bayesian index low-rank kriging multiple membership model

Abstract

The exposome is an ideal in public health research that posits that individuals experience risk for adverse health outcomes from a wide variety of sources over their lifecourse. There have been increases in data collection in the various components of the exposome, but novel statistical methods are needed that capture multiple dimensions of risk at once. We introduce a Bayesian index low-rank kriging (LRK) multiple membership model (MMM) to simultaneously estimate the health effects of one or more groups of exposures, the relative importance of exposure components, and cumulative spatial risk over time using residential histories. The model employs an MMM to consider all residential locations for subjects weighted by duration and LRK to increase computational efficiency. We demonstrate the performance of the Bayesian index LRK-MMM through a simulation study, showing that the model accurately and consistently estimates the health effects of one or several group indices and has high power to identify a region of elevated spatial risk due to unmeasured environmental exposures. Finally, we apply our model to data from a multicenter case-control study of non-Hodgkin lymphoma (NHL), finding a significant positive association between one index of pesticides and risk for NHL in Iowa. Additionally, we find an area of significantly elevated spatial risk for NHL in Los Angeles. In conclusion, our Bayesian index LRK-MMM represents a step forward toward bringing the ideals of the exposome into practice for environmental risk analyzes.

Keywords: exposome; mixture analysis; non-Hodgkin lymphoma; residential history; spatial analysis.

FIGURE 1

Summary of true (black) and estimated (red) betas for the one‐group model in the simulation study. Columns indicate different scenarios and exposure correlation strengths

FIGURE 2

Summary of true (black) and average estimated (red) betas for the three‐group model in the simulation study. Columns indicate different scenarios and rows indicate different exposure correlation strengths

FIGURE 3

Estimated cumulative spatial odds ratios for non‐Hodgkin lymphoma in the Los Angeles study center. Hollywood (triangle), Inglewood (circle), and Long Beach (square) are marked on the map

FIGURE 4

Estimated cumulative spatial odds ratios for non‐Hodgkin lymphoma in the Detroit study center. The Detroit city center (circle) is marked on the map

FIGURE 5

Estimated cumulative spatial odds ratios for non‐Hodgkin lymphoma in the Iowa study center. Cedar Rapids (circle) is marked on the map

FIGURE 6

Estimated cumulative spatial odds ratios for non‐Hodgkin lymphoma in the Seattle study center. The Seattle city center is marked (circle) on the map

FIGURE 7

Areas of significantly elevated cumulative spatial risk for non‐Hodgkin lymphoma in the Los Angeles study center