Use of study-specific MOE-like estimates to prioritize health effects from chemical exposure for analysis in human health assessments

Kevin Hobbie,Kan Shao,Cara Henning,William Mendez,Janice S Lee,Ila Cote,Ingrid L Druwe,J Allen Davis,Jeffrey S Gift

doi:10.1016/j.envint.2020.105986

Abstract

There are unique challenges in estimating dose-response with chemicals that are associated with multiple health outcomes and numerous studies. Some studies are more suitable than others for quantitative dose-response analyses. For such chemicals, an efficient method of screening studies and endpoints to identify suitable studies and potentially important health effects for dose-response modeling is valuable. Using inorganic arsenic as a test case, we developed a tiered approach that involves estimating study-specific margin of exposure (MOE)-like unitless ratios for two hypothetical scenarios. These study-specific unitless ratios are derived by dividing the exposure estimated to result in a 20% increase in relative risk over the background exposure (RRE20) by the background exposure, as estimated in two different ways. In our case study illustration, separate study-specific ratios are derived using estimates of United States population background exposure (RRB-US) and the mean study population reference group background exposure (RRB-SP). Systematic review methods were used to identify and evaluate epidemiologic studies, which were categorized based on study design (case-control, cohort, cross-sectional), various study quality criteria specific to dose-response analysis (number of dose groups, exposure ascertainment, exposure uncertainty), and availability of necessary dose-response data. Both case-control and cohort studies were included in the RRB analysis. The RRE20 estimates were derived by modeling effective counts of cases and controls estimated from study-reported adjusted odds ratios and relative risks. Using a broad (but not necessarily comprehensive) set of epidemiologic studies of multiple health outcomes selected for the purposes of illustrating the RRB approach, this test case analysis would suggest that diseases of the circulatory system, bladder cancer, and lung cancer may be arsenic health outcomes that warrant further analysis. This is suggested by the number of datasets from adequate dose-response studies demonstrating an effect with RRBs close to 1 (i.e., RRE20 values close to estimated background arsenic exposure levels).

Highlights

The evaluation of the potential human health effects of chemicals, like arsenic, that have an extremely large volume of datasets and the wide variance in data quality represented across studies would benefit from an efficient screening approach that helps to narrow the focus of dose-response analyses to studies and health outcomes that are of the most concern for risk assessment
The overall database used for this RRB test case analyses included 255 datasets, resulting in 180 risk exposure (RRE)-US20 and 192 RRE-SP20 estimates, from 64 studies, and is heterogeneous
235 RRE-US20 and 235 RRE-SP20 estimations remained after removing RRE20s that were below background or infinite

Summary

Introduction

The evaluation of the potential human health effects of chemicals, like arsenic, that have an extremely large volume of datasets and the wide variance in data quality represented across studies would benefit from an efficient screening approach that helps to narrow the focus of dose-response analyses to studies and health outcomes that are of the most concern for risk assessment. It is intended to preliminarily inform, not replace, a human health assessment It offers an efficient approach for identifying studies that may be adequate for dose-response analysis and for approximating the relative potency of a chemical with respect to health effects that are known or presumed to be related to increases in exposure. This approach attempts to provide a portion of the information that can be used to prioritize studies and health effects for the more focused and in-depth dose-response analyses that would be performed as part of a full chemical health assessment This margin-of-exposure (MOE)-like analysis uses ratios of the study-specific estimates of exposures associated with a 20% increase in relative risk over a background exposure estimate (RRE20) divided by the background estimate in the same study-specific exposure units (RRB).. Two types of study-specific RRB estimates are derived in this case study, one that uses a background exposure estimate for the U.S population (RRB-US) in the exposure units of the study (see Table 2) and the other that uses the study-specific background exposure estimated for the study population reference group (RRB-SP)

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Discussion

Conclusion