Systematic dose-response of environmental epidemiologic studies: Dose and response pre-analysis

Allen, Bruce C.; Shao, Kan; Hobbie, Kevin A.; Méndez, William; Lee, Janice S.; Cote, Ila; Druwe, Ingrid; Gift, Jeff; Davis, Jerry

doi:10.1016/j.envint.2020.105810

Cited by 10 publications

(19 citation statements)

References 15 publications

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“…This inorganic arsenic example illustrates how an RRB analysis can be helpful for identifying studies and health outcomes that may warrant more detailed and sophisticated analyses. As illustrated in companion case studies, more sophisticated dose-response methods can include model averaging approaches ( Mendez et al, 2020 ) or multistudy Bayesian meta-regression ( Allen et al, 2020a , 2020b ). Although this RRB analysis used inorganic arsenic data as a test case, it can potentially be implemented for any chemical of concern with a large database of epidemiologic studies in order to prioritize health outcomes and datasets, and guide structured and/or tiered dose-response analyses.…”

Section: Discussionmentioning

confidence: 99%

“…Because the exposure-response models for both case-control and cohort studies specifically are fitted to the numbers of cases and non-cases (or cases and controls), it is necessary to adjust the numbers of expected cases to account for covariate adjustment, similar to the way adjusted ORs account for covariates compared with crude ratios. The approach used for this adjustment involves generating effective-counts ( Allen et al, 2020a ). The effective-counts for both cases and controls are then used in the model-fitting (see example input data in Tables 5 and 6 ).…”

Section: Methodsmentioning

confidence: 99%

“…In this way, the RRE 20 values for any dataset using any exposure or dose metric are comparable in terms of potential potency, and thus conversion of all datasets to a common dose metric is not necessary. The cases and controls were adjusted using the effective count method described by Allen et al (2020a) in a companion article to this article. This adjustment, broadly speaking, ensures the cases and controls are appropriately adjusted to account for any covariates.…”

Section: Methodsmentioning

confidence: 99%

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

Hobbie

Shao

Henning

et al. 2020

Environment International

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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 (RRE 20 ) 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 RRE 20 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., RRE 20 values close to estimated background arsenic exposure levels).

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Hobbie

Shao

Henning

et al. 2020

Environment International

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“…For populations from Taiwan/China, an average dietary exposure of 36 μg iAs/day was assumed. This estimate is based on a dietary intake estimate of 0.65 μg iAs/kg per day from a survey of Taiwanese eating habits carried out by the Taiwan Department of Health (TDOH, 2007) reported in the publications by Mendez et al (2020) and Allen et al (2020), and assuming a body weight of 55 kg.…”

Section: Conversion Of Water and Urine Ias Concentrations To Inorgani...mentioning

confidence: 99%

Update of the risk assessment of inorganic arsenic in food

Schrenk,

Bignami,

Bodin

et al. 2024

EFS2

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The European Commission asked EFSA to update its 2009 risk assessment on arsenic in food carrying out a hazard assessment of inorganic arsenic (iAs) and using the revised exposure assessment issued by EFSA in 2021. Epidemiological studies show that the chronic intake of iAs via diet and/or drinking water is associated with increased risk of several adverse outcomes including cancers of the skin, bladder and lung. The CONTAM Panel used the benchmark dose lower confidence limit based on a benchmark response (BMR) of 5% (relative increase of the background incidence after adjustment for confounders, BMDL05) of 0.06 μg iAs/kg bw per day obtained from a study on skin cancer as a Reference Point (RP). Inorganic As is a genotoxic carcinogen with additional epigenetic effects and the CONTAM Panel applied a margin of exposure (MOE) approach for the risk characterisation. In adults, the MOEs are low (range between 2 and 0.4 for mean consumers and between 0.9 and 0.2 at the 95th percentile exposure, respectively) and as such raise a health concern despite the uncertainties.

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“…US FDA's Arsenic in Rice and Rice Products Risk Assessment Report (US Food and Drug Administration [US FDA], 2016) proposed to model incidence ratios of exposure groups as dichotomous data reported in prospective cohort studies for analyzing BMD where confounding covariates were taken into account by using adjusted numbers of cases in BMD modeling. Allen et al (2020b) introduced the idea of using "effective counts" that were adjusted counts for covariates based on the adjusted ORs or RRs of exposure groups reported in the literature for BMD analysis. Shao et al (2021) applied the BMD modeling strategy for summary continuous data to model adjusted ORs or RRs of exposure groups as the continuous response for BMD estimation.…”

Section: Introductionmentioning

confidence: 99%

Benchmark dose modeling for epidemiological dose–response assessment using prospective cohort studies

et al. 2023

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Benchmark dose (BMD) methodology has been employed as a default dose–response modeling approach to determine the toxicity value of chemicals to support regulatory chemical risk assessment. Especially, a relatively standardized BMD analysis framework has been established for modeling toxicological data regarding the formats of input data, dose–response models, definitions of benchmark response, and model uncertainty consideration. However, the BMD approach has not been well developed for epidemiological data mainly because of the diverse designs of epidemiological studies and various formats of data reported in the literature. Although most of the epidemiological BMD analyses were developed to solve a particular question, the methods proposed in two recent studies are able to handle cohort and case–control studies using summary data with consideration of adjustments for confounders. Therefore, the purpose of the present study is to investigate and compare the “effective count”‐based BMD modeling approach and adjusted relative risk (RR)‐based BMD analysis approach to identify an appropriate BMD modeling framework that can be generalized for analyzing published data of prospective cohort studies for BMD analysis. The two methods were applied to the same set of studies that investigated the association between bladder and lung cancer and inorganic arsenic exposure for BMD estimation. The results suggest that estimated BMDs and BMDLs are relatively consistent; however, with the consideration of established common practice in BMD analysis, modeling adjusted RR values as continuous data for BMD estimation is a more generalizable approach harmonized with the BMD approach using toxicological data.

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Systematic dose-response of environmental epidemiologic studies: Dose and response pre-analysis

Cited by 10 publications

References 15 publications

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

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

Update of the risk assessment of inorganic arsenic in food

Benchmark dose modeling for epidemiological dose–response assessment using prospective cohort studies

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