# Strengthening policy-relevant evidence in environmental epidemiology: dose-response curve estimation for varying exposure distributions

> **NIH NIH F31** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $41,640

## Abstract

Project Summary
While environmental epidemiology aims to produce policy-relevant evidence for the protection of the public’s
health, it requires consistent results across studies to create regulatory standards through the identification of
dose-response curves. Current standard practices do not address how to compare estimates across studies
when exposure distributions do not overlap, nor how to ascertain the dose-response curve of the complete
exposure-disease relationship in such situations.
This proposal was borne out of difficulties I encountered performing a systematic review, where the estimates I
wanted to compare came from studies with partially- or non-overlapping exposure distributions. I brought the
idea of utilizing causal inference methods to explore the issue to my sponsor, and she agreed that they could
be applied to the issue to clarify the underlying causal structures that could both result in disparate effect
estimates as well as complicate the ascertainment of accurate dose-response curves across the full range of
exposure levels.
This is the first study to use causal inference methods to articulate the assumptions individual studies must
meet in order for risk estimates to 1) be comparable to other studies with different exposure distributions and 2)
be utilized to create accurate dose-response curves that span the full range of range of exposure levels across
populations. The results will allow us to better inform regulatory standards for polychlorinated biphenyls
(PCBs), the exposure of interest in this proposal; the technique will also be applicable to other types of
endocrine disrupting chemicals (EDCs).
In order to carry out this research, I will analyze sample-specific and pooled data from three birth cohorts,
analyzing the effect of prenatal PCB exposure on birth weight. I will also create simulations informed by the
cohort data to examine the ways in which different underlying causal structure lead to different dose-response
curves.
Deepening our understanding of the ways in which chemicals impact our health is crucial both to inform current
regulatory policy as well as for the future regulation of newer, structurally similar chemicals. Together with the
proposed didactic instruction, this research will also serve as a training vehicle to meet my goals as an
independent researcher in environmental epidemiology, utilizing causal inference methods to explicate
exposure-outcomes relationships that will lead to a career conducting policy-relevant research to improve the
public’s health.

## Key facts

- **NIH application ID:** 10068552
- **Project number:** 1F31ES032331-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Eva Laura Siegel
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $41,640
- **Award type:** 1
- **Project period:** 2020-09-09 → 2022-04-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10068552

## Citation

> US National Institutes of Health, RePORTER application 10068552, Strengthening policy-relevant evidence in environmental epidemiology: dose-response curve estimation for varying exposure distributions (1F31ES032331-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10068552. Licensed CC0.

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