Benchmark dose modeling for epidemiological dose-response assessment using prospective cohort studies.

Area of Science:

• Environmental health
• Toxicology
• Epidemiology

Background:

• Benchmark dose (BMD) methodology is standard for chemical risk assessment using toxicological data.
• Existing BMD frameworks are not well-suited for diverse epidemiological study designs and data formats.
• Recent advancements allow BMD analysis for cohort and case-control studies using summary data.

Purpose of the Study:

• To compare "effective count" and adjusted relative risk (RR)-based BMD modeling for epidemiological data.
• To identify a generalizable BMD modeling framework for published prospective cohort study data.
• To support regulatory chemical risk assessment with robust epidemiological data analysis.

Main Methods:

• Applied two BMD modeling approaches: "effective count" and adjusted RR.
• Utilized published studies on inorganic arsenic exposure and cancer (bladder, lung).
• Compared the generalizability and consistency of estimated BMD and BMDL values.

Main Results:

• Estimated BMD and BMDL values were relatively consistent between the two methods.
• The adjusted RR-based BMD approach demonstrated greater generalizability.
• This approach aligns with established BMD analysis practices for toxicological data.

Conclusions:

• The adjusted RR-based BMD modeling is a more harmonized and generalizable approach for epidemiological data.
• This framework facilitates consistent application of BMD methodology across toxicological and epidemiological studies.
• The findings support improved regulatory risk assessment through standardized analysis of published epidemiological research.