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Measuring transient exposures in epidemiology is challenging. At-risk-measure sampling uses mobile sensor data to estimate exposure, improving transportation injury research.

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Area of Science:

  • Epidemiology
  • Environmental Health
  • Biostatistics

Background:

  • Measuring transient exposures in epidemiological studies is difficult, particularly for built-environment risks like transportation injury.
  • Mobile sensor data offers potential for improved exposure measurement but often lacks individual-level data, limiting traditional case-crossover designs.

Purpose of the Study:

  • To present a novel sampling method, at-risk-measure sampling, for estimating exposure denominators in epidemiological studies.
  • To address challenges in measuring transient exposures using aggregated big data from mobile sensors.

Main Methods:

  • At-risk-measure sampling focuses on sampling the 'measure of the at-risk experience' (e.g., person-distance, person-events) rather than individuals or locations.
  • The method aims to approximate the exposure distribution of the entire cohort using aggregated data.
  • Illustrated with bicycling data from a mobile app, sampling person-distance and person-events by location.

Main Results:

  • The method provides a way to estimate the incidence rate ratio denominator using aggregated cohort data.
  • It extends case-control sampling principles to continuous measures like person-time or person-distance.
  • Demonstrates the utility of mobile sensor data for epidemiological exposure assessment.

Conclusions:

  • At-risk-measure sampling is a statistically and logistically efficient method for analyzing transient exposures from big data.
  • This approach enhances epidemiological research on transportation injury and other time-space varying exposures.
  • Facilitates the use of aggregated mobile sensor data for robust exposure assessment in public health studies.