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Assessing bias in measuring power outage exposure with simulations
Heather McBrien1, Daniel Mork2, Marianthi-Anna Kioumourtzoglou1
1Department of Environmental Health Sciences, Columbia Mailman School of Public Health, New York, New York.
Environmental Epidemiology (Philadelphia, Pa.)
|June 16, 2025
Summary
Researchers can use new power outage data for health studies, but missing data and unknown outage durations can cause bias. Careful analysis and duration choices can yield reliable results for power outage health outcomes.
Area of Science:
- Environmental Epidemiology
- Public Health Data Science
Background:
- New national power outage data since 2020 can aid epidemiologic studies.
- Challenges include missing data and unknown health-relevant power outage durations.
- Bias can affect study results due to data limitations.
Purpose of the Study:
- Assess if existing datasets yield usable effect estimates despite missing data.
- Quantify bias from incorrect assumptions about health-relevant power outage duration.
Main Methods:
- Simulated county-level studies using PowerOutage.us data.
- Estimated the effect of daily power outage exposure on hospitalization rates.
- Measured bias from incorrect duration assumptions and missing exposure data.
Main Results:
- Underestimating health-relevant power outage duration caused substantial bias towards the null (mean bias: -64.7%).
- Overestimating duration led to less bias (mean bias: -6.7%).
- Missing 80%+ of data in 80% of counties severely biased results towards the null (mean bias: -54.4%).
Conclusions:
- Some bias is likely when using available power outage data.
- Sensitivity analyses and careful duration selection can mitigate bias.
- Researchers can produce low-bias estimates for power outage health outcomes.
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