Estimating Spatial and Temporal Patterns of Residential Power Outages in Massachusetts from 2013 to 2022 Using Public Records.

Authors

Chad W Milando, Muskaan Khemani, Allison M James, Jianna Correi-Silva, Jillian Collins, Madeleine K Scammell, Mary D Willis, Jonathan I Levy, Amruta Nori-Sarma

Published in

Journal of urban health : bulletin of the New York Academy of Medicine. Aug 22, 2026. Epub Aug 22, 2026.

Abstract

Power outages are a growing threat to human health. Extreme weather events and strains on the electrical grid can cut off access to critical health-supporting medical equipment. However, the full scope of health impacts related to power outages has not been quantified, in large part because outage data with high spatial and temporal resolution are not widely available. In this work, we present a methodological framework for constructing a longitudinal, high spatial and temporal resolution dataset of power outages using public records. We apply this method to the Commonwealth of Massachusetts, extracting and synthesizing data from daily town-level reports submitted by electricity providers to the Massachusetts Department of Public Utilities from 2013 to 2022. For reference, towns in Massachusetts (N = 351) are smaller than counties (N = 14). For each town and day, we calculated the fraction of electrical circuits experiencing a power outage and classified this into a tertile categorical variable: mild, moderate, or severe. Across the state, the median and inter-quartile range (IQR) of outages per town was 7.3 power outages per year of any type [IQR: 4.3, 11.4], with substantial heterogeneity across months. Mild outages were most associated with equipment failure or planned maintenance, whereas moderate and severe outages were associated with tree interference (35.6% and 50.9% of outages, respectively). Developing suburbs and rural towns experienced the highest frequency of severe outages, while towns with a high density of Environmental Justice populations experienced more frequent mild outages. Our dataset reinforces substantial spatial and temporal heterogeneity in power outages, and our methods provide a framework for building similar datasets for future analyses of population vulnerability and health impacts associated with power outages in other states where curated datasets are not available.

PMID:
42632856
Bibliographic data and abstract were imported from PubMed on 23 Aug 2026.

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