Evidence map›Paper›PMID 35963946›Full record

ArticleJournal of exposure science & environmental epidemiology2023

The 2021 Texas Power Crisis: distribution, duration, and disparities.

Nina M Flores, Heather McBrien, Vivian Do, Mathew V Kiang, Jeffrey Schlegelmilch, Joan A Casey

Abstract read
In one paragraph

Article in Journal of exposure science & environmental epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Article
  2. The Reverse Risk Register Approach to Understanding Systemic Factors in Risk Analysis and Management Case Studies.Risk analysis : an official publication of the Society for Risk Analysis · 2026
    Article
  3. Assessing the Sensitivities of Input-Output Methods for Natural Hazard-Induced Power Outage Macroeconomic Impacts.Risk analysis : an official publication of the Society for Risk Analysis · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Nina M FloresDepartment of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA. nf2497@cumc.columbia.edu.ORCID 0000-0002-2723-1728
Heather McBrienDepartment of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
Vivian DoDepartment of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
Mathew V KiangDepartment of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
Jeffrey SchlegelmilchNational Center for Disaster Preparedness at the Columbia Climate School, Columbia University, New York, NY, USA.
Joan A CaseyDepartment of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.

Funding

True Metal fac coreP30ES009089 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MATTHEW S PERZANOWSKI · 1998 to 2026
$44.7M
Supplemental Training in Making Data FAIR and AI/ML ReadyT32ES007322 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Pam R Factor-Litvak, Allison Kupsco · 2000 to 2026
$12.1M
NIEHS NIH HHS P30 ES009089NIEHS NIH HHS T32 ES007322
6 · The paper itself

Abstract

backgroundPrecipitated by an unusual winter storm, the 2021 Texas Power Crisis lasted February 10 to 27 leaving millions of customers without power. Such large-scale outages can have severe health consequences, especially among vulnerable subpopulations such as those reliant on electricity to power medical equipment, but limited studies have evaluated sociodemographic disparities associated with outages.

objectiveTo characterize the 2021 Texas Power Crisis in relation to distribution, duration, preparedness, and issues of environmental justice.

methodsWe used hourly Texas-wide county-level power outage data to estimate geographic clustering and association between outage exposure (distribution and duration) and six measures of racial, social, political, and/or medical vulnerability: Black and Hispanic populations, the Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI), Medicare electricity-dependent durable medical equipment (DME) usage, nursing homes, and hospitals. To examine individual-level experience and preparedness, we used a preexisting and non-representative internet survey.

resultsAt the peak of the Texas Power Crisis, nearly 1/3 of customers statewide (N = 4,011,776 households/businesses) lost power. We identified multiple counties that faced a dual burden of racial/social/medical vulnerability and power outage exposure, after accounting for multiple comparisons. County-level spatial analyses indicated that counties where more Hispanic residents resided tended to endure more severe outages (OR = 1.16, 95% CI: 1.02, 1.40). We did not observe socioeconomic or medical disparities. With individual-level survey data among 1038 respondents, we found that Black respondents were more likely to report outages lasting 24+ hours and that younger individuals and those with lower educational attainment were less likely to be prepared for outages. SIGNIFICANCE: Power outages can be deadly, and medically vulnerable, socioeconomically vulnerable, and marginalized groups may be disproportionately impacted or less prepared. Climate and energy policy must equitably address power outages, future grid improvements, and disaster preparedness and management.

Indexed as

DisastersMedicareAgedElectricityHumansSocial GroupTexasUnited StatesClimate changeDurable medical equipmentEnvironmental justiceExtreme cold weatherPower outageTexas

Identifiers

PMID35963946
PMCPMC9851928

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.