Addiction Treatment Networks Cannot Withstand Acute Crises: Lessons from 2021 Winter Storm Uri in Texas

Bottner, Richard; Weems, John; Hill, Lucas G.; Ziebell, Christopher; Long, Sharon; Young, S.E.; Sasser, Mike; Ferguson, Aaron J.; Tirado, Carlos

doi:10.31478/202103c

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Although there are numerous peer reviewed articles and reports reviewing the technical causes, policy drivers, and consequences of the power grid failure resulting from Winter Storm Uri (e.g., see Glazer at al. ; Li et al 2022; Nejat et al 2022; Popik and Humphreys 2021; Smead 2021; Vogel and Vogel 2021), there is surprisingly limited published information on the social dimensions of the event in Texas (for exceptions see Bottner et al 2021; Hobby School of Public Affairs 2021; Li et al 2022). In this article, we address the following research question: What is the relationship between critical infrastructure failure and psychosocial stress in the context of Winter Storm Uri?…”

Section: Introductionmentioning

confidence: 99%

Winter Storm Uri: Resource Loss and Psychosocial Outcomes of Critical Infrastructure Failure in Texas

Ritchie¹,

Gill²,

Hamilton³

2022

Journal of Critical Infrastructure Policy

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In February of 2021, Winter Storm Uri affected parts of the United States, Mexico, and Canada. Texas was particularly hard hit, as the state's primary power provider, ERCOT (the Electric Reliability Council of Texas), proved to be unprepared for the event-despite similar storms in 1989 and 2011 that revealed weaknesses in the state's electric grid system. This article investigates psychosocial outcomes of individuals who experienced Winter Storm Uri. Drawing upon survey data collected in Texas in April and May of 2022, we illustrate ways in which loss of critical infrastructure and compounding results influence levels of stress among respondents. Using Hofoll's (1989Hofoll's ( , 1991 Conservation of Resources model of stress, we find that Uri-related losses of objects and conditions resources contribute to elevated stress as measured by the Avoidance subscale of the Impact of Event Scale (Horowitz 1976;Horowitz, Wilner, and Alvarez 1979)-more than one year after the disaster. Our regression model consisting of indicators of objects resource loss, conditions resource loss, and demographic characteristics explains approximately 33 percent of the variance in the Avoidance subscale. Findings suggest that more attention should be paid to the social impacts of critical infrastructure failures and that such impacts should be addressed by improving critical infrastructure policy and regulations, as well as the physical structures.

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Section: Introductionmentioning

confidence: 99%

Winter Storm Uri: Resource Loss and Psychosocial Outcomes of Critical Infrastructure Failure in Texas

Ritchie¹,

Gill²,

Hamilton³

2022

Journal of Critical Infrastructure Policy

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Texas electricity service crisis through an energy justice lens

Tiwari

2022

The Electricity Journal

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Rapid Damage Estimation of Texas Winter Storm Uri from Social Media Using Deep Neural Networks

Ye²,

Duffield³

2022

Urban Science

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The winter storm Uri that occurred in February 2021 affected many regions in Canada, the United States, and Mexico. The State of Texas was severely impacted due to the failure in the electricity supply infrastructure compounded by its limited connectivity to other grid systems in the United States. The georeferenced estimation of the storm’s impact is crucial for response and recovery. However, such information was not available until several months afterward, mainly due to the time-consuming and costly assessment processes. The latency to provide timely information particularly impacted people in the economically disadvantaged communities, who lack resources to ameliorate the impact of the storm. This work explores the potential for disaster impact estimation based on the analysis of instant social media content, which can provide actionable information to assist first responders, volunteers, governments, and the general public. In our prototype, a deep neural network (DNN) uses geolocated social media content (texts, images, and videos) to provide monetary assessments of the damage at zip code level caused by Uri, achieving up to 70% accuracy. In addition, the performance analysis across geographical regions shows that the fully trained model is able to estimate the damage for economically disadvantaged regions, such as West Texas. Our methods have the potential to promote social equity by guiding the deployment or recovery resources to the regions where it is needed based on damage assessment.

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Addiction Treatment Networks Cannot Withstand Acute Crises: Lessons from 2021 Winter Storm Uri in Texas

Cited by 3 publications

References 5 publications

Winter Storm Uri: Resource Loss and Psychosocial Outcomes of Critical Infrastructure Failure in Texas

Winter Storm Uri: Resource Loss and Psychosocial Outcomes of Critical Infrastructure Failure in Texas

Texas electricity service crisis through an energy justice lens

Rapid Damage Estimation of Texas Winter Storm Uri from Social Media Using Deep Neural Networks

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