ArticleGeoHealth2022
Observational Verification of the Cumulative Resilience Screening Index (CRSI) Using Hurricanes, Inland Floods, and Wildfires From 2016 to 2019.
Article in GeoHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Ecosystem Services Profiles for Communities Benefitting from Estuarine Habitats along the Massachusetts Coast, USA.Ecological informatics · 2023Article
- Observational Verification of the Cumulative Resilience Screening Index (CRSI) Using Hurricanes, Inland Floods, and Wildfires From 2016 to 2019.GeoHealth · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Users can apply three processes to develop confidence in decision-making tools like models and indices-validation, verification, and observation. The utility of the Cumulative Resilience Screening Index (CRSI) was demonstrated by combining the processes of verification and observation using real-world natural hazard events (i.e., hurricanes, inland flooding, and wildfires). The ability of CRSI to determine the counties most vulnerable to hazards and least likely to recover quickly from natural hazards is demonstrated using these natural hazard events from outside the original index construction data set. Using Hurricane Harvey and Hurricane Michael, the counties in Texas and Florida/Georgia, respectively, experiencing the most damage and the most extended recovery intervals were determined accurately. Similarly, the most vulnerable and least recoverable counties were correctly identified as those associated with the Great Louisiana Flood of 2016. Finally, three different types of wildfires in California were examined to determine the likelihood of recovery and the strength of pre-event planning. All models and indices developed for use by decision-makers should consider undertaking this verification or a similar validation operation to enhance user confidence.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.