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Record W7075286901

Counting Every Death When Every Death Counts: A Mixed-Methods Study of Hurricane Michael Excess Mortality

2023· dissertation· en· W7075286901 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Commons - University of South Florida (University of South Florida) · 2023
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Mortality rateBayExcess mortalityPopulationCause of deathPublic healthHealth statistics
DOInot available

Abstract

fetched live from OpenAlex

Vulnerable populations have been shown to be disproportionately impacted by natural disasters, such as hurricanes. Hurricane Michael, a Category 5 storm, in October of 2018 hit the rural and socioeconomically vulnerable area of the Florida Panhandle, causing 50 fatalities in Florida, massive destruction to area including the healthcare infrastructure, and led a prolonged recovery period. The purpose of the research was to examine long-term impacts to health in Florida Panhandle after Hurricane Michael; changes to mortality trends, changes to health of survivors, and changes to their access to health care using a mixed-methods, sequential, explanatory study design. The initial phase was excess mortality modeling that used vital statistics death records and a seasonally adjusted ARIMA analyze changes in mortality rates for the year post-storm in two coastal areas, Bay County, and combined Gulf and Franklin Counties. Results were that in Quarter 2 of 2019 (April-June), crude mortality was forecasted for Gulf and Franklin Counties to be 227.8 (95% CI 159.8, 316.0) (per 100,00), though the observed mortality rate was 322.5, showing evidence of excess mortality though no such evidence was found for Bay County. Also in Gulf and Franklin Counties, for Quarter 2 2019, evidence of excess mortality was found for those 55 and older and Whites, and in Quarter 3 of 2019 (July-September) cancer-related mortality was observed at 80.6 though had been forecasted to be 48.7 (90% CI, 28.9, 76.2) (per 100,000). These findings of excess mortality steered the second phase, qualitative data collection and thematic analysis of six focus groups and ten interviews (46 total participants) of Hurricane Michael survivors and responders on the changes to health that they experienced. Findings were that survivors endured prolonged depression, anxiety, PTSD, lingering impacts to their general well-being, extended service disruptions to infrastructure and schools, a housing crisis, and relied on social connections to emotionally support one another because the area had very few mental health providers. Lastly, a third phase of the study used a thematic analysis of the collected qualitative data to conduct a triangulation to explain root causes of excess mortality modeling findings and evaluate whether those results or the official fatality count of 50 deaths was a more accurate measurement of Hurricane Michael's impact on health. This revealed how for months to years post-storm, survivors had delays in accessing health care, particularly specialty care, due to the hurricane having destroyed healthcare infrastructure, leading to them traveled multiple counties or states away for care including for cancer treatment; thus clarifying the excess mortality occurring several months after Hurricane Michael and supporting it as an accurate health measure. This study demonstrated the capability of analyzing excess mortality on smaller, rural populations and highlighted the need to utilize it as another measure of a natural disaster's impact on health along with a fatality count. Furthermore, qualitative data can corroborate disaster-related statistical findings and provide contextually rich data which offer crucial insight; expanding disaster measures overall can elucidate how communities are impacted and recover, therefore growing the knowledge of how to bolster resilience to unavoidable events such as hurricanes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.247
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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