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Record W4396227369 · doi:10.7759/cureus.59230

COVID-19 Vaccination Rates and Predictors of Vaccine Uptake Among Adults With Chronic Obstructive Pulmonary Disease: Insights From the 2022 National Health Interview Survey

2024· article· en· W4396227369 on OpenAlexaff
Oyinlola O Fasehun, Oluwafeyi Adedoyin, Charity Iheagwara, Ifeyinwa H Ofuase-Lasekan, Sapana Manandhar, Natalie A Akoto, Taiwo Ajani, Chuka G Nwume, Joshua T Green, Okelue E Okobi

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsProvincial Health Services Authority
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)VaccinationPulmonary diseaseMedicine2019-20 coronavirus outbreakNational Health Interview SurveySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)DiseaseFamily medicineEnvironmental healthInternal medicineImmunologyVirologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background and objective The coronavirus disease 2019 (COVID-19) vaccination rates and predictors of vaccine uptake among patients with chronic obstructive pulmonary disease (COPD) in the United States are unknown. In light of this, we assessed COVID-19 vaccination rates in this population and evaluated predictors of vaccine uptake. Methods Using 2022 survey data from the National Health Interview Survey (NHIS), 1486 adults with COPD who responded with "yes/no" to whether they had received the COVID-19 vaccine were identified, including those who had received booster doses. A chi-square test was used to ascertain differences between those who had received the vaccine and those who had not, as well as between those who had received booster doses and those who had not. A logistic regression was used to evaluate predictors of COVID-19 vaccination uptake. Results A total of 1195 individuals among 1486 respondents with chronic pulmonary disease (78.4%) had been vaccinated against COVID-19, and 789/1195 (62.5%) had received booster shots. The majority of individuals were aged 65 years and above, exceeded the 1+ threshold for the ratio of family income to poverty (RFIP), and were covered by insurance. Positive predictors of COVID-19 vaccination were as follows: age 40 - 64 years (OR: 2.34, 95% CI: 1.31 - 4.19; p=0.004) and 65 years and above (OR: 1.93, 95% CI: 1.36 - 2.72; p<0.001), RFIP threshold of ≥1 (OR: 2.02, 95% CI: 1.42 - 2.88; p<0.001), having a college degree (OR: 1.92, 95% CI: 1.92 - 3.26, p=0.016), and being insured (OR: 3.12, 95% CI: 1.46 - 6.66, p=0.003). The current smoking habit negatively predicted the uptake (OR: 0.54, 95% CI: 0.33 - 0.87, p=0.012). The positive predictors of COVID-19 vaccination boosters were as follows: age 40 - 64 years (OR: 2.72, 95% CI: 1.39 - 5.30, p=0.003) and 65 years and above (OR: 4.85, 95% CI: 2.45 - 9.58, p<0.001). Being from the non-Hispanic (NH) black ethnicity negatively predicted receiving the COVID-19 booster (OR: 0.55, 95% CI: 0.36 - 0.85, p=0.007). Conclusions While COVID-19 vaccination rates are fairly satisfactory in COPD patients, the uptake of booster vaccines is relatively lower in this population. Socioeconomic and behavioral factors are associated with poor vaccine uptake, and targeted interventions should be implemented to address these factors.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.312
Teacher spread0.286 · 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".

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Citations2
Published2024
Admission routes1
Has abstractyes

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