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Record W4296686126 · doi:10.21203/rs.3.rs-2046871/v1

Trends in pulmonary tuberculosis mortality between 1985 and 2018: an observational analysis

2022· preprint· en· W4296686126 on OpenAlexaboutno aff
Harpreet Singh, Arashdeep Rupal, Omar Al Omari, Chinmay Jani, Alaaeldin Ahmed, Alexander M. Walker, Joseph Shalhoub, Carey C. Thomson, Dominic C. Marshall, Justin D. Salciccioli

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyTuberculosisMortality ratePopulationMedicineGeographyImmigrationEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Introduction Pulmonary tuberculosis (TB) is a major source of global mortality and morbidity, particularly in the developing world. Latent infection has enabled it to spread to approximately a quarter of the world's population. The late 1980s and early 1990s saw an increase in the number of reported TB cases related to the HIV epidemic and immigration, as well as the spread of multidrug-resistant TB (MDR TB). Few studies have reported pulmonary TB mortality trends. Our study reports and compares trends in pulmonary TB mortality between 1985 and 2018 in countries throughout the world. Methods We utilized the World Health Organization (WHO) mortality database to extract TB mortality data based on the International Classification of Diseases (ICD) 10 system. Based on the availability and quality of data, we included Canada and the United States (US) from the Americas; Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Republic of Moldova, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, and United Kingdom from Europe; Australia, New Zealand, and Japan from the Western Pacific region. Crude mortality rates were dichotomized by sex and reported by year. We computed age standardized death rates (ASRDs) per 100,000 population using the world standard population. Pulmonary TB mortality trends were examined using Joinpoint regression analysis and reported using estimated annual percentage changes (EAPCs). Results We observed a decrease in mortality in males and females in all countries except the Republic of Moldova, which showed an increase in female mortality (+0.12%). Among all countries, Lithuania had the greatest reduction in male mortality (-12.01%) between 1993-2018, and Hungary had the greatest reduction in female mortality (-1.57%) between 1985-2017. Male mortality declined at a steady rate across the study period. Slovenia had the most rapid recent declining trend for males with an EAPC of -47% (2003-2016), followed by Australia (-33.6%, 2014-2017), whereas Croatia and Austria showed an increase in EAPC of +25.0% (2015-2017) and +17.8% (2010-2014), respectively. For females, New Zealand had the most rapid recent declining trend (-47.2%, 1985-2015), followed by Hungary (-35.1%, 2004-2007), whereas Croatia showed an increase in EAPC (+24.9%, 2014-2017). Conclusion Pulmonary TB mortality is disproportionately higher among Central and Eastern European countries. This communicable disease cannot be eliminated from any one region without a global approach. Priority action areas include ensuring early diagnosis and appropriate treatment to the most vulnerable groups. In low- and middle-income countries with high TB incidence, attenuation of socioeconomic determinants including extreme poverty, inadequate living conditions, and malnutrition remains crucial.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.321
GPT teacher head0.513
Teacher spread0.191 · 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
Published2022
Admission routes1
Has abstractyes

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