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Record W2787869301 · doi:10.1016/s2468-2667(18)30002-1

Determinants of tuberculosis trends in six Indigenous populations of the USA, Canada, and Greenland from 1960 to 2014: a population-based study

2018· article· en· W2787869301 on OpenAlexaffabout
Kianoush Dehghani, Zhiyi Lan, Peizhi Li, Sascha Wilk Michelsen, Sean Waites, Andrea Benedetti, Pierre Lejeune, Jill Torrie, Elizabeth Robinson, Berenica Vejvoda, Muhammad Abu Shadeque Mullah, Diana Redwood, Michael J. Cooper, Anne Fanning, Wadieh Yacoub, Gonzalo G. Alvarez, Bolette Søborg, Richard Long, Dick Menzies

Bibliographic record

VenueThe Lancet Public Health · 2018
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsOttawa HospitalHealth CanadaMcGill UniversityUniversity of AlbertaGovernment of NunavutUniversity of OttawaCree Board of Health and Social Services of James BayMcGill University Health Centre
Fundersnot available
KeywordsTuberculosisMedicinePopulationIndigenousDemographySocioeconomic statusLatent tuberculosisPsychological interventionEpidemiologyEnvironmental healthLife expectancyIncidence (geometry)Public healthGerontologyGeographyMycobacterium tuberculosisBiologyEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Tuberculosis continues to disproportionately affect many Indigenous populations in the USA, Canada, and Greenland. We aimed to investigate whether population-based tuberculosis-specific interventions or changes in general health and socioeconomic indicators, or a combination of these factors, were associated with changes in tuberculosis incidence in these Indigenous populations. METHODS: For this population-based study we examined annual tuberculosis notification rates between 1960 and 2014 in six Indigenous populations of the USA, Canada, and Greenland (Inuit [Greenland], American Indian and Alaska Native [Alaska, USA], First Nations [Alberta, Canada], Cree of Eeyou Istchee [Quebec, Canada], Inuit of Nunavik [Quebec, Canada], and Inuit of Nunavut [Canada]), as well as the general population of Canada. We used mixed-model linear regression to estimate the association of these rates with population-wide interventions of bacillus Calmette-Guérin (BCG) vaccination of infants, radiographic screening, or testing and treatment for latent tuberculosis infection (LTBI), and with other health and socioeconomic indicators including life expectancy, infant mortality, diabetes, obesity, smoking, alcohol use, crowded housing, employment, education, and health expenditures. FINDINGS: Tuberculosis notification rates declined rapidly in all six Indigenous populations between 1960 and 1980, with continued decline in Indigenous populations in Alberta, Alaska, and Eeyou Istchee thereafter but recrudescence in Inuit populations of Nunavut, Nunavik, and Greenland. Annual percentage reductions in tuberculosis incidence were significantly associated with two tuberculosis control interventions, relative to no intervention, and after adjustment for infant mortality and smoking: BCG vaccination (-11%, 95% CI -6 to -17) and LTBI screening and treatment (-10%, -3 to -18). Adjusted associations were not significant for chest radiographic screening (-1%, 95% CI -7 to 5). Declining tuberculosis notification rates were significantly associated with increased life expectancy (-37·8 [95% CI -41·7 to -33·9] fewer cases per 100 000 for each 1-year increase) and decreased infant mortality (-9·0 [-9·5 to -8·6] fewer cases per 100 000 for each death averted per 1000 livebirths) in all six Indigenous populations, but no significant associations were observed for other health and socioeconomic indicators examined. INTERPRETATION: Population-based BCG vaccination of infants and LTBI screening and treatment were associated with significant decreases in tuberculosis notification rates in these Indigenous populations. These interventions should be reinforced in populations still affected by tuberculosis, while also addressing the persistent health and socioeconomic disparities. FUNDING: Public Health Department of the Cree Board of Health and Social Services of James Bay.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.023
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.097
GPT teacher head0.394
Teacher spread0.297 · 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 teacher head, 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

Citations36
Published2018
Admission routes2
Has abstractyes

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