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Record W2904839344 · doi:10.1016/s2214-109x(18)30435-2

Proximate determinants of tuberculosis in Indigenous peoples worldwide: a systematic review

2018· review· en· W2904839344 on OpenAlexafffundabout
Maxime Cormier, Kevin Schwartzman, Dieynaba S. N’Diaye, Claire Boone, Alexandre M. dos Santos, Júlia Gaspar, Danielle Cazabon, Marzieh Ghiasi, Rebecca Kahn, Aashna Uppal, Martin Morris, Olivia Oxlade

Bibliographic record

VenueThe Lancet Global Health · 2018
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University Health CentreMcGill University
FundersCanadian Institutes of Health Research
KeywordsIndigenousCINAHLMedicinePopulationEnvironmental healthTuberculosisPsychological interventionMEDLINEMalnutritionGerontologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Indigenous peoples worldwide carry a disproportionate tuberculosis burden. There is an increasing awareness of the effect of social determinants and proximate determinants such as alcohol use, overcrowding, type 1 and type 2 diabetes, substance misuse, HIV, food insecurity and malnutrition, and smoking on the burden of tuberculosis. We aimed to understand the potential contribution of such determinants to tuberculosis in Indigenous peoples and to document steps taken to address them. METHODS: We did a systematic review using seven databases (MEDLINE, Embase, CINAHL, Global Health, BIOSIS Previews, Web of Science, and the Cochrane Library). We identified English language articles published from Jan 1, 1980, to Dec 20, 2017, reporting the prevalence of proximate determinants of tuberculosis and preventive programmes targeting these determinants in Indigenous communities worldwide. We included any randomised controlled trials, controlled studies, cohort studies, cross-sectional studies, case reports, and qualitative research. Exclusion criteria were articles in languages other than English, full text not available, population was not Indigenous, focused exclusively on children or older people, and studies that focused on pharmacological interventions. FINDINGS: Of 34 255 articles identified, 475 were eligible for inclusion. Most studies confirmed a higher prevalence of proximate determinants in Indigenous communities than in the general population. Diabetes was more frequent in Indigenous communities within high-income countries versus in low-income countries. The prevalence of alcohol use was generally similar to that among non-Indigenous groups, although patterns of drinking often differed. Smoking prevalence and smokeless tobacco consumption were commonly higher in Indigenous groups than in non-Indigenous groups. Food insecurity was highly prevalent in most Indigenous communities evaluated. Substance use was more frequent in Indigenous inhabitants of high-income countries than of low-income countries, with wide variation across Indigenous communities. The literature pertaining to HIV, crowding, and housing conditions among Indigenous peoples was too scant to draw firm conclusions. Preventive programmes that are culturally appropriate targeting these determinants appear feasible, although their effectiveness is largely unproven. INTERPRETATION: Indigenous peoples were generally reported to have a higher prevalence of several proximate determinants of tuberculosis than non-Indigenous peoples, with wide variation across Indigenous communities. These findings emphasise the need for community-led, culturally appropriate strategies to address smoking, food insecurity, and diabetes in Indigenous populations as important public health goals in their own right, and also to reduce the burden of tuberculosis. FUNDING: Canadian Institutes of Health Research.

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)0.007
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0100.010
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.082
GPT teacher head0.463
Teacher spread0.381 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations50
Published2018
Admission routes3
Has abstractyes

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