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Record W2154018674 · doi:10.1186/1742-6405-8-13

Estimating the impact of expanded access to antiretroviral therapy on maternal, paternal and double orphans in sub-Saharan Africa, 2009-2020

2011· article· en· W2154018674 on OpenAlexafffund
Aranka Anema, Christopher G. Au-Yeung, Michel Joffres, Angela Kaida, Krisztina Vàsàrhelyi, Steve Kanters, Julio Montaner, Robert S. Hogg

Bibliographic record

VenueAIDS Research and Therapy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversity of British ColumbiaPacific Institute for the Mathematical SciencesSimon Fraser UniversitySt. Paul's Hospital
FundersCanadian Institutes of Health ResearchNational Institutes of HealthGlaxoSmithKlineNational Institute on Drug AbuseHealth CanadaBristol-Myers SquibbBoehringer Ingelheim
KeywordsMedicineAntiretroviral therapyPopulationCensusDemographyCohortHuman immunodeficiency virus (HIV)Expanded accessIncidence (geometry)Developing countryEnvironmental healthSocioeconomicsEconomic growthVirologyViral load

Abstract

fetched live from OpenAlex

BACKGROUND: HIV/AIDS has orphaned 11.6 million children in sub-Saharan Africa. Expanded antiretroviral therapy (ART) use may reduce AIDS orphanhood by decreasing adult mortality and population-level HIV transmission. METHODS: We modeled two scenarios to measure the impact of adult ART use on the incidence of orphanhood in 10 sub-Saharan African countries, from 2009 to 2020. Demographic model data inputs were obtained from cohort studies, UNAIDS, UN Population Division, WHO and the US Census Bureau. RESULTS: Compared to current rates of ART uptake, universal ART access averted 4.37 million more AIDS orphans by year 2020, including 3.15 million maternal, 1.89 million paternal and 0.75 million double orphans. The number of AIDS orphans averted was highest in South Africa (901.71 thousand) and Nigeria (839.01 thousand), and lowest in Zimbabwe (86.96 thousand) and Côte d'Ivoire (109.12 thousand). CONCLUSION: Universal ART use may significantly reduce orphanhood in sub-Saharan Africa.

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 score0.189
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.122
GPT teacher head0.413
Teacher spread0.291 · 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

Citations23
Published2011
Admission routes2
Has abstractyes

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