Evidence map›Paper›PMID 21352748›Full record

ArticleEpidemics2009

Incongruent HIV and tuberculosis co-dynamics in Kenya: interacting epidemics monitor each other.

María S Sánchez, James O Lloyd-Smith, Brian G Williams, Travis C Porco, Sadie J Ryan, Martien W Borgdorff, John Mansoer, Christopher Dye, Wayne M Getz

Abstract readComparative Study
In one paragraph

Article in Epidemics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. How can mathematical models advance tuberculosis control in high HIV prevalence settings?The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2014
    Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

María S SánchezDepartment of Environmental Science, Policy and Management, University of California, 137 Mulford Hall, Berkeley, CA 94720-3112, USA. msanchez@nature.berkeley.edu
James O Lloyd-Smith
Brian G Williams
Travis C Porco
Sadie J Ryan
Martien W Borgdorff
John Mansoer
Christopher Dye
Wayne M Getz

Funding

FORECASTING HIV EVOLUTION IN IVDUs AND OTHER POPULATIONSR01DA013510 · NIDA · UNIVERSITY OF CALIFORNIA BERKELEY · PI GETZ, WAYNE M · 2000 to 2004
$1.3M
NIDA NIH HHS R01 DA013510
6 · The paper itself

Abstract

objectiveKenya is heralded as an example of declining HIV in Africa, while its tuberculosis (TB) numbers continue rising. We conducted a comparative investigation of TB-HIV co-dynamics in Africa to determine the likelihood of reported trends. METHODS AND

resultsOur mathematical modeling analysis exposes the notable incongruence of reported trends in Kenya because TB-HIV co-dynamics, tightly knit worldwide and most dramatically in sub-Saharan Africa, suggest that declining HIV trends should trigger reductions in TB trends. Moreover, a continental-scale analysis of TB-HIV trends places Kenya as an outlier in eastern and southern Africa, and shows TB outpacing HIV in western central Africa. We further investigate which TB processes across HIV stages have greater potential to reduce TB incidence via a sensitivity analysis.

conclusionsThere are two parsimonious explanations: an unaccounted improvement in TB case detection has occurred, or HIV is not declining as reported. The TB-HIV mismatch could be compounded by surveillance biases due to spatial heterogeneity in disease dynamics. Results highlight the need to re-evaluate trends of both diseases in Kenya, and identify the most critical epidemiological factors at play. Substantial demographic changes have occurred in Kenya, including rapid urbanization accompanied by poor living conditions, which could disproportionately increase TB incidence. Other possible contributors include immune reconstitution due to the recent delivery of antiretrovirals, and an increased presence of the virulent Beijing/W TB genotype. Results support the importance of integrating information from closely interacting epidemics, because this approach provides critical insights unobtainable when components of generalized epidemics are considered individually.

Indexed as

Anti-Retroviral AgentsHIV InfectionsHumansKenyaModels, BiologicalPrevalenceSentinel SurveillanceTuberculosisAnti-Retroviral Agents

Identifiers

PMID21352748
PMCPMC10548337

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.