Evidence map›Paper›PMID 30012092›Full record

ArticleBMC infectious diseases2018

Variability in distribution and use of tuberculosis diagnostic tests in Kenya: a cross-sectional survey.

J N Oliwa, J Maina, P Ayieko, D Gathara, I A Kathure, E Masini, A H Van't Hoog, M B van Hensbroek, M English

Abstract read
In one paragraph

Article in BMC infectious diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

  1. Pooled it
  2. Trial
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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.

J N OliwaKEMRI-Wellcome Trust Research Programme, Nairobi, Kenya. joliwa@kemri-wellcome.org.ORCID 0000-0002-4575-2447
J MainaKEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
P AyiekoKEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
D GatharaKEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
I A KathureNational TB and Leprosy Programme, Ministry of Health, Kenya, Nairobi, Kenya.
E MasiniNational TB and Leprosy Programme, Ministry of Health, Kenya, Nairobi, Kenya.
A H Van't HoogThe Academic Medical Centre, University of Amsterdam, Department of Global Health, Amsterdam, The Netherlands.
M B van HensbroekThe Academic Medical Centre, University of Amsterdam, Department of Global Health, Amsterdam, The Netherlands.
M EnglishKEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.

Funding

Wellcome TrustWellcome Trust Initiative to Develop African Research Leaders (IDeAL) PHD 013World Health Organization 001
6 · The paper itself

Abstract

backgroundGlobally, 40% of all tuberculosis (TB) cases, 65% paediatric cases and 75% multi-drug resistant TB (MDR-TB) cases are missed due to underreporting and/or under diagnosis. A recent Kenyan TB prevalence survey found that a significant number of TB cases are being missed here. Understanding spatial distribution and patterns of use of TB diagnostic tests as per the guidelines could potentially help improve TB case detection by identifying diagnostic gaps.

methodsWe used 2015 Kenya National TB programme data to map TB case notification rates (CNR) in different counties, linked with their capacity to perform diagnostic tests (chest x-rays, smear microscopy, Xpert MTB/RIF®, culture and line probe assay). We then ran hierarchical regression models for adults and children to specifically establish determinants of use of Xpert® (as per Kenyan guidelines) with county and facility as random effects.

resultsIn 2015, 82,313 TB cases were notified and 7.8% were children. The median CNR/100,000 amongst 0-14yr olds was 37.2 (IQR 20.6, 41.0) and 267.4 (IQR 202.6, 338.1) for ≥15yr olds respectively. 4.8% of child TB cases and 12.2% of adult TB cases had an Xpert® test done, with gaps in guideline adherence. There were 2,072 microscopy sites (mean microscopy density 4.46/100,000); 129 Xpert® sites (mean 0.31/100,000); two TB culture laboratories and 304 chest X-ray facilities (mean 0.74/100,000) with variability in spatial distribution across the 47 counties. Retreatment cases (i.e. failures, relapses/recurrences, defaulters) had the highest odds of getting an Xpert® test compared to new/transfer-in patients (AOR 7.81, 95% CI 7.33-8.33). Children had reduced odds of getting an Xpert® (AOR 0.41, CI 0.36-0.47). HIV-positive individuals had nearly twice the odds of getting an Xpert® test (AOR 1.82, CI 1.73-1.92). Private sector and higher-level hospitals had a tendency towards lower odds of use of Xpert®.

conclusionsWe noted under-use and gaps in guideline adherence for Xpert® especially in children. The under-use despite considerable investment undermines cost-effectiveness of Xpert®. Further research is needed to develop strategies enhancing use of diagnostics, including innovations to improve access (e.g. specimen referral) and overcoming local barriers to adoption of guidelines and technologies.

Indexed as

Diagnostic Tests, RoutineAdolescentAdultChildChild, PreschoolCost-Benefit AnalysisCross-Sectional StudiesFemaleGuideline AdherenceHIV SeropositivityHumansInfantInfant, NewbornKenyaMalePrevalenceAdultsChildrenDiagnosticsDistributionTestsTuberculosisUse

Identifiers

PMID30012092
PMCPMC6048895

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.