Evidence map›Paper›PMID 17181867›Full record

ArticleBMC medicine2006

Prioritising between direct observation of therapy and case-finding interventions for tuberculosis: use of population impact measures.

Richard F Heller, Islay Gemmell, Richard Edwards, Iain Buchan, Shally Awasthi, James A Volmink

Abstract read
In one paragraph

Article in BMC medicine, 2006. 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
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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

6 authors.

Richard F HellerEvidence for Population Health Unit, University of Manchester, Oxford Road, Manchester M13 9PT, UK. Dick.Heller@manchester.ac.uk
Islay Gemmell
Richard Edwards
Iain Buchan
Shally Awasthi
James A Volmink

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPopulation impact measures (PIMs) have been developed as tools to help policy-makers with locally relevant decisions over health risks and benefits. This involves estimating and prioritizing potential benefits of interventions in specific populations. Using tuberculosis (TB) in India as an example, we examined the population impact of two interventions: direct observation of therapy and increasing case-finding.

methodsPIMs were calculated using published literature and national data for India, and applied to a notional population of 100,000 people. Data included the incidence or prevalence of smear-positive TB and the relative risk reduction from increasing case finding and the use of direct observation of therapy (applied to the baseline risks over the next year), and the incremental proportion of the population eligible for the proposed interventions.

resultsIn a population of 100,000 people in India, the directly observed component of the Directly Observed Treatment, Short-course (DOTS) programme may prevent 0.188 deaths from TB in the next year compared with 1.79 deaths by increasing TB case finding. The costs of direct observation are (in international dollars) 5960 I dollars and of case finding are 4839 I dollars or 31702 I dollars and 2703 I dollars per life saved respectively.

conclusionIncreasing case-finding for TB will save nearly 10 times more lives than will the use of the directly observed component of DOTS in India, at a smaller cost per life saved. The demonstration of the population impact, using simple and explicit numbers, may be of value to policy-makers as they prioritize interventions for their populations.

Indexed as

Cost-Benefit AnalysisDirectly Observed TherapyHumansIncidenceIndiaPrevalenceTreatment OutcomeTuberculosis

Identifiers

PMID17181867
PMCPMC1764027

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Registered trials

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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.