Evidence map›Paper›PMID 24565214›Full record

ArticleGlobal health action2014

Collecting and analysing cost data for complex public health trials: reflections on practice.

Neha Batura, Anni-Maria Pulkki-Brännström, Priya Agrawal, Archana Bagra, Hassan Haghparast-Bidgoli, Fiammetta Bozzani, Tim Colbourn, Giulia Greco, Tanvir Hossain, Rajesh Sinha and 2 more

Abstract read
In one paragraph

Article in Global health action, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 4 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

38 citing papers in PubMed, 4 syntheses or guidelines 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

12 authors.

Neha BaturaInstitute for Global Health, University College London, London, UK; n.batura@ucl.ac.uk.
Anni-Maria Pulkki-BrännströmInstitute for Global Health, University College London, London, UK.
Priya AgrawalSNEHA, Mumbai, India.
Archana BagraSNEHA, Mumbai, India.
Hassan Haghparast-BidgoliInstitute for Global Health, University College London, London, UK.
Fiammetta BozzaniGlobal Health and Development, London School of Hygiene and Tropical Medicine, London, UK.
Tim ColbournInstitute for Global Health, University College London, London, UK.
Giulia GrecoHealth Economics and Systems Group, London School of Hygiene and Tropical Medicine, London, UK.
Tanvir HossainBADAS, Dhaka, Bangladesh;
Rajesh SinhaEkjut, Ranchi, India.
Bidur ThapaMIRA, Kathmandu, Nepal.
Jolene Skordis-WorrallInstitute for Global Health, University College London, London, UK; Health Economics and Systems Group, London School of Hygiene and Tropical Medicine, London, UK.

Funding

Wellcome Trust 085417Wellcome Trust 085417ma/Z/08/Z
6 · The paper itself

Abstract

backgroundCurrent guidelines for the conduct of cost-effectiveness analysis (CEA) are mainly applicable to facility-based interventions in high-income settings. Differences in the unit of analysis and the high cost of data collection can make these guidelines challenging to follow within public health trials in low- and middle- income settings.

objectiveThis paper reflects on the challenges experienced within our own work and proposes solutions that may be useful to others attempting to collect, analyse, and compare cost data between public health research sites in low- and middle- income countries.

designWe describe the generally accepted methods (norms) for collecting and analysing cost data in a single-site trial from the provider perspective. We then describe our own experience applying these methods within eight comparable cluster randomised, controlled, trials. We describe the strategies used to maximise adherence to the norm, highlight ways in which we deviated from the norm, and reflect on the learning and limitations that resulted.

resultsWhen the expenses incurred by a number of small research sites are used to estimate the cost-effectiveness of delivering an intervention on a national scale, then deciding which expenses constitute 'start-up' costs will be a nontrivial decision that may differ among sites. Similarly, the decision to include or exclude research or monitoring and evaluation costs can have a significant impact on the findings. We separated out research costs and argued that monitoring and evaluation costs should be reported as part of the total trial cost. The human resource constraints that we experienced are also likely to be common to other trials. As we did not have an economist in each site, we collaborated with key personnel at each site who were trained to use a standardised cost collection tool. This approach both accommodated our resource constraints and served as a knowledge sharing and capacity building process within the research teams.

conclusionsGiven the practical reality of conducting randomised, controlled trials of public health interventions in low- and middle- income countries, it is not always possible to adhere to prescribed guidelines for the analysis of cost effectiveness. Compromises are frequently required as researchers seek a pragmatic balance between rigor and feasibility. There is no single solution to this tension but researchers are encouraged to be mindful of the limitations that accompany compromise, whilst being reassured that meaningful analyses can still be conducted with the resulting data.

Indexed as

Community-Based Participatory ResearchCost-Benefit AnalysisCosts and Cost AnalysisData CollectionDeveloping CountriesHumansMulticenter Studies as TopicPublic HealthRandomized Controlled Trials as Topiccost datacost-effectiveness analysisLMICmultisiterandomised control trials

Identifiers

PMID24565214
PMCPMC3929994

What OpenQuestion holds

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LicenceCC BY
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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.