Evidence map›Paper›PMID 35904279›Full record

ArticleHealth policy and planning2022

Complexity and evidence in health sector decision-making: lessons from tuberculosis infection prevention in South Africa.

Shehani Perera, Justin Parkhurst, Karin Diaconu, Fiammetta Bozzani, Anna Vassall, Alison Grant, Karina Kielmann

Open access · hybridAbstract read
In one paragraph

Article in Health policy and planning, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 32% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 5 institutions in 2 countries.

Shehani PereraDivision of Social and Behavioural Sciences, School of Public Health and Family Medicine, University of Cape Town, Falmouth Road, Observatory 7925, Cape Town, South Africa.ORCID 0000-0003-0165-7998
Justin ParkhurstDepartment of Health Policy, London School of Economics and Political Science, Houghton Street, London WC2A 2AE, England, United Kingdom.ORCID 0000-0003-0831-6213
Karin DiaconuInstitute for Global Health and Development, School of Health Sciences, Queen Margaret University, Musselburgh EH21 6UU, Edinburgh, UK.ORCID 0000-0002-5810-9725
Fiammetta BozzaniDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, England, UK.ORCID 0000-0002-9518-6885
Anna VassallCentre for Health Economics in London, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, England, UK.
Alison GrantTB Centre, London School of Hygiene & Tropical Medicine, Keppel St, London WC1E 7HT, England, UK.ORCID 0000-0002-2437-5195
Karina Kielmann
Queen Margaret University · GBUniversity of London · GBLondon School of Economics and Political Science · GBLondon School of Hygiene & Tropical Medicine · GBUniversity of Cape Town · ZA

Funding

Building Research in Inter-Disciplinary Gender and HIV through the Social Sciences (The BRIDGES Programme)D43TW011308 · FIC · UNIVERSITY OF CAPE TOWN · PI COLVIN, CHRISTOPHER JAMES · 2019 to 2023
$1.7M
Economic and Social Research Council ES/P008011/1FIC NIH HHS D43 TW011308Research England's Connecting Capabilities Fund, Bloomsbury SET
6 · The paper itself

Abstract

To better understand and plan health systems featuring multiple levels and complex causal elements, there have been increasing attempts to incorporate tools arising from complexity science to inform decisions. The utilization of new planning approaches can have important implications for the types of evidence that inform health policymaking and the mechanisms through which they do so. This paper presents an empirical analysis of the application of one such tool-system dynamics modelling (SDM)-within a tuberculosis control programme in South Africa in order to explore how SDM was utilized, and to reflect on the implications for evidence-informed health policymaking. We observed group model building workshops that served to develop the SDM process and undertook 19 qualitative interviews with policymakers and practitioners who partook in these workshops. We analysed the relationship between the SDM process and the use of evidence for policymaking through four conceptual perspectives: (1) a rationalist knowledge-translation view that considers how previously-generated research can be taken up into policy; (2) a programmatic approach that considers existing goals and tasks of decision-makers, and how evidence might address them; (3) a social constructivist lens exploring how the process of using an evidentiary planning tool like SDM can shape the understanding of problems and their solutions; and (4) a normative perspective that recognizes that stakeholders may have different priorities, and thus considers which groups are included and represented in the process. Each perspective can provide useful insights into the SDM process and the political nature of evidence use. In particular, SDM can provide technical information to solve problems, potentially leave out other concerns and influence how problems are conceptualized by formalizing the boundaries of the policy problem and delineating particular solution sets. Undertaking the process further involves choices on stakeholder inclusion affecting whose interests may be served as evidence to inform decisions.

Indexed as

Health PolicyTuberculosisHumansPolicy MakingSouth Africaevidence-based researchhealth policySouth AfricaSystem dynamics modellingtuberculosis

Identifiers

PMID35904279
PMCPMC9557355
OpenAlexW4288691636

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.