Evidence map›Paper›PMID 33298098›Full record

ArticleImplementation science : IS2020

Barriers and facilitators of implementation of a community cardiovascular disease prevention programme in Mukono and Buikwe districts in Uganda using the Consolidated Framework for Implementation Research.

Rawlance Ndejjo, Rhoda K Wanyenze, Fred Nuwaha, Hilde Bastiaens, Geofrey Musinguzi

Abstract read
In one paragraph

Article in Implementation science : IS, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Review
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

5 authors.

Rawlance NdejjoDepartment of Disease Control and Environmental Health, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda. rndejjo@musph.ac.ug.ORCID 0000-0001-9263-557X
Rhoda K WanyenzeDepartment of Disease Control and Environmental Health, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda.
Fred NuwahaDepartment of Disease Control and Environmental Health, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda.
Hilde BastiaensDepartment of Primary and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
Geofrey MusinguziDepartment of Disease Control and Environmental Health, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn low- and middle-income countries, there is an increasing attention towards community approaches to deal with the growing burden of cardiovascular disease (CVD). However, few studies have explored the implementation processes of such interventions to inform their scale up and sustainability. Using the consolidated framework for implementation research (CFIR), we examined the barriers and facilitators influencing the implementation of a community CVD programme led by community health workers (CHWs) in Mukono and Buikwe districts in Uganda.

methodsThis qualitative study is a process evaluation of an ongoing type II hybrid stepped wedge cluster trial guided by the CFIR. Data for this analysis were collected through regular meetings and focus group discussions (FGDs) conducted during the first cycle (6 months) of intervention implementation. A total of 20 CHWs participated in the implementation programme in 20 villages during the first cycle. Meeting reports and FGD transcripts were analysed following inductive thematic analysis with the aid of Nvivo 12.6 to generate emerging themes and sub-themes and thereafter deductive analysis was used to map themes and sub-themes onto the CFIR domains and constructs.

resultsThe barriers to intervention implementation were the complexity of the intervention (complexity), compatibility with community culture (culture), the lack of an enabling environment for behaviour change (patient needs and resources) and mistrust of CHWs by community members (relative priority). In addition, the low community awareness of CVD (tension for change), competing demands (other personal attributes) and unfavourable policies (external policy and incentives) impeded intervention implementation. On the other hand, facilitators of intervention implementation were availability of inputs and protective equipment (design quality and packaging), training of CHWs (Available resources), working with community structures including leaders and groups (process-opinion leaders), frequent support supervision and engagements (process-formally appointed internal implementation leaders) and access to quality health services (process-champions).

conclusionUsing the CFIR, we identified drivers of implementation success or failure for a community CVD prevention programme in a low-income context. These findings are key to inform the design of impactful, scalable and sustainable CHW programmes for non-communicable diseases prevention and control.

Indexed as

Cardiovascular DiseasesCommunity Health WorkersDelivery of Health CareHumansQualitative ResearchUgandaAdoptionCardiovascular diseaseCommunity health workersImplementation

Identifiers

PMID33298098
PMCPMC7726905

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.