Evidence map›Paper›PMID 25854780›Full record

ArticleGlobal health action2015

Referral outcomes of individuals identified at high risk of cardiovascular disease by community health workers in Bangladesh, Guatemala, Mexico, and South Africa.

Naomi S Levitt, Thandi Puoane, Catalina A Denman, Shafika Abrahams-Gessel, Sam Surka, Carlos Mendoza, Masuma Khanam, Sartaj Alam, Thomas A Gaziano

Open access · goldAbstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Global health action, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
3.3field-weighted citation impact, top 8% 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

28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 46 citations in OpenAlex.

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

9 authors at 6 institutions in 6 countries.

Naomi S LevittChronic Disease Initiative for Africa, Cape Town, South Africa.
Thandi PuoaneChronic Disease Initiative for Africa, Cape Town, South Africa.
Catalina A DenmanCentro de Estudios en Salud y Sociedad, El Colegio de Sonora, Mexico.
Shafika Abrahams-GesselBrigham & Women's Hospital, Harvard School of Public Health, Harvard University, Cambridge, MA, USA.
Sam SurkaChronic Disease Initiative for Africa, Cape Town, South Africa; samsurka@gmail.com.
Carlos MendozaInstitute of Nutrition of Central America and Panama (INCAP), Ciudad de Guatemala, Guatemala.
Masuma KhanamSchool of Medicine and Public Health, University of Newcastle, Callaghan, Australia.
Sartaj AlamBrigham & Women's Hospital, Harvard School of Public Health, Harvard University, Cambridge, MA, USA.
Thomas A GazianoBrigham & Women's Hospital, Harvard School of Public Health, Harvard University, Cambridge, MA, USA.
Brigham and Women's Hospital · USSouth African Tuberculosis Vaccine Initiative · ZAEl Colegio de Sonora · MXInstitute of Nutrition of Central America and Panama · GTUniversity of Newcastle Australia · AUUniversity of the Western Cape · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe have found that community health workers (CHWs) with appropriate training are able to accurately identify people at high cardiovascular disease (CVD) risk in the community who would benefit from the introduction of preventative management, in Bangladesh, Guatemala, Mexico, and South Africa. This paper examines the attendance pattern for those individuals who were so identified and referred to a health care facility for further assessment and management.

designPatient records from the health centres in each site were reviewed for data on diagnoses made and treatment commenced. Reasons for non-attendance were sought from participants who had not attended after being referred. Qualitative data were collected from study coordinators regarding their experiences in obtaining the records and conducting the record reviews. The perspectives of CHWs and community members, who were screened, were also obtained.

resultsThirty-seven percent (96/263) of those referred attended follow-up: 36 of 52 (69%) were urgent and 60 of 211 (28.4%) were non-urgent referrals. A diagnosis of hypertension (HTN) was made in 69% of urgent referrals and 37% of non-urgent referrals with treatment instituted in all cases. Reasons for non-attendance included limited self-perception of risk, associated costs, health system obstacles, and lack of trust in CHWs to conduct CVD risk assessments and to refer community members into the health system.

conclusionsThe existing barriers to referral in the health care systems negatively impact the gains to be had through screening by training CHWs in the use of a simple risk assessment tool. The new diagnoses of HTN and commencement on treatment in those that attended referrals underscores the value of having persons at the highest risk identified in the community setting and referred to a clinic for further evaluation and treatment.

Indexed as

BangladeshCardiovascular DiseasesCommunity Health WorkersFemaleGuatemalaHumansHypertensionMaleMexicoPatient Acceptance of Health CarePovertyProgram EvaluationReferral and ConsultationRisk AssessmentSouth AfricaBangladeshcardiovascular risk assessmentcommunity health workersGuatemalalow-middle income countriesMexicoreferral outcomesSouth Africa

Identifiers

PMID25854780
PMCPMC4390559
OpenAlexW2067669784

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.