Evidence map›Paper›PMID 32903709›Full record

ReviewFrontiers in endocrinology2020

The Challenges in the Development of Diabetes Prevention and Care Models in Low-Income Settings.

Feneli Karachaliou, George Simatos, Aristofania Simatou

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in endocrinology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 3 pooled it
8.7field-weighted citation impact, top 2% 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

57 citing papers in PubMed, 3 syntheses or guidelines pooled it, 99 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Feneli KarachaliouUnit of Endocrinology, Diabetes and Metabolism, 3rd University Pediatric Department, Attikon University Hospital, Athens, Greece.
George SimatosDepartment of Breast Surgery, Agios Savvas Hospital, Athens, Greece.
Aristofania SimatouUnit of Endocrinology, Diabetes and Metabolism, 3rd University Pediatric Department, Attikon University Hospital, Athens, Greece.
University General Hospital Attikon · GRSt Savas Hospital · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In low- and middle-income countries (LMICs), the burden of non-communicable diseases such as diabetes is rapidly rising, overpassing the existing burden of communicable diseases. Patients with diabetes living in low-income communities face unique challenges related to lack of awareness, difficulty in accessing health care systems and medications, and consequently failure in achieving optimal diabetes management and preventing complications. Effective diabetes prevention and care models could help reduce the rising burden by standardizing guidelines for prevention and management, improving access to care, engaging community and peers, improving the training of professionals and patients and using the newest technology in the management of the disease. In this article, we review the latest research and evidence on effective models of diabetes prevention and diabetes care delivery in low- income settings. We also provide existing evidence relating to the effectiveness of these models in low-resource contexts, with the aim to highlight characteristics and strengths that make their implementation successful and long-lasting.

Indexed as

Models, TheoreticalPovertyDelivery of Health CareDiabetes MellitusDisease ManagementHumansdiabetes managementdiabetes mellitusdiabetes preventionhealth care modelslow and middle income

Identifiers

PMID32903709
PMCPMC7438784
OpenAlexW3049301313

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.