Evidence map›Paper›PMID 29392352›Full record

ReviewDiabetologia2018

Epidemiology of diabetes and diabetic complications in China.

Ronald C W Ma

Erratum issuedOpen access · bronzeAbstract readReview
PubMed Publisher
In one paragraph

Review in Diabetologia, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 300 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
300citing papers in PubMed, 6 pooled it
24.2field-weighted citation impact, top 1% 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

300 citing papers in PubMed, 6 syntheses or guidelines pooled it, 498 citations in OpenAlex.

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240 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author at 1 institution in 2 countries.

Ronald C W MaDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, 30-32 Ngan Shing Street, Shatin, New Territories, Hong Kong, Hong Kong Special Administrative Region, People's Republic of China. rcwma@cuhk.edu.hk.
Prince of Wales Hospital · CN

Funding

Health and Medical Research Fund 13140761Research Grants Council, University Grants Committee Theme-based Research Scheme (T12-402/13N)The Chinese University of Hong Kong Vice-Chancellor One-off Discretionary Fund
6 · The paper itself

Abstract

The People's Republic of China (herein referred to as China) has witnessed one of the most dramatic rises in diabetes prevalence anywhere in the world. The latest epidemiological study suggests that approximately 11% of the population has diabetes, with a significant proportion remaining undiagnosed. Risk factors for diabetes in the Chinese population are similar to those in other populations, though gestational diabetes and young-onset diabetes is becoming increasingly common. Data on the prevalence of diabetic complications remain limited, though cardio-renal complications account for significant morbidity and mortality. Other diabetes-related comorbidities are becoming increasingly common, with cancer emerging as a major cause of mortality among individuals with diabetes. There are many challenges and obstacles that impede effective diabetes prevention and the delivery of care, though much progress has occurred over recent years. Lessons learnt from how China has responded to the challenges posed by the diabetes epidemic will be invaluable for other countries facing the many threats of diabetes and its complications.

Indexed as

AdultAgedChinaDiabetes ComplicationsDiabetes, GestationalDiabetes MellitusFemaleHumansMaleMiddle AgedObesityPregnancyPrevalenceRisk FactorsYoung AdultChinaCoronary heart diseaseDiabetesDiabetic kidney diseaseEpidemiologyGeneticsGestational diabetesObesityReviewRisk factors

Identifiers

PMID29392352
OpenAlexW2793289663

What OpenQuestion holds

Textmetadata
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.