Evidence map›Paper›PMID 27895928›Full record

ArticleJRSM open2016

Assessing the population impact of low rates of vitamin D supplementation on type 1 diabetes using a new statistical method.

Christos S Zipitis, Zulf M Mughal, Peter E Clayton

Open access · goldAbstract read
In one paragraph

Article in JRSM open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
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.

Christos S ZipitisThe University of Manchester, Manchester Academic Health Science Centre, Department of Paediatrics, Wrightington Wigan & Leigh NHS Foundation Trust, Wigan Lane, Wigan WN1 2NN, UK.
Zulf M MughalThe University of Manchester, Manchester Academic Health Science Centre, Department of Paediatric Endocrinology, Central Manchester University Hospitals NHS Trust, Oxford Road, Manchester M13 9WL, UK.
Peter E ClaytonThe University of Manchester, Manchester Academic Health Science Centre, Department of Paediatric Endocrinology, Central Manchester University Hospitals NHS Trust, Oxford Road, Manchester M13 9WL, UK.
University of Manchester · GBWrightington, Wigan and Leigh NHS Foundation Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vitamin D supplementation for all children <5 is recommended by the UK Department of Health for its skeletal effects. Vitamin D is also linked with a number of extra-skeletal effects; one of them being protection against type 1 diabetes. With a rapid increase in the incidence of type 1 diabetes and the associated costs, measures of curtailing the rapid increase of type 1 diabetes are needed. In this review, we look at type 1 diabetes using a statistical method (PIN-ER-t) and published data in an attempt to quantify the impact on the population of babies born in 2012 of increasing vitamin D supplementation rates. Calculations show that for the population of 729,674 babies born in England and Wales in 2012, 374 cases of type 1 diabetes (out of 1357 total predicted) could be prevented over 18 years if all were supplemented with vitamin D. This could lead to savings in excess of £62 million for the cohort. This piece of work adds to the argument for studying the potential link between vitamin D supplementation and type 1 diabetes further.

Indexed as

other statistics and research methodspaediatricspublic healthvitamins and supplements

Identifiers

PMID27895928
PMCPMC5117162
OpenAlexW2548850157

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.