Evidence map›Paper›PMID 12835444›Full record

ArticleJournal of the Royal Society of Medicine2003

Hidden diabetes in the UK: use of capture-recapture methods to estimate total prevalence of diabetes mellitus in an urban population.

Geoffrey V Gill, Aziz A Ismail, Nicholas J Beeching, Sarah B J Macfarlane, Mark A Bellis

Abstract read
In one paragraph

Article in Journal of the Royal Society of Medicine, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. 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.

Geoffrey V GillDivision of Tropical Medicine, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK. g.gill@liv.ac.uk
Aziz A Ismail
Nicholas J Beeching
Sarah B J Macfarlane
Mark A Bellis

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An early requirement of the UK's Diabetes National Service Framework is enumeration of the total affected population. Existing estimates tend to be based on incomplete lists. In a study conducted over one year in North Liverpool, we compared crude prevalence rates for type 1 and type 2 diabetes with estimates obtained by capture-recapture (CR) analysis of multiple incomplete patient lists, to assess the extent of unascertained but diagnosed cases. Patient databases were constructed from six sources-a hospital diabetes centre; general practitioner registers; hospital admissions with a diagnosis of diabetes; a hospital diabetic retinal clinic; a research list of patients with diabetes admitted with stroke; and a local children's hospital. Log linear modelling was used to estimate missing cases, hence total prevalence. The crude prevalence of diabetes was 1.5% (95% confidence interval [CI] 1.41, 1.52), compared with a CR-adjusted rate of 3.1% (CI 3.03, 3.19). Age-banded CR-adjusted prevalence was always higher in males than in females and the difference became more pronounced with increasing age. Among males, CR-adjusted prevalence rose from 0.4% at age 10-19 years to 18.3% at 80+ years; in females the corresponding figures were 0.4% and 9.3%. The gap between crude and CR-estimated prevalence points to a rate of 'hidden diabetes' that has substantial implications for future diabetes care.

Indexed as

Urban HealthAdolescentAdultAgedAged, 80 and overAge DistributionChildChild, PreschoolDiabetes MellitusEnglandEpidemiologic MethodsFemaleHumansInfantInfant, NewbornMale

Identifiers

PMID12835444
PMCPMC539535

What OpenQuestion holds

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Registered trials

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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.