Evidence map›Paper›PMID 24038008›Full record

ArticleBMJ open2013

Representativeness and optimal use of body mass index (BMI) in the UK Clinical Practice Research Datalink (CPRD).

Krishnan Bhaskaran, Harriet J Forbes, Ian Douglas, David A Leon, Liam Smeeth

Abstract read
In one paragraph

Article in BMJ open, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
101citing papers in PubMed, 2 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

101 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Weight loss as a predictor of cancer in primary care: a systematic review and meta-analysis.The British journal of general practice : the journal of the Royal College of General Practitioners · 2018
    Pooled it
  2. Epidemiology of adult overweight recording and management by UK GPs: a systematic review.The British journal of general practice : the journal of the Royal College of General Practitioners · 2017
    Pooled it
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41 more citing papers are in PubMed but not listed here.

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.

Krishnan BhaskaranFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Harriet J Forbes
Ian Douglas
David A Leon
Liam Smeeth

Funding

Department of Health PDF-2011-04-007Medical Research Council G0802403Medical Research Council MR/K006584/1Wellcome Trust 098504
6 · The paper itself

Abstract

objectivesTo assess the completeness and representativeness of body mass index (BMI) data in the Clinical Practice Research Datalink (CPRD), and determine an optimal strategy for their use.

designDescriptive study.

settingElectronic healthcare records from primary care.

participantsA million patient random sample from the UK CPRD primary care database, aged ≥16 years. PRIMARY AND SECONDARY OUTCOME MEASURES: BMI completeness in CPRD was evaluated by age, sex and calendar period. CPRD-based summary BMI statistics for each calendar year (2003-2010) were age-standardised and sex-standardised and compared with equivalent statistics from the Health Survey for England (HSE).

resultsBMI completeness increased over calendar time from 37% in 1990-1994 to 77% in 2005-2011, was higher among females and increased with age. When BMI at specific time points was assigned based on the most recent record, calendar-year-specific mean BMI statistics underestimated equivalent HSE statistics by 0.75-1.1 kg/m(2). Restriction to those with a recent (≤3 years) BMI resulted in mean BMI estimates closer to HSE (≤0.28 kg/m(2) underestimation), but excluded up to 47% of patients. An alternative strategy of imputing up-to-date BMI based on modelled changes in BMI over time since the last available record also led to mean BMI estimates that were close to HSE (≤0.37 kg/m(2) underestimation).

conclusionsCompleteness of BMI in CPRD increased over time and varied by age and sex. At a given point in time, a large proportion of the most recent BMIs are unlikely to reflect current BMI; consequent BMI misclassification might be reduced by employing model-based imputation of current BMI.

Indexed as

EpidemiologyPrimary CareStatistics & Research Methods

Identifiers

PMID24038008
PMCPMC3773634

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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.