Evidence map›Paper›PMID 25649312›Full record

ArticleBMC family practice2015

Missed opportunities: general practitioner identification of their patients' smoking status.

Jamie Bryant, Mariko Carey, Rob Sanson-Fisher, Elise Mansfield, Tim Regan, Alessandra Bisquera

Abstract readMulticenter Study
In one paragraph

Article in BMC family practice, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Observational
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

6 authors.

Jamie BryantHealth Behaviour Research Group, Priority Research Centre for Health Behaviour, University of Newcastle & Hunter Medical Research Institute, HMRI Building, Callaghan, NSW, Australia. Jamie.bryant@newcastle.edu.au.
Mariko CareyHealth Behaviour Research Group, Priority Research Centre for Health Behaviour, University of Newcastle & Hunter Medical Research Institute, HMRI Building, Callaghan, NSW, Australia. Mariko.carey@newcastle.edu.au.
Rob Sanson-FisherHealth Behaviour Research Group, Priority Research Centre for Health Behaviour, University of Newcastle & Hunter Medical Research Institute, HMRI Building, Callaghan, NSW, Australia. Rob.Sanson-Fisher@newcastle.edu.au.
Elise MansfieldHealth Behaviour Research Group, Priority Research Centre for Health Behaviour, University of Newcastle & Hunter Medical Research Institute, HMRI Building, Callaghan, NSW, Australia. elise.mansfield@newcastle.edu.au.
Tim ReganHealth Behaviour Research Group, Priority Research Centre for Health Behaviour, University of Newcastle & Hunter Medical Research Institute, HMRI Building, Callaghan, NSW, Australia. tim.regan@newcastle.edu.au.
Alessandra BisqueraClinical Research Design IT and Statistical Support Unit, Hunter Medical Research Institute, University of Newcastle, HMRI Building, Callaghan, NSW, 2308, Australia. Alessandra.Bisquera@newcastle.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn order to provide smoking cessation support to their patients in line with clinical practice guidelines, general practitioners must first ascertain whether their patients' use tobacco. This study examined (i) the sensitivity, specificity, positive predictive value and negative predictive value of general practitioner detection of smoking, and (ii) the general practitioner and patient characteristics associated with detection of tobacco use.

methodsEligible patients completed a touchscreen computer survey while waiting for an appointment with their general practitioner. Patients self-reported demographic characteristics, medical history, and current smoking status. Following the patient's consultation, their general practitioner was asked to indicate whether the patient was a current smoker (yes/no/unsure/not applicable). Smoking prevalence, sensitivity, specificity, positive predictive value and negative predictive values (with 95% confidence intervals) were calculated using patient self-report of smoking status as the gold standard. Generalised estimating equations were used to examine the general practitioner and patient characteristics associated with detection of tobacco use.

resultsFifty-one general practitioners and 1,573 patients in twelve general practices participated. Patient self-report of smoking was 11.3% compared to general practitioner estimated prevalence of 9.5%. Sensitivity of general practitioner assessment was 66% [95% CI 59-73] while specificity was 98% [95% CI 97-98]. Positive predictive value was 78% [95% CI 71-85] and negative predictive value was 96% [95% CI 95-97]. No general practitioner factors were associated with detection of smoking. Patients with a higher level of education or who responded 'Other' were less likely to be detected as smokers than patients who had completed a high school or below level of education.

conclusionDespite the important role general practitioners play in providing smoking cessation advice and support, a substantial proportion of general practitioners do not know their patient's smoking status. This represents a significant missed opportunity in the provision of preventive healthcare. Electronic waiting room assessments may assist general practitioners in improving the identification of smokers.

Indexed as

Self ReportAdultCross-Sectional StudiesEducational StatusFemaleGeneral PractitionersHealth SurveysHumansMaleMiddle AgedPrevalenceSmokingSmoking Cessation

Identifiers

PMID25649312
PMCPMC4333157

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.