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Missed opportunities: general practitioner identification of their patients’ smoking status
BACKGROUND: In 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 predicti...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333157/ https://www.ncbi.nlm.nih.gov/pubmed/25649312 http://dx.doi.org/10.1186/s12875-015-0228-7 |
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author | Bryant, Jamie Carey, Mariko Sanson-Fisher, Rob Mansfield, Elise Regan, Tim Bisquera, Alessandra |
author_facet | Bryant, Jamie Carey, Mariko Sanson-Fisher, Rob Mansfield, Elise Regan, Tim Bisquera, Alessandra |
author_sort | Bryant, Jamie |
collection | PubMed |
description | BACKGROUND: In 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. METHODS: Eligible 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. RESULTS: Fifty-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. CONCLUSION: Despite 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. |
format | Online Article Text |
id | pubmed-4333157 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43331572015-02-20 Missed opportunities: general practitioner identification of their patients’ smoking status Bryant, Jamie Carey, Mariko Sanson-Fisher, Rob Mansfield, Elise Regan, Tim Bisquera, Alessandra BMC Fam Pract Research Article BACKGROUND: In 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. METHODS: Eligible 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. RESULTS: Fifty-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. CONCLUSION: Despite 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. BioMed Central 2015-02-04 /pmc/articles/PMC4333157/ /pubmed/25649312 http://dx.doi.org/10.1186/s12875-015-0228-7 Text en © Bryant et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Bryant, Jamie Carey, Mariko Sanson-Fisher, Rob Mansfield, Elise Regan, Tim Bisquera, Alessandra Missed opportunities: general practitioner identification of their patients’ smoking status |
title | Missed opportunities: general practitioner identification of their patients’ smoking status |
title_full | Missed opportunities: general practitioner identification of their patients’ smoking status |
title_fullStr | Missed opportunities: general practitioner identification of their patients’ smoking status |
title_full_unstemmed | Missed opportunities: general practitioner identification of their patients’ smoking status |
title_short | Missed opportunities: general practitioner identification of their patients’ smoking status |
title_sort | missed opportunities: general practitioner identification of their patients’ smoking status |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333157/ https://www.ncbi.nlm.nih.gov/pubmed/25649312 http://dx.doi.org/10.1186/s12875-015-0228-7 |
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