J A Usher-Smith1, A P Kassianos2, J D Emery3, G A Abel1, Z Teoh4, S Hall5, R D Neal6, P Murchie5, F M Walter1. 1. Primary Care Unit, Department of Public Health & Primary Care, University of Cambridge School of Clinical Medicine, Strangeways Research Laboratory, Cambridge, CB1 8RN, U.K. 2. Department of Applied Health Research, University College London, London, U.K. 3. Department of General Practice, Melbourne Medical School Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Carlton, Victoria, Australia. 4. Betsi Cadwaladr University Health Board, Wrexham Maelor Hospital, Wrexham, U.K. 5. Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, U.K. 6. North Wales Centre for Primary Care Research, Bangor University, Wrexham, U.K.
Abstract
BACKGROUND: Melanoma incidence is rising rapidly worldwide among white populations. Defining higher-risk populations using risk prediction models may help targeted screening and early detection approaches. OBJECTIVES: To assess the feasibility of identifying people at higher risk of melanoma using the Williams self-assessed clinical risk estimation model in U.K. primary care. METHODS: We recruited participants from the waiting rooms of 22 general practices covering a total population of > 240 000 in three U.K. regions: Eastern England, North East Scotland and North Wales. Participants completed an electronic questionnaire using tablet computers. The main outcome was the mean melanoma risk score using the Williams melanoma risk model. RESULTS: Of 9004 people approached, 7742 (86%) completed the electronic questionnaire. The mean melanoma risk score for the 7566 eligible participants was 17·15 ± 8·51, with small regional differences [lower in England compared with Scotland (P = 0·001) and Wales (P < 0·001), mainly due to greater freckling and childhood sunburn among Scottish and Welsh participants]. After weighting to the age and sex distribution, different potential cut-offs would allow between 4% and 20% of the population to be identified as higher risk, and those groups would contain 30% and 60%, respectively of those likely to develop melanoma. CONCLUSIONS: Collecting data on the melanoma risk profile of the general population in U.K. primary care is both feasible and acceptable for patients in a general practice setting, and provides opportunities for new methods of real-time risk assessment and risk stratified cancer interventions.
BACKGROUND:Melanoma incidence is rising rapidly worldwide among white populations. Defining higher-risk populations using risk prediction models may help targeted screening and early detection approaches. OBJECTIVES: To assess the feasibility of identifying people at higher risk of melanoma using the Williams self-assessed clinical risk estimation model in U.K. primary care. METHODS: We recruited participants from the waiting rooms of 22 general practices covering a total population of > 240 000 in three U.K. regions: Eastern England, North East Scotland and North Wales. Participants completed an electronic questionnaire using tablet computers. The main outcome was the mean melanoma risk score using the Williams melanoma risk model. RESULTS: Of 9004 people approached, 7742 (86%) completed the electronic questionnaire. The mean melanoma risk score for the 7566 eligible participants was 17·15 ± 8·51, with small regional differences [lower in England compared with Scotland (P = 0·001) and Wales (P < 0·001), mainly due to greater freckling and childhood sunburn among Scottish and Welsh participants]. After weighting to the age and sex distribution, different potential cut-offs would allow between 4% and 20% of the population to be identified as higher risk, and those groups would contain 30% and 60%, respectively of those likely to develop melanoma. CONCLUSIONS: Collecting data on the melanoma risk profile of the general population in U.K. primary care is both feasible and acceptable for patients in a general practice setting, and provides opportunities for new methods of real-time risk assessment and risk stratified cancer interventions.
Authors: Katie Mills; Jon Emery; Rebecca Lantaff; Michael Radford; Merel Pannebakker; Per Hall; Nigel Burrows; Kate Williams; Catherine L Saunders; Peter Murchie; Fiona M Walter Journal: BMJ Open Date: 2017-11-28 Impact factor: 2.692
Authors: Fiona M Walter; Merel M Pannebakker; Matthew E Barclay; Katie Mills; Catherine L Saunders; Peter Murchie; Pippa Corrie; Per Hall; Nigel Burrows; Jon D Emery Journal: JAMA Netw Open Date: 2020-02-05
Authors: Annkathrin Hornung; Theresa Steeb; Anja Wessely; Titus J Brinker; Thomas Breakell; Michael Erdmann; Carola Berking; Markus V Heppt Journal: Int J Environ Res Public Health Date: 2021-02-10 Impact factor: 3.390