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Clearing the air: underestimation of youth smoking prevalence associated with proxy-reporting compared to youth
Eden M Barrett1, Raglan Maddox2, Joanne Thandrayen2
1National Centre for Epidemiology and Population Health, Australian National University, 54 Mills Road, Acton ACT 2601, Australia. Eden.barrett@anu.edu.au.
Background:
Smoking remains a leading cause of disease burden globally. Declining youth smoking prevalence is an essential feature of effective tobacco control; however, accurate data are required to assess progress. This study investigates bias in youth smoking prevalence estimates by respondent type (proxy-reported, self-report with parent present, or self-report independently) for Aboriginal and Torres Strait Islander and total populations of Australia.
Methods:
Repeated cross-sectional analysis of representative Aboriginal and Torres Strait Islander Health and National Health Surveys, 2007-2019. Data were restricted to participants aged 15-17 years. Prevalence ratios (PR) and 95% Confidence Intervals (CI) for ever-smoking by respondent type were calculated using Poisson regression with robust standard errors. National youth current-smoking prevalence was estimated if all data were collected by youth self-report; estimates and trends were compared to observed estimates.
Results:
Over 75% of all smoking status data were reported by proxy or with parent present. Ever-smoking prevalence among youth self-reporting independently versus proxy-reported was 1.29 (95% CI:0.96-1.73) to 1.99 (95% CI:1.39-2.85) times as high for Aboriginal and Torres Strait Islander youth, and 1.83 (95% CI:0.92-3.63) to 2.72 (95% CI:1.68-4.41) times as high for total population youth. Across surveys, predicted national current-smoking prevalence if all youth self-reported independently was generally higher than observed estimate.
Conclusions:
Estimates of youth smoking prevalence are likely inaccurate and underestimated if data are collected by proxy or with parent present. Increased reliance on data reported by youth independently is crucial to improve data accuracy, including to enable accurate assessment of national prevalence.
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