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Cancer Epidemiol. 2017 Aug;49:19-23. doi: 10.1016/j.canep.2017.04.012. Epub 2017 May 18.

Modelling lung cancer mortality rates from smoking prevalence: Fill in the gap.

Author information

1
Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain.
2
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Surgery, Medical and Social Sciences, Universidad de Alcalá, Madrid, Spain.
3
Plan for Oncology of the Catalan Government, L'Hospitalet de Llobregat, Spain; Department of Clinical Sciences, School of Medicine, Universitat de Barcelona, Barcelona, Spain.
4
Department of Clinical Sciences, School of Medicine, Universitat de Barcelona, Barcelona, Spain; Tobacco Control Unit, Cancer Prevention and Control Programme, Catalan Institute of Oncology-ICO, L'Hospitalet de Llobregat, Spain; Cancer Control and Prevention Group, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Spain.
5
Centre Atenció Primària Les Corts, Transverse Group for Research in Primary Care, IDIBAPS, Barcelona, Spain.
6
Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain; Tobacco Control Unit, Cancer Prevention and Control Programme, Catalan Institute of Oncology-ICO, L'Hospitalet de Llobregat, Spain; Cancer Control and Prevention Group, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Spain. Electronic address: jmmartinez@uic.es.

Abstract

BACKGROUND:

The objective of this study is to estimate the gap between smoking prevalence and lung cancer mortality and provide predictions of lung cancer mortality based on previous smoking prevalence.

MATERIALS AND METHODS:

We used data from the Spanish National Health Surveys (2003, 2006 and 2011) to obtain information about tobacco use and data from the Spanish National Statistics Institute to obtain cancer mortality rates from 1980 to 2013. We calculated the cross-correlation among the historical series of smoking prevalence and lung cancer mortality rate (LCMR) to estimate the most likely time gap between both series. We also predicted the magnitude and timing of the LCMR peak.

RESULTS:

All cross-correlations were statistically significant and positive (all above 0.8). For men, the most likely gap ranges from 20 to 34 years. The age-adjusted LCMR increased by 3.2 deaths per 100,000 people for every 1 unit increase in the smoking prevalence 29 years earlier. The highest rate for men was observed in 1995 (55.6 deaths). For women, the most likely gap ranges from 10 to 37 years. The age-adjusted LCMR increased by 0.28 deaths per 100,000 people for every 1 unit increase in the smoking prevalence 32 years earlier. The maximum rate is expected to occur in 2026 (10.3 deaths).

CONCLUSION:

The time series of prevalence of tobacco smoking explains the mortality from lung cancer with a distance (or gap) of around 30 years. According to the lagged smoking prevalence, the lung cancer mortality among men is declining while in women continues to rise (maximum expected in 2026).

KEYWORDS:

Cross-correlation; Gap; Lung cancer; Prediction; Smoking; Time-series

PMID:
28528290
DOI:
10.1016/j.canep.2017.04.012
[Indexed for MEDLINE]

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