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Smoking-related cancer death among men and women in an ageing society (China 2020–2040): a population-based modelling study

Abstract

Background China is experiencing a postpeak smoking epidemic with accelerating population ageing. Understanding the impacts of these factors on the future cancer burden has widespread implications.

Methods We developed predictive models to estimate smoking-related cancer deaths among men and women aged ≥35 years in China during 2020–2040. Data sources for model parameters included the United Nations World Population Prospects, China Death Surveillance Database, national adult tobacco surveys and the largest national survey of smoking and all causes of death to date. The main assumptions included stable sex-specific and age-specific cancer mortality rates and carcinogenic risks of smoking over time.

Results In a base-case scenario of continuing trends in current smoking prevalence (men: 57.4%–50.5%; women: 2.6%–2.1% during 2002–2018), the smoking-related cancer mortality rate with population ageing during 2020–2040 would rise by 44.0% (from 337.2/100 000 to 485.6/100 000) among men and 52.8% (from 157.3/100 000 to 240.4/100 000) among women; over 20 years, there would be 8.6 million excess deaths (0.5 million more considering former smoking), and a total of 117.3 million smoking-attributable years of life lost (110.3 million (94.0%) in men; 54.1 million (46.1%) in working-age (35–64 years) adults). An inflection point may occur in 2030 if smoking prevalence were reduced to 20% (Healthy China 2030 goal), and 1.4 million deaths would be averted relative to the base-case scenario if the trend were maintained through 2040.

Conclusions Coordinated efforts are urgently needed to curtail a rising tide of cancer deaths in China, with intensified tobacco control being key.

  • health services
  • prevention
  • public opinion
  • smoking caused disease
  • surveillance and monitoring

Data availability statement

Data are available on reasonable request. Data supporting this manuscript are available to anyone who wishes to access the data from correspondence to jingmeijiang@ibms.pumc.edu.cn on reasonable request, following publication.

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