How Well Have Projected Lung Cancer Rates Predicted the Actual Observed Rates?

Asian Pac J Cancer Prev. 2021 Feb 1;22(2):437-445. doi: 10.31557/APJCP.2021.22.2.437.

Abstract

Background: While many past studies have constructed projections of future lung cancer rates, little is known about their consistency with the corresponding observed data for the time period covered by the projections. The aim of this study was to assess the agreement between previously published lung cancer incidence and/or mortality rate projections and observed rates.

Methods: Published studies were included in the current study if they projected future lung cancer rates for at least 10 years beyond the period for which rates were used to obtain the projections, and if more recent observed rates for comparison covered a minimum of 10 years from the beginning of the projection period. Projected lung cancer incidence and/or mortality rates from these included studies were extracted from the publications. Observed rates were obtained from cancer registries or the World Health Organization's Mortality Database. Agreement between projected and observed rates was assessed and the relative difference (RD) for each projected rate was calculated as the percentage difference between the projected and observed rates.

Results: A total of 59 projections reported in 14 studies were included. Nine studies provided projections for 20 years or more. RDs were higher for those projections in which the lung cancer rates peaked during the projection period, and RDs increased substantially with the length of the projection period. When lung cancer rates peaked during the projection period, methods incorporating smoking data were generally more successful at predicting the trend reversal than those which did not incorporate smoking data. Mean RDs for 15-year projections comparing methods with or without smoking data were 12.7% versus 48.0% for males and 8.2% versus 42.3% for females.

Conclusions: The agreement between projected and observed lung cancer rates is dependent on the trends in the observed rates and characteristics of the population, particularly trends in smoking.

Keywords: age-period-cohort model; generalized linear model; incidence rates; mortality rates; statistical projections.

MeSH terms

  • Adult
  • Age Distribution
  • Aged
  • Female
  • Global Health / statistics & numerical data*
  • Humans
  • Incidence
  • Lung Neoplasms / diagnosis
  • Lung Neoplasms / epidemiology*
  • Male
  • Middle Aged
  • Models, Statistical
  • Predictive Value of Tests
  • Registries
  • Sex Distribution
  • Survival Rate