Time trends of major cancers incidence and mortality in Guangzhou, China 2004-2015: A Joinpoint and Age-Period-Cohort Analysis

Abstract

Background: Cancer is an important focus of public health worldwide. This study aims to provide a comprehensive overview of temporal trends in incidence and mortality of leading cancer in Guangzhou, China from 2004 to 2015.

Methods: Data were collected from the population-based registry in Guangzhou. Age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) were calculated and Joinpoint regression was used for evaluating the average annual percent changes (AAPC) among the entire study period and the estimated annual percent changes (EAPC) in time segments. The effects of age, period, and birth cohort were assessed by the age-period-cohort model.

Results: The age-standardized incidence and mortality by the world standard population decreased significantly among males with AAPC of -1.7% (95% CI: -3.0%, 0.2%) and -2.7% (95% CI: -4.3%, -1.1%) for all malignancies during 2004-2015, while among females, the age-standardized incidence had a non-significant reduction with AAPC of -1.3% (95% CI: -2.8%, 0.2%) and the age-standardized mortality demonstrated a remarkable decline (AAPC -2.0%, 95% CI: -3.6%, -0.3%). For males, the most commonly diagnosed cancers were trachea, bronchus, and lung (TBL), liver, colorectal, nasopharyngeal, stomach, and prostate cancer. For females, breast, TBL, colorectal, liver stomach, and thyroid cancer ranked the top. Unfavorable trends were observed in ASIR of colorectal, thyroid, and prostate cancer. APC models yielded different ages, periods, and birth cohort effect patterns by cancer sites.

Conclusions: Cancer burden remained a public health challenge in Guangzhou as the aging population and lifestyles changes, despite declines in incidence and mortality rates in some cancers. Surveillance of cancer trends contributed to valuable insights into cancer prevention and control.

Keywords: Age-Period-Cohort analysis; Guangzhou; incidence; mortality; time trend.

FIGURE 1

Age‐standardized incidence and mortality rates of six major cancers for males in Guangzhou, 2004–2015. TBL, trachea, bronchus, and lung.

FIGURE 2

Age‐standardized incidence and mortality rates of six major cancers for females in Guangzhou, 2004–2015. TBL, trachea, bronchus, and lung.

FIGURE 3

Age‐period‐cohort model of the incidence and mortality rates in major cancers among males in Guangzhou, 2004–2015. Notes: TBL, trachea, bronchus, and lung. Each graph includes three curves, with the solid line representing the incidence rate and the dotted line for mortality rate. From left to right, they are trends in the rates by age for the reference cohort (age effect), incidence risk by birth cohort (cohort effect), and incidence risk by calendar year (period effect). The graph has a horizontal axis divided into two parts: one for age (years old) and one for cohort‐period (calendar years). The left vertical axis represents incidence rates for the age effect, and the right vertical axis represents the relative risk for the cohort and period effect. The drift is added to the non‐linear birth cohort effects, and the right plot presents the period effect as residual ratio rates.

FIGURE 4

Age‐period‐cohort model of the incidence and mortality rates in major cancers among females in Guangzhou, 2004–2015. Notes: TBL, trachea, bronchus, and lung. Each graph includes three curves, with the solid line representing the incidence rate and the dotted line for mortality rate. From left to right, they are trends in the rates by age for the reference cohort (age effect), incidence risk by birth cohort (cohort effect), and incidence risk by calendar year (period effect). The graph has a horizontal axis divided into two parts: one for age (years old) and one for cohort‐period (calendar years). The left vertical axis represents incidence rates for the age effect, and the right vertical axis represents the relative risk for the cohort and period effect. The drift is added to the non‐linear birth cohort effects, and the right plot presents the period effect as residual ratio rates.