The Korean Ministry of Health and Welfare initiated a nationwide, hospital-based cancer registry, the Korea Central Cancer Registry (KCCR), in 1980. The history, objectives, and activities of the KCCR have been documented in detail elsewhere [3 (
link)]. Incidence data from 1999 to 2016 were obtained from the Korea National Cancer Incidence Database (KNCI DB). Cancer cases were classified according to the International Classification of Diseases for Oncology, third edition [4 ], and converted according to the International Classification of Diseases, 10th edition (ICD-10) [5 ]. Mortality data from 1993 to 2017 were acquired from Statistics Korea [1 ]. The cause of death was coded and classified according to ICD-10 [5 ].
The cancer sites included in this study were (1) all cancers sites combined and (2) the 24 common cancer sites as follows: lips, oral cavity, and pharynx (C00-C14), esophagus (C15), stomach (C16), colon and rectum (C18-C20), liver and intrahepatic bile duct (liver) (C22), gallbladder and other parts of the biliary tract (gallbladder) (C23-C24), pancreas (C25), larynx (C32), trachea, bronchus and lung (lung) (C33-C34), breast (C50), cervix uteri (C53), corpus uteri (C54), ovary (C56), prostate (C61), testis (C62), kidney (C64), bladder (C67), brain and central nervous system (C70-C72), thyroid (C73), Hodgkin lymphoma (C81), non-Hodgkin lymphoma (C82-C86, C96), multiple myeloma (C90), leukemia (C91-C95), and ‘other and ill defined’ sites.
Population data from 1993 to 2019 were obtained from the resident registration population data, reported by Statistics Korea. Data on the mid-year population, as of July 1 of the respective year, were analyzed. However, we used population data as of December 31, 2018 for the year 2019, because mid-2019 resident registration population data were not yet available at the time of analysis.
Linear regression models [6 ] were used to assess time trends and projections. We first performed a
Joinpoint regression analysis on the data available to detect the year when significant changes occurred in cancer trends according to sex and cancer site. A
Joinpoint regression describes changes in data trends by connecting several different line segments on a log scale at “joinpoints.” This analysis was performed using the
Joinpoint software (ver. 4.3.1,
http://surveillance.cancer.gov/joinpoint) from the Surveillance Research Program of the US National Cancer Institute [7 ]. For the analysis, we arranged to have at least four data points between consecutive joinpoints. Secondly, to predict age-specific cancer rates, a linear regression model was fitted to age-specific rates by 5-year age groups against observed years based on observed cancer incidence data of the latest trend. Finally, we multiply the projected age-specific rates by the age-specific population to get the projected cancer cases and deaths of the year 2019. For thyroid cancer, we used a square root transformation when fitting a linear regression model and converted the predicted values back to the original scale.
We summarized the results by using crude rates (CRs) and age-standardized rates (ASRs) of cancer incidence and mortality. ASRs were standardized using the world standard population [8 ] and expressed per 100,000 persons.
Jung K.W., Won Y.J., Kong H.J, & Lee E.S. (2019). Prediction of Cancer Incidence and Mortality in Korea, 2019. Cancer Research and Treatment : Official Journal of Korean Cancer Association, 51(2), 431-437.