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Title Page 1

ABSTRACT 3

Contents 6

CHAPTER Ⅰ. INTRODUCTION 20

1.1. Background Information 20

1.2. Objectives & Methodology 23

1.3. Structure of the Thesis 27

CHAPTER Ⅱ. LITERATURE REVIEW 28

2.1. Introduction 28

2.2. Decomposition Analysis 29

2.2.1. Das Gupta's Method 29

2.2.2. Bashir's Method 31

2.2.3. Cheng's Method 33

2.3. Population structure-based projection models 37

2.3.1. APC (Age-period-cohort) model 37

2.3.2. NORDPRED (NP) model 39

2.3.3. BAPC (Bayesian Age-period-cohort) Model 41

2.4. Non-population structure-based projection models 43

2.4.1. JOINPOINT (JP) Model 43

2.4.2. Time Series Forecasting Model 45

2.5. Summary 49

CHAPTER Ⅲ. SELECTION AGEING RELATED CANCER AND CURRENT TREND ANALYSIS IN 2000 TO 2019 51

3.1. Introduction 51

3.2. Selection of ageing-related cancer 51

3.2.1. Selection of ageing-related cancer with incidence 52

3.2.2. Selection of ageing-related cancer with mortality 58

3.2.3. Final selection of ageing-related cancer to both incidence and mortality 64

3.3. Validation selection of ageing-related cancer 69

3.3.1. Age-Period-Cohort Effect in 2000 to 2019 69

3.4. Summary 88

CHAPTER Ⅳ. COMAPRISON BY PROJECITON MODELS WITH AND WITHOUT POPULATION STRUCTURE EFFECTS 89

4.1. Introduction 89

4.2. Population structure-based model 90

4.3. Non-population structure-based model 92

4.4. Projection results between population structure-based model and non-population structure-based model 93

4.4.1. Projection results for male incidence and mortality rates by models 93

4.4.2. Projection results for female incidence and mortality rates by models 99

4.5. Compare model characteristics based on outcomes by cancer type 104

4.6. Summary 109

CHAPTER Ⅴ. FUTURE PREDICITONS ACCORDING TO POPULATION AGEING SCENARIOS 111

5.1. Introduction 111

5.2. Scenarios setting 112

5.3. Applying decomposition analysis in future projection 113

5.4. Comparison of projection according to prospective population projection scenarios 116

5.4.1. Comparison male incidence and mortality rates by scenarios 116

5.4.2. Comparison female incidence and mortality rates by scenarios 122

5.5. Comparison of decomposition about projection according to prospective population projection scenarios 128

5.5.1. Comparison decompose male incidence and mortality by scenarios 128

5.5.2. Comparison decompose female incidence and mortality by scenarios 141

5.6. Summary 153

CHAPTER Ⅵ. CONCLUSION 154

BIBLIOGRAPHY 158

List of Tables 11

Table 1. Meaning of mathematical symbols in the decomposition formula 34

Table 2. Male and Interaction effect formula of each year 34

Table 3. Decomposition formula about main effect 35

Table 4. APC Model on an Age-Period Array 38

Table 5-1. Cancer incidence and ranking in all age by sex in 2019 54

Table 5-2. Cancer incidence and rank in 65+ age by sex in 2019 55

Table 5-3. Cancer incidence rate ratio in age groups by sex 56

Table 5-4. JOINPOINT Average Annual Percent Change (AAPC) shifting last 10 years period in incidence by sex 57

Table 6-1. Cancer mortality rate ratio in age groups by sex 60

Table 6-2. Cancer mortality and ranking in 65+ age by sex 61

Table 6-3. Cancer mortality rate ratio in age groups by sex 62

Table 6-4. JOINPOINT Average Annual Percent Change (AAPC) shifting last 10 years period in mortality by sex 63

Table 7-1. Cancer selection for the model projection with adjustment on ageing population in incidence by sex 66

Table 7-2. Cancer selection for the model projection with adjustment on ageing population in mortality by sex 67

Table 7-3. Cancer selection for the model projection with adjustment on ageing population in incidence and mortality by sex 68

Table 8. Key scenarios for prospective population projections 113

Table 9-1. Male incidence rate of each cancer type by year by scenarios 120

Table 9-2. Male mortality rate of each cancer type by year by scenarios 121

Table 10-1. Female incidence rate of each cancer type by year by scenarios 126

Table 10-2. Female mortality rate of each cancer type by year by scenarios 127

Table 11-1. Male incidence case difference based on population aging risk 137

Table 11-2. Male mortality case difference based on population aging risk 138

Table 12-1. Net change by male incidence case 139

Table 12-2. Net change by male mortality case 140

Table 13-1. Female incidence case difference based on population aging risk 149

Table 13-2. Female mortality case difference based on population aging risk 150

Table 14-1. Net change by female incidence case 151

Table 14-2. Net change by female mortality case 152

List of Figures 14

Figure 1. Ageing related cancer selection framework 26

Figure 2-1. Age-Period-Cohort Effect for Male Incidence (Esophagus, Stomach, Colorectal Cancer) 72

Figure 2-2. Age-Period-Cohort Effect for Male Incidence (Liver, Gallbladder, Pancreas Cancer) 73

Figure 2-3. Age-Period-Cohort Effect for Male Incidence (Lung, Prostate, Bladder Cancer) 74

Figure 2-4. Age-Period-Cohort Effect for Male Incidence (Kidney Cancer, Non-Hodgkin Lymphoma) 75

Figure 3-1. Age-Period-Cohort Effect for Male Mortality (Esophagus, Stomach, Colorectal Cancer) 76

Figure 3-2. Age-Period-Cohort Effect for Male Mortality (Liver, Gallbladder, Pancreas Cancer) 77

Figure 3-3. Age-Period-Cohort Effect for Male Mortality (Lung, Prostate, Bladder Cancer) 78

Figure 3-4. Age-Period-Cohort Effect for Male Mortality (Kidney Cancer, Non-Hodgkin Lymphoma) 79

Figure 4-1. Age-Period-Cohort Effect for Female Incidence (Stomach, Colorectal, Liver Cancer) 82

Figure 4-2. Age-Period-Cohort Effect for Female Incidence (Gallbladder, Pancreas, Lung Cancer) 83

Figure 4-3. Age-Period-Cohort Effect for Female Incidence (Cervix Uteri Cancer, Non-Hodgkin Lymphoma, Leukemia) 84

Figure 5-1. Age-Period-Cohort Effect for Female Mortality (Stomach, Colorectal, Liver Cancer) 85

Figure 5-2. Age-Period-Cohort Effect for Female Mortality (Gallbladder, Pancreas, Lung Cancer) 86

Figure 5-3. Age-Period-Cohort Effect for Female Mortality (Cervix Uteri Cancer, Non-Hodgkin Lymphoma, Leukemia) 87

Figure 6-1. Projection for male age-adjusted incidence & mortality rates in 2000 to 2040 using population structure-based models and non-population structure-based models (Esophagus, Stomach, Colorectal, Colorectal & Anus, Liver Cancer) 96

Figure 6-2. Projection for male age-adjusted incidence & mortality rates in 2000 to 2040 using population structure-based models and non-population structure-based models (Gallbladder, Pancreas, Lung Prostate Cancer) 97

Figure 6-3. Projection for male age-adjusted incidence & mortality rates in 2000 to 2040 using population structure-based models and non-population structure-based models (Kidney, Bladder Cancer, Non-Hodgkin Lymphoma) 98

Figure 7-1. Projection for female age-adjusted incidence & mortality rates in 2000 to 2040 using population structure-based models and non-population structure-based models (Stomach, Colorectal, Colorectal & Anus, Liver, Gallbladder 101

Figure 7-2. Projection for female age-adjusted incidence & mortality rates in 2000 to 2040 using population structure-based models and non-population structure-based models (Pancreas, Lung, Cervix uteri Cancer, Non-Hodgkin 102

Figure 7-3. Projection for female age-adjusted incidence & mortality rates in 2000 to 2040 using population structure-based models and non-population structure-based models (Leukemia) 103

Figure 8-1. Projection for male age-adjusted incidence & mortality rates in 2000 to 2040 using NP model by Scenarios (Esophagus, Stomach, Colorectal, Colorectal & Anus, Liver Cancer) 117

Figure 8-2. Projection for male age-adjusted incidence & mortality rates in 2000 to 2040 using NP model by Scenarios (Gallbladder Pancreas, Lung, Prostate Cancer) 118

Figure 8-3. Projection for male age-adjusted incidence & mortality rates in 2000 to 2040 using NP model by Scenarios (Kidney, Bladder Cancer, Non-Hodgkin Lymphoma) 119

Figure 9-1. Projection for female age-adjusted incidence & mortality rates in 2000 to 2040 using NP model by Scenarios (Stomach, Colorectal, Colorectal & Anus, Liver, Gallbladder Cancer) 123

Figure 9-2. Projection for female age-adjusted incidence & mortality rates in 2000 to 2040 using NP model by Scenarios (Pancreas, Lung, Cervix Uteri, Cancer, Non-Hodgkin Lymphoma) 124

Figure 9-3. Projection for female age-adjusted incidence & mortality rates in 2000 to 2040 using NP model by Scenarios (Leukemia) 125

Figure 10-1. Comparison decomposition for male incidence in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Esophagus, Stomach, Colorectal, Colorectal & Anus, Liver Cancer) 131

Figure 10-2. Comparison decomposition for male incidence in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Gallbladder, Pancreas, Lung, Prostate Cancer) 132

Figure 10-3. Comparison decomposition for male incidence in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Kidney, Bladder Cancer, Non-Hodgkin lymphoma) 133

Figure 11-1. Comparison decomposition for male mortality in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Esophagus, Stomach, Colorectal, Colorectal & Anus, Liver Cancer) 134

Figure 11-2. Comparison decomposition for male mortality in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Gallbladder, Pancreas, Lung, Prostate Cancer) 135

Figure 11-3. Comparison decomposition for male mortality in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Gallbladder, Pancreas, Lung, Prostate Cancer) 136

Figure 12-1. Comparison decomposition for female incidence in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Stomach, Colorectal, Colorectal & Anus, Liver, Gallbladder Cancer) 143

Figure 12-2. Comparison decomposition for female incidence in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Pancreas, Lung, Cervix Uteri Cancer, Non-Hodgkin lymphoma) 144

Figure 12-3. Comparison decomposition for female incidence in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Leukemia) 145

Figure 13-1. Comparison decomposition for female mortality in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Stomach, Colorectal, Colorectal & Anus, Liver, Gallbladder Cancer) 146

Figure 13-2. Comparison decomposition for female mortality in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Pancreas, Lung, Cervix Uteri Cancer, Non-Hodgkin lymphoma) 147

Figure 13-3. Comparison decomposition for female mortality in 2000 to 2040 using NP by Median, Fast Ageing Slow Ageing Scenarios (Leukemia) 148

초록보기

 This study focuses on projecting cancer incidence and mortality rates in Korea, with an emphasis on the role of demographic factors. The study compares the differences of various models, including population structure-based and nonpopulation structure-based models, in predicting cancer trends by gender and cancer type. The models used in the analysis include the NP, BAPC, JP, ARIMA, SSML. The study also utilizes APC-analysis to identify age-related cancers and quantify the contribution of aging factors to the increase in cancer incidence and mortality rates.

The study's results reveal variations in the rate of increase and decrease of cancer incidence and mortality rates by cancer type and demographic group across scenarios. The NP model consistently demonstrated the most realistic trends by gender and cancer type. The decomposition analysis consistently identified aging as a major factor contributing to the increase in cancer incidence or mortality. The study identifies certain limitations and challenges associated with predicting cancer incidence and mortality rates and underscores the importance of selecting appropriate models and validating them to obtain reliable and accurate predictions.

Overall, this study highlights the significant role of aging and other demographic factors in cancer incidence and mortality rates and provides insights into the potential impact of these factors on future cancer trends in Korea. The findings have important implications for cancer prevention and control efforts and can help inform policy decisions aimed at reducing the burden of cancer in aging societies. The study's approach and methodology can also be applied to other settings to project cancer trends and quantify the contribution of demographic factors to cancer burden.

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