Cancer Prevalence Projections in Japan and Decomposition Analysis of Changes in Cancer Burden, 2020–2050: A Statistical Modeling Study

In Japan, cancer has been the leading cause of death since 1981 (1), accounting for one third of total deaths in this developed country with a high life expectancy (2). According to the National Cancer Center of Japan, there were estimations of 1,019,000 new cancer cases and 380,400 cancer deaths in 2022. The most common cancers in Japan include colorectal, stomach, lung, prostate, and female breast cancers, which accounted for approximately 60% of all cancer cases in the country (3, 4). Recent trends show a decline in cancer incidence among males and a decrease in mortality rates across both sexes (5), and noteworthy advancements in cancer diagnosis and treatment have contributed to improved survival rates (6). However, Japan's complicated demographic transition, characterized by the emergence of a super-ageing society (with the proportion of adults aged ≥ 65 years projected to rise from 28% in 2020 to 38% in 2050) and a declining population (due to a fertility rate lower than the death rate), presents significant challenges in effectively managing cancer burdens (7, 8).

The projections of future cancer burden in Japan are crucial for health policy planning and resource allocation, as they can assist policymakers and health authorities in developing effective cancer control strategies and interventions (9–13). One such study estimated the increases in number of new cancer cases in both Japanese males (6.9%) and females (30.9%) between 2015 and 2054 due to population ageing and increasing incidence risk. Colorectal and lung cancers were projected to remain as leading cancer burdens of both incidence and mortality in Japan over 2015–2054, while prostate and female breast cancers would be the leading incidence burdens among males and females, respectively (14). While cancer incidence and mortality are commonly used to assess cancer burden and plan for immediate health service's needs, they do not provide information about the period after primary treatment, between cancer diagnosis and the end of life. Prevalence, on the other hand, reflects the number of people who have been previously diagnosed with cancer and are still alive, either at a specific point in time (complete prevalence) or a specific period (limited-duration prevalence; refs. 15–18). Projections of prevalence can provide a comprehensive view of cancer burden and support allocating resources for the care of cancer survivors; thus, it has garnered great interest and has been developed in methods worldwide (19–21). The National Cancer Center of Japan reported that the 5-year prevalence in 2015–2019 were 1,734,060 (2.8% of the population) in males and 1,399,380 (2.2%) in females, and predicted to steadily increase in future (4). These projections highlight the urgent need for effective cancer prevention and control strategies in Japan.

Although cancer prevalence projection is essential in resource allocation for survivorship care, addressing the long-term effects of cancer treatment, and providing palliative care services, there is a lack of comprehensive and up-to-date national-level projections for Japan. The cancer prevalence estimates for 2020 from the Global Cancer Observatory (GCO) relied on prevalence–incidence ratios from Nordic countries during the period 2006 to 2015 and survival ratios between Japan and Nordic countries (22). While this approach is convenient for global estimations, it lacks future projections and fails to consider the unique characteristics of population demographics and the most recent cancer registry data in each country. The Cancer Information Service (CIS) provided some national cancer prevalence projections; however, the data were limited to 2012 and did not consider dynamic survival rates. Notably, it did not provide information on long-term prevalence measures such as 10-year prevalence (1). To bridge this knowledge gap, our study aims to estimate and project the limited-duration (5-year and 10-year) prevalence of all cancer sites in Japan from 2020 to 050, stratified by sex and age group, using the latest data from the national cancer registry. In addition, we decomposed the changes in prevalence counts between 2020 and 2050 into three key components: population demographics, incidence rates, and survival rates, providing valuable insights into the drivers of variations in cancer prevalence.

where |${I}_{t - i,j}$| is the annual number of new cancer cases at year t-i and age j, |${S}_{t - i,j}(i + 0.5$|⁠) is the proportion of cases diagnosed at year t-i age j and alive at time i+0.5 year after diagnosis, n represents the limited duration of prevalence. In brief, to make projections of cancer prevalence in 2020–2050, we need to project cancer incidence cases and absolute survival rates in the same period.

In this investigation, data on cancer new cases were gathered by sex for 22 cancer sites in 1975 to 2019 from the CISs, resulting in 39 sex-site combinations, including 17 sites each for males and females and five sex-specific sites (23). Population data in 1975–2019 were obtained from CIS, while population projections in 2020–2054 were obtained from the National Institute of Population and Social Security Research (IPSS) using medium-fertility and medium-mortality rate assumptions (8).

To predict cancer incidence in Japan, we employed age–period–cohort (APC) models using an empirical approach (24–26). Similar to our previous work, we validated a total of 3,510 models (90 model variants * 39 sex-site combinations) using four-fold time series cross-validation, and assessed their performance using absolute relative bias by comparing predicted and observed values. The chosen models showed relatively good performance with median ranging from 0% to 7.36% (14). The final outputs were single-year incidence cases in 2020 to 2050, which were extracted from new cases of 5-year intervals in 1975 to 2054 by using linear interpolation (27, 28).

The data for analyzing cancer survival rates was obtained from the Monitoring of Cancer Incidence in Japan (MCIJ) project (29). We selected six population-based cancer registries in Japan (Miyagi, Yamagata, Niigata, Fukui, Osaka, and Nagasaki) that have been previously used as high-quality data source to estimate national statistics for cancer survival in Japan (6). Rigorous data quality checks were conducted, adhering to international standards and previous national studies, ensuring consistent and available patient survival data for analysis (6, 30, 31). Our comprehensive approach involved excluding cases of male breast cancer (0.05%), patients over 100 years old (1.1%), in situ instances (1.3%), death certification only records (9.2%), and retrospective reports (13.9%). Ultimately, our analysis encompassed 1,476,058 cancer cases diagnosed from 1993 to 2015 (74.5%). Supplementary Table S1 provides detailed information on data exclusion for cancer survival projection.

To capture contemporary survival trends accurately, we employed a “period analysis” approach instead of the conventional “cohort-wise” approach. This method offers more robust and up-to-date survival rate estimates, as demonstrated in previous research (32–34). It accounts for recent advancements in cancer care, aligning our study's projections closely with Japan's current cancer survival landscape. We included these updated survival rates in our time-to-event models as the projection bases. We fitted multiple parametric survival models, with survival time and censoring status (death) as the response variables for each individual, and included covariates such as age groups and periods in the models. To ensure model stability for rare cancers and young age groups, survival time was measured in 3-month intervals. We created periods by adding the time of follow-up to the year of diagnosis, categorized in 3-year intervals to minimize the role of random variation. We validated the common distributions used in cancer survival analysis, such as exponential, Weibull, Gompertz, gamma, log-normal, and log-logistic distributions (35). The best-performing model was selected for each cancer site and sex as site- and sex-specific approach for our main analysis (Supplementary Table S2). We projected 10-year survival curves up to next decades based on data from five 3-year intervals (periods from 2003–2017) and applied linear interpolation to obtain single-year intervals of 10-year survival rates from 2020 to 2050 (27, 28).

We conducted a comprehensive analysis to project future cancer prevalence, considering various assumptions about incidence and survival rates. We employed two primary assumptions: "dynamic" and "static". The dynamic assumption anticipated that the current trends in incidence and survival rates would continue from 2020 to 2050. Conversely, the static assumption presumed that these rates would remain constant throughout this period 2020 to 2050. Based on these assumptions, we formulated four distinct scenarios for projecting future cancer prevalence, which are outlined in Supplementary Table S3. Our main analysis, Scenario I, incorporated dynamic assumptions for all three factors: population demographics, incidence rates, and survival rates. In contrast, our sensitivity analysis included Scenario II, III, and IV, which involved static assumptions for survival rate, incidence rate, and both survival and incidence rates, respectively.

We used the formula mentioned earlier to calculate the 5-year and 10-year cancer prevalence cases on a single-year basis between 2020 and 2050. We combined the cases of all cancer types and sexes to obtain the all-site and both-sex prevalence cases. Moreover, we computed age-specific rates for six different age groups, namely children + adolescents (aged 0–19 years), young adults (20–39 years), middle-aged individuals (40–64 years), youngest-old (65–74 years), middle-old (75–84 years), and oldest-old (85+ years). We also estimated the cancer prevalence proportions and the ranking of cancer burdens in each time period. To determine the percentage changes in cancer cases from 2020 to 2050, we subtracted the cases in 2020 from those in 2050 and divided the result by the 2020 cases.

In our sensitivity analysis of prevalence projections, we took into account the uncertainties associated with both projected incidence and survival rates. To do this, we projected cancer incidence rates for the period of 2020 to 2050 under different scenarios of drift attenuations, ranging from 4% to 12%. In addition, we projected cancer survival rates using the two highest performing distributions, Weibull and Gompertz distributions (as presented in Supplementary Table S4), for all cancer types and sexes. Using several scenarios for each input (i.e., incidence and survival rates), we calculated cancer prevalence multiple times, resulting in multiple estimations of future cancer prevalence. The results of these analyses are presented as uncertainty ranges of our main projections.

Our projections of cancer prevalence in Japan rely on projected population demographics, cancer incidence, and survival rates, allowing us to attribute future prevalence changes to these components. To comprehensively understand factors driving cancer prevalence shifts, we performed a decomposition analysis, breaking changes in prevalence counts into three components: Population Demographics, reflecting age structure and population size shifts; Cancer Incidence, reflecting shifts in diagnosis risk driven by cancer-related factors; and Survival Rates, reflecting shifts in cancer-related mortality risk due to treatment advancements.

To summarize, the changes in prevalence counts within a specific period (e.g., 2020–2050) can be attributed to different factors based on the scenarios analyzed. In scenario I, which considers dynamic population demographics, incidence, and survival rates, the changes in prevalence are due to all those three components. In contrast, scenario IV, which focuses on dynamic population demographics alone, reflects changes primarily driven by population demographics. Similarly, scenario II and III, which combine dynamic population demographics and incidence rates (II) or survival rates (III), reveals changes resulting from population demographics and incidence rates or survival rates, respectively. By subtracting these indicators, we can isolate the effects of each component—population demographics, cancer incidence, and survival rates—on the changes in cancer prevalence (14, 36). For a more comprehensive understanding of the decomposition analysis, please refer to the detailed descriptions provided in the Supplementary Materials and Methods.

Ethical approval for our study was obtained from the Institutional Review Board of the National Cancer Center Japan, with the approval number 2019–202.

The cancer incidence data analyzed in this study are publicly available in CIS at Ganjoho. The projected population from Institute of Population and Social Security that were analyzed in this study are publicly available at IPSS.

The survival data analyzed in this study are available from MCIJ project. Restrictions apply to the availability of these data, which were used under ethical approval for this study (provided above). Data are available to authorized research members of MCIJ upon reasonable request.

Table 1 presents the projected all-site combined 5-year prevalence for 2020 and 2050. Employing a scenario considering dynamic population, incidence, and survival rates, we projected 3,241,900 cancer survivors in 2020 (1,756,000 males and 1,485,800 females) with a prevalence rate of 25.9‰ (28.8‰ in males, 23.1‰ in females). By 2050, the number of survivors is projected to increase to 3,665,900 (1,770,200 males and 1,895,700 females) with a prevalence rate of 36.0‰ (35.9‰ in males, 36.0‰ in females). This represents an overall increase of 424,000 cancer survivors (+13.1%) in prevalence counts over 2020–2050, including a notable rise of 409,900 female cancer survivors (+27.6%) and a smaller increase of 14,200 male survivors (+0.8%). Both females and males also showed notable increases in prevalence rates, by 55.8% and 24.7%, respectively. Supplementary Table S5 presents gender differences in trajectory with prevalence projections in years 2020, 2030, 2040, and 2050, with male prevalence demonstrates a steady increase until the 2030s, followed by a subsequent decline, while female prevalence exhibits continuous growth, albeit at a more gradual pace, and eventually shows a decline after the 2040s. Notably, Supplementary Table S5 also demonstrates that female cancer prevalence will surpass male prevalence starting from 2040. Figure 1, panel (A), illustrates the scenario-specific projections of 5-year prevalence counts from 2020 to 2050, accompanied by sensitivity ranges. The figure aligns with the mentioned trends: male prevalence is projected to peak in 2033 at 1,948,000 cases, while female prevalence is expected to peak in 2039 at 1,979,200 cases. Supplementary Tables S6–S7 and Supplementary Fig. S1, panel (A), provide additional information on the all-site combined 10-year prevalence projections for a comprehensive analysis, with 5,016,400 survivors (2,649,300 males and 2,367,100 females) in 2020 and 5,883,400 survivors (2,692,200 males and 3,191,200 females) in 2050.

Table 2 and Fig. 2 provide a comprehensive overview of site-specific 5-year prevalence projections, rankings, and percentages for cancer sites stratified by sex in 2020 and 2050. In the both-sex combined analysis, the five most prevalent cancer sites remain consistent from 2020 to 2050, including colorectal, female breast, prostate, lung, and stomach cancers. However, there are changes in their rankings in 2050, with stomach cancer dropping in rank and prostate cancer ascending. Collectively, these cancers contribute to a total of 2,369,100 survivors, constituting 66.4% of the overall survivor population for all types of cancers in 2050. In males, the common leading cancers over 2020–2050 are prostate, colorectal, stomach, and lung cancers, with kidney cancer in 2020 being replaced by malignant lymphoma cancer in 2050. Similarly, in females, the common top cancer sites over 2020–2050 are female breast, colorectal, lung, and corpus uteri, with stomach cancer in 2020 being replaced by malignant lymphoma in 2050. Supplementary Tables S8 presents the site-specific prevalence in 2020, 2030, 2040, and 2050. Supplementary Tables S9–S10 and Supplementary Fig. S2 provide detailed results of site-specific 10-year prevalence projections. Supplementary Figs. S3–S5 present the projections of 5-year prevalence counts for both-sex, males, and females, respectively, and Supplementary Figs. S6–S8 display the corresponding projections for 10-year prevalence.

Table 1 also provides detailed projections of 5-year prevalence count and rate by age groups. In 2050, the middle-old group (aged 75–84 years) is projected to have the highest cancer prevalence, with 1,105,000 survivors (630,000 males and 475,000 females). This corresponds to a prevalence rate of 76.1‰ (95.2‰ in males and 60.1‰ in females). Figure 1, panel (B), visually represents the trends among these age groups, highlighting changes in age distribution of cancer survivors from 2020 to 2050. Visualization of the 10-year prevalence is in Supplementary Fig. S1, panel (B). Figure 3 illustrates the age distributions of cancer survivors in 2020 and 2050, stratified by sex and encompassing all cancer sites. Older cancer survivors (aged ≥ 65) accounted for 69.2% of the total 5-year prevalence counts in 2020, projected to increase to 74.8% in 2050. Prostate cancer had the highest proportion of older survivors (93.4% in 2050), while ovarian cancer had the smallest (35.8% in 2050). Supplementary Figure S9 provides additional information for analysis of the 10-year prevalence. Moreover, Supplementary Figs. S10 and S11 depict the distribution of time since diagnosis for cancer survivors in 2020 and 2050, stratified by cancer site and sex, for both 5-year and 10-year prevalence. A small proportion of cancer survivors has surpassed the 5-year mark, projected to increase from 35.4% in 2020 to 37.6% in 2050.

Table 2 presents changes in cancer prevalence counts from 2020 to 2050, revealing an overall increase of 424,000 cancer survivors. The rise in cancer incidence (+368,100 survivors) primarily drives this increase, accompanied by smaller contributions from population demographics (+28,800) and improvements in survival rates (+27,000). Among males, the modest increase of 14,200 survivors is mainly due to population demographics (+89,500), offset by a decline in incidence rates (−64,700). Conversely, in females, the notable increase of 409,800 survivors is primarily attributed to higher incidence rates (+432,800) and improved survival rates (+37,700), partially counterbalanced by changes in population demographics (−60,700). Figure 1, panel C visually depicts the decomposition of changes over the 2020 to 2050 period, emphasizing the impact of cancer incidence, population demographics, and survival rates for both sexes.

Figure 4 displays the site-specific absolute and relative changes in 5-year prevalence counts from 2020 to 2050, stratified by sex. In males, the highest absolute increases in 5-year prevalence counts over 2020 to 2050 are observed in prostate (+79,700), lung (+40,300), and malignant lymphoma (+21,100) cancers, while the highest relative increases are seen in pancreas (+41.3%), thyroid (+38.7%), and multiple myeloma (+35.2%) cancers. In females, female breast (+152,000), colorectal (+99,100), and corpus uteri (+66,800) cancers demonstrate the highest absolute increases, while corpus uteri (+90.8%), malignant lymphoma (+71.6%), and oral cavity cancers (+59.5%) present the highest relative increases. Stomach, liver, and bladder cancers show the most significant decreases in both absolute and relative terms for both males and females. Results for 10-year prevalence are visualized in Supplementary Fig. S12, while detailed decompositions of changes in 5-year prevalence counts for all sites can be found in Table 2, and corresponding results for 10-year prevalence are provided in Supplementary Table S8. Supplementary Figs. S13–S15 visually depict the decomposition of changes in 5-year prevalence counts over the 2020–2050 period for both-sex, males, and females, respectively, while Supplementary Figs. S16–S18 display the corresponding projections for 10-year prevalence.

This study provides the projections of cancer prevalence counts and rates for 22 cancer sites in both sexes in Japan. Employing a scenario-based approach that incorporates dynamic population, incidence, and survival rates, we projected an increase of 424,000 cancer survivors (+13.1%) over 2020 to 2050 include a notable rise of 409,900 female survivors (+27.6%) and a smaller increase of 14,200 male survivors (+0.8%), resulting in the surpassing of female survivors over male survivors during the projected period. The top prevalent cancer sites in 2050 include colorectal, female breast, prostate, lung, and stomach cancers, accounting for 66.4% of all-site combined survivors. Age-wise, the middle-old group (aged 75–84 years) is projected to have the highest prevalence in 2050. Changes in cancer prevalence are primarily driven by increases in cancer incidence, with smaller contributions from population demographics and improvements in survival rates. Among males, the cancers with the most notable absolute increases in prevalence counts are prostate, lung, and malignant lymphoma cancers. Conversely, among females, the highest absolute increases are observed in female breast, colorectal, and corpus uteri cancers. On the other hand, stomach, liver, and bladder cancers exhibit the most significant declines in prevalence.

Comparing our cancer prevalence projections with currently available data from national (CIS) and international sources (GCO) reveals both consistencies and differences. While the GCO only provides estimation of cancer prevalence in 2020 without any prevalence projection, the CIS only provides projections in 5-year period, with the longest period being 2035 to 2039. Overall, our projected rise in cancer prevalence counts from 2020 to 2050 aligns with the global pattern of increased cancer burden due to aging populations and shifts in lifestyle and risk factors (37). Notably, leading prevalent cancer sites in 2020 and 2040, including colorectal, female breast, prostate, lung, and stomach cancers, closely mirror the current data reported by these sources (4, 38). In addition, our findings consistently demonstrate a prevailing cancer prevalence pattern: male prevalence steadily increases until the 2030s, followed by a subsequent decline, while female prevalence grows gradually until the 2040s before declining (Supplementary Table S5), which is in line with previous projections in Japan (1). However, there are slight differences between our findings and the existing data. For instance, our estimates of 5-year cancer prevalence rates in 2020 (28.8‰ in males, 23.1‰ in females) showed variations from the existing estimates by the GCO (24.5‰ in males, 18.5‰ in females) and CIS (31.0‰ in males, 24.0‰ in females), while still falling within the range of their reported values. Moreover, our prevalence projections in 2040 are 1,896,800 [1,771,700 – 2,067,000] cases or 35.4‰ [33.1–38.6] in males and 1,978,000 [1,687,900 – 2,178,800] cases or 34.5‰ [29.4 – 38.0] in females, while CIS's projections in period 2035–2039 are 1,844,100 and 1,671,600 thousand cases or 33.6‰ and 28.5‰ in males and females, respectively. While CIS projected no changes in the top 5 leading cancers from 2015–2039 in both males and females, our study's projections highlighted a decline in the rank of stomach cancers in both sexes over the period 2020–2040. This phenomenon is later discussed in conjunction with the exploration of the role of cancer incidence in shaping cancer prevalence in Japan.

These differences in cancer prevalence projections, as we mentioned earlier, can be attributed to variations in projection methodologies and data sources. Specifically, the GCO relied on indirect estimations using prevalence–incidence ratios and survival ratios from Nordic countries in the period 2006–2015 (22). In contrast, the CIS used a particular incidence projection model (Poisson regression model) without empirical validation, incorporated only a static survival rate scenario (in 2006–2008), and relied on Japanese incidence data only up until 2012 (1, 14). The observed changes in cancer incidence in Japan from 1985 to 2015 highlight the complexity of the situation, characterized by declines in stomach and liver cancers and increases in female breast, colorectal, and prostate cancers (5). These dynamic trends demonstrate that static estimates alone may not capture the dynamic nature of cancer trends in Japan, and merely relying on changes in population demographics in some previous projections can potentially lead to misleading conclusions (39, 40). In contrast, our study goes beyond the limitations of previous approaches by incorporating dynamic population, incidence, and survival rates, and using the most up-to-date data from the national cancer registry. Furthermore, we perform sensitivity analysis by exploring different hyperparameters of APC models for projecting incidence rates and investigating various distributions of parametric survival models and employing period-approach analysis for projecting survival rates. By considering these methodological and data advancements, this study provides valuable insights for informed decision-making in cancer control and resource allocation in Japan.

Our study reveals that the rise in incidence is a major driver of increased cancer survivors, particularly in colorectal, prostate, and female breast cancers, which are the leading cancer burdens. These findings underscore the ongoing significance of implementing comprehensive cancer control measures to address the growing cancer burden. For instance, colorectal cancer can potentially be reduced through effective cancer control measures (5), highlighting the need for greater efforts from government and policymakers to promote both primary and, more importantly, secondary preventive interventions in Japan. Furthermore, we observed a significant decrease in incidence rates for stomach, liver, and gallbladder cancers, which contributed to a decline in cancer prevalence of those infection-associated cancers throughout the study period. This emphasizes the crucial role of policymakers and governments in sustaining effective cancer control policies that target established environmental, lifestyle, and infectious factors, including oncogenic infections. Notably, factors such as tobacco smoking (linked to cancers of the lung, stomach, bladder, etc.), human papillomavirus (HPV; related to cancers of the cervix and oral cavity), and Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV) (associated with cancers of the liver) warrant focused attention. Our findings can serve as a foundation for further research, aiming to strengthen knowledge and provide valuable guidance to low- and middle-income nations. By enhancing the management of both communicable and non-communicable diseases, these efforts can ultimately contribute to enhanced outcomes in cancer control (41, 42).

Notably, population demographics have different impacts on the projected changes in future cancer survivors across genders, leading to an increase of 89,500 survivors (+5.1%) in males but a reduction of 60,700 survivors (-4.1%) in females (Table 2). In Japan, population demographics encompass the effects of both population aging (linked to increased cancer cases) and population decline (associated with reduced cancer cases) (14), with the direction of impact contingent on the absolute magnitude of these component effects. For cancer sites prone to manifest in younger age groups (such as female breast, corpus uteri, ovary, cervix uteri, thyroid, leukemia, brain, and oral cavity), the influence of population aging is relatively subdued, potentially resulting in a reduced effect on cancer incidence of population demographics. Remarkably, these specific cancer sites constitute a larger proportion among females (48.0%) in contrast to a minor proportion among males (5.6%) in 2020 (Fig. 2), thus contributing to the gender differences observed in this study. In addition, in light of the population aging with the rapid increase of older individuals (rising from 28% in 2020 to 38% in 2050; ref. 8), cancer prevalence rates in 2050 are projected to be highest in those aged 65 to 74, 75 to 84, and 85+ with 60.5‰, 68.6‰, and 52.5‰, respectively. Older patients with cancer often face additional complexities such as comorbidities, frailty, and functional limitations. Thus, this finding underscores the necessity to address the unique healthcare needs and challenges associated with an aging population, including effective cancer prevention, early detection, and tailored treatment strategies (43). While the current clinical practice guidelines for elderly cancer pharmacotherapy in Japan provide a starting point, further improvements are required to cover a broader range of carcinomas and include higher levels of evidence and discussions on supportive and symptomatic therapies (44). Moreover, ongoing reforms of social security systems in Japan should consider the complex challenges posed by population aging, such as the shortage of medical and human resources and the sustainability of the finance and healthcare system (45). Our projections of cancer prevalence provide valuable inputs for future health economics and policy research, which can explore the costs of cancer diagnosis and treatment, secure necessary resources, and establish evidence-based control programs (1).

Our study highlights the modest yet noteworthy improvements in survival rates for both sexes, which have had a positive impact on the prevalence of various cancer sites, such as lung, pancreas, esophagus, leukemia, and multiple myeloma. These improvements can be attributed to advancements in treatment modalities, including the adoption of precision medicine approaches and the implementation of supportive care measures (44, 46). However, the projected increase in the number of cancer survivors emphasizes the urgency of establishing proactive surveillance and tailored screening strategies to address the ongoing risk of recurrences and new primary cancers (47). By combining enhanced surveillance and structured screening, and highlighting the importance of personalized approaches in survivorship care planning, it is possible to effectively manage risks and optimize cancer survivor health. Nonetheless, it is crucial to understand that the impact of survival rates on cancer prevalence was generally less marked than the effects of population demographics and incidence rates across most cancer sites. This may indicate the stability of survival rates for many cancer types over time (6, 30). While molecular-targeted drugs have been developed, their application may not be universal for all patients. Notably, immune checkpoint inhibitor therapy stands out as an exception, as certain proven effective immunotherapies have been included in clinical practice guidelines as standard treatments covered by health insurance in Japan as of August 2020 (48, 49). However, our survival data only covers the period up to 2015, and thus, it does not fully capture the impact of these emerging therapies. On the other hand, we observed certain declines in cancer survivors attributed to survival rates in specific cancer sites, indicating a lack of significant improvements in survival over the study period, which aligns with recent reports from CIS reports (50). Moving forward, comprehensive approaches are required to improve survival rates and enhance overall cancer outcomes in Japan. These approaches encompass improving public awareness, promoting early detection through effective screening programs, ensuring equitable access to quality healthcare services, advancing treatment options, and implementing strategies for lifestyle modifications and risk factor reduction.

This study presents the first comprehensive projections of cancer prevalence for 22 cancer sites in both sexes in Japan, using recent and nationally-representative data. The scenario-based approach incorporating population demographic, incidence, and survival rates ensures a realistic estimation, while the inclusion of sensitivity ranges enhances the robustness and exploration of uncertainty. In addition, the decomposition analysis provides valuable insights into the factors influencing variations in cancer prevalence over the period. Nevertheless, it is crucial to interpret the findings considering the study's limitations. Although the inclusion of sensitivity ranges introduces some level of uncertainty, long-term cancer prevalence prediction remains challenging due to the complex interplay of multiple factors. Specifically, we opted not to include all 12 scenarios of fertility or mortality variations in our population projections for the sake of analytical simplicity. Nevertheless, it's crucial to highlight that our selection of the medium assumption aligns with the National Institute of Population and Social Security Research's primary population projections (8), and has been used in previous projections in Japan (1, 14), rendering it a justifiable choice for this study. In addition, the projections rely on assumptions and models based on available data and current trends, which may not fully capture potential changes in risk factors, treatment options, population dynamics, and newly implemented or future interventions/policies (51, 52). It is also critical to consider the potential overdiagnosis of prostate and female breast cancers in Japan, attributed to the wide adoption of minimally invasive tests and a rapid increase in incidence without a corresponding change in mortality rates (53–55). We, however, did not incorporate covariates for these specific cancers with potential overdiagnosis (e.g., Prostate-specific antigen screening rate for prostate cancer, breast cancer screening rates for female breast cancer) as projecting these covariates into the future would require further assumptions, introducing additional uncertainty. Similarly, we did not incorporate covariates to assess the potential impact of innovations such as direct-acting antiretrovirals (DAA) and HPV vaccination on projected cancer prevalence for hepatocellular carcinoma and HPV-related sites. Still, our incidence data extends up to 2019, offering a partial reflection of these factors in recent trends because DAAs were first approved in 2011 (telaprevir; ref. 56), and HPV vaccination commenced in 2010 (57). Previous works indicate a modest HPV vaccination impact with very low coverage (<1%) due to vaccine hesitancy (58, 59), and challenges in controlling hepatitis persist despite the projected reduction in HBV and HCV burden (60), supporting our expectation of limited influence. Nevertheless, our projections offer baseline estimations against which the impact of future or newly implemented interventions/policies or factors that arise after the study period can be assessed, such as the effects of COVID-19–related delays in cancer screening and treatment (13). While the substantial exclusion of data (25.5%) in our survival analysis might raise concerns about overestimation of survival rates, this practice adheres to international standards and is vital for accurate analysis. Given that a substantial proportion of exclusions were related to uncertain survival times (death certification only and retrospective cases), future studies might investigate multiple imputation techniques to mitigate this inherent limitation of population-based cancer registry data (3). Despite these constraints, this study remains a valuable resource for policymakers and researchers in shaping cancer control and prevention strategies. To advance accuracy in cancer prevalence projections, future research should prioritize model refinement, integration of emerging data, incorporation of additional covariates, and the application of mathematical modeling techniques.

In summary, this study presents a comprehensive analysis of cancer prevalence projections in Japan for 22 cancer sites from 2020 to 2050. Notable increases in cancer survivors are observed, differing by sex and specific cancer types. The study underscores the impacts of aging populations, changing cancer incidence rates, and improved survival on future prevalence trends. These findings carry significant policy implications, emphasizing the need for proactive planning and resource allocation to address the growing cancer burden.

K. Katanoda reports grants from Ministry of Health, Labor, and Welfare, Japan during the conduct of the study. No disclosures were reported by the other authors.

P.T. Nguyen: Conceptualization, resources, data curation, software, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. M. Hori: Data curation, writing–review and editing. T. Matsuda: Data curation, writing–review and editing. K. Katanoda: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, methodology, project administration, writing–review and editing.

This work was supported by Grants-in-aid for Cancer Control Policy from the Ministry of Health, Labor, and Welfare, Japan (20EA1026, 23EA1033, 20EA1017, 23EA0801).

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.