The global population is facing significant changes due to population ageing [1,2]. There were an estimated 771 million older adults in 2022, representing 10% of the global population – a 3-fold increase from 258 million in 1980. This trend is expected to continue, with projections indicating that the older population will reach 994 million (12%) by 2030 and 1.6 billion (16%) by 2050 [3]. Further predictions suggest that older adults will comprise 61% of the population in 155 countries by 2100, while by 2050, approximately 1.1 billion older individuals will reside in less developed countries, constituting two-thirds of their population [3,4]. These demographic shifts will strain health care systems worldwide, especially in regions with limited resources, which is why understanding the regional and national implications of population ageing is essential for enhancing health care systems globally. It is also likely to affect the management of chronic diseases such as ischaemic heart disease, stroke, cancer, and diabetes [5–8]. Aside from being a leading cause of mortality, cancer contributes to a substantial proportion of the global disease burden; this proportion is expected to increase in the future, predominantly due to population ageing [9,10]. For example, data from the GLOBOCAN 2020 show that individuals aged >60 years accounted for most cancer diagnoses and deaths globally [11], while cancer registries, including those in China, have reported a marked increase in cancer burden with advancing age, especially among individuals aged 60–79 years [12].
While these challenges could be mitigated through the strategic allocation of health care resources, there is still no comprehensive evaluation of the impact of population ageing on cancer-related disability-adjusted life years. Existing studies have mostly focussed on specific geographical regions [13,14] or selected types of cancer [15−17], limiting their global applicability. This gap hindered international organisations in adapting health care infrastructures to the evolving needs of the ageing population. We thus aimed to assess and quantify the impact of population ageing on cancer-related disability-adjusted life years (DALYs) across 204 countries/territories over three decades (1990–2019), providing strategic insights to guide health care systems in managing cancer amidst global population ageing.
METHODS
Data source
DALYs are a comprehensive population health metric that combines years of life lost to premature mortality and years lived with disability due to health loss from disease, injury or other conditions [10]. For our analysis, we retrieved data on cancer-related DALYs from 1990 to 2019 for 204 countries and territories from the Global Burden of Disease (GBD) Study 2019 [18]. These data were collected from systematic reviews, authoritative websites, published reports, and primary data sources such as Demographic and Health Surveys. We defined older adults as individuals aged ≥65 years, following the United Nations classification [3]. Our analysis included 30 cancer causes, classified following the International Classification of Diseases, 10th Revision (Table S1 in the Online Supplementary Document). We categorized the study population aged 15–95 into 16 age groups aged.
The sociodemographic index (SDI), which we also retrieved from the GBD database, combines information on the economy, education, and fertility rate of countries around the world, thereby acting as a measure of social and economic development which is closely tied to health outcomes. It is calculated using per capita income, average education achievement, and fertility rates, with the final values of the SDI ranging from 0 to 1. The geometric mean was computed for each location and each year, whereby the 204 countries and territories are classified into five groups based on the SDI quintiles: low, low-middle, middle, high-middle, and high SDI.
Decomposition analysis
We used a robust decomposition analysis method detailed in previous studies [6–8]. This approach separates the net effect of population ageing from population growth and change in age-specific rates by considering the following three key components: the demographic composition, characterised by a shift toward a larger proportion of older individuals (otherwise known as population ageing); population growth; and age-specific mortality rates. DALY changes can be attributed to population ageing, population growth, and changes in age-specific mortality rates (Text S1 in the Online Supplementary Document). The equations for the decomposition analysis were as follows:
Ma, Mp, and Mm represent the main effects of population ageing, population growth, and changes in age-specific DALY rates, respectively; Ipa, Ipm, Iam, and Ipam denote the two-way and three-way interactions of the three components; and mij and sij indicate the age-specific DALY rate and proportion of the population for the ith age group in the jth year (i = 1, 2, . . ., 16; j = 1, 2), respectively. We calculated these terms using 1990 as the reference year. Additionally, N1 and N2 denote population sizes in 1990 and 2019, respectively.
Using decomposition analysis, we quantified the global and national health consequences of population ageing on cancer-related DALYs from 1990 to 2019. Specifically, we calculated the absolute (the number of cancer-related DALYs) and the relative contribution (obtained by dividing attributed cancer-related DALYs by the total DALYs in 1990 and multiplying by 100%). Importantly, this proportion may exceed 100% in cases where the attributed DALYs in a given year exceeded the total DALYs in 1990, indicating an increase in cancer-related DALYs. Conversely, a negative effect denotes a decline in total cancer-related DALYs. Lastly, we used smoothing spline models to examine the relationship between attributed ageing and SDI from 1990 to 2019.
We analysed all data using R, version 4.2.2 (R Core Team, Vienna, Austria). We followed the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement in presenting our findings (Table S2 in the Online Supplementary Document) [19].
RESULTS
Population ageing
The number of cancer-related DALYs population among individuals aged ≥65 years increased by 95.14% from 1990 (52.25 million) to 2019 (101.96 million). Stratified by SDI, we observed greater increases in cancer-related DALYs among adults (≥65 years) in the high-, high-middle-, middle-, and low-middle-SDI countries than in the low-SDI countries (high SDI: 19.23 million (48.32%) in 1990 to 29.68 million (58.28%) in 2019; high-middle SDI:15.72 million (34.10%) in 1990 to 28.29 million (44.83%) in 2019; middle SDI: 11.06 million (27.31%) in 1990 to 28.20 million (37.75%) in 2019; low-middle SDI: 4.44 million (24.55%) in 1990 to 11.74 million (30.76%) in 2019; low SDI: 1.78 million (24.94%) in 1990 to 4.00 million (25.97%) in 2019 (Table 1).
Table 1. Cancer-related DALYs (in 100 000) in 1990 and 2019, worldwide and by regions, according to the SDI*
| Regions according to SDI | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Global | High | High-middle | Middle | Low-middle | Low | |||||||
| 1990 | 2019 | 1990 | 2019 | 1990 | 2019 | 1990 | 2019 | 1990 | 2019 | 1990 | 2019 | |
| Total number | 1516.73 | 2424.12 | 397.96 | 509.28 | 460.92 | 630.88 | 404.99 | 746.92 | 180.76 | 381.69 | 71.38 | 154.05 |
| Sex | ||||||||||||
| Men | 857.34 (56.53) | 1361.93 (56.18) | 223.11 (56.06) | 287.98 (56.55) | 274.74 (59.61) | 376.04 (59.61) | 230.76 (56.98) | 434.43 (58.16) | 93.70 (51.84) | 190.47 (49.90) | 34.67 (48.57) | 72.33 (46.95) |
| Women | 659.38 (43.47) | 1062.19 (43.82) | 174.85 (43.94) | 221.30 (43.45) | 186.18 (40.39) | 254.84 (40.39)) | 174.24 (43.02) | 312.49 (41.84) | 87.06 (48.16) | 191.22 (50.10) | 36.71 (51.43) | 81.72 (53.05) |
| Age in years | ||||||||||||
| 15–29 | 88.28 (5.82) | 92.91 (3.83) | 9.99 (2.51) | 6.73 (1.32) | 20.91 (4.54) | 14.63 (2.32) | 33.04 (8.16) | 29.88 (4.00) | 17.72 (9.80) | 26.51 (6.95) | 6.57 (9.20) | 15.10 (9.80) |
| 30–64 | 905.94 (59.73) | 1311.65 (54.11) | 195.69 (49.17) | 205.76 (40.40) | 282.83 (61.36) | 333.40 (52.85) | 261.36 (64.53) | 435.07 (58.25) | 118.66 (65.65) | 237.75 (62.29) | 47.01 (65.86) | 98.94 (64.23) |
| 65–74 | 332.13 (21.90) | 598.93 (24.71) | 108.24 (27.20) | 147.4 (28.94) | 101.76 (22.08) | 168.68 (26.74) | 77.92 (19.24) | 179.29 (24.00) | 31.19 (17.25) | 76.29 (19.99) | 12.87 (18.03) | 26.98 (17.51) |
| 75–84 | 162.31 (10.70) | 328.92 (13.57) | 68.83 (17.30) | 107.72 (21.15) | 48.53 (10.53) | 89.36 (14.16) | 28.65 (7.07) | 85.49 (11.45) | 11.76 (6.51) | 34.97 (9.16) | 4.45 (6.23) | 11.22 (7.28) |
| 85–95 | 28.07 (1.85) | 91.72 (3.78) | 15.21 (3.82) | 41.68 (8.18) | 6.89 (1.49) | 24.81 (3.93) | 4.03 (1.00) | 17.19 (2.30) | 1.44 (0.80) | 6.17 (1.62) | 0.49 (0.69) | 1.82 (1.18) |
DALYs – disability-adjusted life years, SDI – socio-demographic index
*Presented as n (%) unless specified otherwise.
Global cancer-related DALYs attributed to population growth and ageing
Population growth was the main contributor to cancer-related DALYs globally, followed by population ageing. Using the figures from 1990 as a reference for each subsequent year, global cancer-related DALYs attributed to population growth and ageing increased steadily from 1991 to 2019. The numbers of cancer-related DALYs and proportions attributed to population growth, ageing, and epidemical change were 92.38 million (60.91%), 41.38 million (27.28%), and −43.02 million (−28.36%), respectively. The number and attributed proportion of cancer-related DALYs globally due to population growth consistently increased from 1991 to 2019, both in men (2019: 51.69 million (60.29%)) and women (2019: 40.62 million (61.61%)). The corresponding cancer-related DALYs attributed to the global population ageing in 2019 were 26.31 million (30.69%) for men and 16.27 million (24.68%) for women (Figure 1, Table 2). Our predictions suggest that cancer DALYs attributable to ageing will increase to 74.75 million per 100 000 population by 2030 (Figure S1 in the Online Supplementary Document).
Figure 1. Global changes in cancer-related DALYs attributed to population ageing, population growth and change in age-specific DALY rates from 1991 to 2019. Panel A. Global DALYs. Panel B. Global DALYs in men. Panel C. Global DALYs in women.
Table 2. Number of cancer-related DALYs (in 100 000) and the proportion attributed to population ageing by SDI, 1990–2019*
| Population ageing, n (attributed %) | Population growth, n (attributed %) | Change in age-specific DALY rates, n (attributed %) | Change in total DALYs | |
|---|---|---|---|---|
| Global | ||||
| Men | 263.13 (30.69) | 516.88 (60.29) | −275.43 (−32.13) | 504.58 |
| Women | 162.74 (24.68) | 406.23 (61.61) | −166.16 (−25.20) | 402.81 |
| Total | 413.78 (27.28) | 923.81 (60.91) | −430.20 (−28.36) | 907.39 |
| High SDI | ||||
| Men | 80.38 (36.03) | 74.41 (33.35) | −89.92 (−40.30) | 64.87 |
| Women | 40.13 (22.95) | 49.79 (28.47) | −43.47 (−24.86) | 46.45 |
| Total | 112.12 (28.17) | 122.66 (30.82) | −123.46 (−31.02) | 111.32 |
| High-middle SDI | ||||
| Men | 96.99 (35.30) | 112.55 (40.97) | −108.24 (−39.40) | 101.31 |
| Women | 50.31 (27.02) | 72.43 (38.90) | −54.10 (−29.06) | 68.65 |
| Total | 140.26 (30.43) | 183.88 (39.89) | −154.18 (−33.45) | 169.96 |
| Middle SDI | ||||
| Men | 109.04 (47.25) | 151.17 (65.51) | −56.54 (−24.50) | 203.67 |
| Women | 75.92 (43.57) | 119.30 (68.47) | −56.96 (−32.69) | 138.26 |
| Total | 184.51 (45.56) | 271.82 (67.12) | −114.41 (−28.25) | 341.92 |
| Low-middle SDI | ||||
| Men | 20.19 (21.55) | 79.67 (85.02) | −3.09 (−3.30) | 96.77 |
| Women | 22.10 (25.38) | 82.35 (94.58) | −0.29 (−0.34) | 104.16 |
| Total | 42.88 (23.72) | 162.13 (89.69) | −4.08 (−2.26) | 200.92 |
| Low SDI | ||||
| Men | −2.84 (−8.18) | 42.35 (122.15) | −1.85 (−5.34) | 37.66 |
| Women | −0.63 (−1.72) | 47.18 (128.53) | −1.54 (−4.20) | 45.01 |
| Total | −3.27 (−4.58) | 89.51 (125.40) | −3.58 (−5.01) | 82.67 |
DALYs – disability-adjusted life years, SDI – socio-demographic index
*The attributed proportion was calculated as the change in total DALYs attributed to population ageing between 1990 and 2019 divided by total DALYs in 1990 × 100%. The ratio was calculated as the change in total DALYs attributed to the change in mortality rate divided by that associated with population ageing.
Cancer-related DALYs attributed to population ageing by SDI
Over the past three decades, cancer-related DALYs due to population ageing have steadily increased in high-, upper-middle-, and lower-middle-income countries, with greatest increases in middle-SDI countries and greatest decreases in low-income countries (Figure 2, Table S3 in the Online Supplementary Document). The cancer-related DALYs attributed to population ageing between 1990 and 2019 were 11.21 million (28.17%), 14.03 million (30.43%), 18.45 million (45.56%), 4.29 million (23.72%), and −0.33 million (−4.58%) for high-, upper-middle-, middle-, lower-middle-, and low-income-SDI countries, respectively (Table 2, Figure S2 in the Online Supplementary Document). The proportion of cancer-related DALYs due to ageing linked to increased SDI values (P < 0.05) showed a bell-shaped curve (Figure S3 in the Online Supplementary Document).
Figure 2. Cancer-related DALYs attributed to population ageing by SDI, 1990–2019. Panel A. Number of DALYs attributed ageing. Panel B. Proportion of DALYs attributed ageing. Panel C. Number of DALYs attributed ageing in men. Panel D. Proportion of DALYs attributed ageing in men. Panel E. Number of DALYs attributed to ageing in women. Panel F. Proportion of DALYs attributed to ageing in women.
We observed similar patterns for men and women (Figure 2, Table S4–5 in the Online Supplementary Document). Specifically, the cancer-related DALYs attributed to population ageing among men were 8.04 million (36.03%), 9.70 million (35.3%), 10.90 million (47.25%), 2.02 million (21.55%) and −0.28 million (−8.18%) in high-, upper-middle-, middle-, lower-middle-, and low-income countries between 1990 and 2019, respectively. The corresponding cancer-related DALYs (100 000) attributed to ageing among women were 4.01 million (22.95%), 5.03 million (27.02%), 7.59 million (43.57%), 2.21 million (25.38%), and −0.06 million (−1.72%), respectively (Table 2).
Cancer-related DALYs attributed to population ageing in 204 countries/territories
Cancer-related DALYs attributed to ageing increased in 171 countries/territories between 1990 and 2019 (Table 3). China saw the highest increase in DALYs due to population ageing (24.00 million). DALYs attributed to ageing worldwide were the highest in the USA (3.27 million), India (3.06 million), Japan (2.87 million), and Brazil (1.56 million). Conversely, cancer-related DALYs attributed to ageing decreased in 33 countries/territories from 1990 to 2019. The highest decrease occurred in Afghanistan, Pakistan, Nigeria, Ethiopia, and Mozambique (Figure 3, Table S6 and Figure S4 in the Online Supplementary Document). The attributed proportion of DALYs due to ageing varied from −63.36% in Equatorial Guinea to 155.84% in the United Arab Emirates. Population ageing was strongly associated with increased cancer-related DALYs in the United Arab Emirates (155.84%), Northern Mariana Islands (110.22%), Bahrain (93.78%), United States Virgin Islands (78.45%), and the Republic of Korea (77.44%) (Figure 3, Table S6 and Figure S4 in the Online Supplementary Document).
Table 3. Top 10 countries/territories with the highest increase and decrease in cancer-related DALYs attributed to population ageing between 1990 and 2019
| Total | Men | Women | ||||
|---|---|---|---|---|---|---|
| Country/territory | Attributed to ageing | Country/territory | Attributed to ageing | Country/territory | Attributed to ageing | |
| Attributed numbers (in 100 000) population, ordered by rank | ||||||
| 1 | China | 240.03 | China | 157.20 | China | 84.96 |
| 2 | USA | 32.69 | USA | 22.36 | India | 15.84 |
| 3 | India | 30.60 | Japan | 20.54 | USA | 12.20 |
| 4 | Japan | 28.74 | India | 14.44 | Japan | 10.02 |
| 5 | Brazil | 15.60 | Russian Federation | 10.74 | Brazil | 7.20 |
| 6 | Russian Federation | 12.88 | Germany | 9.21 | Indonesia | 5.61 |
| 7 | Republic of Korea | 11.06 | Brazil | 8.27 | Thailand | 4.38 |
| 8 | Germany | 10.97 | Republic of Korea | 8.16 | Russian Federation | 4.31 |
| 9 | Indonesia | 10.32 | France | 6.59 | Republic of Korea | 3.61 |
| 10 | Thailand | 10.31 | Italy | 6.39 | Germany | 3.48 |
| 195 | Somalia | −0.09 | Burkina Faso | −0.26 | Mali | −0.17 |
| 196 | Chad | −0.13 | Guinea | −0.29 | Cameroon | −0.17 |
| 197 | Guinea | −0.13 | Uganda | −0.33 | Burkina Faso | −0.19 |
| 198 | Mali | −0.16 | Mali | −0.33 | Mozambique | −0.25 |
| 199 | Democratic Republic of the Congo | −0.17 | Democratic Republic of the Congo | −0.40 | Uganda | −0.33 |
| 200 | Mozambique | –0.18 | Mozambique | −0.42 | Democratic Republic of the Congo | −0.35 |
| 201 | Ethiopia | −0.25 | Ethiopia | −0.72 | Ethiopia | −0.46 |
| 202 | Nigeria | −0.33 | Pakistan | −1.73 | Nigeria | −0.98 |
| 203 | Pakistan | −0.52 | Nigeria | −1.93 | Pakistan | −1.27 |
| 204 | Afghanistan | −0.78 | Afghanistan | −1.99 | Afghanistan | −1.27 |
| Attributed proportion, ordered by rank | ||||||
| 1 | United Arab Emirates | 155.84 | United Arab Emirates | 162.25 | United Arab Emirates | 149.50 |
| 2 | Northern Mariana Islands | 110.22 | Northern Mariana Islands | 104.85 | Northern Mariana Islands | 117.57 |
| 3 | Bahrain | 93.78 | Bahrain | 101.26 | Bahrain | 85.14 |
| 4 | United States Virgin Islands | 78.45 | Republic of Korea | 94.25 | Taiwan (Province of China) | 75.59 |
| 5 | Republic of Korea | 77.44 | United States Virgin Islands | 93.12 | Guam | 68.67 |
| 6 | Djibouti | 72.25 | Djibouti | 90.89 | Thailand | 67.10 |
| 7 | Thailand | 71.51 | Greenland | 81.42 | Palau | 66.46 |
| 8 | Taiwan (Province of China) | 69.45 | Singapore | 78.39 | Kuwait | 64.67 |
| 9 | Singapore | 69.30 | Venezuela (Bolivarian Republic of) | 77.40 | Republic of Korea | 64.10 |
| 10 | Guam | 68.92 | Thailand | 75.44 | United States Virgin Islands | 62.65 |
| 195 | Burkina Faso | −22.38 | Sao Tome and Principe | −30.52 | Sao Tome and Principe | −12.36 |
| 196 | Nigeria | −22.54 | Chad | −31.41 | Somalia | −15.15 |
| 197 | Guinea | −23.56 | Cabo Verde | −32.18 | Mozambique | −18.78 |
| 198 | Mali | −25.26 | Benin | −32.40 | Guinea | −20.92 |
| 199 | Mozambique | −27.31 | Sierra Leone | −37.61 | Sierra Leone | −22.84 |
| 200 | Sierra Leone | −29.23 | Burkina Faso | −39.05 | Mali | −23.59 |
| 201 | Chad | −33.52 | Mozambique | −43.59 | Liberia | −28.04 |
| 202 | Liberia | −43.84 | Liberia | −61.80 | Equatorial Guinea | −31.18 |
| 203 | Afghanistan | −61.62 | Afghanistan | −88.72 | Chad | −35.36 |
| 204 | Equatorial Guinea | −63.36 | Equatorial Guinea | −100.62 | Afghanistan | −43.72 |
Figure 3. Proportion of DALYs attributed to population ageing between 1990 and 2019 in 204 countries and territories. Panel A. Number of DALYs attributed to ageing. Panel B. Proportion of DALYs attributed to ageing.
For men and women, the top 10 countries/territories with the highest increase and decrease of cancer-related DALYs associated with population ageing were similar. China has the highest number of DALYs due to ageing, and the United Arab Emirates has the highest increase in the proportion of DALYs attributed to population ageing. Whereas the highest decrease of DALYs attributed to population ageing was in Afghanistan and Equatorial Guinea (Figure 3, Table 3, Table S6 and Figure S4 in the Online Supplementary Document).
The top 10 cancer types with the highest increase in cancer-related DALYs attributed to population ageing
The top 10 cancer types with the highest increase in the absolute number of cancer-related DALYs associated with population ageing between 1990 and 2019 were tracheal, bronchus, and lung (8.72 million); stomach (5.06 million); colorectal (4.28 million); breast (3.13 million); oesophagal (2.42 million); liver (2.38 million); prostate (1.95 million); pancreatic (1.90 million); and cervical cancers (1.25 million) (Figure 4, Table 4; Table S7 in the Online Supplementary Document). Notably, prostate (44.75%); pancreatic (40.93%); non-melanoma skin (38.03%); bladder (37.10%); multiple myeloma (37.03%); kidney (35.32%); colorectal (34.67%); gallbladder and biliary tract (33.97%); and tracheal, bronchus, and lung cancers (32.20%) had the highest increases in the proportion of cancer-related DALYs attributed to ageing. We found similar results for men and women (Table 4). Moreover, 13 out of 30 cancer types display the highest DALYs attributed to ageing in middle-SDI regions, such as oesophagal cancer, stomach cancer, and cervical cancer (Figure S5 in the Online Supplementary Document), while 16 out of 30 cancer types had the highest DALYs in high-SDI regions, such as tracheal, bronchus, and lung cancer; colorectal cancer; and prostate cancer.
Figure 4. Cancer-related DALYs attributed to population ageing in 30 cancer types, 1990–2019. Panel A. Number of DALYs attributed to ageing. Panel B. Number of DALYs attributed to ageing in men. Panel C. Number of DALYs attributed to ageing in women.
Table 4. Top 10 cancer types with the highest increase in cancer-related DALYs attributed to population ageing between 1990 and 2019*
| Total | Men | Women | ||||
|---|---|---|---|---|---|---|
| Cancer causes | Attributed to ageing | Cancer causes | Attributed to ageing | Cancer causes | Attributed to ageing | |
| Attributed numbers (in 100 000), ordered by rank | ||||||
| 1 | Tracheal, bronchus and lung cancer | 87.23 | Tracheal, bronchus and lung cancer | 68.95 | Breast cancer | 29.53 |
| 2 | Stomach cancer | 50.55 | Stomach cancer | 35.60 | Tracheal, bronchus, and lung cancer | 22.51 |
| 3 | Colon and rectum cancer | 42.80 | Colon and rectum cancer | 25.47 | Colon and rectum cancer | 18.08 |
| 4 | Breast cancer | 31.30 | Prostate cancer | 23.37 | Stomach cancer | 16.50 |
| 5 | Esophageal cancer | 24.23 | Esophageal cancer | 18.73 | Cervical cancer | 12.22 |
| 6 | Liver cancer | 23.83 | Liver cancer | 17.61 | Pancreatic cancer | 7.93 |
| 7 | Prostate cancer | 19.46 | Pancreatic cancer | 11.19 | Ovarian cancer | 7.56 |
| 8 | Pancreatic cancer | 19.00 | Bladder cancer | 8.18 | Liver cancer | 6.59 |
| 9 | Cervical cancer | 12.49 | Larynx cancer | 6.20 | Esophageal cancer | 6.30 |
| 10 | Other malignant neoplasms | 9.66 | Lip and oral cavity cancer | 5.98 | Other malignant neoplasms | 4.18 |
| Attributed proportion, ordered by rank | ||||||
| 1 | Prostate cancer | 44.75 | Prostate cancer | 53.74 | Pancreatic cancer | 39.24 |
| 2 | Other neoplasms | 41.97 | Other neoplasms | 48.68 | Other neoplasms | 37.40 |
| 3 | Pancreatic cancer | 40.93 | Bladder cancer | 43.08 | Tracheal, bronchus and lung cancer | 33.36 |
| 4 | Non-melanoma skin cancer | 38.03 | Pancreatic cancer | 42.70 | Multiple myeloma | 33.34 |
| 5 | Bladder cancer | 37.10 | Multiple myeloma | 41.38 | Bladder cancer | 32.51 |
| 6 | Multiple myeloma | 37.03 | Kidney cancer | 39.95 | Colon and rectum cancer | 30.65 |
| 7 | Kidney cancer | 35.32 | Colon and rectum cancer | 39.52 | Kidney cancer | 30.24 |
| 8 | Colon and rectum cancer | 34.67 | Gallbladder and biliary tract cancer | 39.27 | Gallbladder and biliary tract cancer | 30.07 |
| 9 | Gallbladder and biliary tract cancer | 33.97 | Mesothelioma | 37.32 | Lip and oral cavity cancer | 28.17 |
| 10 | Tracheal, bronchus and lung cancer | 32.20 | Breast cancer | 36.65 | Ovarian cancer | 28.00 |
*The attributed proportion for total was calculated as the number of DALYs attributed to population ageing for each cause of DALY/151.67 million (total DALYs in 1990) × 100%. The attributed proportion for men was calculated as the number of DALYs attributed to population ageing for each cause of DALY/85.73 million (total DALYs for men in 1990) × 100%. The attributed proportion for women was calculated as the number of DALYs attributed to population ageing for each cause of DALY/2.42 million (total DALYs for women in 1990) × 100%.
DISCUSSION
Our decomposition analysis of the global and national impact of population ageing on cancer-related DALYs from 1990 to 2019 had three key findings. First, the global cancer-related DALYs attributed to population growth and ageing increased rapidly from 1991 to 2019. Second, the increase in cancer-related DALYs due to ageing showed considerable diversity across regions and countries, with a bell-shaped pattern emerging in relation to socioeconomic development, affecting middle-SDI countries the most. Overall, 83.8% of countries were associated with increased DALYs due to ageing, with the highest increase in China (number of DALYs) and the United Arab Emirates (proportion). Third, certain cancer types were disproportionately affected by population ageing. Specifically, tracheal, bronchus, and lung cancer; stomach cancer; and colorectal cancer had the highest increase in DALYs associated with ageing. In contrast, prostate cancer, pancreatic cancer, and non-melanoma skin cancer showed the highest attributed proportions of DALYs due to ageing. These findings highlight the need for countries to adopt tailored preventive and therapeutic strategies for older individuals to effectively manage the rising health care demands resulting from population ageing.
The impact of ageing on DALYs is direct and significant. With an increase in the proportion of elderly individuals in the population, the incidence of age-related chronic diseases (such as cancer, cardiovascular diseases, diabetes, etc.) increases. Elderly individuals, due to declining immune system function and the coexistence of multiple chronic conditions, are more susceptible to health damage, leading to a decline in quality of life and premature death. Therefore, ageing directly increases the loss of healthy life years due to disease and disability, significantly increasing the burden of DALYs on society. The impact of population ageing can be attributed to population growth, increases in life expectancy, and decreases in fertility [3,20]. First, the global population growth rate, which was 2.1% annually in 1962–65, has declined to <1% in 2020 and is expected to decrease further to approximately 0.5% by 2050. Countries like Japan and South Korea are experiencing population declines, and China reported its first negative population growth in 2022 [21–23]. Second, following consistent increases in recent years, the global average life expectancy is projected to rise from 72.8 years in 2019 to 77.2 years by 2050. Lastly, the global average fertility rate has decreased from 5.0 in 1950 to 2.3 in 2021, with projections suggesting further reductions in the future. These trends collectively contribute to the exacerbation of population ageing [3,20], emphasising the importance of initiatives promoting healthy ageing to prevent and control cancer.
According to the World Health Organization report, East and Southeast Asia, Latin America, and the Caribbean are experiencing the fastest rates of population ageing [3]. These regions, including countries such as China, Korea, Japan, Thailand, and Singapore, also saw the highest increases in cancer burden in our study. Interestingly, population ageing was linked to a decrease in cancer-related DALYs in several low-SDI countries such as Afghanistan, Guinea, and Liberia. This decrease may be influenced by data reporting quality; diagnostic and treatment capacities; sociopolitical stability; and health care conditions, rather than a true decline in cancer burden. We further observed a bell-curved relationship, with a transitional stage where middle SDI countries are most affected. First, the disease burden in middle-income countries is mostly transitioning from a high burden of infectious diseases to a high burden of chronic diseases. Specifically, infectious diseases become more effectively controlled with socioeconomic developments, while lifestyle changes (such as urbanisation and Westernised diets) significantly increase cancer risk. Therefore, non-communicable diseases (such as cancer) have become major health threats. These diseases are more common in the elderly population, thereby exacerbating the health burden associated with ageing [24]. Second, while middle-SDI countries are experiencing rapid economic development, their social welfare systems, health care resource allocation, and infrastructure construction may not fully keep pace with population ageing. This leads to issues with health care accessibility, health insurance coverage, and pensions, with coverage still incomplete and protection levels limited [25]. The most populated middle-SDI countries, such as India, have a large and rapidly ageing population, facing significant income disparity, uneven regional development and limited resources in the public system. Further expanding health insurance coverage, particularly in rural and impoverished areas, could ensure more people can afford cancer treatment. Healthcare systems need to prepare for and manage the substantial impact of population ageing, especially in highly populated middle-SDI countries with an existing cancer epidemic.
A study focussing on fractions and trends of cancer deaths attributable to population ageing in China used a different methodological approach, whereby the authors decomposed four components: population ageing, population growth, age-specific incidence, and deaths due to case fatality rates [14]. The core indicator was cancer deaths due to ageing, and their findings are consistent with our study. Another decomposition analysis of global cancer occurrence used the decomposition method as we did in our study, but with the analysis indicators being incidence and mortality; they found that the global increase in cancer cases and deaths was mainly attributed to population ageing [6]. The greatest attribution in our study, in turn, was to population growth. Additionally, other studies using decomposition analysis of DALYs and deaths associated with liver cancer [26], diabetes [7], ischemic stroke [27], and the top 10 causes of global death [5] had consistent results, with the largest attribution coming from population growth, followed by population ageing. This highlights how ageing in upper to middle-income countries and population growth in low to middle-income countries hinder efforts to reduce the burden of disease.
Based on the analysis of cancer types, we observed that lung and gastrointestinal cancers are related to the highest increase in the number of cancer-related DALYs associated with ageing, mainly resulting from high incidence and mortality globally. Some effective prevention measures include strengthening smoking cessation programmes for the elderly to reduce the impact of tobacco exposure on lung cancer incidence and improving the living environment of the elderly to reduce exposure to air pollution and harmful substances. Regular lung cancer check-ups for high-risk elderly individuals for early detection and intervention are also recommended. Stomach cancer, meanwhile, has a high incidence in East Asian countries, such as China, Japan, and Korea. With global population ageing, the incidence and mortality rates of gastric cancer in the elderly are significantly increasing, leading to a higher DALY burden. Prevention measures could include the eradication treatment of Helicobacter pylori; reducing the intake of high-salt or pickled foods; and increasing the consumption of fruits and vegetables. Considering the invasiveness and low compliance of endoscopy, it might also be necessary to explore novel screening technologies such as magnetic capsule endoscopy, or to develop innovative and feasible non-invasiveness biomarkers for gastric cancer diagnosis, such as cell-free DNA, DNA methylation, or exosomes. Notably, prostate and pancreas cancer contributed the highest attributed proportion due to ageing, aligning with findings of studies on the occurrence and mortality of cancer in ageing populations [6]. Therefore, tailored preventative and therapeutic strategies focussing on the specific cancer types most affected by population ageing are essential.
The impact of ageing on cancer-related DALYs differs between men and women, largely due to the incidence and disease burden of gender-specific cancers. For example, breast cancer is specific to women and has a high incidence rate among elderly women; prostate cancer is specific to men, and its incidence increases significantly in elderly men. Compared with elderly women, elderly men may face less family care and social support, leading to lower adherence to disease management, and they may also be more significantly impacted by occupational exposures (e.g. smoking, occupational carcinogens). Establishing social support networks for this population by providing psychological counselling and family care support could help address these issues. Moreover, men may be less likely to seek preventive health care services due to cultural or personal factors, particularly in old age, resulting in late cancer diagnoses and poorer treatment outcomes. While younger women may frequently utilise prenatal and gynaecological check-ups, elderly women, particularly in low-income countries, may have reduced access to such services [28], as these systems are more likely to face health care resource shortages. Screening programmes for breast and cervical cancers should be maintained or redesigned for sustainability, ensuring elderly women have access to necessary screening and preventive services.
Cultural attitudes towards ageing and cancer treatment in different regions likewise affect DALYs. For example, they may influence early detection and willingness to seek treatment [29], whereby people may lack sufficient awareness of health check-ups and cancer screening, leading to late cancer detection. Increasing health education and promotion can change this attitude and increase participation in early screening programmes. In some Asian countries, cultural taboos surrounding illness may discourage people from undergoing routine checks. Cultural distrust of modern medicine, cultural beliefs, traditional customs, religious views, folk remedies, and preferences for traditional treatment methods may delay early cancer diagnosis and intervention with modern treatments. For example, rapid ageing in Japan and the emphasis on health in traditional culture, along with the universal health care system, resulted in higher participation rates in early screening and treatment, as was the case with the national gastric cancer screening programme [30]. Conversely, in India, cultural diversity; the coexistence of traditional and modern medicine; taboos and sensitivities surrounding illness in traditional customs; religious views; and distrust of modern medicine may lead to delayed medical visits [31].
An in-depth examination of health care system variations is essential for addressing the challenges posed by the burden of serious diseases. China’s successful policy and health care reform, which has expanded the coverage and reimbursement rates of medical insurance, especially for the high costs of cancer treatment, has provided more support to citizens and reduced poverty due to illness. This has improved accessibility to medical services, particularly for rural and low-income populations, such as through the New Rural Cooperative Medical Scheme. Densely populated regions have struggled to establish private health insurance policies [3,4,32], with the growing elderly population imposing budgetary challenges for families and societies, as have costs related to diagnostics and treatment, drugs, hospitalisation and care. These challenges have been exacerbated by a related loss of productivity, growing burdens on caregivers, and social welfare costs. Future economic research should use novel health economic evaluation methods to explore the cost-effectiveness of various strategies for cancer prevention, early screening, treatment, and rehabilitation in middle-SDI countries. These could include evaluations of the applicability and feasibility of these strategies to optimise resource allocation and enhance the sustainability of health care systems, as well as cost-effectiveness or cost-utility analyses to quantify the impact of this burden on the national economy and health care systems, ensuring long-term economic and social benefits.
There are several possible targeted health care strategies for tackling these challenges [33]. The first option is prevention strategies, governments could develop or strengthen laws and regulations, such as public smoking bans; implement environmental governance measures; promote the widespread vaccination against human papillomavirus (HPV) and hepatitis B to prevent related cancers such as cervical and liver cancer; or enhance vaccination coverage through free or low-cost immunisation programs. Another approach is the establishment of screening programmes, where governmental bodies could implement national cancer screenings and early detection programs for older adults (<75 years); formulate clear screening guidelines and ensure the accessibility of screening programs; or launch mobile screening services in rural and remote areas to ensure early detection opportunities for these populations. They could also collaborate with non-governmental organisations to provide technical and financial support. Lastly, they should further develop cancer screening plans tailored to local conditions. For resource-limited, but high-incidence regions, health care authorities could focus on low-cost cancer screening options and building basic medical service capabilities. The adoption of telemedicine and digital health solutions can likewise enhance care access, particularly for older patients with mobility issues. A third possible avenue could be health care infrastructure enhancements, whereby health care stakeholders could establish or upgrade cancer treatment centres in the most burdened areas, equipped with advanced diagnostic and treatment facilities, or increase training for oncologists, nurses, and other relevant health care professionals to enhance their diagnostic and treatment capabilities. They could also act by improving diagnostic accuracy and accelerating the diagnostic process by introducing the application of artificial intelligence and ensuring equitable distribution of medical resources, especially in rural and remote areas, to reduce disparities in medical services. Lastly, promoting collaborations among government agencies, health care providers, and advocacy organisations is crucial to effectively addressing the multifaceted challenges of an ageing population.
Cancer screening offers various benefits in reducing cancer risk and premature death for the entire population [34–36]. However, in older adults, concerns arise that the potential disadvantages of screening might outweigh these benefits. Screening detects slow-growing cancers that may require ten or more years to appear. Older individuals, therefore, face a higher competing risk of cancer-unrelated death, diminishing their potential advantages, and they may also experience psychological stress due to positive screening results and unnecessary diagnostic interventions. Moreover, over-screening is common with such screening programmes, occurring when the life expectancy in older adults is less than 10–15 years or when they exceed the recommended age thresholds of screening [37,38]. For example, a nationwide study in the USA found that over half of older individuals had recently undergone unnecessary screenings for breast, colorectal, and cervical cancer [39], indicating a trend towards over-screening in this age group. The specific benefits and harms of screening in older adults are often underestimated due to the extrapolation of indirect evidence from younger populations, as most clinical trials exclude participants aged >75 years and as screening guidelines offer limited evidence-based recommendations for discontinuation [40]. More inclusive screenings and real-world research involving older adults could help address these gaps.
The main strength of this study is our quantification of the influence of ageing on global and national cancer-related DALYs between 1990 and 2019. This rigorous decomposition analysis enabled us to make cross-country comparisons and identify countries at high risk of cancer burden due to population ageing. Moreover, we included 30 cancer types in our analysis, and also explored the relationship between the attributed ageing proportion and the SDI, providing valuable insights into health care policies promoting healthy ageing. However, this study also has some limitations. First, population ageing results from declining fertility and increasing life expectancy [41], yet our analysis did not differentiate between these factors. Second, data gaps in the GBD data set, primarily due to limited high-quality country-level data, especially in resource-poor, low-income countries, constrained our study. Future research could address these gaps by using more rigorous, higher-quality data collection approaches.
CONCLUSIONS
Population ageing contributed to global cancer-related DALYs significantly between 1990 and 2019, with cancer-related DALYs attributed to ageing continuing to rise. The increase due to ageing varied considerably across regions and countries, showing a bell-shaped pattern when analysed by socioeconomic development, whereby middle-SDI countries are affected the most. We observed increased DALYs due to ageing in 83.8% of countries, with the highest increases in China (numbers) and the United Arab Emirates (proportion). Globally, specific cancer types, particularly tracheal, bronchus, and lung cancer, stomach cancer, and colorectal cancer were disproportionately affected by population ageing, with the highest attributed proportions of DALYs due to ageing seen in prostate, pancreatic, and non-melanoma skin cancers. These findings provide a valuable foundation for tailored health care strategies and resource allocation to address this critical public health issue.
Additional material
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Acknowledgements
We appreciate the valuable work done by the GBD Collaborators.