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The United Nations Sustainable Development Goals for 2030 include reducing premature mortality from noncommunicable diseases by one-third. Although previous modeling studies have predicted premature mortality from noncommunicable diseases, the predictions for cancer and its subcategories are less well understood in China.
The aim of this study was to project premature cancer mortality of 10 leading cancers in Hunan Province, China, based on various scenarios of risk factor control so as to establish the priority for future interventions.
We used data collected between 2009 and 2017 from the Hunan cancer registry annual report as empirical data for projections. The population-attributable fraction was used to disaggregate cancer deaths into parts attributable and unattributable to 10 risk factors: smoking, alcohol use, high BMI, diabetes, physical inactivity, low vegetable and fruit intake, high red meat intake, high salt intake, and high ambient fine particulate matter (PM2.5) levels. The unattributable deaths and the risk factors in the baseline scenario were projected using the proportional change model, assuming constant annual change rates through 2030. The comparative risk assessment theory was used in simulated scenarios to reflect how premature mortality would be affected if the targets for risk factor control were achieved by 2030.
The cancer burden in Hunan significantly increased during 2009-2017. If current trends for each risk factor continued to 2030, the total premature deaths from cancers in 2030 would increase to 97,787 in Hunan Province, and the premature mortality (9.74%) would be 44.47% higher than that in 2013 (6.74%). In the combined scenario where all risk factor control targets were achieved, 14.41% of premature cancer mortality among those aged 30-70 years would be avoided compared with the business-as-usual scenario in 2030. Reductions in the prevalence of diabetes, high BMI, ambient PM2.5 levels, and insufficient fruit intake played relatively important roles in decreasing cancer premature mortality. However, the one-third reduction goal would not be achieved for most cancers except gastric cancer.
Existing targets on cancer-related risk factors may have important roles in cancer prevention and control. However, they are not sufficient to achieve the one-third reduction goal in premature cancer mortality in Hunan Province. More aggressive risk control targets should be adopted based on local conditions.
Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020 or nearly one in six deaths [
Cancer is a disease commonly believed to be preventable. In a nationwide study on the risk factors of cancer across 31 provinces of mainland China, Chen et al [
In 2016, the United Nations (UN) set a target to achieve a relative one-third reduction from the 2015 level in premature mortality from noncommunicable diseases (NCDs), including cancer, by 2030 in its Sustainable Development Goal (SDG) target 3.4 [
Hunan Province, located in the central-south of China, is one of the most populous provinces with over 73 million residents. The gross domestic product of Hunan Province was 4.18 trillion yuan (approximately US $606.03 billion) in 2020, ranking 9th among 32 provincial administrative divisions in mainland China. Cancer incidence and mortality rates in Hunan Province in 2018 were 248.24/100,000 and 154.50/100,000, respectively, representing a medium level across the nation [
Since publication of the World Health Organization (WHO) Global Monitoring Framework in 2013 [
In general, most previous modeling studies focused on overall NCDs at a national level, and there is limited evidence on cancer and its subcategories at a local provincial level. Moreover, the WHO’s voluntary global targets did not include dietary and environmental factors, which are known to be important risk factors for cancer, and it remains unknown whether and how control of these factors may help with cancer prevention.
In light of such research gaps, we performed this study to project premature mortality from cancer in Hunan Province through 2030 under different risk factor control scenarios. Specifically, we projected whether SDG target 3.4 can be met for cancer prevention in Hunan Province and how many deaths from cancers can be prevented if all selected risk factors were controlled. The risk factors were selected based on the Global Monitoring Framework [
This analysis centered on publicly available data with no identifiable information on the subjects studied. Therefore, research ethics board approval was not required for this study.
Cancer sites were selected based on the rank of cancer deaths in the Hunan cancer registry annual report series over the past 10 years, while also taking into account endemic cancer types associated with special local lifestyle habits in Hunan, such as oral cavity cancer and nasopharynx cancer. Finally, 10 leading subcategories were selected, including cancers of the lung, esophagus, liver, stomach, pancreas, prostate, breast, colorectum, oral cavity, and nasopharynx.
Correspondingly, risk factors were selected based on the following criteria: (1) causally associated with cancers as evidenced by the latest Global Burden of Disease (GBD) study [
The National Program of Cancer Registries (NPCR), launched in 2008, is responsible for the collection, evaluation, and publication of cancer data in China. In Hunan Province, 70.4% of cancer patients have registered in the NPCR to date [
To ensure data quality, the relative risk (RR) estimates for risk-cancer pairs were preferentially derived from summary results published by the GBD series and the CUP Expert Report. If these were not available, priority was given to meta-analyses or systematic reviews conducted in China or Asia. Studies that provided RRs on our predefined metrics were preferred and estimates for both genders were assumed to be equal if no separate values were available.
Based on the Global Monitoring Framework and Healthy China 2030, we constructed 12 separate scenarios of risk factor exposure for the year 2030. Among them, the baseline scenario projected cancer mortality to 2030 assuming that all risk factors continue to follow current trends (see
Scenario specifications in risk factor exposure projection according to the World Health Organization Global Monitoring Framework.
Scenario | Scenario specification |
Natural trend | Age- and sex-specific risk factor exposures were projected assuming the annual change rate remained similar to that between 2009 and 2017. |
Harmful alcohol use | Age- and sex-specific prevalence of harmful alcohol use is reduced relatively by 10% from the 2013 level. All other risk factors follow the natural trends. |
Smoking | Age- and sex-specific prevalence of smoking in 2030 is reduced relatively by 30% from the 2013 level. All other risk factors follow the natural trends. |
Physical inactivity | Age- and sex-specific prevalence of physical inactivity in 2030 is 10% relatively lower than that in 2013. All other risk factors follow the natural trends. |
Diabetes | Age- and sex-specific prevalence of diabetes in 2030 is the same as in 2013. All other risk factors follow the natural trends. |
High BMI | Age- and sex-specific distributions of BMI in 2030 are the same as in 2013. All other risk factors follow the natural trends. |
Low vegetable intake | Age- and sex-specific prevalence of low vegetable intake in 2030 is reduced relatively by 30% from the 2013 level. All other risk factors follow the natural trends. |
Low fruit intake | Age- and sex-specific prevalence of low fruit intake in 2030 is reduced relatively by 30% from the 2013 level. All other risk factors follow the natural trends. |
High red meat intake | Age- and sex-specific prevalence of high red meat intake in 2030 is reduced relatively by 30% from the 2013 level. All other risk factors follow the natural trends. |
High salt intake | Age- and sex-specific mean population salt intake in 2030 is reduced relatively by 30% from the 2013 level. All other risk factors follow the natural trends. |
PM2.5a | The annually averaged PM2.5 concentration in 2030 is reduced to 15 μg/m3, according to grade I of air quality standard GB3095-2012. All other risk factors follow the natural trends. |
All targets are achieved in 2030 | All targets described above are achieved in 2030. |
aPM2.5: fine particulate matter.
Our analysis was focused on examining premature mortality under 12 different scenarios. Consistent with the global documents, we defined premature cancer mortality as the probability of dying from cancers between the ages of 30 and 70 years [
The premature mortality rates from selected cancers are shown in
Estimated cancer deaths and mortality rates in Hunan Province, China, 2009-2017.
Year | Total | Men | Women | |||||||
|
Deaths, n | Mortality rate (1/100,000) | Standardized mortality rate (1/100,000) | Deaths, n | Mortality rate (1/100,000) | Standardized mortality rate (1/100,000) | Deaths, n | Mortality rate (1/100,000) | Standardized mortality rate (1/100,000) | |
2009 | 81,159 | 117.56 | 89.53 | 50,480 | 140.88 | 108.62 | 30,679 | 92.49 | 69.51 | |
2010 | 83,332 | 127.03 | 87.13 | 53,938 | 159.69 | 110.53 | 29,394 | 92.07 | 62.85 | |
2011 | 88,269 | 123.61 | 83.53 | 56,536 | 152.84 | 104.19 | 31,733 | 92.34 | 62.21 | |
2012 | 95,044 | 132.08 | 91.14 | 62,883 | 168.78 | 117.78 | 32,161 | 93.11 | 63.74 | |
2013 | 96,173 | 133.84 | 87.14 | 63,640 | 171.43 | 112.76 | 32,533 | 94.71 | 61.18 | |
2014 | 102,821 | 142.47 | 92.58 | 67,338 | 180.00 | 119.36 | 35,483 | 102.51 | 65.26 | |
2015 | 102,310 | 140.91 | 91.50 | 66,543 | 176.93 | 117.34 | 35,767 | 102.75 | 65.11 | |
2016 | 108,097 | 147.48 | 97.57 | 70,590 | 185.88 | 125.61 | 37,507 | 106.52 | 68.81 | |
2017 | 111,719 | 152.91 | 99.38 | 72,708 | 192.26 | 128.18 | 39,011 | 111.00 | 69.91 | |
APCa (95%CI) | N/Ab | 3.13 (2.50-3.80) | 1.60 (0.50-2.70) | N/A | 3.43 (2.40-4.50) | 2.19 (0.60-3.40) | N/A | 2.54 (1.70-3.30) | 1.65 (0.60-2.70) | |
N/A | <.001 | .011 | N/A | <.001 | .002 | N/A | <.001 | .007 |
aAPC: annual percentage change.
bN/A: not applicable.
Premature mortality (%) from selected cancers in Hunan Province, China, 2009-2017.a
Year | Lung cancer | Gastric cancer | Liver cancer | Colorectal cancer | Esophageal cancer | Pancreatic cancer | Nasopharynx cancer | Oral cavity cancer | Prostate cancer | Breast cancer | All cancers |
2009 | 1.95 | 0.66 | 1.36 | 0.44 | 0.16 | 0.11 | 0.18 | 0.05 | 0.02 | 0.65 | 6.64 |
2010 | 2.02 | 0.60 | 1.64 | 0.34 | 0.16 | 0.10 | 0.19 | 0.04 | 0.07 | 0.32 | 6.53 |
2011 | 1.87 | 0.50 | 1.63 | 0.36 | 0.23 | 0.08 | 0.25 | 0.02 | 0.03 | 0.36 | 6.26 |
2012 | 2.25 | 0.55 | 1.46 | 0.54 | 0.29 | 0.12 | 0.28 | 0.05 | 0.03 | 0.49 | 7.06 |
2013 | 2.15 | 0.53 | 1.13 | 0.48 | 0.24 | 0.13 | 0.23 | 0.08 | 0.07 | 0.44 | 6.74 |
2014 | 2.42 | 0.57 | 1.27 | 0.55 | 0.25 | 0.13 | 0.23 | 0.09 | 0.06 | 0.55 | 7.31 |
2015 | 2.41 | 0.48 | 1.24 | 0.56 | 0.26 | 0.14 | 0.22 | 0.09 | 0.07 | 0.57 | 7.22 |
2016 | 2.55 | 0.56 | 1.35 | 0.62 | 0.29 | 0.16 | 0.23 | 0.11 | 0.07 | 0.62 | 7.83 |
2017 | 2.57 | 0.59 | 1.39 | 0.63 | 0.26 | 0.17 | 0.21 | 0.11 | 0.07 | 0.62 | 7.88 |
aPremature mortality is the probability of dying between the ages of 30 and 70 years from a specific cause that was calculated using the life table method.
Annual percentage change (APC) in premature mortality from selected cancers in Hunan Province, China, 2009-2017.
Cancer type | APC (95% CI) | ||||
Lung | 4.06 (2.50 to 5.60) | <.001 | |||
Gastric | –1.16 (–4.00 to 1.80) | .38 | |||
Liver | –1.92 (–5.40 to 1.70) | .24 | |||
Colorectal | 7.13 (3.00 to 11.50) | .005 | |||
|
|||||
|
2009-2012 | 21.59 (2.20 to 44.60) | .04 | ||
|
2012-2017 | 0.03 (–7.50 to 8.00) | .99 | ||
|
Average | 7.60 (1.60 to 13.90) | <.001 | ||
Pancreatic | 7.10 (2.90 to 11.40) | .005 | |||
|
|||||
|
2009-2012 | 13.91 (3.20 to 25.70) | .02 | ||
|
2012-2017 | –4.59 (–8.70 to –0.30) | .04 | ||
|
Average | 2.00 (–1.30 to 5.30) | .20 | ||
Oral cavity | 17.26 (3.60 to 32.80) | .02 | |||
Prostate | 11.91 (–1.30 to 26.80) | .07 | |||
Breast | 4.82 (–2.40 to 12.60) | .17 | |||
All cancers | 2.61 (1.40 to 3.80) | .001 |
The premature mortality rates for all cancers and each subcategory were consistently higher in men than in women, with differences of more than 5-fold (
Deaths and premature mortality of main cancers for people aged 30-70 years in 2013 and projections for 2030 if risk factor trends continue in Hunan Province, China.
Disease | 2013 | 2030 | Percent change | ||||||||
|
Premature deaths, n | Mortality rate (1/100,000) | Premature mortality (%) | Premature deaths, n | Mortality rate (1/100,000) | Premature mortality (%) | Premature deaths | Mortality rate | Premature mortality | ||
|
|||||||||||
|
Total | 36,886 | 188.18 | 8.82 | 71,920 | 382.42 | 14.24 | 94.98 | 103.22 | 61.47 | |
|
Lung cancer | 12,700 | 64.79 | 3.28 | 22,599 | 120.17 | 4.54 | 77.95 | 85.47 | 38.35 | |
|
Gastric cancer | 2785 | 14.21 | 0.69 | 2290 | 12.17 | 0.46 | –17.78 | –14.30 | –32.51 | |
|
Liver cancer | 7373 | 37.61 | 1.71 | 12,360 | 65.72 | 2.61 | 67.64 | 74.72 | 52.34 | |
|
Colorectal cancer | 2405 | 12.27 | 0.60 | 7539 | 40.09 | 1.60 | 213.49 | 226.74 | 164.82 | |
|
Esophageal cancer | 1624 | 8.29 | 0.43 | 5916 | 31.46 | 1.35 | 264.19 | 279.58 | 210.91 | |
|
Pancreatic cancer | 591 | 3.01 | 0.15 | 1414 | 7.52 | 0.28 | 139.34 | 149.45 | 82.95 | |
|
Nasopharynx cancer | 1508 | 7.70 | 0.34 | 2852 | 15.16 | 0.63 | 89.07 | 97.06 | 82.95 | |
|
Oral cavity cancer | 591 | 3.01 | 0.13 | 7255 | 38.58 | 1.71 | 1128.22 | 1180.13 | 1194.33 | |
|
Prostate cancer | 253 | 1.29 | 0.07 | 2504 | 13.31 | 0.52 | 889.01 | 930.81 | 592.15 | |
|
Other cancers | 7046 | 23.68 | 1.72 | 7193 | 38.25 | 1.46 | 2.08 | 61.53 | –14.85 | |
|
|||||||||||
|
Total | 17,687 | 97.25 | 4.51 | 25,867 | 138.19 | 5.11 | 46.25 | 42.10 | 13.46 | |
|
Lung cancer | 3409 | 18.75 | 0.95 | 6738 | 36.00 | 1.38 | 97.63 | 92.02 | 44.93 | |
|
Gastric cancer | 1319 | 7.25 | 0.36 | 763 | 4.08 | 0.17 | –42.13 | –43.78 | –52.16 | |
|
Liver cancer | 1928 | 10.60 | 0.51 | 3185 | 17.01 | 0.59 | 65.19 | 60.50 | 14.04 | |
|
Colorectal cancer | 1380 | 7.59 | 0.36 | 4803 | 25.66 | 0.92 | 248.04 | 238.15 | 158.84 | |
|
Esophageal cancer | 112 | 0.61 | 0.04 | 61 | 0.33 | 0.01 | –45.30 | –46.86 | –64.61 | |
|
Pancreatic cancer | 365 | 2.01 | 0.10 | 271 | 1.45 | 0.06 | –25.73 | –27.84 | –45.08 | |
|
Nasopharynx cancer | 497 | 2.73 | 0.11 | 400 | 2.14 | 0.09 | –19.52 | –21.81 | –21.86 | |
|
Oral cavity cancer | 101 | 0.56 | 0.03 | 215 | 1.15 | 0.04 | 112.05 | 106.02 | 55.81 | |
|
Breast cancer | 1827 | 10.04 | 0.44 | 3185 | 17.01 | 0.69 | 74.37 | 69.42 | 57.56 | |
|
Other cancers | 6748 | 29.51 | 1.69 | 6245 | 33.36 | 1.27 | –7.45 | 13.03 | –24.88 | |
|
|||||||||||
|
Total | 54,572 | 143.54 | 6.74 | 97,787 | 260.59 | 9.74 | 79.19 | 81.55 | 44.47 | |
|
Lung cancer | 16,109 | 42.18 | 2.15 | 29,337 | 78.18 | 2.96 | 82.12 | 85.33 | 37.57 | |
|
Gastric cancer | 4104 | 10.79 | 0.53 | 3053 | 8.14 | 0.32 | –25.61 | –24.62 | –39.93 | |
|
Liver cancer | 9301 | 24.35 | 1.13 | 15,545 | 41.42 | 1.59 | 67.13 | 70.11 | 40.72 | |
|
Colorectal cancer | 3785 | 9.97 | 0.48 | 12,342 | 32.89 | 1.25 | 226.09 | 229.86 | 158.76 | |
|
Esophageal cancer | 1736 | 4.52 | 0.24 | 5977 | 15.93 | 0.68 | 244.29 | 252.39 | 182.06 | |
|
Pancreatic cancer | 956 | 2.52 | 0.13 | 1685 | 4.49 | 0.17 | 76.26 | 78.18 | 30.58 | |
|
Nasopharynx cancer | 2006 | 5.26 | 0.23 | 3252 | 8.67 | 0.35 | 62.15 | 64.77 | 53.36 | |
|
Oral cavity cancer | 692 | 1.81 | 0.08 | 7470 | 19.91 | 0.87 | 979.24 | 1001.08 | 971.53 | |
|
Prostate cancer | 253 | 1.29 | 0.07 | 2504 | 13.31 | 0.52 | 889.01 | 930.81 | 592.15 | |
|
Breast cancer | 1827 | 10.04 | 0.44 | 3185 | 17.01 | 0.69 | 74.37 | 69.42 | 57.56 | |
|
Other cancers | 13,794 | 26.54 | 1.70 | 13,438 | 35.81 | 1.36 | –2.58 | 34.91 | –19.87 |
The modeling scenarios seek to avert one-third of premature mortality by 2030. However, this goal is hard to accomplish. For all cancers combined, premature mortality among the total population was expected to increase by 23.65% compared to the baseline year of 2013, even if all risk factor control targets were reached by 2030 (
For subcategories, all cancers showed increases in premature mortality compared with that in 2013 in the combined risk factor control target-achieved scenarios, except for gastric cancer with a decrease of 41.63% in the total population (34.45% for men and 53.40% for women). However, it should be noted that the premature deaths and mortality of gastric cancer would still decrease substantially even if all risk factors continue their current trends, as shown in the baseline scenario. A decrease in premature mortality was also found for women in esophageal cancer (72.63%), pancreatic cancer (48.71%), and nasopharynx cancer (29.44%) under combined target–achieved scenarios. Although the combined risk factor control targets failed to achieve the one-third reduction of the cancer mortality rate set by the UN, they could still lead to notable decreases in premature mortality compared with the baseline scenario in 2030.
Moreover, the impact on cancer premature mortality varied substantially across different risk factors. For all cancers combined, diabetes and low fruit intake were the top two leading risk factors of cancer premature mortality for both genders. For instance, halting the rise in the prevalence of diabetes may contribute to nearly half of the reductions in cancer premature mortality for both genders (
Premature cancer mortality for people aged 30-70 years in 2030 if all risk factor targets are achieved in Hunan Province, China, and the comparison with baseline values.
Disease | 2030 (if all risk factor targets are achieved) | Percent change compared with baseline in 2013, % | Percent change compared with baseline in 2030, % | ||||||
|
Deaths, n | Premature mortality, % | Deaths | Premature mortality | Deaths | Premature mortality | |||
|
|||||||||
|
Total | 60,750 | 12.15 | 64.70 | 37.71 | –15.53 | –14.72 | ||
|
Lung cancer | 16,207 | 3.26 | 27.62 | –0.69 | –28.28 | –28.22 | ||
|
Gastric cancer | 2220 | 0.45 | –20.29 | –34.45 | –3.05 | –2.88 | ||
|
Liver cancer | 10,782 | 2.25 | 46.24 | 31.48 | –12.76 | –13.69 | ||
|
Colorectal cancer | 5956 | 1.27 | 147.66 | 109.73 | –21.00 | –20.80 | ||
|
Esophageal cancer | 4870 | 1.11 | 199.82 | 156.42 | –17.67 | –17.53 | ||
|
Pancreatic cancer | 1267 | 0.25 | 114.53 | 62.42 | –10.36 | –11.22 | ||
|
Nasopharynx cancer | 2794 | 0.61 | 85.23 | 79.30 | –2.03 | –2.00 | ||
|
Oral cavity cancer | 6878 | 1.62 | 1064.35 | 1126.49 | –5.20 | –5.24 | ||
|
Prostate cancer | 2429 | 0.50 | 859.36 | 572.08 | –3.00 | –2.90 | ||
|
Other cancers | 7193 | 1.46 | 2.08 | –14.85 | 0 | 0 | ||
|
|||||||||
|
Total | 22,463 | 4.46 | 27.00 | –1.09 | –13.16 | –12.83 | ||
|
Lung cancer | 5120 | 1.05 | 50.17 | 10.71 | –24.01 | –23.61 | ||
|
Gastric cancer | 743 | 0.17 | –43.68 | –53.40 | –2.68 | –2.59 | ||
|
Liver cancer | 2811 | 0.51 | 45.80 | –0.45 | –11.74 | –12.71 | ||
|
Colorectal cancer | 3932 | 0.76 | 184.92 | 112.25 | –18.14 | –18.00 | ||
|
Esophageal cancer | 47 | 0.01 | –57.72 | –72.63 | –22.70 | –22.66 | ||
|
Pancreatic cancer | 253 | 0.05 | –30.66 | –48.71 | –6.65 | –6.60 | ||
|
Nasopharynx cancer | 360 | 0.08 | –27.61 | –29.44 | –10.05 | –9.71 | ||
|
Oral cavity cancer | 186 | 0.04 | 83.38 | 35.34 | –13.52 | –13.14 | ||
|
Breast cancer | 2737 | 0.59 | 49.83 | 35.58 | –14.08 | –13.95 | ||
|
Other cancers | 6245 | 1.27 | –7.45 | –24.88 | 0 | 0 | ||
|
|||||||||
|
Total | 83,213 | 8.33 | 52.48 | 23.65 | –14.90 | –14.41 | ||
|
Lung cancer | 21,327 | 2.15 | 32.39 | 0.11 | –27.30 | –27.23 | ||
|
Gastric cancer | 2963 | 0.31 | –27.81 | –41.63 | –2.96 | –2.82 | ||
|
Liver cancer | 13,593 | 1.37 | 46.15 | 21.61 | –12.55 | –13.58 | ||
|
Colorectal cancer | 9888 | 1.00 | 161.25 | 107.52 | –19.88 | –19.80 | ||
|
Esophageal cancer | 4917 | 0.56 | 183.26 | 132.30 | –17.73 | –17.64 | ||
|
Pancreatic cancer | 1520 | 0.15 | 59.05 | 16.93 | –9.77 | –10.45 | ||
|
Nasopharynx cancer | 3154 | 0.34 | 57.26 | 48.79 | –3.01 | –2.98 | ||
|
Oral cavity cancer | 7064 | 0.82 | 920.53 | 912.83 | –5.44 | –5.48 | ||
|
Prostate cancer | 2429 | 0.50 | 859.36 | 572.08 | –3.00 | –2.90 | ||
|
Breast cancer | 2737 | 0.59 | 49.83 | 35.58 | –14.08 | –13.95 | ||
|
Other cancers | 13,438 | 1.36 | –2.58 | –19.87 | 0 | 0 |
Probability of premature death due to cancers for people between ages 30 and 70 years in Hunan Province, China.
Probability of premature death due to cancers in men aged 30-70 years in Hunan Province, China.
Probability of premature death due to cancers in women aged 30-70 years in Hunan Province, China.
In this study, we projected premature mortality from 10 leading cancers in Hunan Province under 12 different risk factor control scenarios in 2030 and evaluated whether SDG target 3.4 can be met for cancer prevention. The results suggest that the one-third reduction goal in premature cancer mortality would not be achieved in Hunan, even if all related risk factor control targets were reached by 2030. This finding is similar to that of a previous study conducted by Li et al [
As for various cancer subcategories, it is interesting that risk factor control targets appeared to generate relatively minor additional benefits for cancers that had already experienced dramatic reductions under the BAU scenario. For other cancers that had not experienced reductions under the BAU scenario, the joint control of all related risk factors may generate larger additional benefits. For instance, the control of all five modifiable risk factors of lung cancer would reduce premature mortality by 28.2% for men and by 23.6% for women compared to the BAU scenario in 2030. These findings suggest that it is more cost-effective to control risk factors for cancers with tendencies toward worse conditions.
Through modeling, our estimates also illustrated significant discrepancies in premature mortality reduction across various risk factor control targets. In parallel with the previous study conducted by Li et al [
The past decade has also seen significant decreases in the mean population salt intake and prevalence of harmful alcohol use in Hunan, both of which led to more favorable trends than those of the WHO targets. Of note, the prevalence of current alcohol use was still maintained at a high level and was much higher in men than women, especially for hazardous and harmful alcohol use. The high alcohol use prevalence among men in China may be explained by the traditional Chinese culture that encourages drinking as a socially acceptable way to show their dominant positions in society. Disturbingly, there was a substantial increase in alcohol use following the rapid economic transition in China [
Apart from the above risk factors, whether SDG target 3.4 can be realized in 2030 largely depends on the high-impact factors, including diabetes, high BMI, and insufficient intake of fruits and vegetables. China has the largest population of individuals with diabetes in the world, and the prevalence of diabetes has been sharply increasing in recent decades in China, including in Hunan Province [
Overweight and obesity prevalence among adolescents and adults has been increasing steadily in China, including Hunan Province, for the past two decades [
Fruit and vegetable intake is an indispensable part of a healthy diet. Studies have shown that the average daily intake of fruit in Hunan Province has been increasing steadily in recent years, while the intake of vegetables has been declining [
We also validated our projection with other methods or assumptions. In the baseline scenario, where all risk factors continue their current trends, the number of premature deaths from cancers increased from 65,443 in 2017 to 97,787 in 2030. Given that approximately 4 million deaths are expected to occur in 2030 in China [
Some limitations of our work sƒhould be considered. First, Hunan launched its cancer registry program in 2009, and the work at the early stages might be imperfect with low population coverage. Even by the end of 2020, the cancer registration in Hunan had only covered approximately 70% of the population, without achieving full coverage, which may to some extent cause a certain bias in the estimation of actual cancer deaths. Nevertheless, with the efforts of local governments, the coverage of cancer registration has rapidly expanded and the data quality has steadily improved in recent years. Some of the data were even cited in monographs of the International Agency for Research on Cancer. In addition, our data for risk factors were drawn from the CCDRFS survey; therefore, all limitations in estimates of levels in the CCDRFS study apply to this analysis.
Second, we used RR estimates primarily from the GBD series and the CUP Expert Report due to the lack of high-quality meta-analyses and prospective cohort studies in China, which may make our results statistically unstable. However, with more and more large cohort studies being carried out in China, more reliable RRs for China could be available in future studies. Third, due to a lack of dynamic monitoring data, some region-specific risk factors such as betel quid, which is classified as a class 1 carcinogen and has a high prevalence in Hunan, has not been included in this analysis. Considering that the Chinese government has made great efforts on sales restriction and increasing the public awareness of its harm in recent years, the prevalence of betel quid chewing in Hunan may decline steadily; thus, modeling without consideration of betel quid may lead to overestimation of future cancer mortality. Furthermore, there are potential interactions among the selected risk factors; however, due to the absence of information on most interactions, we simply calculated their combined effects on cancers based on the assumption of independence, which may lead to some uncertainties in our results. Hence, solid evidence–based joint RRs on cancers are warranted for future studies.
Fourth, we did not investigate the impact of population aging on premature cancer mortality due to insufficient technological conditions and time. In further studies, we will try to examine the fractions and trends attributable to population aging and its interactions with various risk factors on premature cancer mortality. Fifth, since the current health outcome reflects the cumulative effect of past exposures, risk factors such as smoking and alcohol were subdivided by duration and amount whenever possible. However, no lag effect was considered when data on specific information were unavailable. Nevertheless, calculations of PAFs in our study referred to the comparative risk assessment model from the latest GBD series, and the results were similar to those of previously published literature [
In summary, this modeling study illustrates that the absolute burden of premature deaths due to cancers will continue to increase over the next dozen years in the Hunan province of China. Notable health gains could be achieved by addressing unhealthy risk factors for cancers. However, existing targets on related risk factors are not sufficient, particularly in men, to achieve the one-third reduction goal in premature cancer mortality. More aggressive risk targets based on local conditions are urgently needed.
Details on risk factors and cancer sites.
Risk factor exposure estimation.
Analysis methods.
Scenario projections for each cancer by gender.
Death projection for people of all ages.
Comparison between the baseline scenario and proportional change model.
average annual percentage change
business as usual
Chinese Chronic Disease and Risk Factor Surveillance
Continuous Update Project
disability-adjusted life year
global burden of disease
noncommunicable disease
National Program of Cancer Registries
population-attributable fraction
fine particulate matter
relative risk
Sustainable Development Goal
United Nations
World Health Organization
We thank all those who provided data for our analyses. This research was supported by the Science and Technology Innovation Program of Hunan Province (grant number 2022SK2050); Hunan Cancer Hospital Climb Plan (grant number YF2021007); and the Natural Science Foundation of Hunan Province, China (grant number 2021JJ40326).
The data will be available from the corresponding author on request.
WW and SY secured funding for this work. JW and WW are joint first authors, and equally conceived, designed, and performed the work. XL, KX, YZ, ZS, YH, HX, CL, SC, SW, JG, ZL, ML, MX, DJ, ZF, MC, and SY contributed to the data collection and extraction. CL, ZL, and SY revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work, ensuring integrity and accuracy.
None declared.