May 27, 2024

We estimated the number of new cancer cases in the year 2050 for the four most common cancers (breast, prostate, lung, and colorectal cancer) in Denmark under different prevalence scenarios of tobacco smoking, overweight and obesity, and alcohol consumption. We compared the number of new cancer cases in 2050 to the number of cancer cases in Denmark in 2021 under the different prevalence scenarios to evaluate the potential effects of prevention seen from today’s perspective.

We used the macrosimulation model Prevent, a program developed to model prevention scenarios. In summary, the model estimates disease incidence in future years based on different scenarios in the prevalence of risk factors. It is based on data from a starting year, such as cancer incidence rates, population size, and age distribution. The model simulates changes in specific age groups, and in our study, we used Prevent to estimate changes in future cancer incidence. The model was adapted for the EUROCADET project26,27,28 and is, in general, described in papers by Gunningschepers et al.29 and Soerjomataram et al.30;

Prevent requires input data in the form of disease incidence, risk factor prevalence (historical and current), population size (including future projections), relative risk (RR) estimates, and the changes in prevalence of risk factors in the different projected scenarios. All these inputs can vary by age and sex.

When estimating the preventive effect of multiple risk factors for one disease, Prevent assumes that relative risks are multiplicative and that the risk factors are distributed independently in the population.

Disease incidence

Incidence rates of female postmenopausal breast cancer (International Classification of Diseases 10th revision (ICD10): C50), prostate cancer (ICD10: C61), lung cancer (ICD10: C33-3C4), and colon (ICD10: C18) and rectal cancer (ICD10: C19-C20) were retrieved from the Nordic cancer statistics database NORDCAN24,25. For postmenopausal breast cancer, only the incidence in women from 50 years of age was considered.

To avoid variation in the data due to late registration and the COVID-19 pandemic, we used the average incidence for 2018–2021. The incidence was included for 5-year age groups (15–19, 20–24, …, 80–84, 85+).

Disease trend

For lung cancer, we applied a disease trend based on the estimated annual percentage change (EAPC) in the period from 1996 to 2021 from NORDCAN using the NORDPRED-model31 implemented in the database. We calculated the mean EAPC and extended the trend to 2050, which was applied to 2022 to 2050 in our model.

For breast, prostate, and colorectal cancer, we used the constant rate 2018–2021, with no change in disease trend. This was due to the implementation of screening for breast and colorectal cancer in 2009 and 2014, respectively, and the excessive increase in prostate-specific antigen (PSA) use in Denmark.

Risk factors and risk estimates

Risk factors for the four cancer types were included based on the classification by the International Agency for Research on Cancer (IARC) as “Carcinogenic agents with sufficient evidence in humans”32 or by the World Cancer Research Fund (WCRF) as “convincing evidence” as a cause of the specific cancer33,34,35,36. For breast cancer, overweight and obesity, and alcohol consumption were considered. For colon and rectal cancer, smoking, overweight and obesity, and alcohol consumption were considered, and for lung cancer, smoking was considered the only factor. No preventable risk factors matched the criteria for prostate cancer. We did not consider risk factors with protective effects on cancer. Tobacco smoking status was categorized as never smoker, secondhand smoker (never-smokers exposed to smoking 30 min a day), former smoker (used to be smoking but quit), or current smoker (daily smoker). Occasional smokers were considered never smokers. The RR estimates used were based on findings from Gandini37 and Kim38, also used in a study by Andersson in 201818 and Tybjerg et al. in 202239 (Table 1).

Table 1 Cancer type and relative risk estimates (RR) in different exposure categories for tobacco smoking status, BMI, and alcohol consumption in Denmark.

Overweight and obesity were defined by Body Mass Index (BMI) (weight (in kg)/height (in m)2). The groups were categorized according to the World Health Organization (WHO) criteria40: healthy weight BMI < 25 (underweight was considered as healthy weight), overweight 25 ≤ BMI < 30, and obesity BMI ≥ 30. We used the same RR estimates for overweight and obese individuals compared to healthy-weight individuals, as reported by Xue41 and Munsell42 and WCRF33 (Table 1).

Alcohol consumption in the population was categorized as non/occasional drinkers, light drinkers (≤ 1 drink per day), moderate drinkers (> 1 and ≤ 4 drinks per day), or heavy drinkers (> 4 drinks per day), assuming 12.5 g of alcohol (ethanol) to be equal to one drink. The RR estimates used were based on findings from Bagnardi et al.16, also used in studies by Andersson et al.17 and Tybjerg et al.39 (Table 1). Due to different estimates for colon and rectal cancer, estimates were calculated independently for the two types of cancer and then added together in the results for colorectal cancer.

Estimated population size

Demographic data on Denmark’s estimated future population size were collected from population projections in Statistics Denmark43 by sex and 5-year age groups (0–4, 5–9…,80–84, 85+). The base year was 2021, and estimates were used for 2023–2050. For 2021 and 2022, actual numbers were utilized according to sex and 1-year age groups (0, 1,…, 84, 85+).

Risk factor prevalence

Prevalence data on tobacco smoking status, BMI, and alcohol consumption were collected from The Danish National Health Survey44 by sex and 10-year age groups (16–24, 25–34 … 75+) from the most recent years available (2010, 2013, 2017 and 2021). The prevalence of risk factors was treated as categorical. Data on children under 16 were not obtainable and, therefore, excluded.

Disease latency and lag years

When individuals are no longer exposed to a risk factor, their risk of disease decreases with time towards that of unexposed individuals, e.g., when quitting smoking, the risk of lung cancer is unchanged for a period until it starts declining and levels with individuals who are never smokers. The Prevent model considers this as latency time and lag years. Latency time was defined as the time, in years, from a change in exposure to a risk factor until the risk of disease started to change. Lag years were defined as the number of years from the onset of the transition in disease risk to the point at which the disease risk among previously exposed individuals aligns with the risk observed in the unexposed population. Assumptions for each disease and risk factor can be found in the supplementary material (Supplementary A).

Intervention scenarios

We assumed the intervention would start in 2022 and continue until 2050. When the prevalence was lowered, the age groups were allocated to the reference risk estimates (data shown in Supplementary A).

A: Half reduction.

This implies a 50% reduction in the prevalence of secondhand, former, and current smokers, overweight and obese, and light, medium, and heavy drinkers from 2022.

B: Full elimination.

This implies a total reduction in the prevalence of secondhand, former, and current smokers, overweight and obese, and light, medium, and heavy drinkers from 2022.

We performed sensitivity analyses in all scenarios using the risk estimates’ lowest and highest 95% confidence intervals, presented in the supplementary material (Supplementary B). We also compared the no-intervention scenario predictions with the NORDCAN predictions, shown in the supplementary material (Supplementary A).

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