Project Spotlight - Projecting the Future Cancer Burden in Singapore: National Incidence and Direct Medical Cost Estimates to 2050

From left to right: Ian Wee, Tey Han Jieh, Kimberly Chang, Miao Hui, Dawn Chong, Tay See Boon and Evelyn Wong

1. What motivated your team to undertake this projection study of Singapore's future cancer trends? (e.g. ageing population concerns, rising healthcare costs, or need for long-term healthcare planning.) 

There were two key motivations behind this study. First, Singapore has a rapidly ageing population. The risk of developing cancer increases with age, thus increasing the demand for cancer services. Second, there is a rapid escalation in cancer-related healthcare costs, driven by increased incidence, longer survival, and more expensive treatments such as immunotherapy and targeted therapy. Our team initiated this study to provide evidence-based projections of future cancer burden and associated healthcare costs, with the aim of informing healthcare policies. Understanding how incidence, patient demographics and costs may evolve over the next 25 years will help ensure that Singapore remains prepared to meet the rising demand and maintain sustainable, high-quality cancer care. 

2. Why did you choose to use the TRUST platform for this study? 

The TRUST platform provides a secure environment that enables us to work with linked, population-level administrative and health-related datasets. This is essential for a study of this scale, which relies on population-based longitudinal data to model cancer epidemiology and healthcare utilisation comprehensively. 

3. How is the TRUST platform supporting your ongoing methodology and analysis process? 

TRUST allows us to access curated datasets and perform complex analyses within a controlled environment. The platform’s support for data linkage enables a more complete picture of patient journeys, from diagnosis to treatment utilisation and cost. Its analytic tools and BYOD (Bring Your Own Data) capabilities also provide flexibility for customised modelling. 

4. Could you briefly explain the approach and methods your study is using to project cancer cases and costs to 2050? (e.g. cancer registry data, medical claims analysis, statistical modelling approaches) 

Our study integrates cancer incidence data from the National Registry of Diseases Office (NRDO) cancer registry with harmonised inpatient and outpatient and claims data on the TRUST platform to establish historical trends in cancer incidence from 2000 to 2021, one-year and five-year post-diagnosis costs from 2005 to 2016.

Future population demographics were obtained from the DEMOS model (a synthetic population model developed by the Department of Statistics (DOS), and incorporated into our projections. Future cancer incidence to 2050 was projected using generalised additive models (GAMs) stratified by age and sex. Cost projections were generated using generalised linear models with a gamma distribution and log link. These per-patient cost estimates were then multiplied by projected incidence to derive national-level expenditure estimates through 2050.

5. What are some preliminary insights or trends you're observing so far in your research? 

Between 2000 and 2021, cancer cases nearly doubled. The age-standardised incidence rate increased from 200.8 to 246.4 per 100,000 in women and remained stable in men (235.3 per 100,000). Projections indicate that overall annual cancer incidence will rise 3.3-fold, from 2.35 to 7.59 per 1,000 population from 2000 to 2050. Similar trends are observed for both genders. Individuals aged ≥80 years will account for the largest proportion of cases (37.10%) by 2050, which is three times higher than 13.30% in 2000. 

The financial impact is large: first-year direct medical costs escalated by four-fold from SGD 135.6 million in 2005 to SGD 400.6 million in 2016. Five-year cumulative costs rose 3.5-fold from SGD 195.7 million to SGD 671.3 million during the same period. By 2050, projected national expenditure is expected to reach SGD 1.7 billion in the first year after diagnosis and SGD 30.7 billion for the five-year cumulative cost. 

6. How do you anticipate the findings will improve Singapore's healthcare planning and cancer care preparedness in the future? 

The projections will offer a long-term outlook on how cancer incidence and healthcare utilisation may shift due to an ageing population and rising treatment costs. These insights can support strategic planning in several areas—for example, estimating future resource needs, guiding workforce development, planning screening, and prevention programmes.

Furthermore, this will inform healthcare financing and sustainability, allowing policymakers to stress-test whether current financing structures remain viable under future cancer demand.

Ultimately, the study aims to help healthcare systems allocate resources efficiently and design interventions that reduce future healthcare burden.  

7. What advice would you give to fellow researchers interested in working with the TRUST platform? 

Start early by understanding the data dictionary and governance requirements and planning your data linkage and analysis workflow. Engage with the TRUST team—they are very knowledgeable about dataset structures and can help refine your data request. It is also helpful to have detailed documentation of your data cleaning, harmonisation and analysis steps so that other users can benefit from it. Overall, TRUST is a powerful resource, but success depends on having clear objectives and well-defined datasets.