Cancer Epidemiology & Prevention

Note: This page is educational and reflects the state of the literature in 2025. It does not replace medical advice.

TL;DR

Cancer is the second leading cause of death worldwide. Roughly 40 % of cancers are preventable through known interventions: tobacco control, vaccination (HPV, HBV), reducing alcohol and obesity, screening, and avoiding occupational and environmental carcinogens. Brazil-specific data come from INCA (Instituto Nacional de Câncer); global data come from GLOBOCAN/IARC (International Agency for Research on Cancer). The hard part is translating evidence into population-level behavior change and policy — not the science of what works.

1. Why epidemiology matters for technologists

Epidemiology answers questions that pure molecular biology cannot:

How common is this cancer in this population? (incidence)
How many people die of it? (mortality)
How long do patients live after diagnosis? (survival)
What modifiable factors cause it? (etiology)
Does intervention X reduce risk? (prevention efficacy)

Without these answers you cannot prioritize research, allocate health-system resources, or measure the impact of any intervention. For a data-savvy reader, public cancer registries are some of the cleanest, most longitudinal datasets in human biology — and they are largely under-used by the tech community.

2. Key data sources

Source	Scope	Granularity	Access
GLOBOCAN / IARC	Global incidence, mortality, prevalence	By country, sex, cancer site	gco.iarc.fr
SEER (US)	US incidence + survival	Detailed registries	seer.cancer.gov
INCA / Brazil	Brazilian incidence estimates	National + state	inca.gov.br
CDC WONDER	US mortality	Counties	wonder.cdc.gov
WHO Mortality DB	Global mortality	Country-year	who.int
OurWorldInData	Visualizations + downloadable CSVs	Country-year	ourworldindata.org/cancer

For Brazilian context: INCA publishes the Estimativa report every two years with state-level incidence projections. The Atlas On-line de Mortalidade also from INCA covers cancer mortality by ICD code, state, and year.

3. Modifiable risk factors (where prevention bites)

Roughly 40 % of cancers in high-income countries are attributable to known modifiable risks. The biggest contributors: Sources: [1]

Tobacco — by far the largest single avoidable cause; lung, head & neck, bladder, esophageal, pancreatic.
Obesity & physical inactivity — colorectal, breast (post-menopausal), endometrial, kidney, pancreatic, liver, esophageal adenocarcinoma.
Alcohol — liver, esophageal, breast, head & neck, colorectal.
Infections — HPV (cervical, anal, head & neck); HBV/HCV (liver); H. pylori (gastric); EBV (some lymphomas, NPC); HIV (Kaposi, NHL).
Ultraviolet radiation — melanoma and non-melanoma skin cancers.
Occupational and environmental carcinogens — asbestos, benzene, diesel exhaust, radon, certain pesticides, arsenic-contaminated water.
Diet — processed and red meat (colorectal, modest effect); insufficient fruit/vegetables.

Some risk factors are non-modifiable but still useful for stratification: age, sex, family history (Lynch, BRCA1/2, Li-Fraumeni…), prior radiation exposure, certain genetic syndromes.

Caveat. Risk attribution is statistical; individual cancer cases rarely have a single cause. Many people with all the risk factors never get cancer; many with none get it anyway.

4. The three levels of prevention

Primary prevention — prevent the cancer from arising.
- Tobacco control (taxation, advertising bans, smoke-free laws — the most cost-effective public-health intervention ever measured).
- HPV vaccination (eliminates ~90 % of HPV-driven cervical cancers; in Brazil offered free via SUS for boys and girls).
- HBV vaccination (prevents ~80 % of hepatocellular carcinomas attributable to HBV).
- Sun protection.
- Healthy weight, physical activity, alcohol reduction.
- Occupational regulation (asbestos bans, etc.).
Secondary prevention — find the cancer early when treatment is curative.
- Cervical cancer: HPV testing and Pap smears.
- Breast cancer: mammography in target age groups.
- Colorectal cancer: fecal immunochemical test (FIT), colonoscopy.
- Lung cancer: low-dose CT (LDCT) in heavy smokers.
- Prostate cancer: PSA testing — controversial; modern reviews argue net benefit when combined with MRI/biomarkers to avoid overdiagnosis. Sources: [2]
Tertiary prevention — reduce mortality and morbidity from established cancer (treatment, rehabilitation, palliative care).

5. Screening 101: benefit vs. harm

Screening is not free of harm. Every screening program must balance:

Benefit — fewer late-stage diagnoses, lower disease-specific mortality.
Harms — false positives (anxiety, biopsies), overdiagnosis (cancers that would never have caused symptoms), radiation exposure, complications of follow-up procedures.
Equity — uptake varies by income, geography, race/ethnicity.

Statistical concepts every analyst should know:

Sensitivity / specificity — operating point of the test.
Lead-time bias — earlier diagnosis appears to extend survival even if it does not.
Length-time bias — slow-growing (less lethal) cancers are over-represented in screening detections.
Number needed to screen (NNS) — how many must be screened to prevent one death.

These concepts also matter for machine-learning-based screening (e.g., AI mammography). See ML pitfalls in oncology.

6. Brazil-specific context

Most common in men (per INCA estimates 2023–2025): non-melanoma skin, prostate, colorectal, lung, stomach.
Most common in women: non-melanoma skin, breast, colorectal, cervical, lung.
Cervical cancer still has high incidence/mortality vs. high-income peers — primary issue is screening coverage, not the absence of an effective program.
Stomach cancer is more frequent than the global average — H. pylori prevalence and dietary patterns.
HPV vaccine in SUS since 2014; expansion to boys in 2017; coverage still below WHO targets (90 % girls by age 15).
National Cancer Care Policy (Política Nacional de Atenção Oncológica) coordinates SUS oncology — long waits in some states are a major access issue, addressed by Lei 12.732/12 (60-day rule for treatment start).

For up-to-date estimates and policy, the canonical Brazilian sources are INCA, Ministério da Saúde / BVS, and Fundação do Câncer. Sources: [3], [4], [5]

7. Health disparities

Cancer outcomes are highly unequal. Within a single country, mortality varies 2–4× by region, race, income, and education. Drivers include:

Access to screening and treatment (the largest single factor).
Late-stage presentation (delayed diagnosis).
Differences in risk-factor exposure (occupational hazards, food deserts, tobacco marketing).
Treatment-decision biases in some health systems.
Trial enrollment under-representing minority populations — limits generalizability of evidence.

For Brazil this manifests as North/Northeast vs. South/Southeast outcome gaps. For the US, Black/White, urban/rural, and insurance-status gaps. These gaps are largely not biological — they are health-system gaps.

8. Policy levers that actually work

The empirical base is strong for the following population-level interventions:

Tobacco taxation (price elasticity is real; Brazil has used this effectively).
Smoke-free indoor laws.
Plain packaging and graphic warnings on tobacco products.
HPV and HBV vaccination programs with school-based delivery.
Sugar-sweetened beverage taxes (early evidence for obesity-related cancers, longer-term).
Mandatory occupational exposure limits and asbestos bans.
National screening programs with active recall (vs. passive opportunistic screening).
UV protection campaigns (Australia is the textbook example).

References

Bergengren O, Pekala KR, Matsoukas K, et al. 2022 Update on Prostate Cancer Epidemiology and Risk Factors—A Systematic Review. Eur Urol 2023;84:191-206. PMID 37202314. https://doi.org/10.1016/j.eururo.2023.04.021 (example of a modern incidence + risk-factor systematic review using GLOBOCAN data)
International Agency for Research on Cancer (IARC). GLOBOCAN / Global Cancer Observatory. https://gco.iarc.fr
Instituto Nacional de Câncer (INCA, Brasil). Estimativa de incidência e Atlas On-line de Mortalidade. https://www.inca.gov.br/
Ministério da Saúde / BVS. ABC do câncer — abordagens básicas para o controle do câncer. https://bvsms.saude.gov.br/bvs/publicacoes/abc_do_cancer.pdf
Fundação do Câncer (Brasil). https://www.cancer.org.br/
A.C. Camargo Cancer Center. https://accamargo.org.br
U.S. National Cancer Institute (NCI). What is cancer? https://www.cancer.gov/about-cancer/understanding/what-is-cancer
American Cancer Society. Cancer A-Z. https://www.cancer.org/cancer.html
Cleveland Clinic. Cancer (overview). https://my.clevelandclinic.org/health/diseases/12194-cancer

Cancer Epidemiology & Prevention ​

TL;DR ​

1. Why epidemiology matters for technologists ​

2. Key data sources ​

3. Modifiable risk factors (where prevention bites) ​

4. The three levels of prevention ​

5. Screening 101: benefit vs. harm ​

6. Brazil-specific context ​

7. Health disparities ​

8. Policy levers that actually work ​

See also ​

References ​