Global Impact of Cancer

Slide 1 · Global Impact of Cancer

Cancer burden, population trends, and disparities

Pathology and cancer epidemiology

Dr Abu Suraih Sakhri

MBBS, MD (Pathology)

www.abusuraihsakhri.com

Slide 2 · Learning outcomes

Distinguish incident cancer cases, cancer deaths, and age-adjusted rates.

Interpret the worldwide burden and projected demographic pressure reported.

Compare leading cancer sites and cancer mortality by sex in the U.S.

Explain major historical mortality trends, screening effects, and inequities.

Slide 3 · Why cancer epidemiology matters

Describe cancer occurrence

Compare groups and time

Recognize associations

Generate causal hypotheses

Population patterns complement morphological and molecular observations.

Association alone is not proof of causation.

Slide 4 · Four dimensions of cancer burden

Occurrence

Outcome

New diagnoses within a period

Relative distribution of tumor sites

Cancer-attributed deaths

Mortality trends across time

Counts and rates answer different questions.

Slide 5 · Incidence is not mortality

Incidence

Mortality

New cancer diagnoses

Reflects occurrence and detection

May rise even if survival improves

Deaths attributed to cancer

Reflects lethality and clinical care

Can decline while incidence is stable

Avoid ranking cancer lethality from diagnosis counts alone.

Slide 6 · A count needs a population context

Count of cancer events

Population at risk

Time period

Population-based rate

Cancer counts describe workload; Rates facilitate comparisons.

Slide 7 · What an age-adjusted death rate means

The textbook expresses age-adjusted cancer mortality as deaths per 100,000 population.

Adjustment supports comparisons when populations have different age structures.

A change in deaths can reflect both changing rates and changing population size.

Specify age-adjustment when interpreting long-run mortality trends.

Age adjustment is not the same as an absolute death count.

Slide 8 · Absolute burden versus underlying risk

Absolute count

Age-adjusted rate

Affected by population size

Affected by population age composition

Relevant to health-system capacity

Standardizes age structure

Useful for temporal comparisons

Does not itself measure total service demand

A falling standardized rate can coexist with a growing total burden.

Slide 9 · Read every cancer statistic with its denominator

Identify geography: Worldwide or U.S.

Identify sex, age strata, and tumor site.

Identify calendar year and whether values are observed, estimated, or projected.

Check exclusions such as nonmelanoma skin cancer and in situ tumors.

Slide 10 · How population patterns inform etiology

Detect a pattern

Assess temporal or geographic concordance

Investigate exposure and other explanations

Test causal evidence

Smoking–lung cancer and diet–colon cancer as epidemiologic examples.

Slide 11 · Avoid treating ecological associations as proof

Regional differences suggest candidate determinants; They do not identify an individual cause.

Calendar trends may involve both exposure changes and medical care.

Health-care access and diagnostic practices can affect measured incidence.

Use population findings to frame, not replace, causal investigation.

Slide 12 · Worldwide cancer mortality burden in 2020

>9.9

Million estimated cancer deaths worldwide

≈1 in 6

Of all deaths were attributed to cancer

Both figures refer to 2020 worldwide estimates.

Slide 13 · Interpreting “nearly one in six deaths”

Cancer accounted for nearly one-sixth of all deaths worldwide in 2020.

Cancer

Other causes

Other causes

Other causes

Other causes

Other causes

Schematic approximation of a fraction — not six equally sized epidemiologic populations.

Slide 14 · The textbook’s worldwide projection for 2030

21.4

Million new cancer cases projected

13.2

Million cancer deaths projected

These are projections reported, not independently updated surveillance estimates.

Slide 15 · Why projected global counts increase

Population grows

Population ages

More people are exposed to ages of higher cancer burden

Total cases and deaths rise

Population growth and aging as the stated drivers.

These figures are projections, not observed 2030 outcomes.

Slide 16 · A projection is not a measured outcome

Estimate for 2020

Projection for 2030

Global deaths: Over 9.9 million

Cancer: Nearly one in six deaths

New cases: 21.4 million

Deaths: 13.2 million

Do not plot these unlike metrics as one continuous time series.

Slide 17 · Three constraints on interpreting projections

The projected value refers to a future calendar year relative to its modeling assumptions.

The textbook explicitly emphasizes demographic drivers, not a quantified change in intrinsic cancer risk.

New cases and cancer deaths have different denominators and clinical meanings.

A historical projection should not be relabeled as a current global estimate.

Slide 18 · Worldwide burden and U.S. Site patterns

Worldwide

United States

2020 cancer death burden

2030 projected cases and deaths

Population growth and aging

2023 sex-stratified diagnoses

2023 sex-stratified mortality

Historical age-adjusted rates

Do not combine worldwide and U.S. Totals as if they share one denominator.

Slide 19 · Estimated new U.S. Cancer diagnoses in 2023

New cases, United States, sex-stratified

Males

1,010,310

Females

948,000

Numbers are estimated new cases and exclude the categories stated in Fig. 7.16.

Slide 20 · Estimated U.S. Cancer deaths in 2023

Cancer deaths, United States, sex-stratified

Males

322,080

Females

287,740

Unlike the diagnosis panel, this panel represents mortality.

Slide 21 · What the two 2023 U.S. Totals describe

1,958,310

Combined estimated new cases across males and females

609,820

Combined estimated cancer deaths across males and females

Derived arithmetic sums; New cases and deaths are different events and must not be divided to estimate individual case fatality.

Slide 22 · Why deaths divided by new cases is misleading

Diagnosed during 2023

Died during 2023

Not necessarily the same individuals or diagnosis years

Annual deaths / annual diagnoses is NOT a patient-level survival estimate.

Slide 23 · Male cancer diagnosis pattern in the source

Prostate is the leading site in the U.S. Male incidence list.

Lung and bronchus contribute a major share of male diagnoses.

Colon and rectum are also prominent among male new cases.

Site frequencies should be interpreted within the male denominator.

Slide 24 · Female cancer diagnosis pattern in the source

Breast is the leading site among U.S. Female diagnoses.

Lung and bronchus remain prominent among female new cases.

Colon and rectum are another major component of female incidence.

The profile differs from the ranking for female cancer deaths.

Slide 25 · Dominant sites in sex-specific new diagnoses

Percentage of new diagnoses in 2023

Male prostate

29%

Female breast

31%

Shares are within each sex and should not be added to a combined percentage.

Slide 26 · A core set of sites dominates cancer burden

High-frequency sites

Textbook’s synthesis

Lung and bronchus

Female breast and prostate

Colon and rectum

Together represent >50% of U.S. Diagnoses

Also >50% of U.S. Cancer deaths

Distribution differs by sex and endpoint

Concentration of burden suggests where population-level prevention and care may matter most.

Slide 27 · Lung cancer and 2023 U.S. Mortality

Percentage of cancer deaths within each sex

Males: Lung

21%

Females: Lung

21%

Mortality fractions are cancer-death shares, not lung cancer case-fatality risks.

Slide 28 · Cancer incidence and mortality rankings can diverge

Frequent diagnoses

Prominent mortality

Breast cancer: High incidence in females

Prostate cancer: High incidence in males

Lung cancer: Prominent in both sexes

Mortality distribution reflects more than diagnosis frequency

The leading cancer by diagnoses need not be the leading cancer by deaths.

Slide 29 · Why site-specific rankings deserve separate panels

A diagnosis panel answers which cancers arise most frequently.

A mortality panel answers which cancers cause the most deaths.

Neither identifies individual prognosis without further follow-up data.

Annual counts alone cannot separate stage, treatment, and disease biology.

Slide 30 · The female breast–lung paradox

Breast carcinoma

Lung carcinoma

Occurs about 2.5 times as often as lung carcinoma in women, per Robbins.

Most frequent new female cancer diagnosis.

Causes more female cancer deaths than breast carcinoma.

Lung is a leading category in the death panel.

Incidence and mortality measure different features of cancer burden.

Slide 31 · How to read the 2023 figure exclusions

The source excludes basal cell and squamous cell carcinomas of the skin.

In situ carcinomas are excluded, except urinary bladder.

The figure stratifies totals by males and females.

These design choices define the scope of comparisons and totals.

Slide 32 · Why the “all other sites” category matters

Cancer occurs across many organs beyond the leading listed sites.

A residual category prevents a site-ranking figure from appearing exhaustive.

The combined category does not represent one biological cancer entity.

Never compare a pooled residual category with a specific tumor type as if equivalent.

Slide 33 · From burden description to trend analysis

Define a time window

Standardize the rates

Track incidence and mortality separately

Interpret plausible contributors

The historical U.S. Series is reported using age-adjusted cancer death rates.

Slide 34 · Historical background: Trends were not always favorable

Age-adjusted cancer death rates increased during the last 50 years of the twentieth century.

The described subsequent improvement began on different dates for males and females.

A single long-run trend can therefore hide an important directional reversal.

Always define both interval and endpoint when reporting change.

Slide 35 · Cancer incidence stabilized in the U.S.

Males

Females

Incidence stable since 1995 account.

Stability concerns rates, not necessarily absolute new-case counts.

Incidence stabilized in 1995 account.

Sex-specific trend requires its own denominator.

Stabilized incidence does not imply that cancer mortality ceased to change.

Slide 36 · Reported decline in cancer death rates

Approximate reduction in sex-specific cancer death rates

Males (since 1990)

20%

Females (since 1991)

10%

Historical approximate percent declines as stated by Robbins; Not a comparison of current national rates.

Slide 37 · The incidence–mortality decoupling

Incidence observation

Mortality observation

Male and female cancer incidence both stabilized around 1995.

Incidence measures newly diagnosed cancer.

Death rates declined beginning in 1990–1991.

Reduced deaths can reflect prevention, detection, and treatment.

A stable incidence rate can coexist with improving mortality.

Slide 38 · Cancer mortality change is site-dependent

Improvements in specific cancers contribute disproportionately to total population change.

Changes in disease exposure can alter future lung cancer mortality.

Detection and treatment can reduce deaths for selected sites.

The total mortality trend is an aggregate of heterogeneous cancer types.

Slide 39 · Which cancers drove the male mortality decline?

About 80% of the reported male cancer mortality decline

≈80%

Principal contributing sites:

Lung

Prostate

Colorectal

Share of the decline — not the share of all cancer deaths.

Slide 40 · Which cancers drove the female mortality decline?

About 60% of the reported female cancer mortality decline

≈60%

Principal contributing sites:

Breast

Colorectal

Share of the decline — not the share of all cancer deaths.

Slide 41 · Tobacco use and lung cancer mortality

Changes in smoking behavior

Changes in tobacco exposure

Changed burden of lung cancer

Changes in lung cancer deaths

Robbins explicitly attributes reduced lung cancer deaths to reduced use of tobacco.

Population trend concordance supports, but does not alone establish, causal attribution.

Slide 42 · Detection and treatment: Mortality consequences

Earlier detection

Opportunity for intervention

Improved treatment

Reduced cancer mortality

Improved detection and treatment as drivers for colorectal, breast, and prostate cancer mortality reductions.

Slide 43 · Prevention differs from improved cancer care

Exposure change

Improved care

Reduced tobacco use limits exposure to a carcinogen.

May change future occurrence of an exposure-related cancer.

Detection identifies lesions earlier.

Therapy changes outcomes after disease is found.

Different pathways can contribute to the same mortality trend.

Slide 44 · The Pap smear and cervical cancer mortality

Cervical precursor lesion

Detection by Pap smear

Management before invasive progression

Fewer cervical cancer deaths

The marked decline in cervical cancer deaths in high-resource countries largely to cervical cytology.

Slide 45 · Why cervical screening changed outcomes

The Pap smear can identify precursor lesions before progression to invasive cancer.

It can also identify early cancers at a more curable stage.

The source reports a sharp decline in cervical cancer deaths in highly resourced countries.

This is an example of pathology informing population screening.

Slide 46 · Precursor detection versus early cancer diagnosis

Precursor lesion

Early invasive cancer

Abnormality precedes invasive disease.

Recognition creates an opportunity to prevent progression.

Disease is already present.

Recognition may permit effective curative intervention.

Both pathways can reduce cervical cancer mortality, by different mechanisms.

Slide 47 · Female lung cancer mortality reversed direction

A substantial rise in female lung cancer death rates during 1990–2004.

Rates then declined in subsequent years.

The source relates both phases to changes in smoking habits.

The direction of a trend depends critically on the chosen period.

Slide 48 · Changing smoking habits: A time-pattern model

Qualitative pattern only — no numeric historical series reconstructed

Changing smoking habits

Female lung cancer mortality rises, then falls

Time-window matters

Do not infer an exact exposure–response lag from this excerpt.

Slide 49 · The role of social determinants in cancer outcomes

Upstream determinants

Observed patterns

Socioeconomic circumstances

Access to screening and health care

Other environmental and cultural differences

Incidence can differ between groups

Outcomes can differ between groups

Differences need causal evaluation

Disparities require structural and clinical interpretation, not biological essentialism.

Slide 50 · Race is not a discrete biological variable

Robbins explicitly rejects interpreting race as a discrete biological category.

Cancer disparities across racial and ethnic groups are largely linked to social determinants of health.

Health-care access and socioeconomic conditions are named examples.

Population categories should not be treated as substitutes for a causal exposure.

Slide 51 · How unequal access can shape outcomes

Barriers to health-care access

Different opportunities for screening/diagnosis

Different care pathways

Differences in observed outcomes

Access to health care is explicitly cited among the social determinants contributing to disparities.

Diagram is a conceptual teaching model, not a quantified mediation analysis.

Slide 52 · Inherited susceptibility is a separate dimension

The source acknowledges hereditary contributors to cancer risk in some populations.

It names increased breast and ovarian cancer risk in women of Ashkenazi Jewish descent.

The cited mechanism is a higher frequency of germline BRCA1 and BRCA2 variants.

This specific genetic example does not establish race as a biologically discrete category.

Slide 53 · Disentangling group differences

Contextual drivers

Specific hereditary factors

Health-care access

Socioeconomic circumstances

Environmental and cultural exposures

Defined susceptibility alleles

Germline BRCA1 or BRCA2 variants in a specified group

Not a general explanation for all disparities

Environmental and inherited contributions must be assessed rather than assumed.

Slide 54 · Application: High incidence versus high mortality

A female cancer is the most frequent diagnosis, but another cancer causes more deaths. What does this demonstrate?

A. Annual case counts are equivalent to lethality

B. Incidence and mortality rankings can differ

C. Screening always increases deaths

Slide 55 · Application: Interpret a falling mortality rate

A cancer death rate falls while incidence is stable. Which inference is best supported?

A. Cancer has been eradicated

B. Improved outcomes may coexist with stable occurrence

C. The population necessarily became younger

Slide 56 · Application: An observed population disparity

Cancer outcomes differ across two racialized population groups. What should be examined first?

A. Race as a fixed biological mechanism

B. Access, socioeconomic conditions, and specific exposures

C. A presumed universal inherited defect

Slide 57 · Application: Interpret a 2030 figure

Robbins gives 13.2 million cancer deaths for 2030. Which description is accurate?

A. Directly observed worldwide deaths in 2030

B. The textbook’s projected worldwide deaths

C. U.S. Deaths during 2023

Slide 58 · Integrated interpretation: Cancer burden is multidimensional

Global totals reveal a major and potentially growing service burden.

Incidence and mortality must be reported separately by site, sex, and time.

Historical mortality improvement reflects a mixture of prevention and better care.

Disparity analysis requires attention to social determinants and selected inherited risks.

Slide 59 · Reference and provenance

Primary teaching source

Robbins, Cotran & Kumar: Pathologic Basis of Disease. 11th ed. Chapter 7, “Neoplasia,” section “Epidemiology of Cancer” → “Global Impact of Cancer,” pp. 244–246.

Statistical reference within that source

Figure 7.16: U.S. cancer incidence and mortality by sex and tumor site, 2023. Values on these slides are historical textbook estimates and projections.