Measuring Health and Disease in Populations

College of Health @ LEHIGH | Eric Delmelle
February 9, 11, 23 2026

Table of Contents

Part 1: Comparing Health Events

• Introduction and Key Concepts
• Types of Comparisons (Time, Place, Group, Event)
• Policy Implications

Part 2: Measuring Health and Disease

• Incidence and Prevalence
• Mortality Metrics
• Life Tables and Life Expectancy
• Demographic Transitions and Aging Populations
• Fertility Rates

Part 1: Comparing Health Events

Introduction

• Definition of Health Events: Disease outbreaks, chronic conditions, injuries, and health behaviors.
• Importance of Comparisons: Understanding disparities, identifying risk factors, guiding public health interventions.
• Key Concepts: Population health, epidemiology, and biostatistics.

Objectives of Population Health

Four Key Objectives:

1. Describe: Understand population-level health outcomes.
2. Explain: Identify determinants and drivers of health outcomes.
3. Predict: Anticipate future health trends and patterns.
4. Control: Implement interventions to improve outcomes.

Historical Context

Key Figures

• John Snow: Cholera outbreak mapping.
• Ignaz Semmelweis: Importance of handwashing.
• Joseph Goldberger: Nutritional causes of pellagra.

Types of Comparisons

Time-Based

Key Metrics:

• Incidence: New cases over time.
• Prevalence: Existing cases at a given time.

Place-Based

Example:

• Urban vs. Rural Heart Disease Mortality:
  • Urban: 50 per 100,000.
  • Rural: 75 per 100,000.

Group-Based

Example:

• Health disparities by race, age, and income.

Event-Based

Key Concept:

• Natural experiments: Before vs. after policy changes or interventions.

Additional Event-Based Example

Part 2: Measuring Health and Disease

Introduction to Measurement

Key Concepts in Measuring Health and Disease

• Definitions of incidence and prevalence.
• Importance of population health metrics.
• Ageing in the population
• Demographic transition
• Life expectancy
• The importance to standardize

Incidence and Prevalence

Definitions

• Incidence: Number of new cases in a specified time period.
  • Formula: (New cases during time period / Population at risk) × multiplier.
• Prevalence: Total number of existing cases at a given time.
  • Formula: (Existing cases / Total population) × multiplier.

Differences

• Incidence: Measures risk; useful for studying causation.
• Prevalence: Measures disease burden; important for planning healthcare services.

Impact of New and Existing Cases

• Prevalence Formula Relation:
  • Prevalence = Incidence × Duration of Disease
  • check this youTube video
• Factors affecting prevalence:
  • Increase: Longer disease duration, improved survival without a cure.
  • Decrease: Shorter duration, high mortality rates, or prevention.

Rates, Ratios, and Proportions

• Rate: A measure of change per unit time (e.g., incidence density).
  • pay attention to rate instability
• Ratio: Comparison of two numbers, unrelated (e.g., sex ratio).
• Proportion: A part of a whole (e.g., percentage of women in a study group).

Cumulative Incidence

• Proportion of an initially disease-free group of individuals who develop the disease within a specified period of observation.
  • pay attention to censoring (when individuals drop out or are lost to follow-up)

\[ CI = \frac{\text{new cases}}{\text{number of individuals at start of the period}} \]

• Example: Start with 100 disease-free individuals. After one year, 10 develop the disease.

\[ CI = \frac{10}{100} = 0.10\]

• After one year, 10% of the population developed the disease.

Incidence Density

• Rate that measures how often new cases of a disease occur while accounting for different lengths of time that people are at risk

\[ ID = \frac{\text{new cases}}{\text{total person-time at risk}}\]

• Study follows 5 individuals:
  • Person A: 2 years at risk
  • Person B: 3 years at risk
  • Person C: 1 year at risk
  • Person D: 4 years at risk
  • Person E: 2 years at risk
• Total person-time at risk = 12 person-years

Why different times?

• Developed disease
• Died (other causes)
• Lost to follow-up
• Study ended

Incidence Density

• Total person-time at risk = 2 + 3 + 1 + 4 + 2 = 12 person-years
• New cases = 3

\[ ID = \frac{3}{12} = 0.25 \text{ cases per person-year} \]

• 0.25 cases per person-year means that, on average, for every 4 people followed for a year, 1 will develop the disease.

Measures of Comparison

Key Metrics:

• Age-Standardized Rates: Adjusted to eliminate age structure differences.
• Attributable Risk: Measures the impact of specific risk factors on outcomes.

Demographic Changes and Health Metrics

• Fertility Rate: Key measure of reproductive behavior.
• Mortality: Affects life expectancy and health metrics.
• Migration: Alters population composition, health service demands.

Population Structure

Age Pyramids

• Graphical representation of age and sex distribution.
• Shows aging trends, and reveals impact of demographic events like wars, migrations.

Age Pyramids

• Developed Countries: Narrow base, wider top (aging population).
• Developing Countries: Broad base, narrow top (younger population).
• Interactive pyramid.

Qatar and Japan Age Pyramids

Qatar

Japan

Interactive Activity

Analysis Instructions

• Choose an age pyramid.
  • international pyramid.
  • local level pyramid.
• Explain to class what you see and what you expect the population to look like in 15, 30 and 50 years.

Demographic Transition

• 4 stages of transitions
• There is actually a fifth stage!

Demographic Transition

• Stage 1: high burden of infectious diseases, high maternal/child mortality
• Stages 2 & 3: transition to chronic diseases; improved health infrastructure
• Stages 4 and 5: dominance of Non-Communicable Diseases (NCDs), and ageing related conditions

Preston Curve

GDP vs. Life Expectancy

• How do country’s economic status and life expectancy relate?
  • Higher GDP often correlates with longer life expectancy.
  • Plateau effect beyond a certain GDP.

Preston Curve

• A: the new technology is equally applicable in all countries regardless of their level of income.
• B: the new technology has a disproportionately larger effect in rich countries.
• C: poorer countries benefit more.

Aging Populations

• Trends:
  • Increasing proportion of elderly.
  • Dependency ratios rise, impacting economic and health systems.

Aging Populations

Aging Populations

Rectangularization of Life Expectancy

• Survival curves become more rectangular as mortality compresses toward older ages.
• Implications:
  • Shift to chronic diseases as leading health concerns.
  • Need for age-specific interventions.

Survival Curves in Sweden

Source: Schoder, J., & Zweifel, P. (2011). Flat-of-the-curve medicine: a new perspective on the production of health. Health Economics Review, 1, 1-10.

Survival Curves in USA

Source: Bell, F. C., & Miller, M. L. (2005). Life tables for the United States social security area, 1900-2100 (No. 120). Social Security Administration, Office of the Chief Actuary.

Mortality Metrics

Crude Death Rate (CDR)

• Number of deaths per 1,000 individuals per year.
• Can be refined by breaking it down by age, sex, and socioeconomic status.
• Example: CDR in Mexico < CDR in the USA, but age distribution must be considered.

Case Fatality Rate (CFR)

• Proportion of individuals with a disease who die from it.
• Indicates severity and lethality of a disease.
• Example Calculation: CFR = (Disease deaths / Total diagnosed cases) × 100

Standardized Mortality Ratio (SMR)

• Adjusts mortality rates for different population age structures.
• Formula: SMR = (Observed Deaths / Expected Deaths) × 100
• Example Calculation:
  • Observed deaths: 93
  • Expected deaths: 70.5
  • SMR = (93 / 70.5) × 100 = 132 (indicating 32% higher mortality than expected)

Years of Life Lost

• Years of Life Lost (YLL) quantifies the impact of premature mortality in a population.
• Used in public health to evaluate disease burden and prioritize interventions.

Methodology for Calculating YLL

Definition of Premature Mortality Age

• Premature mortality is defined relative to a threshold age, typically the average life expectancy.
• For this example, we use 75 years.

Identification of Age at Death

• Essential for determining premature deaths.
• Each death’s impact varies depending on age at death.

Calculation of Individual YLL

• Formula: \(\text{YLL} = T - a_i\) where:
  • \(T\) is the threshold age,
  • \(a_i\) is the individual’s age at death.

Summation of YLL

• Total population impact calculated as: \(\text{Total YLL} = \sum \text{YLL}_i\)

Example Calculation

Consider three individuals dying at ages 25, 35, and 60, with a threshold of 75 years:

• Individual 1:
  • Age at death: 25
  • YLL = 75 - 25 = 50 years
• Individual 2:
  • Age at death: 35
  • YLL = 75 - 35 = 40 years

• Individual 3:
  • Age at death: 60
  • YLL = 75 - 60 = 15 years
• Total YLL: \(50 + 40 + 15 = 105 \text{ years}\)

Discussion

• The 105 years of YLL highlight the burden of premature deaths.
• Helps health authorities prioritize interventions.
• Modifications include:
  • Discounting future years,
  • Weighting younger deaths more heavily,
  • Adjusting for epidemiological and demographic context.
• Think about large campaigns to reduce road related deaths at young age (= preventable)

Summary

• Crude Death Rate (CDR) and Case Fatality Rate (CFR) provide baseline mortality measures.
• Standardized Mortality Ratio (SMR) adjusts for age distribution differences.
• Years of Life Lost (YLL) quantifies premature mortality and is crucial for measuring disease burden.
• YLL helps prioritize public health interventions by identifying the most significant sources of early death.

Fertility

• Some definitions
  • crude birth rate (CBR): number of births per 1,000 population per year. The denominator, however, refers to the total population, including men and boys, young girls and old women!
  • general fertility rate (GFR): restricts inclusion in its denominator only to women in the reproductive age range
  • total fertility rate (TFR): summing all age-specific fertility rates for women in the childbearing ages.

Birth Rates in US, Canada, Mexico

Deriving Total Fertility Rate

Age-Specific Fertility Rates (ASFRs) for the U.S. (2000)

• 15–19: 48.5/1,000

• 20–24: 112.3/1,000

• 25–29: 121.4/1,000

• 30–34: 94.1/1,000

• 35–39: 40.4/1,000

• 40–44: 7.9/1,000

• 45–49: 0.5/1,000

Total: 425/1,000

Explanation

• The ASFR represents the birth rate per 1,000 women in each age group.

• Summing the ASFRs gives 425/1,000 for all age groups.

• Since each ASFR represents a five-year average, multiplying by 5 gives 2,125/1,000. This means the TFR = 2.1 births per woman, the replacement-level fertility rate typical of developed countries.

• Including ages 10–14 or 50–54 has little impact on the overall TFR.

Aging Populations

• Policy Implications:
  • Need for long-term care services.
  • Redesign of healthcare systems.

Book Exercise

Exercise 2.1: State of the Population

In the 2000 census, the total population of the United States was 281,423,000, with the following age-sex distribution (rounded to the nearest thousand):

Age Group	Male Population	Female Population	Total Population
0–1	1,949,000	1,857,000	3,806,000
1–4	7,862,000	7,508,000	15,370,000
5–14	21,044,000	20,034,000	41,078,000
15–44	62,647,000	61,577,000	124,224,000
45–64	30,143,000	31,810,000	61,953,000
65+	14,410,000	20,582,000	34,992,000
Total	138,055,000	143,368,000	281,423,000

Calculations

• 1. Proportion of the Elderly (65+ years) in the Population
• 2. Proportion of the Young (0–14 years) in the Population
• 3. Sex Ratio in Age Group 0–14 (expressed as 100 males per females)
• 4. Sex Ratio in Age Group 65+ (expressed as males per 100 females)

Summary

Key Takeaways

• Differences between incidence and prevalence.
• Importance of understanding demographic changes.
• Using tools like age pyramids to predict health trends.

Discussion Questions

• How do changes in prevalence reflect healthcare advances?
• What policies can address the challenges of aging populations?

References

• Young, T. K. (2004). Population Health: Concepts and Methods.
• WHO Reports on Health Metrics.
• National Center for Health Statistics.
• Preston, S. H. (1975). The changing relation between mortality and level of economic development. Population Studies, 29(2), 231-248.
• Omran, A. R. (1971). The Epidemiologic Transition: A Theory of the Epidemiology of Population Change. The Milbank Quarterly, 49(4), 509–38.
• Fries, James F. (2002). Aging, natural death, and the compression of morbidity.Bulletin of the World Health Organization 80, no. 3 (2002): 245-250.
• Nusselder, W.J. and Mackenbach, J.P. (1996). Rectangularization of the survival curve in the Netherlands, 1950-1992. The Gerontologist, 36(6), pp.773-782.