NURS 434-001: public / community lecture- unit 4

Janis Barner

adjusted vs attack rates

• Adjusted rate: Statistical procedure that removes the effects of differences in the composition of a population, such as age, when comparing one to another.

• Attack rate: An incidence or occurrence rate. (rate that a disease ATTACKS hosts)

incidence and prevalence rates

• Incidence rates= the probability that people who are free of a condition will develop it over a specific period of time—often a year. They show how quickly new cases of a disease occur in a population. Only new cases are counted in the numerator, and the time of onset (or diagnosis) must be identified. Incidence is especially useful for acute conditions like COVID-19 but can be harder to determine for chronic diseases such as cancer or depression.

• Prevalence rates= the total number of existing cases (both new and old) of a condition in a population at a specific point or over a period of time.

  • Point prevalence= the number of cases at a single moment.

  • Period prevalence= cases over a time interval.

Prevalence is influenced by both incidence (new cases) and duration of the disease—chronic conditions with long durations tend to have high prevalence even if incidence is low. Prevalence data help public health professionals identify the burden of disease, determine community health needs, and plan prevention programs and resource allocation.

types of incidence rates

Incidence rates measure how often new cases of a disease occur in a population. Several specific types of incidence rates help public health professionals understand risk patterns and causes of illness.

1. Mortality Rates

Mortality rates measure the probability of death within a population over a specific time period.

• Crude mortality rate: Probability of death from any cause in the entire population.

• Cause-specific mortality rate: Probability of death from a specific disease

• Case fatality rate: Proportion of people with a particular disease who die from it (e.g., deaths from lung cancer ÷ total people with lung cancer × 100).

• Proportional mortality ratio (PMR): The proportion of all deaths due to a specific cause. Unlike rates, its denominator includes total deaths, not the total population.

2. Incidence Density

Used when individuals are observed for different lengths of time. This gives a more accurate rate when people enter or leave a study at different times, die, or are lost to follow-up.

• Ex- comparing hospital-acquired pneumonia between short-stay acute care patients and long-term residents requires incidence density to account for time differences.

3. Attributable Risk

Identifies how much of a condition in the exposed group is due to the exposure itself, beyond what would occur naturally. Highlights the impact of eliminating a specific risk factor

• Formula:
  Attributable Risk = (Incidence rate in exposed group) − (Incidence rate in unexposed group)

4. Relative Risk Ratio 

Compares the incidence rate in the exposed group to that in the unexposed group.

• Relative Risk = 1.0: No difference in risk.

• Relative Risk > 1.0: Exposure increases risk.

• Relative Risk < 1.0: Exposure may be protective.

• Example: A relative risk of 6.0 means those exposed are six times more likely to develop the condition.

crude rate

General or summary rates that measure the occurrence of the health problem being investigated in the entire population.

• Limitation: Subgroups within the population may have different risks, so crude rates can mask important differences.

• Example: The crude birth rate uses the total population in the denominator, even though only people of childbearing age can give birth. This can distort the true risk for that specific subgroup.

crude death rate calculation

• (total # of deaths / total population for place and time) x 1000 population

example- in 2022, the total deaths in Jamaica was 21,400, and the total population was 2,827,377.

• (21,400 / 2,827,377) x 1000 = 7.57 per 1,000 population.

sensitivity vs specificity

• Sensitivity- Ability of a test to correctly identify people who have a health problem; the probability of testing POSITIVE if the health problem is truly present.

• Specificity- Ability of a test to correctly identify people who do NOT have a health problem; the probability of testing NEGATIVE if the health problem is truly absent.

There is an inverse relationship between sensitivity and specificity—improving one often reduces the other.

• In practice, achieving 100% for both is nearly impossible, so multiple screening tests may be used to improve accuracy. Health professionals must balance the risks of false negatives (missing real cases) and false positives (incorrectly labeling healthy people as sick).

• For rare diseases, high specificity is difficult to maintain, so sensitivity becomes more valuable for identifying potential cases.

attributable risk

The difference between the incidence rates in an exposed and an unexposed group of people

case fatality rate

A rate in which the number of people with a specific disease becomes the subgroup being studied out of the entire population in a designated geographic area.

• Calculated by dividing the number of deaths from a specific disease by the number of people living with that disease during the year, then multiplying by 100.

epidemic curve

A graph that plots the distribution of cases by the time of onset of the disease.

epidemiologic triad

Model based on the belief that health status is determined by the interaction of the characteristics of the triad: host, agent, and environment.

• helps guide planning of interventions.

• Host: person, family, or population; includes modifiable factors (lifestyle, exercise, nutrition) and non-modifiable factors (age, genetics, race)

• Agent: can be biologic (bacteria, viruses), chemical (pollutants, drugs),  physical (trauma, heat), nutritional (deficiencies/excess), or psychosocial (stress, isolation)

• Environment: biologic (plants, animals, vectors), physical (light, air, radiation), social (culture, technology, education, politics, economics)

web of causation & wheel of causation

web

• Epidemiologic model that strongly emphasizes the concept of multiple causation while de-emphasizing the role of agents in explaining illness.

• Focuses on multiple causation and complex interrelated factors.

• Identifies all direct and indirect factors influencing a health condition.

• Useful for chronic diseases and planning interventions at multiple points.

• Example: analyzing adolescent drug misuse through interconnected social, biological, and environmental factors.

Wheel

• Epidemiologic model that de-emphasizes the agent as the sole cause of disease, while emphasizing the interplay of physical, biologic, and social environments.

• Core: genetic makeup of host. Outer wheel: physical, biological, and social environment.

• Highlights interplay of host and environment in determining health outcomes.

in simple terms:

web:

• Think: “many factors all connected”

• Disease is caused by a complex network of factors (not just one cause)

• Includes: Lifestyle, Environment, Genetics, Social factors

• 👉 Example: Heart disease: Smoking + poor diet + stress + genetics → all interact together

wheel:

• Think: “genetics in the center, environment around it”

• Disease is caused by:

  • Core = genetic makeup

  • Outer wheel = environment (biologic, physical, social)

• 👉 Example: for Diabetes: Genetics (center) + environment (diet, activity, culture)

• ✔ Key idea: Genetics is the core driver, environment modifies risk
  ✔ Removes the idea of a single “agent” (like bacteria)

Hippocrates

considered the father of modern medicine. He emphasized environmental factors, lifestyle, diet, and exercise in health and wrote On Air, Waters, and Places, an early milestone in epidemiology.

development of epidemiology

Early Foundations

• John Graunt

  • Analyzed London Bills of Mortality

  • Identified patterns in births, deaths, infant mortality, and seasons

  • Introduced early statistical inference

• Other key contributors:

  • James Lind – early clinical trial (scurvy treatment)

  • Percivall Pott – linked soot to cancer

  • Edward Jenner – developed first vaccine

Florence Nightingale

• Improved sanitation during Crimean War

• Showed infection > wounds as cause of death

• Advocated for sanitary reform using statistics

Advancing Data & Methods

• William Farr

  • Standardized death data collection

  • Defined populations at risk

Disease Investigation & Causality

• John Snow “it’s SNOWing diseases”

  • Linked cholera outbreak to contaminated water

  • Famous Broad Street pump intervention (removed handle → ↓ deaths)

  • Established cause-and-effect before germ theory

natural history of disease

Describes the course of disease from onset to resolution.

Stages:

• Prepathogenesis – initial interaction of agent, host, and environment; primary prevention applied.

• Pathogenesis – biologic or psychological responses; secondary prevention focuses on early diagnosis and treatment.

• Clinical disease – manifestation; tertiary prevention focuses on rehabilitation and maximizing function.

Applying Epidemiologic Principles in Nursing Practice

Collect data from individuals → Analyze → Formulate diagnoses/hypotheses → Plan interventions → Implement → Evaluate outcomes.

• Nursing focuses on individuals/families, while epidemiology focuses on populations/communities.

•Connection Between Nursing and Epidemiology

•Assessment of Health Needs and Assets

•Planning and Implementing Interventions

•Preventing and controlling outbreaks

•Contributing to a safe and healthy environment

•Evaluating the effectiveness of health services