Epidemiology: Morbidity, Mortality, Incidence, and Prevalence – Key Concepts and Data Examples

Morbidity: refers to the number of people who have a given disease; the sick population. Often discussed as a morbidity rate.
- Can be expressed as a percent or per a fixed population size, e.g. per 10,000 or per 100,000 people.
- Core idea: count of illnesses in the population.
Mortality: refers to deaths due to a disease; the number of people who die from that disease.
- Mortality rate: number of deaths from a particular disease in a population.
- Core idea: count of deaths in the population.
Key distinction: morbidity vs mortality differ in what is counted (illness vs death).
Relative magnitudes: mortality rates are usually lower than morbidity rates, because not all who are ill die from the disease.

Both incidence and prevalence are subsets of morbidity.
Incidence:
- Definition: the number of new cases of a disease during a specific timeframe.
- Emphasizes appearance of new illnesses within the period.
- Often illustrated as a rate when tied to a population at risk.
- Example from transcript: the blue line in the HIV figure represents incidence (new HIV infections per year).
Prevalence:
- Definition: the total number of cases of a disease during a given timeframe.
- Emphasizes the total burden of disease at a point or over a period.
- Example from transcript: the prevalence of HIV in the US rising over time, even though new infections per year remained relatively stable.
Relationship between incidence, prevalence, and morbidity:
- Both concepts depend on the time period chosen for measurement.
- It is possible for incidence to stay relatively flat while prevalence increases, as cumulative new cases accumulate over time and people live with the disease longer or longer survival adds to the total number with disease.

Time period shown: 1980 to 2010.
Incidence (new HIV infections): relatively stable over the period; the line is fairly flat after an initial blip in the 1980s.
Prevalence (total people living with HIV at any time during the period): grows over time as new cases accumulate.
Numerical takeaway: by the end of the period, prevalence approaches about $1.2 imes 10^6$ (1.2 million) cases in the United States.
Summary from figure: stable annual new cases but increasing total number of people living with the disease due to accumulation over time.

Morbidity and Mortality Weekly Report (MMWR) has been rebranded in style:
- Now presents tables of nationally notifiable infectious diseases and conditions.
- It provides morbidity and mortality information on a weekly basis.
Data collection process:
- CDC gathers information from health care providers and other state agencies.
- CDC compiles the data and publishes updates.
Practical use: If you want to know what diseases are “hot” this week in the United States, the MMWR is a primary source.
Terminology note: the term used in the current format is often described as morbidity and mortality information, but the presentation is still weekly and nationally oriented.

Data format examples show breakdown by disease type and by geographic region (part of the country).
Botulism categories reported:
- Foodborne botulism
- Infant botulism
- Other botulism (e.g., wound-related, etc.)
2019 annual counts reported in the format:
- Foodborne botulism: 6 cases
- Infant botulism: 18 cases
- Other botulism: 1 case
Significance: illustrates how surveillance reports provide a snapshot of incidents by disease subtype and region, helping identify public health priorities and geographic patterns.
Data origin: collected by health care providers and state agencies, compiled by the CDC for weekly reporting and public dissemination.

Morbidity vs mortality:
- Morbidity = number of illnesses in the population; reflects disease burden.
- Mortality = number of deaths due to a disease; reflects lethality and severity.
Incidence vs prevalence:
- Incidence focuses on new cases in a defined time window, highlighting risk of contracting the disease.
- Prevalence focuses on all existing cases in a defined time window, highlighting overall disease burden and resource needs.
Both incidence and prevalence depend on the chosen time period; definitions must specify the timeframe.
Practical implications:
- A stable incidence with rising prevalence can indicate longer survival, improved treatment, or disease chronicity increasing the number of people living with the disease.
- Surveillance data (like MMWR) guide public health actions, resource allocation, and policy decisions.
Connection to broader epidemiology foundations:
- These measures help quantify population health, monitor trends, compare populations, and assess the impact of interventions.
- Understanding the difference between rate and count is essential for proper interpretation of public health data.

Morbidity rate (general form):
$\text{Morbidity rate} = \frac{\text{Number of people with disease}}{\text{Total population}} \times k,$ where $k$ is a multiplier such as $100$ , $10^2$ , $10^4$ , or $10^5$ depending on reporting scale.
Mortality rate (general form):
$\text{Mortality rate} = \frac{\text{Number of deaths due to disease}}{\text{Total population}} \times k,$ with an appropriate multiplier $k$ .
Incidence (definition, count):
$\text{Incidence} = \text{Number of new cases during a specified time period}.$
Incidence rate (typical epidemiology formulation):
$\text{Incidence rate} = \frac{\text{Number of new cases during } T}{\text{Population at risk during } T} \times k.$
Prevalence (definition):
$\text{Prevalence} = \text{Total number of existing cases during a given time period (or at a point in time).}$
Notes:
- Both incidence and prevalence depend on the chosen time period.
- A disease can have rising prevalence even if incidence is flat or decreasing, due to factors like longer survival and better case ascertainment.

These concepts underpin epidemiologic surveillance and public health decision-making.
Surveillance systems (e.g., CDC, MMWR) operationalize these concepts to track disease burden, identify outbreaks, and inform interventions.
Ethical and practical considerations include ensuring timely reporting, data quality, regional breakdowns, and balancing privacy with public health transparency.