3 Measures of Disease Occurrence and Surveillance
Rachel A. Hoopsick
Measures of Disease Occurrence and Surveillance
Overview
Measures of disease occurrence and surveillance are fundamental in epidemiology for understanding the frequency, distribution, and patterns of health-related states and events within populations. Key measures include prevalence, incidence, mortality rate, and case-fatality rate. Public health surveillance systems are used to monitor these metrics to detect outbreaks, assess the burden of diseases, and guide public health interventions. Effective surveillance allows for timely responses to emerging public health threats and the evaluation of the impact of prevention and control measures.
Learning Objectives
By the end of this chapter, you will be able to:
- Define and calculate measures of morbidity, including incidence and prevalence
- Define and calculate measures of mortality, including mortality rate and case-fatality rate
- Discuss the role of epidemiology in disease surveillance
- Describe different types of public health surveillance
Importance of Measuring Disease and Death
Measuring disease and death is fundamental to effective public health practice, guiding actions to improve health outcomes and enhance the quality of life. Specifically, measuring disease and death is used for:
Understanding Health Status: It provides a comprehensive picture of the health status of a population, identifying prevalent diseases, risk factors, and health needs.
Resource Allocation: Accurate data on disease and mortality inform decision-makers on where to allocate resources, such as funding, medical staff, and equipment, ensuring they are directed to areas of greatest need.
Program Evaluation: Tracking disease and death rates helps evaluate the effectiveness of public health interventions and policies. It allows public health officials to adjust strategies based on what is or isn’t working.
Early Detection and Response: Surveillance of disease incidence and mortality can detect emerging health threats, enabling a prompt public health response to prevent the spread of diseases.
Setting Priorities: Data on disease burden and mortality help prioritize public health issues, focusing efforts on diseases and conditions that cause the most harm or are the most preventable.
Health Planning: Long-term planning and goal setting in public health are guided by trends in disease and death, helping to establish realistic and impactful health targets.
Education and Awareness: Communicating data on disease and mortality to the public raises awareness and educates individuals about health risks and prevention strategies.
Research and Development: Understanding the epidemiology of diseases guides research efforts and the development of new treatments, vaccines, and other interventions.
Morbidity vs. Mortality
In epidemiology, morbidity and mortality are distinct concepts that describe different aspects of health outcomes in populations. Morbidity refers to the occurrence of disease, injury, or disability in a population and indicates how widespread or common a health condition is. Mortality, on the other hand, refers to the occurrence of death within a population, which indicates the frequency of deaths due to specific causes over a certain period. While morbidity highlights the burden of illness and its impact on quality of life, mortality focuses on the ultimate outcome of death, often used to assess the severity of health conditions and the effectiveness of public health interventions.
Measures of Morbidity
There are numerous measures of morbidity that epidemiologists leverage to understand the burden of disease. However, there are two primary measures we will focus on here: prevalence and incidence.
Prevalence
Prevalence is a fundamental epidemiological measure used to describe the proportion of individuals in a population who have a particular disease or condition at a specific point in time or over a specified period. We can further define two distinct measures of prevalence: point prevalence and period prevalence. Point prevalence and period prevalence are two different measures used in epidemiology to describe the proportion of individuals in a population who have a particular disease or condition, but they differ in the timeframe considered.
Point Prevalence: The proportion of a population that has a specific disease or condition at a single point in time. It is calculated as the number of existing cases of a disease at a particular moment divided by the total population at that same moment. The formula is:
[latex]\text{Point Prevalence} = \frac{\text{Number of existing cases at a specific point in time}}{\text{Total population at that time}} X 100[/latex]
For example, if a survey is conducted on January 1st and identifies that 5 out of 100 people have the flu, the point prevalence of flu on January 1st is 5%: [latex]\text{Point Prevalence} = \frac{\text{5}}{\text{1,000}} X 100[/latex]
Point prevalence provides a snapshot of the disease burden at a particular instant, which is useful for assessing the current state of a health issue in a population.
Period Prevalence: The proportion of a population that has a specific disease or condition during a specified time period. This might include all cases, both new and existing, within a specified time frame. It includes all cases that existed at any time during the period, both new (incident) cases and those that were present at the beginning of the period. The formula is:
[latex]\text{Period Prevalence} = \frac{\text{Number of cases present at any time during the period}}{\text{Average or mid-period population}} X 100[/latex]
For example, if a study covers the entire month of January and finds that 20 out of 100 people had the flu at some point during the month, the period prevalence for January is 20%: [latex]\text{Period Prevalence} = \frac{\text{20}}{\text{100}} X 100[/latex]
Period prevalence provides a broader picture of the disease burden over a duration, capturing both existing and newly diagnosed cases within that timeframe.
Incidence
Incidence is a key epidemiological measure that quantifies the occurrence of new cases of a disease or health condition in a population over a specified period. It provides insight into the risk of developing the disease within a given timeframe. We can further define two distinct measures of incidence: cumulative incidence and incidence rate. Cumulative incidence and incidence rate are both measures of disease occurrence in epidemiology, but they differ in how they account for the population at risk and the time component.
Cumulative Incidence: The proportion of a population that develops a disease over a specified period. It is calculated as the number of new cases divided by the number of individuals at risk at the beginning of the period. Cumulative incidence considers a specific period (e.g., one year, five years) and includes only those individuals who were at risk at the beginning of this period. It represents the probability or risk that an individual in the population will develop the disease during the specified period. Cumulative incidence is particularly useful for studying diseases with a clearly defined onset in a population where individuals are followed for the same period. The formula is:
[latex]\text{Cumulative Incidence} = \frac{\text{Number of new cases during a specific period}}{\text{Total number of individuals at risk at the start of the period}}[/latex]
For example, imagine a study that follows 1,000 people over one year to track the development of a particular disease, such as influenza. Suppose that at the beginning of the study, none of the participants had influenza, and by the end of the year, 100 participants had developed the disease.
[latex]\text{Cumulative Incidence} = \frac{\text{100}}{\text{1,000}}[/latex]
The cumulative incidence of influenza during the time period was 0.1 or 10%, which means that there was a 10% risk of developing influenza in this population over the one-year period.
Incidence Rate: Accounts for varying time periods of risk among individuals and is particularly useful when individuals enter and leave the study population at different times. Incidence rate measures the frequency of new cases of a disease in a population per unit of person-time at risk. Person-time is the sum of the periods that each individual in the study was at risk. It accounts for individuals entering or leaving the study and for varying lengths of follow-up. Incidence rates represent the rate at which new cases occur in the population, providing a measure of how quickly individuals in the population develop the disease. The formula is:
[latex]\text{Incidence Rate} = \frac{\text{Number of new cases during a specific period}}{\text{Total person-time at risk during that period}}[/latex]
Examples
For example, consider a study of 500 individuals to observe the occurrence of a particular condition, like a sports injury, over time. Each individual is followed for different lengths of time depending on when they join the study, and some may drop out before the study ends. During the study period, a total of 50 new cases of the sports injury are identified. The total person-time at risk is calculated as 2,000 person-years (sum of the time each individual was at risk and participating in the study).
[latex]\text{Incidence Rate} = \frac{\text{50 cases}}{\text{2,000 person-years}}[/latex]
The incidence rate of the sports injury was 0.025 per person-year or 25 new cases per 1,000 person-years.
Measures of Mortality
There are several measures of mortality that epidemiologists use to quantify death within a population. We will focus on mortality rate and case-fatality rate.
Mortality Rate
The mortality rate is a key measure in epidemiology that quantifies the frequency of deaths in a specific population during a certain period. It provides crucial insights into the health and well-being of populations and helps in understanding the impact of diseases, injuries, and other health conditions. The mortality rate is typically expressed as the number of deaths per a certain population size within a specified time frame, usually per 1,000 or 100,000 people per year. The formula is:
[latex]\text{Mortality Rate} = \frac{\text{Number of deaths during a specific period}}{\text{Total population at that time}} X 10^n[/latex] where 10n is a scaling factor (e.g., 1,000 or 100,000) to make the rate more interpretable.
For example, suppose a city with a population of 500,000 experiences 2,000 deaths over the course of one year:
[latex]\text{Mortality Rate} = \frac{\text{2,000}}{\text{500,000}} X 1,000[/latex]
This means that, on average, there were 4 deaths per 1,000 people in the city during that year.
Case-Fatality Rate
Case-fatality rate is an important measure in epidemiology that quantifies the severity of a disease by calculating the proportion of individuals diagnosed with a specific condition who die from it within a specified period. This measure helps in understanding how lethal a disease is and the risk of death for those who are affected. Case-fatality rate represents the proportion of individuals with a particular disease who die from that disease within a defined time period. It is typically expressed as a percentage. The formula is:
[latex]\text{Case Fatality Rate} = \frac{\text{Number of deaths from the disease}}{\text{Number of diagnosed cases of the disease}} X 100[/latex]
For example, in a study of a new infectious disease, 500 people are diagnosed with the disease. Out of these 500 cases, 50 people die from the disease within the study period.
[latex]\text{Case Fatality Rate} = \frac{\text{50}}{\text{500}} X 100[/latex]
This means that 10% of the individuals diagnosed with the disease died from it.
Public Health Surveillance
Public health surveillance is the continuous, systematic collection, analysis, interpretation, and dissemination of health-related data. Epidemiology plays a crucial role in public health surveillance by providing the scientific foundation and methodology necessary for understanding and controlling health-related events. In public health, surveillance systems are categorized as either active or passive, based on how data is collected and reported.
Active surveillance involves actively seeking out information about health events. Active surveillance is often used during outbreaks, for targeted studies, or in specific high-risk settings where detailed and immediate data are crucial. Public health officials, researchers, or healthcare workers contact healthcare providers, laboratories, or the public to collect data. This approach uses regular, systematic data collection efforts, often through surveys, field visits, or direct communication with health facilities. Because data collection is actively pursued, active surveillance generally results in more accurate and complete data, capturing a broader range of cases, including those that might not be reported otherwise. However, this approach is resource-intensive and more costly because it requires dedicated staff and time to gather information continuously and systematically. Examples of active surveillance systems in the United States include the National Health and Nutrition Examination Survey (NHANES), the National Survey on Drug Use and Health, and the Youth Risk Behavior Surveillance System (YRBSS).
In passive surveillance, data is reported by healthcare providers or laboratories to public health authorities without solicitation. Data are collected as part of routine healthcare activities, such as when a patient visits a clinic or is diagnosed with a condition. This approach is commonly used for monitoring widespread conditions, in settings where the burden of data collection needs to be minimized, or when resources are limited. Passive surveillance is less costly and requires fewer resources than active surveillance because it leverages existing healthcare processes and does not require extensive outreach efforts. However, this method can lead to underreporting or incomplete data, as it depends on the compliance and thoroughness of the reporting entities. Examples of passive surveillance systems in the United States include the Adverse Events Reporting System, which is focused on patient safety related to all marketed drug and therapeutic products, and the Vaccine Adverse Events Reporting System, which collects data on the side effects of vaccines.
Key Takeaways
This chapter highlighted the importance of measuring disease occurrence and implementing effective surveillance systems in epidemiology. Key metrics like prevalence, incidence, mortality rate, and case-fatality rate help epidemiologists assess the health status of populations, detect emerging threats, and inform public health interventions. Public health surveillance, both active and passive, is crucial for timely data collection, guiding resource allocation, and evaluating the impact of prevention and control measures. Together, these epidemiological tools play a vital role in monitoring population health and responding to health challenges efficiently.