What is covered on this page →
Differentiate between the term incidence and prevalence.
Assess the relationship between morbidity and mortality data.
Calculate mortality rates such as proportionate mortality, infant mortality, maternal mortality.
Judge the relevance of adjusted rates from specific epidemiologic data.
Describe at least two methods for presenting epidemiological data.
At its core, epidemiology seeks to answer two fundamental questions: “how” and “why.” How do diseases or health-related events spread within a population and secondly why do certain individuals or groups bear a greater burden of illness? To unravel these mysteries, we rely on various measures of occurrence, which provide us with valuable insights into the patterns of diseases and their impact on communities.
Let’s begin by examining the distribution and determinants of health events, which lie at the heart of epidemiology. Distribution, in this context, refers to the patterns or frequencies with which diseases or health-related factors occur across different populations, places, and times. Determinants, on the other hand, are the factors or conditions that influence the occurrence and spread of these health events. By comprehensively studying both distribution and determinants, epidemiologists aim to uncover the underlying reasons behind disease outbreaks, allowing for the development of targeted interventions.
Another fundamental concept in epidemiology is proportion, which help quantify the occurrence of diseases in relation to the population size or specific time period. These proportion rates (usually expressed as percentages) provide us with essential tools for comparing disease burdens across populations of varying sizes. By expressing the number of cases as a proportion of the population, we can make meaningful comparisons between communities, regions, and countries, aiding in the identification of areas with higher disease prevalence. Conversely, a rate (how fast) involves the measurement of an event (such as disease cases) in relation to the size of a population and the duration of time. Rates are expressed per unit of time and often per unit of population, such as cases per 1,000 people per year. Rates provide a measure of how quickly an event is happening in a population.
Moving forward, we delve into the dynamic interplay of incidence and prevalence. Incidence measures the rate at which new cases of a disease develop within a population over a specific period. It offers insights into the probability of individuals developing a particular health condition, shedding light on the risk factors at play. In contrast, prevalence quantifies the total number of cases, old and new, within a population at a given point in time. This measure provides us with a snapshot of the disease burden within a community, indicating how many individuals are currently affected.
As we explore these measures of occurrence in epidemiology, we will gain a deeper understanding of how diseases spread, why certain populations are more susceptible, and how public health interventions can be tailored to mitigate these challenges.
Incidence and Prevalence of Diseases
In epidemiology, understanding the occurrence and distribution of diseases within populations is paramount for effective public health interventions. To achieve this, epidemiologists employ measures of disease occurrence, primarily expressed as ratios, to assess the burden of health conditions. These ratios consist of two fundamental components: the numerator, representing the case count or specific health events, and the denominator, which reflects the population size or person-time under observation. When we delve into the realm of epidemiological research, two key concepts emerge—incidence and prevalence. Incidence count, referring to the number of new cases that occur within a defined time frame, focuses on disease onsets alone. In contrast, prevalence count encompasses all cases existing within a population at a given point or period. Both incidence and prevalence, with their distinct numerators and denominators, play pivotal roles in unraveling the complex landscape of disease dynamics and informing targeted public health strategies.
Incidence Rate/Density
Incidence rate, often referred to as incidence density or simply incidence, is a crucial epidemiological measure used to quantify the rate at which new cases of a specific disease occur within a population over a defined period. It is a ratio providing essential insights into the risk and dynamics of disease occurrence.
In discussing incidence risk one might refer to incidence rate, incidence density or incidence proportion. The key difference lies in the denominators used for these measures. Incidence rate and density consider person-time at risk, making them suitable for populations with varying follow-up times, while incidence proportion focuses on the initial population size. Incidence rate and incidence density can be used interchangeably. Likewise, incidence proportion and incidence risk can be used interchangeably.
Table 3.1 Morbidity rates- Measuring Incidence.
Attack Rate
Attack rate is a valuable measure of incidence proportion expressed as a percentage. It quantifies the number of new cases of a disease within a population per 100 persons in the population at risk. Expressed as a percentage (%), it provides a straightforward way to assess the risk of disease within a defined group.
For example, if 75 individuals attended a church picnic, and 46 of them subsequently developed ciguatera toxicity, you can calculate the attack rate as follows:
In this example, the attack rate of 61% indicates that among the individuals who attended the picnic, there was a 61% probability of developing ciguatera toxicity. This measure is particularly useful in assessing the risk of disease in specific outbreak situations or within defined populations, providing valuable information for public health interventions and control measures.
Secondary Attack Rate
The Secondary Attack Rate is crucial in assessing the spread of diseases within close contacts, especially in situations where a disease is transmitted from person to person. It quantifies the risk of contracting a disease among individuals who have had close contact with an index case (an initial case of the disease). It is calculated by subtracting the initial cases from the total new cases and expressing it per 100 susceptible individuals. Secondary attack rate helps with helps public health professionals evaluate the risk of disease transmission within households, schools, workplaces, or any setting with close contacts. Diseases like tuberculosis, measles, shigellosis, and varicella, which spread through close contact, are particularly important for these calculations. It provides critical data for implementing control measures. High secondary attack rate values may indicate an urgent need for interventions like quarantine, vaccination, or improved hygiene practices to prevent further transmission. Understanding secondary attack rate can aid in resource allocation and planning for outbreaks. It helps identify which contacts are at the highest risk and need immediate attention.
Consider a daycare centre with nine cases of hepatitis B among 78 attending children. Later, seven family members of the nine infected children also develop hepatitis B. The total number of family members of the nine children is 43. To calculate secondary attack rate:
The information provided about the attack rate (11.54%) and secondary attack rate (20.59%) can offer several inferences about the transmission and impact of a disease within a population:
A higher secondary attack rate (20.59%) compared to the initial attack rate (11.54%) suggests that the disease has a significant potential for secondary transmission within close contacts. This implies that individuals who have contact with already infected cases, such as family members or close friends, are at a greater risk of contracting the disease.
A secondary attack rate of 20.59% indicates a notable risk of disease transmission within households. This could be particularly concerning for diseases that are highly contagious, as it suggests that household members of an infected individual are more likely to become infected themselves.
The higher secondary attack rate may also suggest that there could be a cluster of cases within a specific population or community. It may warrant further investigation into the reasons behind this clustering, such as shared exposures or behaviors.
Public health authorities may consider implementing control measures to reduce secondary transmission, such as isolating infected individuals, providing prophylactic treatment to close contacts, or promoting preventive measures like vaccination or improved hygiene practices.
It’s important to note that attack rates can vary depending on the population, the nature of the disease, and the effectiveness of control measures. Therefore, these rates should be interpreted in the context of the specific disease and population under investigation. Overall, a higher secondary attack rate compared to the initial attack rate suggests that the disease can spread efficiently within close contacts, particularly within households. This information can guide public health interventions and preventive measures to limit further transmission and protect vulnerable populations.
Let’s assess how incidence rate changes with the following examples:
Example #1 Outbreak of highly Contagious Disease.
The number of new cases would increase dramatically over a short period. Since the incidence rate considers both the number of cases and the person-time at risk, this sudden surge in cases would lead to a significant increase in the incidence rate. The rate would reflect the rapid transmission of the disease within the population.
Example #2- Vaccination or Herd Immunity
A substantial portion of the population is vaccinated or achieves immunity, the transmission of the disease is disrupted. As a result, the number of new cases decrease, leading to a reduction in the incidence rate. Herd immunity, which occurs when a sufficient proportion of the population is immune, effectively lowers the risk of disease transmission, further reducing the incidence rate.
Example #3- Death from a Communicable Disease
If individuals with a communicable disease die from the condition, they are no longer considered at risk for developing new cases of the disease. If they die from the communicable disease of interest they are also no longer contributing to disease risks for the population. This reduction in the number of people at risk will lead to a decrease in the incidence rate over time.
Example #4- Cure of a Non-Communicable Disease
In the context of non-communicable diseases, a “cure” typically means that individuals are no longer at risk of developing new cases of the disease. This cessation of risk would result in a decrease in the number of new cases and, consequently, a decrease in the incidence rate.
Prevalence
Prevalence is another critical epidemiological measure that assesses the total number of existing cases of a specific disease or condition within a population at a particular point in time or over a defined period. It provides insights into the overall burden of the disease in a population.
“Number of Existing Cases” refers to the total number of individuals who currently have the disease of interest, and “Total Population at Risk” represents the entire population susceptible to the disease within the defined time frame.
Point Prevalence
Point prevalence is a measure that reflects the proportion of individuals in a population who have a specific condition or disease at a particular point in time. To calculate point prevalence, you need two numbers: the number of existing cases of the disease at a specific moment (the numerator) and the total population at risk at that exact moment (the denominator).
Point prevalence is useful for providing a snapshot of the disease burden within a population at a specific moment. It helps in identifying the current magnitude of a health issue and can be valuable for healthcare planning and resource allocation. Researchers and policymakers often use point prevalence data to understand the immediate impact of a disease, assess healthcare needs, and prioritize interventions.
Period Prevalence
Period prevalence, on the other hand, measures the proportion of individuals who have a specific condition or disease within a defined period. It takes into account both existing and new cases over a specified time frame. To calculate period prevalence, you need the number of people with the condition during the defined period (numerator) and the total population at risk during that same period (denominator).
Period prevalence is valuable for understanding the disease’s cumulative impact over time. It helps in assessing not only the current disease burden but also the historical burden within a specific timeframe. This information can be essential for evaluating the effectiveness of interventions, tracking trends in disease occurrence, and planning long-term healthcare strategies. Period prevalence is particularly useful when dealing with chronic diseases or conditions with long-term consequences.
Example #5- Diabetes Prevalence
In the case of diabetes, prevalence refers to the proportion of individuals within a population who have been diagnosed with diabetes at a particular point in time or over a specific period, such as a year. For instance, if there are 1,000 individuals in a community, and 100 of them have been diagnosed with diabetes, the prevalence of diabetes in that community is 10%. This means that at the given point in time or during the specified period, 10% of the population has diabetes.
Example #6- Prevalence and Behaviour
Prevalence can also be applied to behaviours or habits, such as smoking. Smoking prevalence measures the proportion of individuals in a population who are current smokers at a specific point in time or over a certain period. For example, if a survey conducted in a city with a population of 50,000 reveals that 10,000 people are current smokers, the smoking prevalence in that city is 20%. This indicates that, at the time of the survey, 20% of the population was actively engaged in smoking.
So prevalence provides an estimate of the probability or risk that one will be affected at a point in time. In essence, it is a proportion with condition or the probability a person selected at random will have the condition of interest.
Table 3.2 Factors or Events Influencing Prevalence Rates in the Population
Prevalence represents the proportion of individuals in a population who currently have the disease. It is a valuable tool for understanding the public health impact of a condition, estimating healthcare resource needs, and guiding policy decisions. However, prevalence has some limitations. It does not provide information about the risk of developing the disease or the temporal aspects of the condition’s occurrence. Interpretation should consider factors like population characteristics and the natural history of the disease. Prevalence can be used for comparing disease burdens among different populations but should be complemented by incidence data for a comprehensive understanding of a disease’s impact.
The Cistern Analogy- The Interplay between Incidence and Prevalence
Mortality and Morbidity Data
Morbidity and mortality data are two essential components of epidemiological studies that provide insights into the health of populations and the burden of diseases. They are closely related but serve distinct purposes. Morbidity data focus on the prevalence and incidence of diseases, injuries, and other health conditions in a population. These data include information on the number of people affected by a particular condition, the duration of illness, and the impact on individuals’ quality of life. Mortality data, on the other hand, focus on the number of deaths attributed to specific causes within a population over a given period. Together, morbidity and mortality data provide a comprehensive picture of the overall health status of a population.
Morbidity data help assess the severity of diseases by quantifying the number of people living with a condition and the associated disability or impaired function. Mortality data provide information on the most severe outcome of a disease [death]. The relationship between morbidity and mortality data allows researchers to understand how a specific condition affects individuals in terms of both illness and fatality. The relationship between morbidity and mortality data is instrumental in identifying risk factors associated with diseases and their fatal outcomes. For instance, high morbidity rates for a particular condition may prompt further investigation into the factors contributing to the increased risk of death. This can lead to the development of preventive strategies and targeted interventions to reduce mortality.
Public health interventions often aim to reduce both morbidity and mortality associated with specific diseases. By tracking changes in morbidity and mortality rates over time, policymakers and healthcare professionals can assess the effectiveness of interventions. For example, if vaccination programs result in a significant decrease in morbidity (e.g., reduced cases of a vaccine-preventable disease), this should eventually lead to a decline in mortality rates due to that disease. Another example, if screening is used as an intervention tool for the early detection of a specific cancer, more individuals will be treated before the disease progresses to an advanced stage, potentially resulting in a higher likelihood of a cure (cancer-free period) and a reduction in disease morbidity.
Morbidity and mortality data are crucial for resource allocation in healthcare systems. High morbidity rates can strain healthcare resources, such as hospital beds and healthcare personnel, while high mortality rates can indicate the need for improved access to healthcare services. Balancing resources based on these data is essential for providing appropriate care and reducing the impact of diseases.
Crude Rates
Crude rates are basic epidemiological measures that provide a straightforward overview of disease occurrence or other health events in a population. They are calculated without adjusting for any factors such as age or sex. Crude rates are often used as an initial step in epidemiological analysis to get a general sense of the health issue in a population. They are expressed as a ratio, typically per a specific unit of the population (e.g., per 1,000 or 100,000 people). Crude rates can be useful for making broad comparisons between populations or over time. However, they may not provide a precise picture when there are significant age or sex differences within a population.
Specific Rates
Specific rates provide a more detailed view of disease occurrence by considering specific factors such as age or cause of death. These rates are calculated for subgroups within a population and are valuable for identifying patterns and variations. Two common types of specific rates are:
Age-Specific Mortality Rates- These rates focus on specific age groups within a population. Age-specific mortality rates are calculated by dividing the number of deaths within a particular age group by the total population of that age group and are usually expressed per 1,000 or 100,000 people. They help identify age-related trends in mortality and are crucial for understanding diseases that predominantly affect certain age groups.
To adjust data for differences in the age composition of two populations, age-standardization or age-adjusted rates (either direct or indirect) is utilized to eliminate the influence of varying age distributions when comparing mortality or morbidity data. It is important to note that making comparisons based solely on calculated rates for each population can yield incorrect results. For example, in a population with a significant proportion of elderly individuals, the death rate is likely to be higher than in a younger population. Comparing the crude death rates of two countries might lead to the conclusion that death is more frequent in the country with the older population. However, when age differences are taken into account and removed before making comparisons, the conclusion may differ. The same principle applies to disease occurrences, as certain age groups may have a higher propensity for specific diseases. Relying solely on prevalence rates for comparison can potentially lead to erroneous conclusions. Adjusting for age allows for a more accurate assessment of health outcomes between populations with varying age structures.
Infant Mortality Rate- The infant mortality rate is a specific rate that measures the number of deaths among infants (usually under one year of age) per 1,000 live births in a given year. It is a critical indicator of a population’s overall health and access to healthcare services, as it reflects the risk of death during infancy.
Adjusted Rates
Adjusted rates are used to account for differences in population characteristics that may affect the interpretation of crude rates. These differences can include age, sex, or other factors that influence the rates of health events. Adjusted rates are calculated by applying statistical methods to control for these confounding factors, allowing for more accurate comparisons between different populations or over time. They are particularly useful when analyzing the impact of risk factors or interventions on health outcomes. Adjusted rates provide a more precise understanding of disease patterns and can help identify associations that may be masked by crude rates.
Rates Definitions, Equations and Examples
One of the key advantages of mortality rates is their ability to assess the effectiveness of treatments. When healthcare interventions or therapies are introduced, monitoring changes in mortality rates over time can help determine whether these measures are reducing the fatal consequences of a disease. A decline in mortality rates may suggest that treatments or preventive strategies are successfully saving lives. Furthermore, mortality rates are often more accessible and easier to obtain than incidence data. Recording deaths is a well-established process in healthcare systems worldwide. This accessibility makes mortality data a valuable resource for epidemiologists, enabling them to quickly assess the impact of diseases and health interventions. For instance, when analyzing the impact of a vaccination program on a specific disease, epidemiologists can observe changes in mortality rates associated with that disease. A decrease in mortality following widespread vaccination may indicate the program’s effectiveness in reducing the lethal outcomes of the disease.
Infant Mortality Rate
Total number of infant death (0-11 months) per 1000 live births within a population for a particular place and period of time. All subjects counted in the numerator should have the risk characteristics of the denominator and must satisfy the age limit and time period to be eligible for inclusion.
In 2003, 28,025 babies died before their first birthday while globally live births were at 4,089,950 babies. Calculate the infant mortality rate for 2003.
Maternal Mortality Rate
The denominator used for maternal mortality rates differs significantly from that of other mortality rates, such as general or disease-specific mortality rates. Maternal mortality rates are unique in their focus on a specific population group and a particular timeframe. The denominator for maternal mortality rates is calculated by dividing the total number of maternal deaths by the total number of live births for a specific place and time, typically expressed per 100,000 live births. This distinction is crucial because maternal mortality rates are designed to capture deaths related to pregnancy, labor, and delivery within a specific reproductive age group (usually 15-49 years) and a limited timeframe encompassing both the antenatal and postnatal periods. This focus allows for a more accurate assessment of the risks and outcomes associated with maternal health.
In contrast, general mortality rates, such as crude death rates or age-specific mortality rates, use denominators that encompass the entire population or specific age groups within the population over a broader timeframe, often a calendar year. These rates provide a more comprehensive view of mortality across all causes and age groups in a given population. The uniqueness of the maternal mortality rate denominator reflects the importance of tracking and addressing maternal health issues separately. It allows healthcare professionals and policymakers to monitor the specific risks and challenges faced by pregnant individuals and those giving birth, ultimately guiding interventions and policies to improve maternal health outcomes. By focusing on this distinct population and timeframe, maternal mortality rates provide a more targeted and meaningful measure of the impact of pregnancy-related deaths within a community.
Jamaica, like many countries, classifies maternal deaths into two distinct groups: direct obstetric deaths and indirect obstetric deaths. Direct obstetric deaths are those tragic occurrences resulting from complications directly linked to pregnancy, labor, or the postnatal period. These complications can include severe hemorrhage, infections, or other pregnancy-related issues. In contrast, indirect obstetric deaths encompass fatalities attributed to pre-existing health conditions or diseases that either worsened during pregnancy or were exacerbated by the pregnancy. These distinctions are vital for healthcare systems and policymakers, as they help target interventions and resources toward preventing and managing specific maternal mortality causes. By understanding and categorizing maternal deaths in this manner, Jamaica can tailor its healthcare strategies to reduce both direct and indirect obstetric deaths, ultimately improving maternal health outcomes for its population
Crude Birth Rates
Crude birth rate is a demographic measure that provides important insights into a population’s dynamics. It is defined as the number of live births occurring in a given population during a specific period (usually one year) per 1,000 people in the population. Crude birth rate is called “crude” because it doesn’t take into account the age or sex distribution of the population, making it a simple and easily calculated measure. Crude birth rate provides valuable insight on population growth, age structure, and global population trends. There are socio-cultural factors to be considered when interpreting crude birth rates; it informs healthcare needs, policy implementation, and family planning and education.
The crude birth rates for England, Germany, Spain, and Canada are 11.322 per 1,000, 9.1 per 1,000, 7.5 per 1,000, and 9.8 per 1,000, respectively. Meanwhile, the crude birth rates for Haiti, Ghana, Nigeria, and Bolivia are 23.1 per 1,000, 27.1 per 1,000, 36.6 per 1,000, and 20.867 per 1,000 population, respectively, for the year 2022. Various factors influence birth rates, and lower-income countries tend to have higher birth rates, possibly due to limited access to healthcare services. Cultural norms, such as religious practices, education levels (especially among women), and urbanization, can have a profound impact on crude birth rates.
Total Fertility Rate
The Total Fertility Rate (TFR) is a demographic measure that quantifies the average number of children a woman would give birth to during her reproductive years, typically between the ages of 15 and 49, assuming current birth rates remain constant. TFR provides a projection of future fertility patterns and is not limited to a single year. It takes into account the potential variations in fertility rates among different age groups of women within a population. It is an essential indicator for understanding a population’s reproductive behavior and has several key implications for societal development and planning.
Fertility Rate and TFR are both measures used to assess the reproductive patterns within a population, but they differ in terms of the specific aspects they capture. The fertility rate is a measure that calculates the number of live births per 1,000 women of childbearing age (typically aged 15-49) in a given year. It provides a snapshot of the current birth rate within a specific population and time frame. The fertility rate does not take into account the potential variations in the number of children each woman may have over her lifetime.
As countries undergo the process of development and modernization, there is often a noticeable shift in reproductive patterns. One significant trend is the increase in the mean age at which women choose to have children. This change is influenced by various factors, including improved access to education and employment opportunities for women, greater gender equality, and increased urbanization. As women delay childbirth to pursue educational and career goals, the Total Fertility Rate tends to decrease.
In essence, the TFR reflects not only the average number of births per woman but also the dynamics of a society’s demographic transition. It provides critical insights into the reproductive choices made by a population, the impact of social and economic factors on these choices, and the resulting implications for population growth, dependency ratios, healthcare services, and policy planning.
Crude Death Rate
The crude death rate, often simply referred to as the death rate, is a demographic measure that quantifies the number of deaths occurring within a population during a specific period, typically expressed as the number of deaths per 1,000 people in that population over that period. This rate provides a fundamental indicator of mortality within a community and is usually calculated on an annual basis.
The usefulness of the crude death rate lies in its ability to offer a basic assessment of a population’s overall mortality level, allowing for comparisons between different populations or regions. It is a vital tool for demographers, epidemiologists, and policymakers to monitor trends in mortality, assess the impact of healthcare interventions, and allocate resources for healthcare planning, as it provides a foundational understanding of a population’s mortality profile. However, it should be noted that the crude death rate does not consider age or sex distribution, which can limit its ability to provide a comprehensive analysis of mortality patterns, particularly in populations with varying age structures.
Life Expectancy
A statistical measure that represents the average number of years a person can expect to live, assuming current mortality rates remain constant throughout their lifetime. It serves as a critical indicator of a population’s overall health and well-being. Measuring life expectancy provides valuable insights into the quality of life, healthcare systems, and social and economic conditions within a specific region or country. It is commonly used to compare the health and longevity of populations across different countries or to monitor changes in life expectancy over time within a single population. Let us discuss how some of the factors mentioned might impact life expectancy:
The availability and accessibility of healthcare services significantly influence life expectancy. In countries with robust healthcare systems, timely medical interventions, preventive care, and disease management contribute to longer life expectancies. Conversely, in regions with limited access to healthcare, especially in rural or underserved areas, life expectancy tends to be lower due to delayed, inadequate medical treatment or simply an inability of the healthcare system to satisfy the needs of the population .
Socioeconomic factors such as income, education, and employment opportunities play a crucial role in life expectancy. Individuals with higher socioeconomic status often have better access to education, nutrition, and healthcare, which can lead to healthier lifestyles and longer life expectancies. Conversely, individuals facing poverty, unemployment, or lack of education may experience reduced access to healthcare and increased exposure to health risks, leading to shorter life expectancies.
Personal behaviors and lifestyle choices, such as diet, physical activity, smoking, and alcohol consumption, significantly impact life expectancy. A healthy lifestyle with a balanced diet and regular exercise tends to promote longevity, while tobacco use, excessive alcohol consumption, and poor dietary habits can shorten life expectancy by increasing the risk of chronic diseases like heart disease, cancer, and respiratory disorders. The methods for calculating life expectancy can involve complex statistical analysis. However, one common method is to use life tables, which summarize age-specific mortality rates for a given population. We can look at a hypothetical example to better under the concept:
We will use the following data for the population with the following age-specific mortality rates (qx):
q0 = 0.01 q1 = 0.02 q2 = 0.03 q3 = 0.04 q4 = 0.05
Using the formula, we can calculate life expectancy at birth (e0)
e0 = (1- q0) + (1- q1) + (1- q2) + (1- q3) + (1- q4) = 4.85
Therefore, in this example, the life expectancy at birth for this population is approximately 4.85 years. Note carefully, this is a simplified example, and in real-life calculations, more detailed data and statistical techniques are used to estimate life expectancy.
The formula (Life expectancy at birth) focuses solely on the probability of surviving from birth (age 0) to the end of life. It sums the probabilities of surviving (nqx) for all age groups (intervals) starting from birth and continuing until the end of life. There are additional information that may be obtained from the statistical data provided. We could also calculate life expectancy, often denoted as “ex,” represents the average number of years a person can expect to live at any given age. It doesn’t specifically focus on birth but can be calculated for any age. It reflects the remaining years of life for individuals in a certain age group. The formula for life expectancy at age x (ex) is:
ex = Σ [(nMx + nqx)] / Σ nMx
This formula calculates life expectancy at a specific age “x.” It considers both the age-specific death rates (nMx) and the probabilities of surviving (nqx) for all age groups starting from age “x” and continuing until the end of life. The key difference between the two formulas is the starting point. The original formula (e₀) starts from birth (age 0) and looks at the expected lifespan of a newborn throughout their entire life while the modified formula (ex) starts from a specific age “x” and calculates the expected remaining lifespan for individuals who have already reached that age.
Let us use this formula and the nMx (age-specific death rate between ages x and x+n) from the Life Table for Jamaica (WHO, 2020) to work a practical example. We will focus on the year 2019 and use the combined probabilities for male and female for that time period.
Table 3.3- Mortality Statistics- Life Table Jamaica 2019
ex0 = Σ [(nMx + nqx)] / 2
Where → Σ denotes sum of the values for all age groups.
nMx = the age-specific death rate for each age group.
nqx = the probability of dying between ages x and x + n for each age group.
Now, calculate the age-specific person-years lived (Lx) for each age group, which is = 1/(nMx).
For age group less than 1 year: L0 = 1 / nM0 = 1 / 0.012194726 ≈ 82.05 years
For age group 1-4 years: L1-4 = 1 / nM1-4 = 1 / 0.000487306 ≈ 2053.23 years
For age group 5-9 years: L5-9 = 1 / nM5-9 = 1 / 0.000249219 ≈ 4010.99 years
For age group 10-14 years: L10-14 = 1 / nM10-14 = 1 / 0.000302794 ≈ 3303.77 years
Next, Calculate the probability of surviving beyond each age group (q_x) by subtracting the probability of dying (nqx) from 1.
For age group less than 1 year: q0 = 1 – nq0 = 1 – 0.012194726 ≈ 0.987805274
For age group 1-4 years: q1-4 = 1 – nq1-4 = 1 – 0.001946946 ≈ 0.998053054
For age group 5-9 years: q5-9 = 1 – nq5-9 = 1 – 0.001245318 ≈ 0.998754682
For age group 10-14 years: q10-14 = 1 – nq10-14 = 1 – 0.001512826 ≈ 0.998487174
Now, you can perform the calculations to find the life expectancy at birth (ex0) for this population. Please note that the above calculation assumes a population of 1,000 for simplicity, and the result represents the average number of years a person in this population (Jamaica) is expected to live. Additionally, there are 19 age-groups in this population but for demonstration purposes only four age-groups were used. In real-life scenarios, these calculations are more complex and involve larger populations and more detailed age-specific death rates. If you want to continue the calculation you may also copy the data from World Health Organization’s repository and use the formulas given above in the spreadsheet to calculate for the entire population.
Life expectancy is a vital tool for policymakers and public health professionals. It helps in the allocation of healthcare resources, the development of public health policies, and the assessment of the effectiveness of healthcare interventions. It also aids in identifying health disparities, as differences in life expectancy among various demographic groups can highlight inequalities in access to healthcare and social determinants of health. By measuring life expectancy, we gain a clearer understanding of the health status and needs of a population, which is crucial for informed decision-making and improving overall health outcomes.
Table 3.4 Life Expectancy at Birth by Country for the Year 2019
By simply assessing the life expectancy for the countries in table 3.4, it is evident that higher-income countries generally exhibit longer life expectancies for both males and females, whereas lower-income countries tend to have lower life expectancies. It’s important to note that both Zimbabwe and Haiti have been plagued by long-standing political and social unrest, as well as natural disasters, which may also contribute to variations in life expectancy. As mentioned earlier, when evaluating life expectancy, one must consider all the factors contributing to it for the specific time period and location.
Cause-Specific Death
Cause-specific death, in epidemiology and public health is a rate measurement (frequency and time period); refers to the classification of deaths based on the underlying cause or disease that directly led to an individual’s demise. This approach aims to categorize deaths into specific disease or injury categories, such as heart disease, cancer, accidents, or infectious diseases. It allows researchers, policymakers, and healthcare professionals to gain a comprehensive understanding of the primary health threats within a population. Cause-specific death data are typically collected and recorded through death certificates, which include information on the immediate cause of death, as well as any contributing factors or comorbid conditions. By analyzing these data, epidemiologists can identify trends, patterns, and disparities in mortality, ultimately informing targeted interventions and healthcare strategies.
Cause-specific death data provide insights into the leading causes of mortality in a population. This information is crucial for prioritizing disease prevention efforts, allocating healthcare resources, and developing effective health policies. For example, if heart disease consistently ranks as the primary cause of death in a particular region, public health authorities can implement campaigns to promote heart-healthy lifestyles, increase access to cardiovascular care, and reduce risk factors like smoking and poor diet. Additionally, cause-specific death data enable researchers to assess the impact of specific interventions or medical treatments by tracking changes in mortality rates over time. By focusing on the diseases or conditions responsible for the most deaths, public health initiatives can be tailored to address the specific health needs of a community, ultimately working toward improving overall population health and longevity.
Case Fatality Rate
The case fatality rate is used to calculate the proportion of individuals diagnosed with a specific disease or condition who subsequently die from that disease. Unlike other mortality rates, which consider deaths from all causes, the case fatality rate zooms in on the outcome of interest, providing a disease-specific perspective. Case fatality rate is typically expressed as a percentage, with the number of deaths among confirmed cases divided by the total number of confirmed cases, multiplied by 100. This makes it easy to understand and compare across diseases and populations.
The denominator in case fatality rate calculations includes only individuals who have been confirmed as cases of the disease or condition. This distinction is important because it excludes individuals who may have been exposed to the disease but did not develop symptoms or seek medical attention. As a result, the case fatality rate provides insights into the severity of the disease among confirmed cases. Case fatality rate is often used to assess the severity or lethality of a disease outbreak or epidemic. A high case fatality rate suggests that a significant proportion of diagnosed cases result in fatal outcomes, indicating a more severe impact of the disease within the affected population.
Case fatality rate is sensitive to changes in the disease’s characteristics, such as its virulence, treatment availability, or healthcare system capacity. For example, the case fatality rate for a disease may change if a more virulent strain emerges or if improved treatment intervention becomes available. Likewise, while case fatality rate provides valuable insights into the risk of death among confirmed cases, it does not necessarily reflect the overall risk of death from the disease within the entire population. It is primarily relevant to individuals who have already been diagnosed with the disease.
Epidemiologists commonly use case fatality rate calculations during disease outbreak investigations to assess the impact of the outbreak and guide public health responses. It helps public health officials allocate resources, implement control measures, and evaluate the effectiveness of interventions. Case fatality rate can change over time as more data becomes available and as the course of the disease evolves. It is not a fixed value and can vary during the course of a single outbreak or epidemic.
Proportionate Mortality Rate
The proportionate mortality rate assesses the contribution of a specific cause of death to the overall mortality within a population during a given time period. It is expressed as a percentage and is particularly useful for understanding the relative impact of a specific cause of death, such as COVID-19, in relation to all other causes of mortality. To determine whether COVID-19 contributed to an increased risk or cause of death, one can calculate the the proportionate mortality of COVID-19 as follows:
The proportionate mortality rate provides insight into the proportion of deaths attributed to COVID-19 compared to all other causes of death. A high proportionate mortality rate indicates that COVID-19 is a significant contributor to mortality within the population. Proportionate mortality rate can be calculated for specific time periods, allowing for temporal analysis. This means that one can assess how the contribution of COVID-19 to mortality may have changed over time, such as during different phases of an epidemic or pandemic. Proportionate mortality rate can be calculated for different geographic regions, helping to identify areas where COVID-19 is having a more substantial impact on mortality. This information is valuable for targeting public health interventions. Proportionate mortality rate can also be calculated for specific subpopulations, such as age groups or individuals with underlying health conditions. This allows for a more granular understanding of which groups are at higher risk of COVID-19-related mortality.
By tracking proportionate mortality rate over time, public health authorities and researchers can monitor trends in COVID-19-related mortality and assess the effectiveness of interventions, vaccination campaigns, and other control measures. Proportionate mortality rate data can inform policy decisions and resource allocation. For example, if COVID-19 is found to have a high proportionate mortality rate in a particular region, policymakers may prioritize healthcare resources, testing, contact tracing, and vaccination efforts in that area. Communicating the proportionate mortality rate to the public can raise awareness of the impact of COVID-19 on mortality, leading to increased adherence to preventive measures and vaccination. Proportionate mortality rate can be compared to historical data on causes of death to assess whether COVID-19 has led to a significant increase in mortality compared to previous years. Such comparisons can help identify excess mortality associated with the disease.
Excess Mortality
Excess mortality refers to the difference between the observed number of deaths and the expected number of deaths during a specific time period. It is used to quantify the additional deaths that occur during a particular event or period, such as a disease outbreak, natural disaster, or other crisis. Excess mortality is often expressed as a numerical value (e.g., excess deaths) and is typically used to highlight the impact of a specific event on mortality rates. Excess mortality is calculated as follows:
Excess Mortality = Observed Number of Deaths – Expected Number of Deaths.
Comparing excess mortality and proportionate mortality, proportionate mortality, on the other hand, assesses the distribution of causes of death. It focuses on the proportion or percentage of deaths attributed to specific causes or conditions among all-cause deaths. Proportionate mortality is useful for understanding the relative contribution of different diseases or conditions to overall mortality. It is typically expressed as percentages, and it helps identify which diseases or conditions are the leading causes of death within a population.
Presentation of Epidemiological Data
Epidemiological data are critical in understanding the occurrence and distribution of diseases in populations. When presenting such data, it’s essential to consider the data’s nature, whether nominal, ordinal, interval, or ratio, and whether variables are discrete or continuous. Moreover, understanding key terminologies like incidence, prevalence, attack rates, and birth rates is crucial for effective communication. These data can be presented using various formats, such as tables, charts, and graphs. However, it’s important to note that this summary doesn’t encompass the full breadth of epidemiological data presentation. Epidemiologists are expected to possess a comprehensive understanding of biostatistics and to employ diverse techniques to ensure clear and informative data presentation. There are various methods for presenting epidemiological data, each catering to different needs and audiences. Here are three common methods:
Tables
Tables are a straightforward way to present data in a structured format. For instance, a table can display the incidence rates of a disease in different age groups, allowing readers to compare the rates easily. For example, a table might show that the incidence of COVID-19 is highest in the 20-29 age group and lowest in the 0-9 age group, helping policymakers target interventions.
Graphs and Charts
Graphical representations like bar charts, line graphs, and pie charts can simplify complex data. For instance, a bar chart could illustrate the prevalence of various chronic diseases in a population, making it easy to identify which disease is most common. This visual aid can be especially useful when conveying information to the general public or non-specialist audiences.
Maps
Geographic Information System (GIS) maps are used to present spatial data. For example, an epidemiologist might create a map showing the distribution of malaria cases in a region, highlighting areas with the highest incidence. This visual representation helps identify disease hotspots and guide resource allocation for interventions.
Summary
Understanding measures of disease occurrence in epidemiology, such as incidence and prevalence rates, is vital for assessing the impact of diseases on populations and for designing effective public health interventions. These measures provide essential insights into disease burden, risk factors, and trends over time. By quantifying the occurrence of diseases, researchers and policymakers can prioritize resource allocation, evaluate the effectiveness of interventions, and monitor changes in health outcomes. Epidemiologists use these measures to identify patterns of diseases, plan preventive strategies, and contribute to evidence-based decision-making in healthcare. A solid grasp of these measures is fundamental in the field of epidemiology.
References
Benjamin, E. J., et al. (2017). Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association. Circulation.
Jemal, A., et al. (2019). Annual Report to the Nation on the Status of Cancer, 1975-2015, Featuring Cancer in Men and Women Ages 20-49. Journal of the National Cancer Institute.
Kazak, A. E., Gorman, A. H., & Derosa, B. W. (2016). COVID-19 Nomenclature: A Failure in Normology, Not Biology. Journal of the National Cancer Institute, 108(11), djw214. doi:10.1093/jnci/djw214.
Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology. Lippincott Williams & Wilkins.
World Health Organization. (2016). Monitoring Emergency Obstetric Care: A Handbook. World Health Organization
Last updated- 2023- 9- 27