Epidemiologic and Research Applications.
Epidemiology, Demography – Applications in Community Health Nursing.
After studying this chapter, you should be able to:
- Interpret and use basic epidemiologic, demographic, and statistical measures of community health.
- Apply principles of epidemiology and demography to the practice of community health.
Introduction
Epidemiology and demography are sciences for studying population health. To promote, restore, and maintain the health of populations, the community health professional integrates and applies concepts from these fields. Use of the epidemiologic process can significantly enhance community health practice, providing both a body of knowledge and a methodology for investigating health problems and evaluating health services. This chapter introduces epidemiologic and demographic concepts that are essential for the practice of community health nursing.
Epidemiology
The term epidemiology originates from the Greek terms logos (study), demos (people), and epi (upon)—literally, “the study of what is upon the people.” The focus of study is disease occurrence among population groups; therefore, epidemiology is referred to as population medicine. Population in this context refers to people with a common characteristic such as gender, age, and place of residence. Although epidemiologic investigations examine conditions in population groups, it is important to remember that a population consists of individuals, each of whom is a person with a particular condition.
Epidemiology is the study of the distribution and determinants of health-related states or events in specified populations and the application of this study to control health problems. Epidemiology is a quantitative discipline based on principles of statistics and research methodologies. Epidemiologic studies have made a significant contribution to the identification of risk factors, such as smoking and lung cancer. In addition, epidemiologic studies identify modifiable risk factors for heart disease resulting in lifestyle changes for individuals along with changes in public health policy. Thus, epidemiologic research methods are a powerful tool for investigating health-related events.
Early epidemiologic studies were concerned chiefly with the control of epidemics (an outbreak of an illness beyond the levels expected in a population). John Snow’s study of a cholera epidemic in
Many epidemiologic studies have a disease morbidity/mortality focus; however, the dimensions of health and well-being extend beyond these components. Epidemiology as practiced today has expanded its scope to include investigation of lifestyles, health-promotion strategies, injury, environmental conditions, and other factors that influence health. Public health practitioners use the knowledge gained from the epidemiologic process to guide decision-making and aid in developing and evaluating interventions for health promotion and disease prevention. The epidemiologic process is analogous to the nursing process in that critical analysis is required to gain further insight into public and community health issues.
Descriptive Epidemiology
Descriptive epidemiology focuses on the distribution of frequencies and patterns of health events with groups in a population. Descriptive studies examine disease patterns and other health-related phenomena according to “person” (who is affected?), “place” (where were they affected?), and “time” (when were they affected?). Descriptive statistics provide data, information, and insight into the characteristics present in a group or population with a disease or the absence of disease in unaffected groups or populations. The question addressed is, “Are there characteristics present in the affected population that are not present in the unaffected population?” For example, why are breast cancer rates lower in women who have had children and breast-fed them than in women who have not had children? Descriptive statistics provide epidemiologic studies with data to develop rates, ratios, and proportions of morbidity and mortality statistics for use in public health and vital statistics. Data from descriptive studies suggest hypotheses for further testing and usually involve some form of quantification and statistical analysis. Descriptive studies generally precede analytic studies.
Analytic Epidemiology
In contrast to descriptive epidemiology, analytic epidemiology seeks to identify associations between a particular disease or health problem and its etiology. Analytic studies are directed toward finding answers to the “how” and “why” of health and disease to determine causality. Analytic studies are concerned with the determinants of disease and seek to identify the causes of the problem. They test hypotheses or seek to answer specific questions and can be retrospective or prospective in design.
Demography
Demography (literally, ‘writing about the people’ from the Greek demos [people] and graphos [writing]) is the statistical study of human populations with reference to size and density, distribution, and vital statistics. Demographic statistics provide information about significant characteristics of a population that influence community needs and the delivery of health care services. Demographic studies (that is, demographic research) provide descriptions and comparisons of populations according to the characteristics of age; race; sex; socioeconomic status; geographic distribution; and birth, death, marriage, and divorce patterns. Demographic studies often have health implications that may or may not be addressed by the investigators. The census of the
Levels of Prevention in Community Health Practice
The concept of prevention is a key component of modern community health practice. In popular terminology, prevention means inhibiting the development of disease before it occurs. For the community health practitioner, three levels of prevention—primary, secondary, and tertiary—guide practice.
Primary prevention applied to a generally healthy population precedes disease or dysfunction. Primary prevention is divided into two component areas: 1) general health promotion such as nutrition, hygiene, exercise, and environmental protection; and 2) specific health promotion, which includes immunizations and the wearing of protective devices to prevent injuries. If a disease is environmentally induced, primary prevention can prevent a person’s exposure to the environmental factor involved and thereby prevent development of the disease. Prevention is difficult to measure and demonstrate empirically; however, it is less costly both in terms of human suffering and in terms of economic savings than crisis intervention and treating disease and conditions after they have occurred.
Secondary prevention is the early detection and treatment of adverse health conditions. The goal of this level of prevention is to detect and treat a problem at the earliest possible stage when disease or impairment already exists. Secondary prevention may result in the cure of illnesses that would be incurable at later stages, the prevention of complications and disability, and confinement of the spread of communicable diseases. Examples of secondary prevention include the Pap smear for cervical cancer, audiometric testing for hearing impairment, skin test for tuberculosis, and phenylalanine test for phenylketonuria in infancy. On a community basis, early treatment of people with infectious disease, such as a sexually transmitted disease (STD), may protect others from acquiring infection and thus provides secondary prevention for infected people and primary prevention for their potential contacts.
Tertiary prevention is employed after diseases or events have already resulted in damage to people. The purpose of tertiary prevention is to limit disability and to rehabilitate or restore the affected people to their maximum possible capacities. Examples of tertiary prevention include provision of “meals on wheels” for the homebound, physical therapy services for stroke victims, halfway houses for recovering alcoholics, fitness programs for heart attack victims, and mental health counseling for rape victims.
The goal of intervention at each of the three levels is to prevent the progression process. To plan appropriate methods of primary, secondary, and tertiary prevention, the community health nurse must initially assess the current health status of the community.
Descriptive Measures of Health
Demographic Measures
Certain human characteristics, or demographics, may be associated with wellness or illness. Age, race, sex, ethnicity, income, and educational level are important demographics that may affect health outcomes. For example, men are more likely than women to develop certain heart diseases, and African Americans are more likely than Caucasians to have low-birthweight infants. To plan for the health of a community, the nurse must be familiar with the demographic characteristics of the community and with the health problems associated with those characteristics.
Morbidity and Mortality
Although the discipline of epidemiology encompasses both wellness and illness states, wellness is difficult to measure. Therefore, many measures of “health” are expressed in terms of morbidity (illness) and mortality (death). An excellent source of morbidity and mortality data, by state and for select cities, is the Centers for Disease Control and Prevention, Morbidity and Mortality Weekly Report (http://www.cdc.gov/mmwr).
Incidence
The incidence of any health or disease condition refers to the number of people in a population who develop the condition during a specified period of time.Incidence rates measure the rate at which people without a disease develop the disease during a specific period of time (i.e., the number of new cases of a disease in a population over a period of time). Mathematically, incidence rate over a period of time is expressed as:
Number of new cases of disease
―――――――――――――――――×100,000
Total population at risk
Incidence is particularly important for analytic epidemiologic research because it allows the estimation of risk necessary to assess causal association (relative risk). The calculation of incidence generally requires that a defined population initially free of the disease in question must be followed over a period of time in what is called a prospective (forward-looking) study.
Prevalence
The prevalence of a disease or condition refers to the total number of people in the population who have the condition at a particular time. Thus, prevalence may be calculated in a “one-shot” cross-sectional (“slice of time”) or retrospective (backward looking) study. Mathematically, prevalence is expressed as:
Number of existing cases diseases
――――――――――――――― ×100,000
Number in Total population
Prevalence, therefore, examines the extent of morbidity in a community and is influenced by the rate of new cases, the number of existing cases, effective new treatment modalities, and deaths. It can be classified as period prevalence (existence during a period of time) or point prevalence (a specific point in time).
Understanding Incidence and Prevalence
Measures of incidence and prevalence provide different information and have distinct implications. To understand the relationship between incidence and prevalence, consider the number of passengers on a train. The number of passengers represents prevalence (existing disease, old and new cases); the number of boarding passengers represents incidence (new cases of disease); and passengers who get off the train represent individuals who either recover or die. Both the number of new cases entering and the number of individuals with disease who leave either through death or through recovery from the illness influence prevalence. The number of passengers on board will increase if the number of boarding passengers (new cases) is high; if the number of passengers exiting is low (fewer deaths or increased survival rate due to new treatment); or both occur. Conversely, prevalence will decrease when the number of new cases is low or when individuals die or persons are cured of disease or both.
Consider another example. You read about an increase in the prevalence of cancer; this increase may mean that there are a higher number of people with cancer in the population. This higher number may be due to more new cases (in other words, increased incidence) or because people with cancer are living longer. In either case, the community may need to allocate additional resources toward cancer. Incidence and prevalence rates are useful tools in identifying health disparities along with tracking the frequency of diseases.
Ratios, Proportions, and Rates
In epidemiologic studies, data and statistics make comparison possible among populations. Therefore, it is necessary to convert raw data into ratios, proportions, and rates to provide a more valid description of health problems. A ratio is simply one number divided by another in which there is no specified relationship between the numerator and denominator. For example, of 1,000 motorcycle fatalities, 950 are male and 50 are female. The sex ratio is the number of males compared to number of females (950/50 or 19 males to 1 female).
In contrast, a proportion is one number divided by another in which the numerator is a subset of the denominator (i.e., is included in the denominator) and is expressed as a percentage. Using the same data, you can calculate the proportion of males to females. Of 1,000 motorcycle fatalities, 950 are male and 50 are female. What percentage of fatalities is male? A proportion is expressed by the formula X/(X + Y); thus the percentage of males to females would be 950/1,000 multiplied by 100, which equals 95%. Neither ratio nor proportion has a population base or specific unit of time.
Because epidemiology is the study of population health, statistical measures must relate the occurrence of a health condition to the population base. To assess the probability that one group is at higher risk than another, rates are calculated. Rates measure the amount of disease, injury, disability, or death within a unit of the population and within a unit of time. Rates express a mathematical relationship in which the numerator relates to the number of persons experiencing the condition, and the denominator expresses the population at risk or the total number of persons who have the possibility of experiencing the condition. Rates improve the ability to make comparisons because they reduce the standard of comparison to a common denominator, the unit size of the population.
Do not confuse rates with other proportions that do not use the population at risk as the denominator. For example, the death rate from cancer is not the same as the proportion of deaths from cancer. In each, the numerator is the number of deaths from cancer. However, the denominator differs. In the death rate, the denominator represents all people at risk of dying from cancer. Therefore, the cancer death rate is an expression of the risk of dying from cancer. In the proportion of deaths, also called proportionate mortality rate (PMR), the denominator is the total number of deaths from all causes. Mathematically, the PMR is expressed as:
Number or deaths due to a specific cause
——————————————————- ×100
Total number of deaths from all causes
Therefore, the proportionate cancer mortality simply describes the proportion of deaths attributable to cancer. For example, if the PMR of heart disease is 37%, this indicates that 37% of all deaths, regardless of age, sex, or race, are related to diseases of the heart. However, this statistic provides no indication of the actual rate involved.
Calculation of Epidemiologic Rates
Rates are calculated by the formula:
Number of people experiencing condition
————————————————————— ×Κ
population at risk for experiencing condition
K is a constant (usually 1,000 or 100,000) that allows the ratio, which may be a very small number, to be expressed in a meaningful way. The concept of rates can be understood more easily by applying this formula to the calculation of the infant mortality rate, which estimates an infant’s risk of dying during the first year of life.
Example of a Rate: The Infant Mortality Rate
The infant mortality rate (IMR) is usually calculated on a calendar-year basis. The number of infant deaths (deaths before the age of 1 year) during the year is divided by the number of live births (infants born alive) during the year. The numerator represents the number of infants experiencing the “condition” of dying in the first year of life, and the denominator represents the population of infants at risk for dying in the year.
If within a given year 34,400 infant deaths and 4,084,000 live births were reported for the
34,400
―――― ×1,000 = 8,4 infant deaths per live births
4,084,000
Interpretation of Rates
Rates enable researchers to compare different populations in terms of health problems or conditions. To assess if one community is at greater or lesser risk for the problems or conditions, compare the rates for the community with rates from similar communities, from the state, or from the
Caution is required in interpreting rates. Like most statistical measures, rates are less reliable when based on small numbers. This is important when assessing relatively infrequent events or conditions, or communities with small populations.
The majority of rates are based on data from a calendar year, which may also present some difficulties. Populations may increase or decrease during a calendar year. To adjust for population changes over the year, the midyear population estimate is generally used because the population at risk cannot be determined accurately. A study that follows a cohort, or specified group, forward in time can help overcome the limitations of the conventionally calculated calendar-year rate.
Commonly Used Rates
summarizes a number of important rates. Note that the measures of natality and mortality are, in essence, measures of incidence of the conditions of “being born” and “dying.” Note also the various ways in which the denominator, or population at risk, is determined in different rates.
Crude, Specific, and Adjusted Rates
The basic concept of a rate can be broken down into three categories: crude, specific, and adjusted. Rates computed for a population as a whole are crude rates. When calculating deaths in the total population, irrespective of age, the rate obtained is the crude mortality rate or crude death rate. With the crude mortality rate, there is no allowance for the age distribution of the population or comparisons between populations with different age groups.
Subgroups of a population may have differences not revealed by the crude rates. Rates calculated for subgroups are specific rates. Specific rates relate to demographic factors such as age, race, and gender, or they may refer to the entire population but be specific for some single cause of death or illness. For example, to eliminate the effects of different age structures in the population of comparison, the age-specific death rate would be appropriate. Mortality rates for specific diseases such as heart disease relate to a specific cause of death, any subgroup or any age, race, gender, religion, or for an entire population. Information gained from specific rates can aid in identification of groups at increased risk within the population and facilitate comparisons between populations that have different demographic compositions.
In comparing populations with different distributions of a factor known to affect the health condition of interest, the use of adjusted rates may be appropriate. An adjusted rate is a summary measure in which statistical procedures remove the effect of differences in the composition of the various populations. In essence, adjustment produces an estimate of what the crude rate would be if the populations were identical in respect to the adjusted factor. Rates are adjusted for age, race, sex, or any factor or combination of factors suspected of affecting the rate. Adjusted rates are helpful in making community comparisons, but they are imaginary: caution is necessary when interpreting.
Commonly Used Rates
Measures of Natality:
Number of live births during time interval
Crude birth rate = —————————————————- × 1,000
Estimated midinterval population
Number of live births during time interval
Fertility rate = ————————————————————– × 1,000
Number of women aged 15-44 age midinterval
Measures of Morbidity
and Mortality:
Number of new cases of specified health conditions
during time interval
Incidence rate = —————————————————-×1,000
Estimated midinterval population at risk
Number of deaths of infants aged < 1year
during time interval
Infant mortality rate = ————————————————————- × 1,000
Total live births during time interval
Number of deaths of infant aged < 28 days
during time interval
Neonatal mortality rate = ———————————————————- × 1,000
Total live births during time interval
Number of deaths of infants aged _ 28
days but < 1 year during time interval
Postneonatal mortality rate = ————————————————– × 1,000
Total live births during time interval
Number of deaths from puerperal causes
during one year
Maternal mortality rate = —————————————————– ×100,000
Number of live births during same year
Analytic Measures of Health
As discussed previously, rates describe and compare the risks of dying, becoming ill, or developing other health conditions. In epidemiologic studies, it is also desirable to determine if health conditions are associated with, or related to, other factors. The research findings may provide the theoretical foundation by which preventive actions are identified (e.g., the linking of air pollution to health problems has led to environmental controls). To investigate potential relationships between health conditions and other factors, analytic measures of community health are required. In this section, three analytic measures are discussed: relative risk, odds ratio, and attributable risk.
Relative Risk
To determine if a relationship or association exists between a health condition and a suspected factor, it is necessary to compare the risk of developing the health condition for the population exposed to the factor with the risk for the populatioot exposed to the factor. The relative risk (RR) expresses the risk ratio of the incidence rate of those exposed (e.g., smokers) and those not exposed to the suspected factor (e.g., nonsmokers). The relative risk indicates the benefit that might accrue to the client if the risk factor is removed. In this situation, calculation of the relative risk illustrates to the clinician how much the risk for the smoker increases compared with nonsmokers. The relative risk only applies to studies that determine incidence (prospective data). Relative risk is used to make causal inferences. Relative risk can be calculated as follows:
Incidence rate among those exposed
RR = —————————————————
Incidence rate among those not exposed
The relative risk indicates whether the rate in the exposed population is higher than the rate in the non-exposed population and, if so, how many times higher. A high relative risk in the exposed population suggests that the factor is a risk factor in the development of the health condition. Relative risk does not indicate that someone with the factor will develop the disease. It is important to note that risk estimates are probability statements; therefore, 1) all those exposed to the factor do not develop the disease but merely have an increased probability of doing so; and 2) some people who have not been exposed to the factor will develop the disease. If the relative risk associated with the presence of the factor is 10, this merely means that the probability for the disease is 10 times greater in someone with the factor than in someone without the factor. A relative risk less than 1 indicates a protective factor. For example, the relative risk of postmenopausal breast cancer among women who breastfed is 0.87 lower than those who did not report breastfeeding, indicating a protective effect associated with breastfeeding.
Internal and External Risk Factors
The concept of relative risk applies when one group of people clearly is exposed and another is not exposed to an external agent such as a virus, cigarette smoke, or an industrial pollutant. However, it may be confusing to see relative risks applied to internal factors such as age, race, or sex. Nevertheless, as can be seen in the following example, people also are “exposed” to intrinsic factors that may carry as much risk as extrinsic ones.
Relative Risk: Homicide
Sinauer and associates (1999) studied female homicide (termed femicide) over a 5-year period in
19,5per 100,000
RR = ———————- = 3,61
5,4 per 100,000
In other words, the risk of dying from homicide was three and one half times greater for black women than for white women. Clearly, race is a risk factor. Race cannot be altered, but the information provided by this analysis can be used to plan protective services for the population at greatest risk.
Odds Ratio
Calculation of the relative risk is straightforward when incidence rates are available. Unfortunately, not all studies are prospective as is required for the computation of incidence rates. In a retrospective study, the relative risk is approximated by the odds ratio.
As shown in Table 2-1, the odds ratio is a simple mathematical ratio of the odds in favor of having a specific health condition when the suspected factor is present and the odds in favor of having the condition when the factor is absent. The odds of having the condition when the suspected factor is present are represented by a/b in the table. The odds of having the condition wheot exposed to the factor are c/d. The odds ratio is thus:
a/b ad
―― = ――
c/d bc
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Crosstabulation for Calculation of Odds Ratio |
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An example frequently cited in epidemiologic literature is that of toxic shock syndrome (TSS). When TSS, a severe illness involving high fever, vomiting, diarrhea, rash, and hypotension or shock, first occurred, it was neither practical nor ethical to consider cases only on a prospective basis. Therefore, existing cases were compared retrospectively with non-cases, or controls. Early studies noted an association between TSS and tampon use and suggested that users of a specific brand of super-absorbent tampon might be at especially high risk. To clarify the issue, researchers analyzed data from TSS cases and controls, all of whom used tampons. TSS data in Table 2-2 can be used to calculate the odds ratio for users of the specific brand of tampon.
ad 30(84)
odds ratio = ―― = ——— = 7
bc 30 (12)
An odds ratio of 1.0 implies that the odds of exposure are equal and suggest that a particular exposure is not a risk factor for the disease of study. Users of the specific brand of tampons were seven times more likely to develop TSS than were tampon users of other brands. Based on the results of studies, the brand (Rely) was removed from the market.
Relative Risk and Odds Ratio: Caution in Interpretation
A high odds ratio or relative risk must be regarded with appropriate concern; however, the finding should not obscure the potential involvement of other factors. As illustrated in Table 2-2, 12 people in the sample had TSS although they did not use the specific brand of tampon. In other words, this product was not the sole cause of TSS. Subsequent research showed that certain super-absorbent materials in tampons or certain aspects of tampon use foster growth of Staphylococcus aureus, the probable causal organism in TSS (Centers for Disease Control [CDC], 1983, 1981, 1980; Davis, Chesney, Ward, LaVenture, & the Investigation and Laboratory Team, 1980).
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Table 2-2 Toxic Shock Syndrome Cases Among 156 Tampon Users |
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Attributable Risk and Attributable Risk Percentage
Another measure of risk is attributable risk (AR), which measures the difference between the incidence rates for those exposed and those not exposed to the risk factor. The measure estimates the excess risk attributable to exposure to the risk factor. It shows the potential reduction in the overall incidence rate if the factor is eliminated. The AR is calculated by subtracting the incidence among those unexposed to a risk factor (nonsmokers) from those who were exposed (smokers):
AR = incidence rate in exposed group minus incidence rate in nonexposed group
AR usually is further quantified into attributable risk percentage
Epidemiologic Approaches to Community Health Research
Epidemiologic models guide investigators in examining the determinants of population health. This section describes three models and explains how each might guide the approach to the same problem.
Application of each model is illustrated using the problem of an increase in the infant mortality rate in a hypothetical community. The infant mortality rate is a particularly important health index that health professionals should understand even if their main concern is not maternal or child health. Because infant mortality is influenced by a variety of biologic and environmental factors affecting the infant and mother, the infant mortality rate is both a direct measure of infant health and an indirect measure of community health as a whole.
The Epidemiologic Triangle
The epidemiologic triangle or agent–host–environment model is a traditional view of health and disease, developed when epidemiology was concerned chiefly with communicable disease. The model, however, is applicable to other health conditions. In the model, the agent is an organism capable of causing disease. The host is the population at risk for developing the disease. The environment is a combination of physical, biologic, and social factors that surround and influence both the agent and the host. The epidemiologic triangle is used to analyze the role and interrelatedness of each of the factors (i.e., the influence, reactivity, and effect each factor has on the other two). According to this model, the agent, host, and environment can coexist. Disease and injury occur only when there is an interaction or altered equilibrium between them.
Figure 2-1 shows the triangle in its normal state of equilibrium. Equilibrium does not signify optimum health but simply the usual pattern of illness and health in a population. Any change in one of the factors (agent, host, or environment) will result in disequilibrium—in other words, a change in the usual pattern.
How could this model guide the investigation of increased infant mortality? To understand this, one must consider the three facets of the model.
Agent
If one thinks of the epidemiologic triangle model from an infectious disease perspective, it might appear that the investigation should focus on types of infections as agents that cause infant deaths. However, major causes of infant mortality in the
Host
The characteristics of the host (the infant population) will be the second area of assessment. This assessment involves examining infant birth and death patterns in terms of age, ethnicity, sex, and birth weight. These characteristics have been shown to be important risk factors for infant mortality. By studying these factors, it may be possible to identify groups of infants who are at particularly increased risk of dying.
Environment
Lastly, the environment is assessed. The mother is a significant part of the infant’s prenatal and postnatal environment. Therefore, the investigators will analyze birth and infant mortality patterns according to factors such as maternal age, ethnicity, parity (number of previous live births), prenatal care, and education or socioeconomic status. Analysis of these factors, which are also related to infant mortality, will help provide further identification of at-risk groups. Other conditions in the environment also need to be considered. For instance, has migration into the community from other areas increased? Has adult morbidity or mortality, particularly among pregnant women, increased? Have there been changes in health services, policies, personnel, funding, or other factors that could affect infant health?
Practical Application
The analysis of these three areas ‘the agent, host, and environment’ should provide information regarding groups at risk for increased infant mortality and a means of reducing a risk factor. Thus, the epidemiologic triangle, although it was designed with a communicable disease orientation, can provide a useful guide for studying the multifaceted problem of infant mortality, along with other health problems.
The Person – Place – Time Model
In an epidemiologic study, variables can be considered in terms of person (who is affected), place (where affected), and time (when affected) relationships. The person – place – time model examines the characteristics of the people affected (the host in the triangle model), the place (environment) or location, and the time period involved (which could relate to the agent, host, or environment). In studying infant mortality according to this model, infant and maternal factors are considered traits of “person.” Aspects of “place” are such factors as whether the community is rural or urban and affluent or poor. Aspects of “time” include seasonal or age-specific patterns or trends in mortality.
The Web of Causation
The web of causation (MacMahon & Pugh, 1970) views a health condition as the result not of individual factors but of complex interrelationships of numerous factors interacting to increase or decrease the risk of disease. The essence of this concept is the multifactorial nature in that a number of interrelated variables are almost always involved in the cause of a particular outcome. The web of causation attempts to identify all the possible influences on the health and illness processes. Creating the web identifies the most direct causes of conditions, factors contributing to those causes, factors influencing each of these factors, and so on.
Synergism and Factors in the Web
Central to the web of causation model is the concept of synergism, wherein the whole is more than the sum of its separate parts. For example, the effects of a Shigella infection of the infant, combined with the effects of poverty, youth, and low educational level of the mother, are more deleterious to infant health than the sum of the effects of the individual risk factors.
Use of the web of causation may result in a more expansive study of infant mortality than one guided by other models. Ideally, investigators using this model first identify all factors related to infant mortality. Next, they identify factors that are related to each of these factors. These two comprehensive steps provide the outline for the web of causation for infant mortality. Finally, the investigators examine the relationships among all the identified components of the web and attempt to determine the most feasible point of intervention to improve infant mortality in the community. Figure 2-2 depicts a web of causation for infant mortality.
Practical Application
This multifaceted approach taken in the web of causation model addresses the concept of causation in a manner consistent with current knowledge of human health. However, it may be overwhelming to carry out in everyday practice. In fact, it is more common to examine only a portion of the web, acknowledging that other relationships exist. Thorough examination of one portion of the web may provide sufficient information for initiation of useful actions to improve community health.
Models: Guides to Investigation and Practice
In this section, three models, each providing a slightly different approach to a community health problem, were discussed. As you continue to study community health, you will find other models that can guide your practice. There is no one “correct” model; as you gain experience, you will be able to choose or adapt those that are most appropriate for your work
Epidemiology Applications in Community Health Nursing.
Environmental Health
After studying this chapter, you should be able to:
· Identify basic concepts of Epidemiology
· Describe methodology data, morbidity, mortality
· Common epidemiological study designs
· Agent, Host, Environment triad
· The Role of the Community Health Nurse
A question frequently asked is, “What is epidemiology”? There are many different definitions of the term. In the main, people attempting to define epidemiology have normally done so in the context of their own particular interests or needs. A useful general definition is that given by Schwabeet al (1977), which defines epidemiology as the study of disease in populations. It thus differs from the more conventional medical approaches to the study of disease that is normally concerned with the study of disease processes in affected individuals. While the objective of the latter is to find cures for diseases in individuals already affected, epidemiology is basically concerned with the reasons why those individuals became diseased in the first place.
Inherent in the epidemiological approach is the belief that the frequency of occurrence of a disease in a population is governed by the interaction of a large number of different factors or determinants. The epidemiologist believes that by studying these interactions it may become possible to manipulate some of the determinants involved, and so reduce the frequency with which the disease in question occurs m a population.
At this stage it is necessary to ascertain what is meant by the terms population and determinant.
A population can be defined as the complete collection of individuals that have some particular characteristic(s) in common. Depending on the characteristic(s) being considered, a population can be very large or very small. For example, one may wish to study a particular disease in a particular cattle population in a particular country. That cattle population could consist of:
All the cattle in the country
or All the dairy cattle in the country or
All the dairy cattle of a certain breed in the country etc.
Another term often used in epidemiological studies is population at risk. This is usually a subset of the original, defined population and comprises the total number of individuals in that original population that are considered capable of acquiring the particular disease or disease characteristic being studied.
For instance, we might be interested in studying the frequency with which abortion occurs in a population of dairy cattle of a certain breed in a certain country. The population at risk would not be all the individual animals of that particular dairy breed in that country, since this would include males, steers and immature females, all of which would not or could not be pregnant and therefore could not abort! It would consist of female cattle of that breed which were of breeding age. However, if the characteristic being studied was infection by one of the infectious agents that can cause abortion, such as Brucella abortus, the population at risk would have to include all calves, adult males, steers and immature females of the particular breed in question, since all these individuals could potentially become infected with this organism?
A determinant is any factor or variable that can affect the frequency with which a disease occurs in a population. Determinants can be broadly classified as being either intrinsic or extrinsic iature. Intrinsic determinants are physical or physiological characteristics of the host or disease agent (or intermediate host or vector, if present) which are generally determined genetically. Extrinsic determinants are normally associated with some form of environmental influence on the host or disease agent (or intermediate host or vector, if present). They may also include interventions made by man into the disease process by the use of drugs, vaccines, dips, movement controls and quarantines. The role of determinants in the disease process is discussed in more detail later on in this chapter.
Since the determinants of disease are often varied, the epidemiologist may have to draw on a number of different scientific disciplines and techniques if he is to study them. The epidemiological approach is, therefore, a holistic one and the “art” of epidemiology lies in the ability of the epidemiologist to coordinate the use of such disciplines and techniques in a disease investigation, and to produce from the results generated a composite and comprehensive picture of how a particular disease maintains itself iature.
If we accept the premise that the frequency with which a disease occurs in a population is governed by a large number of determinants, it would be expected that some of these, particularly the extrinsic ones, would vary in space and time. It follows, therefore, that disease is a dynamic process. The type and pattern of diseases in livestock differ from country to country, area to area, species to species and production system to production system. Furthermore, the range and importance of the disease problems encountered may change dramatically over time within the criteria mentioned. The effective control of disease depends as much on a thorough understanding of the many complex factors that govern the changes taking place in a disease process as it does on the provision of veterinary inputs such as drugs, vaccines and dips.
Intrinsic determinants of disease
Disease agents as determinants of disease
Agents associated with disease can be categorized into two broad groups:
· “Living” agents, such as viruses, bacteria, rickettsia, protozoa, helminths, arthropods etc.
· “Non-living” agents, such as heat and cold, water, nutrients, toxic substances etc.
Since infectious diseases of livestock are generally regarded as being of prime importance in
In instances of infectious disease, the presence or absence of the aetiological agent is the main determining factor in the epidemiology of the disease. Obviously, disease cannot occur in the absence of the agent, but, conversely, disease need not always result from the presence of the agent. This leads us to the important epidemiological distinction between infection and disease.
· Infection can be defined as the invasion of a living organism, the host, by another living organism, the agent.
· Disease can be defined as a derangement in the function of the whole body of the host or any of its parts.
Infectivity, virulence and pathogenicity
Whether infection takes place or not may depend on a whole range of determinants, both intrinsic and extrinsic, which affect the host and the agent (and the intermediate host or the vector, if present).
Infectivity
is a measure of the ability of a disease agent to establish itself in the host. This term can be used qualitatively, when an agent is referred to as being of low, medium or high infectivity, or quantitatively. Attempts to quantify infectivity normally involve the use of a statistic known as ID50. This refers to the individual dose or numbers of the agent required to infect 50% of a specified population of susceptible animals under controlled environmental condition.
Having become infected, the host may or may not become diseased, and this is again determined by a range of intrinsic and extrinsic determinants affecting the agent and the host. Two terms – virulence and pathogenicity – are often used to describe the ability of the agent to cause disease.
Virulence
can be defined as a measure of the severity of a disease caused by a specified agent. In its strict sense, virulence is a laboratory term and is used to measure the varying ability of disease agents to produce disease under controlled conditions. It is often quantified by a statistic known as LD50 which refers to the individual dose or numbers of the agent which will kill 50% of a specified population of susceptible animals under controlled environmental conditions.
Pathogenicity
Is an epidemiological term used to describe the ability of a particular disease agent of known virulence to produce disease in a range of hosts under a range of environmental conditions?
Host/agent relationships
The relationships between infection and disease are frequently dynamic iature. They centre on the “balance” that can be achieved between the resistance mechanism of the host and the infectivity and virulence of the agent. Disease outbreaks caused by the introduction of an agent into a susceptible host population which has not been previously exposed to that agent normally result in a disease of high pathogenicity with commensurate severe losses in the host population. Such a process is actually detrimental to the agent’s survival, since by killing off the host population it adversely affects both its ability to reproduce and its chances of gaining access to new susceptible hosts. An agent can therefore improve its chances of survival by increasing its infectivity and decreasing its pathogenicity, and some agents have a natural tendency to do this under certain circumstances.
Since a commensal or parasitic relationship confers no benefits to the hosts, they tend to develop means of resisting infection by disease agents. While the agents, in order to survive, develop methods of circumventing the hosts’ defences. Disease agents normally have much shorter generation intervals and can multiply much more rapidly than their hosts, and therefore tend to evolve much quicker. This rapid evolution usually enables the agents to keep comfortably ahead of the hosts’ defence mechanisms. There are many mechanisms by which infectious agents can avoid or overcome the defences of the host. The two mechanisms whose consequences are of particular importance in the field of livestock disease control are the carrier state and antigenic variation.
Creation of the carrier state.
The term “carrier” is used to describe an individual that is infected by a disease agent and is capable of disseminating that disease agent but shows no sign of clinical disease. Three types of carrier state are recognised:
· The true carrier, which is an infected individual capable of disseminating the infectious agent but which never exhibits clinical signs of disease. True carriers occur in various diseases, including salmonellosis.
· The incubatory carrier, which is an infected individual capable of disseminating the infectious agent while the disease is still in the incubatory stage. In foot-and-mouth disease, for instance, infected animals are most infectious 12 to 24 hours before the clinical signs of the disease appear.
· The convalescent carrier, which is an individual that continues to disseminate the infectious agent after the clinical signs of the disease have disappeared. Convalescent carriers occur in such diseases as contagious bovine pleuropneumonia.
Antigenic variation. Some species of disease agent seek to evade the hosts’ defence mechanisms by altering their antigenic characteristics. The most extreme case of antigenic variation occurs in trypanosomiasis, where infection in the host usually takes the form of a series of parasitaemias each one of which involves a form of trypanosome antigenically different from the preceding one. This type of antigenic variation occurs during the course of a single infection.
Another type of antigenic variation occurs in certain agents, such as the foot-and-mouth disease virus, that are highly infectious iature and that depend for their survival on a continuous cycling through host populations of relatively long-lived animals. The ability to reinfect the same host at a later date is obviously desirable for the agent’s survival, and this is dependent on the generation of a relatively short-lived immunity combined with the ability of the agent to undergo antigenic variation during its passage through the host population. In such circumstances there is a strong selection pressure for antigenic variants. The two main types of variation are:
· Antigenic drift, which involves only minor changes in antigenicity, so that hosts previously infected with the agent retain a certain degree of immunity to the drifted strain.
· Antigenic shift, which involves a major change in antigenicity, so that previously infected individuals possess little or no immunity to the shifted agent.
Antigenic shifts are of particular significance when the control of a disease is being attempted by vaccination, since in effect they represent the introduction of a new agent against which the existing vaccine is likely to confer little or no immunity.
The capacity of parasites to evolve rapidly has important implications in other areas of disease control. The very act of introducing a control measure or disease treatment may, in itself, create conditions whereby a strong pressure is exerted on the agent population to select strains which are resistant to the measures or treatments imposed. The evolution of such resistant strains will, in turn, jeopardise the effectiveness of the control measure or treatment. Resistant strains of agents are most likely to develop when the measures or treatments are carried out on a wide scale but improperly – as, for example, in the case of antibiotic resistance arising through the widespread, unsupervised use of antibiotics by livestock producers.
Other terms used to further define host/agent relationships include:
· Incubation period, which is the period of time that elapses from the infection of the host by the agent to the appearance of clinical symptoms.
· Prepatent period, which is the period between the infection of the host by the agent and the detection of the agent in the tissues or secretions of the host.
· Period of communicability, which is the period of time during which an infected host remains capable of transmitting the infective agent.
Methods of transmitting infectious agents
Ascertaining the means by which disease agents are transmitted is a major objective in epidemiological studies, since once the mechanisms by which a particular disease is transmitted are understood, it may become possible to introduce measures to prevent transmission from taking place.
There are three main ways by which disease agents are transmitted from infected to susceptible hosts. An agent may be transmitted through contact between infected and susceptible individuals, or it may be conveyed between these individuals by means of an inanimate object or via another animal serving as a vector or intermediate host. These methods of transmission are not mutually exclusive; the same disease agent may be transmitted by more than one of the following ways.
Contact transmission.
In contact transmissions the agent is conveyed between hosts through direct physical contact, as in the case of venereally transmitted diseases such as vibriosis or trichomoniasis, or through indirect contact.
In cases of indirect contact the agent is normally contained in the excretions, secretions or exhalations of the infected host i.e. in the faeces, urine, milk, saliva, placenta and placental fluids, or as aerosols or droplets in the breath. Susceptible hosts contract the infection either by direct exposure to these or through exposure to substances contaminated by them. Diseases spread in this fashion include rinderpest, foot-and-mouth disease,
Contact transmissions can be further distinguished according to whether they occur horizontally between individuals of the same generation or vertically between individuals of different generations. In vertical transmissions the infectious agent is usually passed from dam to offspring either in the uterus or through the colostrum.
The main factors determining whether or not transmission takes place in contact-transmitted diseases are:
· The ability of the agent to survive in the environment. Rinderpest virus, for example, is easily destroyed in the environment, so contact between infected and susceptible individuals must be close and immediate for transmission to take place, whereas, under certain circumstances, foot-and-mouth disease can spread between widely separated stock.
· The extent of the contact that occurs between infected and susceptible individuals of the host populations and their mobility within these populations. The control of livestock movements is, therefore, a vital factor in the control of contact-transmitted diseases which, in
Vehicular transmission.
In vehicular transmission the agent is transferred between infected and susceptible hosts by means of an inanimate substance or object (sometimes called fomite), such as water, foodstuffs, bedding materials, veterinary equipment and pharmaceuticals, or on the skin, hair or mouthparts of animals. In contrast to indirect transmission, the survival time of the agent in or on the vehicle is usually prolonged. This means, in effect, that vehicular transmission can take place over greater distances and over longer time periods. Hygiene, disinfection and control over the distribution of likely vehicles of transmission are important factors in the control of vehically transmitted diseases.
Certain agents may take the opportunity to reproduce themselves during vehicular transmission. This occurs in the transmission of food-borne bacteria, such as salmonella and coliforms, and underlines the importance of strict hygiene in the handling of foodstuffs and livestock feeds, since a small initial contamination may eventually result in the gross contamination of a whole batch of food or feed.
Vectors and intermediate hosts.
Confusion frequently arises between the terms “vector”, “intermediate host” and “definitive host”. The latter two terms are essentially parasitological terms and describe the different types of hosts that are biologically necessary in the lives of agents with relatively complicated life cycles.
· A definitive host is a host in which the agent undergoes a sexual phase of its development.
· An intermediate host is a host in which the agent undergoes an asexual phase of its development.
The definitive host is usually a vertebrate, while intermediate hosts can be either vertebrates or invertebrates.
· A vector is an invertebrate animal that actively transmits an infectious agent between infected and susceptible vertebrates.
Essentially, vectors can transmit infectious agents in two ways. They can serve as a vehicle whereby the infectious agent is conveyed from one host to another without undergoing a stage of development or multiplication. This is known as mechanical transmission. Alternatively, the infectious agent can undergo some stage of development or multiplication in the vector – this is known as biological transmission – and in this case the vector is serving either as an intermediate or definitive host, depending on which stage of the development cycle of the agent takes place within it. Vertebrate intermediate hosts play the same role in the transmission of their disease agents as biological vectors.
In mechanical transmission the agent is carried on the skin or mouthparts of the vector from an infected to a susceptible host. The survival time of the agent in or on the vector is usually short, and as a result the transmission of the agent has to be accomplished rapidly. The carriers are normally winged haematophagous insects, and transmission usually takes place when susceptible and infected hosts are in close proximity and when large numbers of vectors are present.
In biological transmission, since the agent develops in the vector, a period of time elapses between the acquisition of the infectious agent by the vector and its becoming infective. Once it has become infective, the vector may remain so, normally for a considerable period if not the rest of its life. This provides more than a single opportunity for disease transmission.
In addition, vectors may be able to pass the agent on to their own offspring transovarially. Transovarial transmission enables an infectious agent to be maintained in a vector population through many generations without that population having to be reinfected, and, as such, the vector population remains a continuous source of risk. If transovarial transmission does not occur, at least one stage in each generation of the vector must become infected before transmission of the agent can take place.
Arthropod vectors that undergo metamorphosis have the capacity to pass an agent from one developmental stage to the next. This is known as transtadial transmission. Usually in transtadial transmission, one developmental stage becomes infected with the disease agent and the following stage transmits it. If different developmental stages feed on different host species, transtadial transmission can provide a mechanism for an inter-species transmission of disease agents.
The main intrinsic determinants in the host which can influence the frequency of occurrence of infection and disease are species, breed, age and sex.
Species susceptibilities and natural reservoirs
Most disease agents are capable of infecting a range of animal species, both vertebrate and invertebrate. The severity of the disease resulting from such infections may, however, vary between the species concerned. While certain host species may be refractory to infection with certain disease agents, e.g. equines to the foot-and-mouth disease virus, very few disease agents are in fact restricted to one host species.
The multi-species susceptibility to disease agents is particularly important if the species concerned are able to maintain the disease agent within their populations i.e. to function as natural reservoirs of infection. The failure of programmes aimed at controlling a certain disease in one species has often been blamed on the presence of natural reservoir species, because they can reintroduce the infectious agent.
When investigating the potential of a certain species to act as a natural reservoir of a particular disease agent, and the implications this would have on disease control policy, the following considerations need to be borne in mind:
Infection with the disease agent.
Although it may be possible to infect a certain host species with a disease agent under laboratory conditions, this may only be achievable by using a method of transmission that does not occur naturally (e.g. intracerebral inoculation). If this is the case, that particular host species is unlikely to play a significant role in the epidemiology of the disease.
Ability of a host species to maintain a disease agent.
It may prove possible to demonstrate that a particular host species can be infected by a certain disease agent and that that infection can be accomplished by a natural means of transmission. A further question then needs to be asked, namely, is that species capable of maintaining the agent within its populations for significant periods of time? If this is not the case, then although that particular species may be involved in the localised spread of the disease agent during an outbreak, it will not serve as a continuous source of infection. As such, the importance of that species in the overall epidemiology of the disease may be reduced, and it may become possible to contemplate a disease control programme in which control measures do not have to be applied to that particular host species. In rinderpest control, for example, it has proved possible to control and perhaps even eradicate the disease by concentrating control measures solely on cattle populations, in spite of the presence of species of wild game which are also susceptible to the disease.
Transmission from the natural reservoir.
Even if a species can function as a natural reservoir for a particular disease agent, transmission from that reservoir to domestic livestock may only occur rarely and in certain, clearly defined circumstances. If this is the case, the reservoir species is unlikely to cause a major problem in the initial control of the disease in question. However, when the frequency of occurrence of the disease has been reduced to a low level, and eradication of the disease becomes a possibility, the implications of the presence of reservoir host species for the success of the proposed eradication programme may have to be re-assessed.
Breed susceptibilities
Within a host species, wide ranges of susceptibility to a particular disease are often observed between different breeds. In
Within breeds too, differences in susceptibility to the same disease agent have beeoted between strains or families. This has led, in recent years, to the development of breeding programmes designed to select for disease resistance. Selective breeding has been pioneered in the poultry industry where a large number of different “lines” of poultry have been developed that are resistant to such diseases as Marek’s disease, salmonellosis, and even vitamin D and manganese deficiencies. Pigs, too, can be selected for their resistance to atrophic rhinitis and some forms of colibacillosis. There are breeding programmes in
Breeding for disease resistance is probably most applicable as a disease control option in instances where particular disease agents are ubiquitous in the environment, or of non-infectious diseases caused by multi-causal determinants, or where other methods of control have proved unsatisfactory.
Differences in species or breed susceptibility to disease must be taken into account when introducing new breeds or species into new environments. The new breed or species may be exposed to disease agents to which the local breeds or species are resistant but to which the new breed or species is highly susceptible. Conversely, the imported breed or species may itself introduce a new disease agent to which it is resistant but to which local breeds or species are susceptible. This factor has become the cause for much concern in recent years given the rapid development of international transport facilities whereby livestock and their products can easily be conveyed from one part of the world to another. Furthermore, because of improvements in the disease investigation and diagnostic facilities of many veterinary services, disease agents are being identified that cause little or no disease in indigenous livestock populations but which have the potential to cause a severe problem in the more susceptible livestock populations of other countries should these agents be imported. Bluetongue is an example of a disease which has attained prominence in this way.
Age susceptibilities
Differences in susceptibility to disease are often seen between different age groups. For example, young animals are generally less susceptible to tick-borne diseases than older animals. There is, however, often a problem in distinguishing between true age resistance in young animals and passive resistance occasioned by the transfer of maternal antibodies via the placenta or in the colostrum. A false impression of age susceptibility may also be created when a highly infectious disease occurs frequently in a population. It may, for instance, appear that only young individuals are affected by the disease in question. This may not be due to a difference in age susceptibility but simply because the older individuals, who had been infected previously, represent a surviving and immune population.
Sex associations in disease
In these associations the clinical signs of disease are associated with sexual attributes, as in the case of diseases of the reproductive tract, rather than with the fact that males may be more susceptible than females or vice versa. Sometimes, too, one particular sex may be regarded by farmers as being of greater value than the other and will therefore receive a correspondingly greater amount of care and attention when sick.
Extrinsic determinants of disease
Extrinsic determinants of disease are important in epidemiology in that they can have effects on the host, on the agent, and on the interactions between the host and the agent. They can also affect any intermediate hosts or vectors involved in the transmission of a disease, and thus determine the type and extent of the disease transmission taking place.
There are three major extrinsic determinants. The first two are climate and soils, which, by interacting in a variety of ways, affect the environment of the host, the agent, and the intermediate host or vector, if they are present. The third major factor is man, who, uniquely among animals, has the ability to modify both the environment in which he lives and the environment in which he keeps his livestock.
When considering climate as a determinant of disease, a distinction is normally made between the macroclimate or weather, and the microclimate. The term microclimate refers to the actual climatic conditions prevailing in the specific, restricted environment where the host, agent, vector or intermediate host actually live. While man is as yet largely incapable of deliberately manipulating macroclimates, he can control and manipulate microclimates to some extent.
Macroclimates. A large number of different factors combine to make up the microclimate. Some of these factors (heat, cold, rainfall, wind, humidity etc) can act as disease agents in their own right, either individually or in combinations. As such they can cause disease in young and newborn animals which are particularly sensitive to heat, cold and dehydration. In older animals they tend to act more as indirect determinants of disease in that they can produce either alone or in combinations with other managemental and nutritional determinants – “stress” conditions in the host, which may lower its resistance both to infection and, if infection takes place, to disease.
Macroclimates can also affect the ability of a disease agent, or its intermediate host or vector, to survive in the environment. If the effects of weather on disease agents and their intermediate hosts or vectors are known, it may be possible to predict when host populations are at a particular risk of contracting disease and thereby to implement appropriate control measures at strategic times. This approach has been used with success in the control of such diseases as helminthiasis, ticks and tick-borne diseases, trypanosomiasis, foot-and-mouth disease, and in mineral and other nutritional deficiencies.
Microclimates. While macroclimates can have a direct effect on microclimates, the study of macroclimates alone can frequently be misleading in achieving an understanding of the epidemiology of a disease. Regions where existing macroclimatic conditions might be thought unsuitable for the transmission of a disease may, in fact, contain limited areas where the microclimatic conditions are suitable for the survival of the disease agent and its vector or intermediate host. (An example may be a water hole or an irrigated pasture in an arid environment). Such areas often provide enhanced conditions for disease transmission, since they may prove attractive to livestock, particularly at those times of the year when the macroclimate is at its most severe. If the host and the agent (and the vector or intermediate host, if they exist) are in close contact, the transmission of disease can be effected rapidly and easily. Thus, in arid areas, the transmission of such diseases as helminthiasis and trypanosomiasis may in fact take place during the dry season when the hosts, the agent and the vector are all concentrated around permanent water sources. High contact rates in these areas also favour the introduction and transmission of rinderpest, foot-and-mouth disease and contagious bovine pleuropneumonia.
By interacting with climate, soils determine vegetation and the environment in which the livestock are kept. The main effect of vegetation is outrition. Soils therefore act indirectly as determinants of disease by causing starvation, if there is little or no vegetation, or nutritiorial imbalances such as protein, energy, vitamin or mineral deficiencies. Malnutrition can be the direct cause of disease, or it can stress the host and thus increase its susceptibility to infection and disease from other sources. Soils can also have an effect on the ability of the agent to survive in the environment, through such factors as waterlogging, pH etc.
Man is often able to create favourable, artificial microclimates for livestock rearing by providing such inputs as housing, water supplies, irrigation etc. Unfortunately, this often results in the creation of conditions favourable for the survival of disease agents and their intermediate hosts or vectors. This means that, by altering the environment, man can alter the determinants of the diseases present in that environment. The changes in determinants will favour some diseases and be detrimental to others. Thus changes in systems and methods of production will result in changes in the relative importance of the diseases present, with perhaps some new diseases being introduced and others disappearing. The epidemiologist should be alert to such changes and should attempt to predict the likely effect that these will have on the overall disease picture, so that potentially dangerous situations can be averted or controlled.
Man is also able to interfere directly in the disease process through the use of drugs, vaccines, movement controls, quarantines etc. Among the main tasks of the epidemiologist is the investigation of the efficacy such measures, as well as to design ways in which they can be used most efficiently and to monitor the effects of their introduction on disease incidence.
Describing disease events in populations
The first priority in investigating the epidemiology of a disease is to describe accurately the nature of the problem being investigated. Comprehensive and accurate description of disease problems often provides valuable insights into the epidemiology of the disease being investigated and allows hypotheses about likely determinants to be formulated.
A description of a disease problem should specify the disease and the population at risk, give information on the distribution of events in time and space, and include an attempt to quantify disease events.
Disease diagnosis. If the disease is infectious iature, the disease agent involved should also be identified. For the disease agent to be infectious it must fulfil Koch’s postulates that:
– The agent should be present in all cases of the disease;
– It can be isolated and grown in pure culture; and
– It should be capable of producing the disease when innoculated into healthy animals.
One of the problems associated with these postulates is that they do not take into account the differences between different strains of agents, particularly in their virulence, pathogenicity, and infectivity, which may be important in the epidemiology of the disease. We shall have more to say on the problems of disease diagnosis in Chapter 4.
Populations at risk. These can be identified by studying the distribution of the disease within host populations by species, breed, age and sex. Descriptions of population densities and movements are also of great value, particularly when the disease is transmitted by contact.
Distribution of disease events in time and space. This generally involves looking for the “clustering” of disease events in time, space or both.
The clustering of disease events in space can often be demonstrated by the use of conventional mapping techniques. This type of clustering may indicate the presence of a particular determinant or determinants (e.g. a vector, a mineral deficiency etc) in an area. It should be remembered, however, that clustering in space occurs naturally in the case of contact – transmitted diseases, and that it may also be a function of host-population density.
The clustering of disease events in time may indicate that the host population was exposed to a common source of the disease or its determinant. Outbreaks of diseases transmitted by such vehicles as water or foodstuffs frequently exhibit clustering in time, as in the case of food poisonings. Seasonal clustering of disease events often indicates the influence of climatic determinants in some form or other.
The distribution of disease events in populations in time and space can be described by three basic descriptive terms. These are: endemic, epidemic and sporadic.
An endemic disease is a disease that occurs in a population with predictable regularity and with only minor deviations from its expected frequency of occurrence. In endemic diseases, disease events are clustered in space but not in time. Note that a disease may be endemic in a population at any frequency level, provided that it occurs with predictable regularity. Additional terms can be used to describe endemic diseases according to their frequency of occurrence. Thus:
· Hyperendemic is an endemic disease that affects a high proportion of the population at risk.
· Mesoendemic is an endemic disease that affects a moderate proportion of the population at risk.
· Hypoendemic is an endemic disease that affects a small proportion of the population at risk.
An epidemic disease is a disease that occurs in a population in excess of its normally expected frequency of occurrence. In an epidemic disease, disease events are clustered in time and space. Note that a disease may be epidemic even at a low frequency of occurrence, provided that it occurs in excess of its expected frequency.
A pandemic is a large epidemic affecting several countries or even one or more continents.
A sporadic disease is a disease that is normally absent from a population but which can occur in that population, although rarely and without predictable regularity.
Many epidemics of infectious disease occur in a regular, cyclical fashion over a prolonged period of time. This is because with an increasing frequency of occurrence of the disease in a host population, the number of susceptible hosts decreases as individuals within that population become infected, and then either die or recover and become immune to reinfection. As the number of susceptible hosts decreases, so does the opportunity for disease transmission. This, in turn, means that the frequency of occurrence of new cases of the disease declines. A period of time then elapses during which new susceptible individuals are born into the host population. The number of susceptible hosts in the population thus increases, and the opportunities for the disease agent to find a susceptible host are enhanced. As a result the frequency of occurrence of the disease may increase and a new epidemic may take place.
When assessing the efficacy of measures introduced to control epidemics, an attempt should be made to distinguish between a decline in the frequency of occurrence of the disease due to a control measure, and a natural decline in the epidemic cycle. Epidemics can be prevented if the level of immunity in the host population can be sustained. It is important, therefore, in instances where the control of an infectious disease is being attempted by vaccination, that coverage be maintained in the. host population even when the disease is occurring rarely.
Morbidity and Mortality
Morbidity and Mortality conferences (M&M) are traditional, recurring conferences held by medical services at academic medical centers and by most large private medical and surgical practices. They are essentially peer reviews of mistakes occurring during the care of patients. The objectives of a well-run M&M conference are to learn from complications and errors, to modify behavior and judgment based on previous experiences, and to prevent repetition of errors leading to complications. Conferences are nonpunitive and focus on the goal of improved patient care. M&M conferences occur with regular frequency, often weekly, biweekly or monthly, and highlight recent cases and identify areas of improvement for clinicians involved in the case. They are also important for identifying systems issues (e.g., outdated policies, changes in patient identification procedures, arithmetic errors, etc.) which affect patient care.
Morbidity and Mortality conferences have a long tradition in the practice of medicine, having originated in the early 1900s with Dr. Ernest Codman at Massachusetts General Hospital in Boston. It is almost 100 years since Ernest Codman wrote a monograph on this subject, which caused his colleagues to banish him from the
How do I choose a study type?
· Ensure that it directly addresses your research question. There is no point performing a study which will not answer the question in hand. Many study types can address the same question, so choose a design carefully.
· Make sure the study is ethical. Epidemiology usually involves the active cooperation of patients. They usually participate for the good of research rather than for any benefit which they stand to gain. The ideal study must be ethically sound and ought to minimise the invasiveness to the participants.
· Work within your budget. Epidemiological studies tend to be much larger than other scientific experiments and resources are always limited. Patient recruitment and assessment take time and money. You need to consider the cost of collection and testing of biological samples, telephone calls and postage, and of course salaries–personnel resources have high costs attached. The cost of the research can preclude certain study designs.
· Make sure the results will be valid. The most frequent flaws in epidemiology are bias and confounding. Bias produces incorrect results and can rarely be rectified after data collection. Confounding can be addressed during statistical analysis (number crunching), provided that sufficient information was gathered during the study. Ways to avoid these two hazards will be discussed in the next article
The six study designs that you need to know
- Case series—what clinicians see
- Ecological—geographical comparisons
- Cross sectional–survey, a snapshot in time
- Case-control–compare people with and without a disease
- Cohort–follow people over time to see who gets the disease
- Randomised controlled trial (RCT)–the human experiment
Hierarchy of evidence
There are six main types of epidemiological study to choose from. Each design is ranked according to what was traditionally thought of as its usefulness in providing accurate results. This ranking is sometimes referred to as the hierarchy of evidence, with stronger evidence coming from the studies later rather than earlier on in the list.
Case series
Lowest in the pecking order is the case series. This is simply a description of cases. Importantly, there is no comparison group and so case series are ofteot considered as epidemiology. They describe patients’ characteristics, and may generate ideas for future studies. They can also be misleading though. For example, in the early 1980s, doctors noticed that patients with immune system deficiencies tended to be young homosexuals. That observation suggested that their symptoms were caused by the use of “poppers” (amyl nitrates–sexual stimulants). Experiments of the immunological properties of amyl nitrates were underway before the HIV virus was identified.1 The use of case series is clearly limited, although that should not deter clinicians from observant monitoring of unusual patients.
Ecological studies
Second in our hierarchy comes the ecological study, when we compare groups of people not individuals. Assuming that associations seen on a group level also hold on an individual level leads to ecological bias. Consider a study of suicide and religion.2 Higher suicide rates occurred in regions where a higher proportion of protestants lived. To infer that protestants are more likely to commit suicide than people of other religions may be incorrect. Although the rates are higher in the regions with more protestants, we do not know the religion of the people committing suicide. Suicide could be more common among religious minorities who live in predominantly protestant regions. However, with careful interpretation of results, ecological studies do have benefits. They are generally quick, cheap, and can be performed from data which are published routinely–for example, death rates, per caput income, national food consumption.
Cross sectional study
A cross sectional study is a snapshot in time–that is, a survey. Participants are picked from a well defined population. Exit polls on voting day are cross sectional studies, where the population is all voters exiting from a particular polling station. We contact people only once, so these studies are relatively cheap. But that limits their usefulness, since we can study only current diseases (prevalence), and cannot identify when people first get a disease (incidence). If we are interested in whether a certain behaviour may cause a disease we may infer incorrect results. People may change their behaviour once they get a disease. This concept of reverse causality will be discussed in the next article.
Case-control studies
Choosing your participants on account of their disease is the basis of a case-control study. On face value, this seems a simple study design, where cases are people with the condition and controls are those without. Suitable control selection can be tricky, and unsuitable controls can invalidate your results. Suppose that you want to know whether smokers are at an increased risk of colon cancer. Comparisons are made between the smoking habits (probably current and past) of cases and controls. It is no good choosing controls who have lung cancer or heart disease, as they will be more likely to be smokers than the population from which the cases came, and that will distort your results. Another risk of case-control studies is recall bias, which occurs if cases and controls recall past events differently. Because we actively choose the cases, a case-control study ensures that we find enough people when a disease is rare. These studies are rarely suitable for investigating causes of death, since dead people cannot provide vital information. Carefully designed case-control studies can provide useful results, and such studies should not be ignored without a careful evaluation of the methodology used.
Cohort studies
In a cohort or follow up study, a healthy group of people (cohort) are identified. They are then followed over time to see who develops the disease of interest and who does not. The important point to remember about cohort studies is the time factor–that at the beginning of the study neither the people themselves nor the researchers know who is going to get what disease. This effectively avoids recall bias, although other types of bias can still hinder these studies. However, the costs of cohort studies are sometimes prohibitive. If you are studying mobile phone use and cancer development you need to follow up people for a long time before the cancer becomes evident. The resource implications is often why cohort studies are not chosen in epidemiology.
Randomised controlled trials
Finally, what is often called the gold standard of epidemiological studies–the randomised controlled trial or clinical trial. This is a human experiment, where people are randomly assigned to receive one treatment or the other. This treatment is often a drug as clinical trials of drugs are required before being licensed for prescription. The treatment may also be a health intervention, such as a weight loss or smoking cessation programme. It is necessary that the individual (and preferably also the health professional) are “blind” to which treatment a person receives, although this is not always possible. In clinical trials, the aim is to replicate the “real life” situation, so that the results obtained are as close to what would happen if the treatment was used in a real life situation. Clinical trials tend to be extremely expensive, and are unsuitable for use in some situations. For example, it is not ethical to randomise people to heavy drinking to investigate the effect of its use on breast cancer.
Considerations when choosing a study type
- Does this design address my question?
- Is this study design ethical?
- What resources do I have (time, money, personnel)?
- Is there a cheaper or quicker way of answering the same question?
Example of study design
As an example of how to choose a study, I will return to the study of orgasms and mortality in middle aged men.3 Which of the study types described above would answer the question of whether sex and death are related?
A case series of people who have died would not be useful–given the frequency of the exposure (orgasms) and the outcome (death), there would be nothing that would stand out in a study of all people who died. Remember, there is no comparison group.
In an ecological study, we would compare rates of orgasm and death across different geographical areas. But how would we find out orgasm rates per area? And would these differ sufficiently across areas to be able to correlate them with death rates?
A cross sectional study by its nature excludes dead people. We could do a survey of women, asking them about the frequency of their partner’s orgasms, and ask if the partner is still alive. But how accurate are reports from women regarding their partner’s orgasms? (That approach might be more accurate than asking men directly!) In addition, it may be too distressing to ask recently widowed women about their sex life.
Similar problems would be encountered with a case-control study. Such studies of mortality always rely on proxy information–for example, from a partner rather than the individual. The cases would be people who had recently died. But who would be the controls? Brothers? Neighbours?
The method the authors chose was the cohort study, when people were asked about their sex life then followed up for 10 years. This is not without its problems, although these are more issues of interpretation than design, so will be considered in the next article.
Finally, could they have done a randomised controlled trial, allocating men to groups that have frequent, infrequent, or no sex. In that case recruitment would have been pretty difficult–would you open yourself to the option of no sex?
And finally…
As you will have gathered from the series of questions above, there is no clear cut answer to which study design to use. Often two or more designs are possible, and then practical (“how much time and money do I have?”) and qualitative (“which study will give better results?”) considerations help to make the choice. The ability to choose a useful study design is an art which improves with practice
The Role of the Occupational and Environmental Health Nurse
Occupational and Environmental Health Nursing is a specialty that provides health and safety programs to workers and community groups. An Occupational Health Nurse (OHN) focuses on promotion and restoration of health, prevention of illness and injury, and protection from work-related and environmental hazards. Occupational and environmental health nursing began in the
OHNs make independent nursing judgments and are capable of designing and delivering health care to employee-based populations. According to a survey conducted by the American Association of Occupational Health Nurses in 1992, approximately 50% of nurses are sole health-care providers at the worksite. Their position requires a broad scope of knowledge and skills in many areas. The nurse provides emergency care and follow-up case management for job-related injuries and illnesses, providing the most efficient and effective care for the injured worker. The goal is to return the injured worker to his or her job as soon as possible. Early intervention by the nurse reduces workers’ compensation costs and also gets the employee back to work. The nurse acts as a counselor and also recommends referrals to the company’s employee-assistance program and other community resources.
OHNs develop health promotion programs for employees and teach them the skills needed to become responsible for their own health. Most health services offer smoking-cessation programs, exercise and fitness, nutrition, and weight-control programs. If necessary, the OHN can educate an employee in the control of chronic illness and can provide informatioeeded to direct them to effective health-care resources. Health education and prevention programs are effective in the early detection of symptoms that can lead to long-term catastrophic illness.
There are numerous regulations that protect today’s worker, and the OHN must work with employees and employers on compliance with the many regulations and laws affecting the workplace. The OHN must have knowledge of the following laws and regulations: Occupational Safety and Health Administration (OSHA), Family Medical Leave Act (FMLA), The Americans with Disabilities Act (ADA), Workers’ Compensation Act, and the Health Information Portability and Accountability Act (HIPPA).
The role of the nurse will continue to expand as more nurses are hired by businesses to manage and operate occupational health services. Employers pay about $1 trillion annually for employee health, so business executives are looking to OHNs to maximize employee productivity and to decrease cost through lowered disability claims, reduction of on-the-job injuries, and improvement in absentee rates.
OHNs are registered nurses (RNs), licensed to practice in the states in which they are employed. Typically, nurses entering the field have a baccalaureate degree iursing and experience in community health, ambulatory care, critical care, or emergency nursing.
Certification in Occupational and Environmental Health Nursing is highly recommended. Criteria for certification requires 4,000 hours of work experience in the field within 5 years, 50 contact hours of continuing education in the specialty, and successful completion of a national examination.
The OHN’s role ranges from clinician to case manager, health educator, manager, policy maker, consultant, and program evaluator. While it’s challenging to be all things to everyone, the occupational health service affords the OHN a great opportunity for providing excellence in the delivery of health care while it provides an excellent career opportunity.
As the workplace continues to change, nurses must be prepared to handle the challenges and opportunities as employers look to OHNs to be cost-effective providers of work place health care. The most fundamental goal of any organization is to earn a profit while providing a safe and healthy working environment for its employees. The occupational health service exists to support that goal.
Summary
References
Brucker, M. C. (2005). Providing evidence-based care: You can understand research and use it in practice. AWHONN Lifelines, 9(1), 47–55.
Bradford-Hill, A. (1971). Principles of medical statistics (9th ed.).
Centers for Disease Control and Prevention. (1993, September 10). Public health focus: Physical activity and the prevention of coronary heart disease. Morbidity and Mortality Weekly Report, 42, 398–400.
Centers for Disease Control and Prevention. (1983, August 5). Update: Toxic shock syndrome—
Centers for Disease Control and Prevention. (1981, June 30). Toxic shock syndrome—
Centers for Disease Control and Prevention. (1980, September 19). Follow up on toxic shock syndrome. Morbidity and Mortality Weekly Report, 29(37), 441–445.
McFarlane, J,
MacMahon, B., & Pugh, T. F. (1970). Epidemiology: Principles and methods.
Sinauer, N., Bowling, J. M., Moracco, K. E., Runyan, C. W., & Butts, J. D. (1999). Comparisons among female homicides occurring in rural, intermediate, and urban counties in
Snow, J. (1936). Snow on cholera, being a reprint of two papers by John Snow, M.D., together with a biographical memoir by B. W. Richardson, M.D., and an introduction by Wade Hampton Frost, M.D.
Further
Aschengrau, A. & Seage, G. R. (2003). Essentials of epidemiology in public health.
Chernick, K. M. & Friis, R.(2003). Introductory biostatistics for the health sciences.
Cravens, G., & Mair, J. L. (1977). The black death.
Crichton, M. (1969). The Andromeda strain.
Dunne, T. L. (1978). The scourge.
Everitt, B. (2003). Modern medical statistics: A practical guide.
Friis, R. & Sellers, T. (2004). Epidemiology for public health practice. (3rd ed.)
Savitz, D. (2003). Interpreting epidemiologic evidence: Strategies for study design and analysis.
Selvin, S. (2004). Statistical analysis of epidemiologic data (3rd ed.).
Timmreck, T. (1998). An introduction to epidemiology (2nd ed.).
Valanis, B. (1992). Epidemiology in nursing and health care (2nd ed.).
Internet Resources
http://www.cdc.gov/mmwr, The Centers for Disease Control and Prevention has morbidity and mortality weekly reports by state and select cities along with reportable disease trends. This is an excellent site for information on measures and determinants of community health.
http://www.cdc.gov/nchs, CDC’s
http://www.epidemiolog.net, This site contains epidemiology learning materials, including a free online evolving textbook.
