Principles of Epidemiology and Epidemiologic Methods PDF

# Principles of Epidemiology and Epidemiologic Methods ## "I keep six honest serving men; they taught me all I know. Their names are what, why, when, how, where and who." Epidemiology is the basic science of preventive and social medicine. Although of ancient lineage, it made only slow progress up...

# Principles of Epidemiology and Epidemiologic Methods ## "I keep six honest serving men; they taught me all I know. Their names are what, why, when, how, where and who." Epidemiology is the basic science of preventive and social medicine. Although of ancient lineage, it made only slow progress upto the start of the 20th century. Epidemiology has evolved rapidly during the past few decades. Its ramifications cover not only study of disease distribution and causation (and thereby prevention), but also health and health-related events occurring in human population. Modern epidemiology has entered the most exciting phase of its evolution. By identifying risk factors of chronic disease, evaluating treatment modalities and health services, it has provided new opportunities for prevention, treatment, planning and improving the effectiveness and efficiency of health services. The current interest of medical sciences in epidemiology has given rise to newer off-shoots such as infectious disease epidemiology, chronic disease epidemiology, clinical epidemiology, serological epidemiology, cancer epidemiology, malaria epidemiology, neuro epidemiology, genetic epidemiology, occupational epidemiology, psychosocial epidemiology, and so on. This trend is bound to increase in view of the increasing importance given to the pursuit of epidemiological studies. That these studies have added substantially to the advancement of medical knowledge is indisputable. This Chapter studies the basic concepts and principles of epidemiology as an introduction to the subject. ## History Epidemiology began with Adam and Eve, both trying to investigate the qualities of the "forbidden fruit". Epidemiology is derived from the word epidemic (epi=among; demos=people; logos=study), which is a very old word dating back to the 3rd century B.C. The foundation of epidemiology was laid in the 19th century, when a few classic studies made a major contribution to the saving of life. Mention is made of an Epidemiological Society in London in 1850s under the presidency of the Earl of Shaftesbury (1). The Society's main concern was the investigation of infectious diseases. The sudden growth of bacteriology had smothered the development of epidemiology in the Universities. In the United States, Winslow and Sedgwick both lectured in epidemiology in the early 1920s, although the subject was not given departmental status. In 1927, W.H. Frost became the first professor of epidemiology in US. Later Major Greenwood became the first professor of epidemiology and medical statistics in the University of London (1). Epidemiology has grown rapidly during the past few decades. It has now become firmly established in medical education. ## Aims of Epidemiology There appears to be almost as many definitions of epidemiology as there are authors who have written on the subject, ranging from Hippocrates to those of the present day. A short list is given below (2, 3): 1. That branch of medical science which treats epidemics (Parkin, 1873). 2. The science of the mass phenomena of infectious diseases (Frost, 1927). 3. The study of disease, any disease, as a mass phenomenon (Greenwood, 1934), and 4. The study of the distribution and determinants of disease frequency in man (MacMahon, 1960). ## Definition "The study of the occurrence and distribution of health-related events, states, and processes in specified populations, including the study of the determinants influencing such processes, and the application of this knowledge to control relevant health problems.") Study includes surveillance, observation, screening, hypothesis testing, analytic research, experiments, and prediction. Distribution refers to analysis by time, place (or space), and population (i.e. classes or subgroups of persons affected in an organization, population or society, or at regional and global scales). Determinants are the geophysical, biological, behavioural, social, cultural, economic, and political factors that influence health. Health-related events, states and processes include outbreaks, diseases, disorders, causes of death, behaviours, environmental and socio-economic processes, effects of preventive programmes, and use of health and social services. Specified populations are those with common contexts and identifiable characteristics. Application to control makes explicit the aim of epidemiology - to promote, protect, and restore health, and to advance scientific knowledge (4). The wide variety of meanings attached to epidemiology is the expression of the wide ranging subject-matter. The diseases included in the subject-matter have increased from those which occur in epidemics to include those infectious diseases which are endemic in nature, and more recently chronic diseases, accidents and mental health. Modern epidemiology has also taken within its scope the study of health-related states, events and "facts of life" occurring in human population. This includes study of the health services used by the population, and to measure their impact.. Epidemiology, like public health itself, is often more concerned with the well-being of society as a whole, than with the well-being of individuals. Although there is no single definition to which all epidemiologists subscribe, three components are common to most of them. First, studies of disease frequency; second, studies of the distribution; and third, studies of the determinants. Each of these components confers an important message. ## Disease Frequency Inherent in the definition of epidemiology is measurement of frequency of disease, disability or death, and summarizing this information in the form of rates and ratios (e.g., prevalence rate, incidence rate, death rate, etc). Thus the basic measure of disease frequency is a rate or ratio. These rates are essential for comparing disease frequency in different populations or subgroups of the same population in relation to suspected causal factors. Such comparisons may yield important clues to disease aetiology. This is a vital step in the development of strategies for prevention or control of health problems. Equally, epidemiology is also concerned with the measurement of health-related events and states in the community (e.g., health needs, demands, activities, tasks, health care utilization) and variables such as blood pressure, serum cholesterol, height, weight, etc. In this respect, epidemiology has the features of a quantitative science. Much of the subject matter of measurement of disease and health-related events falls in the domain of biostatistics, which is a basic tool of epidemiology. ## Distribution of Disease It is well-known that disease, or for that matter health, is not uniformly distributed in human populations. The basic tenet of epidemiology is that the distribution of disease occurs in patterns in a community (3) and that the patterns may lead to the generation of hypotheses about causative (or risk) factors. An important function of epidemiology is to study these distribution patterns in the various subgroups of the population by time, place and person. That is, the epidemiologist examines whether there has been an increase or decrease of disease over time span; whether there is a higher concentration of disease in one geographic area than in others; whether the disease occurs more often in men or in a particular age-group, and whether most characteristics or behaviour of those affected are different from those not affected (5). Epidemiology addresses itself to a study of these variations or patterns, which may suggest or lead to measures to control or prevent the disease. An important outcome of this study is formulation of aetiological hypothesis. This aspect of epidemiology is known as "descriptive epidemiology". ## Determinants of Disease A unique feature of epidemiology is to test aetiological hypotheses and identify the underlying causes (or risk factors) of disease. This requires the use of epidemiological principles and methods. This is the real substance of epidemiology. This aspect of epidemiology is known as "analytical epidemiology". Analytical strategies help in developing scientifically sound health programmes, interventions and policies. In recent years, analytical studies have contributed vastly to our understanding of the determinants of chronic diseases, e.g., lung cancer and cardiovascular diseases. ## Basic Measurements in Epidemiology Epidemiology focuses, among other things, on measurement of mortality and morbidity in human populations. The first requirement is therefore definition of what is to be measured and establishment of criteria or standards by which it can be measured. This is not only a prerequisite of epidemiological studies, but also one of its goals (13). The clinician may not require a precise definition of disease (e.g., migraine) for immediate patient care, but the epidemiologist needs a definition (a) that is acceptable and applicable to its use in large populations; and (b) that is precise and valid, to enable him to identify those who have the disease from those who do not (10). Clear definitions help to minimize errors in classification of data. Standardized methods of observation and recording are therefore essential before commencing any epidemiological study. ## Measurements in Epidemiology The scope of measurements in epidemiology is very broad and unlimited and includes the following: (14) 1. Measurement of mortality 2. Measurement of morbidity 3. Measurement of disability 4. Measurement of natality 5. Measurement of the presence, absence or distribution of the characteristic or attributes of the disease 6. Measurement of medical needs, health care facilities, utilization of health services and other health-related events 7. Measurement of the presence, absence or distribution of the environmental and other factors suspected of causing the disease, and 8. Measurement of demographic variables. Inspite of a wide range of presently available measurements, there are many areas which are not fully covered. As for example, measurement of the psycho-social aspects of health and disease. The components of well-being need to be better identified. The basic requirements of measurements are validity, reliability, accuracy, sensitivity and specificity. These are discussed in the next chapter. Finally, measurement errors are unavoidable, no matter where and by whom measurements are taken. The purpose of quality control in measurement is, therefore, not to eliminate errors, but to reduce them as much as possible or at least to an acceptable level. In the above connection, the following terminology needs explanation: (a) Variate: Any piece of information referring to the patient or his disease is called a variate. A variate can be discrete, that is it can be present or absent, e.g., cancer lung, broken leg, or rash in measles or it can be continuously distributed, e.g., blood pressure, serum cholesterol, height, etc. (b) Circumstance: Any factor in the environment that might be suspected of causing a disease, e.g., air pollution, polluted water, etc (10). The frequency of a discrete variable or circumstance can be expressed as a rate in relation to population. The frequency of continuously distributed variables or circumstances is expressed in the form of a frequency distribution using the summarizing indices of mean, centiles, standard deviations, etc. ## Tools of measurement The epidemiologist usually expresses disease magnitude as a rate, ratio or proportion. A clear understanding of the term is required for proper interpretation of epidemiological data. The basic tools of measurement in epidemiology are: 1. Rates 2. Ratios, and 3. Proportions ## Rate When we say there were 500 deaths from motor vehicle accidents in City A during 2010, it is just nothing more than counting deaths in that city during that particular year. Such a statement might be sufficient for the municipal administrator to provide necessary health services. But it conveys no meaning to an epidemiologist who is interested in comparing the frequency of accidents in City A with that in City B. To allow such comparisons, the frequency must be expressed as a rate. A rate measures the occurrence of some particular event (development of disease or the occurrence of death) in a defined population during a given time period. It is a statement of the risk of developing a condition. It indicates the change in some event that takes place in a population over a period of time. An example of a typical rate is the death rate. It is written as below: $Death Rate = \frac{Number of deaths in one year}{Mid-year population} \times 1000$ A rate comprises the following elements: numerator, denominator, time specification and multiplier. The time dimension is usually a calendar year. The rate is expressed per 1000 or some other round figure (10,000; 100,000) selected according to the convenience or convention to avoid fractions. The various categories of rates are : 1. Crude rates: These are the actual observed rates such as the birth and death rates. Crude rates are also known as unstandardized rates. 2. Specific rates: These are the actual observed rates due to specific causes (e.g., tuberculosis); or occurring in specific groups (e.g., age-sex groups) or during specific time periods (e.g., annual, monthly or weekly rates). 3. Standardized rates: These are obtained by direct or indirect method of standardization or adjustment, e.g., age and sex standardized rates (see page 66, 67). ## Ratio Another measure of disease frequency is a ratio. It expresses a relation in size between two random quantities. The numerator is not a component of the denominator. The numerator and denominator may involve an interval of time or may be instantaneous in time. Broadly, ratio is the result of dividing one quantity by another. It is expressed in the form of: $x: y$ or $\frac{x}{y}$ Example 1: The ratio of white blood cells relative to red cells is 1:600 or 1/600, meaning that for each white cell, there are 600 red cells. Example 2: The number of children with scabies at a certain time The number of children with malnutrition at a certain time Other examples include: sex-ratio, doctor-population ratio, child-woman ratio, etc. ## Proportion A proportion is a ratio which indicates the relation in magnitude of a part of the whole. The numerator is always included in the denominator. A proportion is usually expressed as a percentage. Example: The number of children with scabies at a certain time The total number of children in the village at the same time × 100 ## Concept of Numerator and Denominator ### Numerator Numerator refers to the number of times an event (e.g., sickness, birth, death, episodes of sickness) has occurred in a population, during a specified time-period. The numerator is a component of the denominator in calculating a rate, but not in a ratio. ### Denominator The lower portion of a ratio. Numerator has little meaning unless it is related to the denominator, The epidemiologist has to choose an appropriate denominator while calculating a rate. It may be (a) related to the population, or (b) related to the total events. ### Related to the population The denominators related to the population comprise the following: (i) MID-YEAR POPULATION: Because the population size changes daily due to births, deaths and migration, the mid-year population is commonly chosen as a denominator. The mid-point refers to the population estimated as on the first of July of an year. (ii) POPULATION AT-RISK: This is an important concept in epidemiology because it focuses on groups at risk of disease rather than on individuals. The term is applied to all those to whom an event could have happened whether it did or not. For example, if we are determining the rate of accidents for a town, the population at risk is all the people in the town. But sometimes, it may be necessary to exclude people because they are not at risk, as for example, in food poisoning, only those who ate the food are at risk of becoming ill. Similarly in calculating "general fertility rate", the denominator is restricted solely to women of child-bearing age (i.e., 15-49 years); older women and little girls are excluded because they are not "at risk" of becoming pregnant. In short, "population at risk" is restricted solely to those who are capable of having or acquiring the disease or condition in question. (iii) PERSON-TIME: In some epidemiological studies (e.g., cohort studies), persons may enter the study at different times. Consequently, they are under observation for varying time periods. In such cases, the denominator is a combination of persons and time. The most frequently used person-time is person-years. Sometimes, this may be person-months, person-weeks or man-hours. For example, if 10 persons remain in the study for 10 years, there are said to be 100 person-years of observation. The same figure would be derived if 100 persons were under observation for one year. These denominators have the advantage of summarizing the experience of persons with different durations of observation or exposure. (iv) PERSON-DISTANCE: A variant of person-time is person-distance, as for example passenger-miles. (v) SUB-GROUPS OF THE POPULATION: The denominator may be subgroups of a population, e.g., age, sex, occupation, social class, etc. ### Related to total events In some instances, the denominator may be related to total events instead of the total population, as in the case of infant mortality rate and case fatality rate. In the case of accidents, the number of accidents "per 1000 vehicles" or "per million vehicle-miles" will be a more useful denominator than the total population, many of them may not be using vehicles. ## Measurement of Mortality Traditionally and universally, most epidemiological studies begin with mortality data. Mortality data are relatively easy to obtain, and, in many countries, reasonably accurate. Many countries have routine systems for collecting mortality data. Each year, information on deaths is analyzed and the resulting tabulations are made available by each government. Mortality data provide the starting point for many epidemiological studies. In fact, they are the major resource for the epidemiologist. ## Death Certificate (Certificate of cause of death) The basis of mortality data is the Death Certificate. For ensuring national and international comparability, it is very necessary to have a uniform and standardized system of recording and classifying deaths. The death certificate recommended by WHO for international use is given in Fig. 1 (4). In India, Death Certificate is a vital record signed by a licensed physician or another designated health worker, that includes cause of death, decedant's name, sex, birth date, adhar number, place of residence and of death, and whether the deceased had been medically attended before death. Occupation, birth place, and other information may be included. It will be seen from Fig. 1 that the international certificate of cause of death is in two parts. Part I deals with the immediate cause of death and the underlying cause. "Causes of death: The causes of death to be entered on the medical certificate of cause of death are all those diseases, morbid conditions, or injuries that either resulted in or contributed to death and circumstances of the accident or violence which produced any such injuries. Underlying cause of death: The underlying cause of death is (1) the disease or injury that initiated the train of events leading to death or (2) the circumstances of the accident or violence that produced the fatal injury." In Part II is recorded any significant associated disease that contributed to the death but did not directly lead to it. ## Death Certificate used in India In order to improve the quality of maternal mortality and infant mortality data and to provide alternative method of collecting data on deaths during pregnancy and infancy, a set of questions are added to the basic structure of international death certificate for use in India. ## Limitations of mortality data Mortality data are not without limitations. Problems are posed by (a) Incomplete reporting of deaths. This is not a problem in developed countries, but in India and other developing countries, this may be considerable. (b) Lack of accuracy: That is inaccuracies in the recording of age and cause of death. The practice of medical certification of death is not widespread. If it does exist, the cause of death is often inaccurate or incomplete due to such difficulties as lack of diagnostic evidence, inexperience on the part of the certifying doctor and absence of postmortem which may be important in deciding the cause of death. (c) Lack of uniformity: There is no uniform and standardized method of collection of data. This hampers national and international comparability (d) Choosing a single cause of death: Most countries tabulate mortality data only according to the underlying cause of death. Other diseases (or risk factors) and conditions which contribute to the patient's death are not tabulated, and valuable information is thereby lost. (e) Changing: Changing coding systems and changing fashions in diagnosis may affect the validity. We also need uniform definitions and nomenclature. (f) Diseases with low fatality: Lastly, mortality statistics are virtually useless, if the disease is associated with low fatality (e.g., mental diseases, arthritis). ## Uses of mortality data Statistics on causes of death are important and widely used for a number of purposes. They may be employed in explaining trends and differentials in overall mortality, indicating priorities for health action and the allocation of resources, in designing intervention programmes, and in the assessment and monitoring of public health problems and programmes moreover, they give important clues for epidemiological research. ## Mortality Rates and Ratios The commonly used measures are described below : ### Crude death rate The simplest measure of mortality is the 'crude death rate'. It is defined as "the number of deaths (from all causes) per 1000 estimated mid-year population in one year, in a given place". It measures the rate at which deaths are occurring from various causes in a given population, during a specified period. The crude death rate is calculated from the formula: $Crude Death Rate = \frac{Number of deaths during the year}{Mid-year population} \times 1000$ It is important to recognize that the crude death rate summarizes the effect of two factors: 1. population composition 2. age-specific death rates (which reflect the probability of dying) Table 1 shows the crude death rates of two populations, A and B. The crude death rate for population A is 15.2 per 1000. The crude death rate for population B is 9.9 per 1000. Apparently, population B appears healthier, than population A. The limitation of the crude death rate is exposed, when we compare the age-specific rates between the two populations as shown in Table 1. It can be seen that population B has higher age-specific rates in all age groups. This seeming contradiction is due to differences in the age-composition of the population. The higher crude death rate in population A is due to its older population compared with population B which has a relatively younger population. Currently, this is the prevailing situation in most developing countries with low crude death rates, but high age-specific death rates. In summary, the crude death rates have a major disadvantage, that is, they lack comparability for communities with populations that differ by age, sex, race, etc. However, they should always be examined first, and later the age-specific death rates which are the most useful single measures of mortality. By moving away from the crude death rate to the more detailed age-specific rates, an attractive feature of the crude death rate, that is, its ability to portray an impression in a single figure is lost. ### Specific death rates When analysis is planned to throw light on aetiology, it is essential to use specific death rates. The specific death rates may be (a) cause or disease specific e.g., tuberculosis, cancer, accident; (b) related to specific groups - e.g., age-specific, sex-specific, age and sex specific, etc. Rates can also be made specific for many other variables such as income, religion, race, housing, etc. Specific death rates can help us to identify particular groups or groups "at-risk", for preventive action. They permit comparisons between different causes within the same population. Specific death rates are obtained mainly in countries in which a satisfactory civil registration system operates and in which a high proportion of deaths is certified medically. Table 2 illustrates how some specific death rates in common use are computed : 1. Specific death rate due to tuberculosis 2. Specific death rate for males 3. Specific death rate in age group 15-20 years 4. Death rate for January 5. Weekly death rate ### Case fatality rate (Ratio) $Case Fatality Rate = \frac{Total number of deaths due to a particular disease}{Total number of cases due to the same disease} \times 100$ Case fatality rate represents the killing power of a disease. It is simply the ratio of deaths to cases. The time interval is not specified. Case fatality rate is typically used in acute infectious diseases (e.g., food poisoning, cholera, measles). Its usefulness for chronic diseases is limited, because the period from onset to death is long and variable. The case fatality rate for the same disease may vary in different epidemics because of changes in the agent, host and environmental factors. Case fatality is closely related to virulence. ### Proportional mortality rate (Ratio) It is sometimes useful to know what proportion of total deaths are due to a particular cause (e.g., cancer) or what proportion of deaths are occurring in a particular age group (e.g., above the age of 50 years). Proportional mortality rate expresses the "number of deaths due to a particular cause (or in a specific age group) per 100 (or 1000) total deaths". Thus we have: (a) Proportional mortality from a specific disease $\frac{Number of deaths from the specific disease in a year}{Total deaths from all causes in that year} \times 100$ (b) Under-5 proportionate mortality rate $\frac{Number of deaths under 5 years of age in the given year}{Total number of deaths during the same period} \times 100$ (c) Proportional mortality rate for aged 50 years and above $\frac{Number of deaths of persons aged 50 years and above}{Total deaths of all age groups in that year} \times 100$ Proportional mortality rate is computed usually for a broad disease group (such as communicable diseases as a whole) and for a specific disease of major public health importance, such as cancer or coronary heart disease in industrialized countries (15). Proportional rates are used when population data are not available. Since proportional mortality rate depends upon two variables, both of which may differ, it is of limited value in making comparison between population groups or different time periods. However, proportional rates are useful indicators within any population group of the relative importance of the specific disease or disease group, as a cause of death. Mortality from communicable diseases is especially important as it relates mostly to preventable conditions. Since the prevailing causes of death vary according to age and sex, it is desirable to compute proportionate mortality separately for each age and sex group in order to determine measures directed to particular age-sex groups for the reduction of preventable mortality (15). Proportional mortality rate does not indicate the risk of members of the population contracting or dying from the disease. ### Survival rate It is the proportion of survivors in a group, (e.g., of patients) studied and followed over a period (e.g., a 5-year period). It is a method of describing prognosis in certain disease conditions. Survival experience can be used as a yardstick for the assessment of standards of therapy. The survival period is usually reckoned from the date of diagnosis or start of the treatment. Survival rates have received special attention in cancer studies. $Survival Rate = \frac{Total number of patients alive after 5 years}{Total number of patients diagnosed or treated} \times 100$ ### Adjusted or standardized rates If we want to compare the death rates of two populations with different age-composition, the crude death rate is not the right yardstick. This is because, rates are only comparable if the populations upon which they are based are comparable. And it is cumbersome to use a series of age specific death rates. The answer is "age adjustment" or "age standardization", which removes the confounding effect of different age structures and yields a single standardized or adjusted rate, by which the mortality experience can be compared directly. The adjustment can be made not only for age but also sex, race, parity, etc. Thus one can generate age-sex, and race-adjusted rates. Standardization is carried out by one of two methods - direct or indirect standardization. Both the methods begin by choosing a "standard population", not the age-structures of the populations. #### Direct standardization Two examples of direct standardization are given. In the first, a "standard population" is selected. A standard population is defined as one for which the numbers in each age and sex group are known. A frequently used standard age-composition (15) is shown in Table 3. The standard population may also be "created" by combining 2 populations; this is shown in the second example. The next step is to apply to the standard population, the age-specific rates of the population whose crude death rate is to be adjusted or standardized. As a result, for each age group, an "expected" number of deaths (or events) in the standard population is obtained; these are added together for all the age groups, to give the total expected deaths. The final operation is to divide the "expected" total number of deaths by the total of the standard population, which yields the standardized or age-adjusted rate. Example 1 Example 1 shows: (a) the computation of age-specific death rates per 1000 population for city X (Table 3); and (b) application of these rates to a standard population to obtain the "expected deaths" and the standardized or age-adjusted death rate (Table 4). It can be seen from Tables 3 and 4 that standardizing for age distribution has reduced the crude death rate from 8.3 to 6.56. The choice of the standard population is, to some extent, arbitrary. Clearly, use of a different standard population will give rise to a different value for the standardized death rate, but it must be remembered that these standardized rates have been calculated so that they can be compared between themselves - they have no intrinsic meaning other than for this purpose (16). It is usual to use the national population as standard when inter-regional comparisons between cities within a range are made. In order that comparisons can be made over a period of years, a 'standard population' can be maintained for that period (16). The standard population used in Table 4 is given by WHO in its publication "Health for All" Series No. 4, on page 77 (15). Example 2 Table 5 shows that in a study of lung cancer and smoking, 42 per cent of cases and 18 per cent of controls were heavy smokers. The direct method of standardization is feasible only if the actual specific rates in subgroups of the observed population are available, along with the number of individuals in each subgroup. #### Indirect age standardization The simplest and most useful form of indirect standardization is the Standardized Mortality Ratio (SMR). In England, it is the basis for the allocation of government money to the health regions of the country. The concept is that the regions with higher mortality also have the higher morbidity, and should therefore receive proportionately higher funding to combat ill-health (16). Standard mortality ratio is a ratio (usually expressed as a percentage) of the total number of deaths that occur in the study group to the number of deaths that would have been expected to occur if that study group had experienced the death rates of a standard population (or other reference population). In other words. SMR compares the mortality in a study group (e.g., an occupational group) with the mortality that the occupational group would have had if they had experienced national mortality rates. In this method, the more stable rates of the larger population are applied to the smaller study group. It gives a measure of the likely excess risk of mortality due to the occupation. $SMR = \frac{Observed Deaths}{Expected Deaths} \times 100$ If the ratio had value greater than 100, then the occupation would appear to carry a greater mortality risk than that of the whole population. If the ratio had value less than 100, then the occupation risks of mortality would seem to be proportionately less than that for the whole population. Table 7 shows that the mortality experience of coal workers was 129 per cent, which meant that their mortality was 29 per cent more than that experienced by the national population. Values over 100 per cent represent an unfavourable mortality experience and those below 100 per cent relatively favourable mortality experience. Table 7 displays the calculations. The SMR has the advantage over the direct method of age adjustment in that it permits adjustment for age and other factors where age-specific rates are not available or are unstable because of small numbers. One needs to know only the number of persons in each age group in the study population and the age-specific rates of the national population (or other reference population). It is possible to use SMR if the event of interest is occurrence of disease rather than death. ## Other standardization techniques 1. A more complicated method of indirect adjustment which yields absolute age adjusted rate, involves the calculation of an index death rate and a standardizing factor for each population of interest. The reader is referred to A.B. Hill's "Principles of Medical Statistics". 2. Life table is an age-adjusted summary of current all-causes mortality. 3. Regression techniques: These are an efficient means of standardization. 4. Multivariate analysis: A computer, using regression or similar methods, can standardize for many variables simultaneously (17). ## Measurement of Morbidity Morbidity has been defined as "any departure, subjective or objective, from a state of physiological well-being" (18, 19). The term is used equivalent to such terms as sickness, illness, disability etc. The WHO Expert Committee on Health Statistics noted in its 6th Report (18) that morbidity could be measured in terms of 3 units (a) persons who were ill; (b) the illnesses (periods or spells of illness) that these persons experienced; and (c) the duration (days, weeks, etc) of these illnesses. ## Incidence Incidence rate is defined as "the number of NEW cases occurring in a defined population during a specified period of time". It is given by the formula : $Incidence =\frac{Number of new cases of specific disease during a given time period}{Population at-risk during that period} \times 1000$ For example, if there had been 500 new cases of an illness in a population of 30,000 in a year, the incidence rate would be: $\frac{500}{30,000} \times 1000 = 16.7 per 1000 per year$ Note: Incidence rate must include the unit of time used in the final expression. If you write 16.7 per 1000, this would be inadequate. The correct expression is 16.7 per 1,000 per year (21). It will be seen from the above definition that incidence rate refers 1. only to new cases 2. during a given period (usually one year) 3. in a specified population or "population at risk", unless other denominators are chosen. 4. it can also refer to new spells or episodes of disease arising in a given period of time, per 1000 population. For example, a person may suffer from common cold more than once a year. If he had suffered twice, he would contribute 2 spells of sickness in that year. The formula in this case would be: $Incidence Rate (spells) = \frac{Number of spells of sickness starting in a defined period}{Mean number of persons exposed to risk in that period} \times 1000$ Incidence measures the rate at which new cases are occurring in a population. It is not influenced by the duration of the disease. The use of incidence is generally restricted to acute conditions. ## Special Incidence Rates Examples include: Attack rate (case rate), Secondary attack rate, Hospital admission rate, etc. ### Attack Rate An attack rate is an incidence rate (usually expressed as a per cent), used only when the population is exposed to risk for a limited period of time such as during an epidemic. It relates the number of cases in the population at risk and reflects the extent of the epidemic. Attack rate is given by the formula: $Attack Rate = \frac{Number of new cases of a specified disease during a specified time interval}{Total population at risk during the same interval} \times 100$ ### Secondary Attack Rate It is defined as the number of exposed persons developing the disease within the range of the incubation period following exposure to a primary case. (see page 103). ## Uses of Incidence Rate The incidence rate, as a health status indicator, is useful for taking action (a) to control disease, and (b) for research into aetiology and pathogenesis, distribution of diseases, and efficacy of preventive and therapeutic measures (15). For instance, if the incidence rate is increasing, it might indicate failure or ineffectiveness of the current control programmes. Rising incidence rates might suggest the need for a new disease control or preventive programme, or that reporting practices had improved. A change or fluctuation in the incidence of disease may also mean a change in the aetiology of disease, e.g., change in the agent, host and environmental characteristics. Analysis of differences in incidence rates reported from various socio-economic groups and geographical areas may provide useful insights into the effectiveness of the health services provided (15). ## Prevalence The term "disease prevalence" refers specifically to all current cases (old and new) existing at a given point in time, or over a period of time in a given population. A broader definition of prevalence is as follows: "the total number of all individuals who have an attribute or disease at a particular time (or during a particular period) divided by the population at risk of having the attribute or disease at this point in time or midway through the period (2)". Although referred to as a rate, prevalence rate is really a ratio. Prevalence is of two types : 1. Point prevalence 2. Period prevalence ### Point prevalence Point prevalence of a disease is defined as the number of all current cases (old and new) of a disease at one point of time, in relation to a defined population. The "point" in point prevalence, may for all practical purposes consist of a day, several days, or even a few weeks, depending upon the time it takes to examine the population sample (21). Point prevalence is given by the formula: $\frac{Number of all current cases (old and new) of a specified disease existing at a given point in time}{Estimated population at the same point in time} \times 100$ When the term