Epidemiologic Data Measurements PDF

Epidemiologic Data Measurements 2 CHAPTER OUTLINE I.â ‡ FREQUENCY I. FREQUENCYâ ‡ 16 A. Incidence (Incident C...

Epidemiologic Data Measurements 2 CHAPTER OUTLINE I.â ‡ FREQUENCY I. FREQUENCYâ ‡ 16 A. Incidence (Incident Cases)â ‡ 16 The frequency of a disease, injury, or death can be measured B. Prevalence (Prevalent Cases)â ‡ 16 in different ways, and it can be related to different denomina- 1. Difference between Point Prevalence and Period Prevalenceâ ‡ 16 tors, depending on the purpose of the research and the avail- C. Illustration of Morbidity Conceptsâ ‡ 16 ability of data. The concepts of incidence and prevalence are D. Relationship between Incidence and Prevalenceâ ‡ 17 of fundamental importance to epidemiology. II. RISKâ ‡ 19 A. Definitionâ ‡ 19 A.â ‡ Incidence (Incident Cases) B. Limitations of the Concept of Riskâ ‡ 19 Incidence is the frequency of occurrences of disease, injury, III. RATESâ ‡ 20 or death—that is, the number of transitions from well to ill, A. Definitionâ ‡ 20 from uninjured to injured, or from alive to dead—in the B. Relationship between Risk and Rateâ ‡ 20 study population during the time period of the study. The C. Quantitative Relationship between Risk and Rateâ ‡ 21 term incidence is sometimes used incorrectly to mean inci- D. Criteria for Valid Use of the Term Rateâ ‡ 21 dence rate (defined in a later section). Therefore, to avoid E. Specific Types of Ratesâ ‡ 22 confusion, it may be better to use the term incident cases, 1. Incidence Rateâ ‡ 22 rather than incidence. Figure 2-1 shows the annual number 2. Prevalence Rateâ ‡ 23 of incident cases of acquired immunodeficiency syndrome 3. Incidence Densityâ ‡ 23 (AIDS) by year of report for the United States from 1981 to IV. SPECIAL ISSUES ON USE OF RATESâ ‡ 23 1992, using the definition of AIDS in use at that time. A. Crude Rates versus Specific Ratesâ ‡ 23 B. Standardization of Death Ratesâ ‡ 25 B.â ‡ Prevalence (Prevalent Cases) 1. Direct Standardizationâ ‡ 25 2. Indirect Standardizationâ ‡ 26 Prevalence (sometimes called point prevalence) is the C. Cause-Specific Ratesâ ‡ 27 number of persons in a defined population who have a speci- V. COMMONLY USED RATES THAT REFLECT MATERNAL fied disease or condition at a given point in time, usually the AND INFANT HEALTHâ ‡ 27 time when a survey is conducted. The term prevalence is A. Definitions of Termsâ ‡ 27 sometimes used incorrectly to mean prevalence rate (defined B. Definitions of Specific Types of Ratesâ ‡ 27 in a later section). Therefore, to avoid confusion, the awkward 1. Crude Birth Rateâ ‡ 27 term prevalent cases is usually preferable to prevalence. 2. Infant Mortality Rateâ ‡ 27 3. Neonatal and Postneonatal Mortality Ratesâ ‡ 28 1.â ‡ Difference between Point Prevalence 4. Perinatal Mortality Rate and Ratioâ ‡ 28 and Period Prevalence 5. Maternal Mortality Rateâ ‡ 28 This text uses the term prevalence to mean point preva- VI. SUMMARYâ ‡ 29 lence—i.e., prevalence at a specific point in time. Some REVIEW QUESTIONS, ANSWERS, AND EXPLANATIONSâ ‡ articles in the literature discuss period prevalence, which refers to the number of persons who had a given disease at any time during the specified time interval. Period preva- lence is the sum of the point prevalence at the beginning of the interval plus the incidence during the interval. Because period prevalence is a mixed measure, composed of point Clinical phenomena must be measured accurately to develop prevalence and incidence, it is not recommended for scien- and test hypotheses. Because epidemiologists study phenom- tific work. ena in populations, they need measures that summarize what happens at the population level. The fundamental epi- C.â ‡ Illustration of Morbidity Concepts demiologic measure is the frequency with which an event of interest (e.g., disease, injury, or death) occurs in the popula- The concepts of incidence (incident cases), point prevalence tion of interest. (prevalent cases), and period prevalence are illustrated 16  C h a p t e râ … 2â … Epidemiologic Data Measurements 17 50,000 (N = 253,448) 45,000 40,000 Cases Reported cases of AIDS 35,000 Known dead 30,000 25,000 20,000 15,000 10,000 5,000 0 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 Year Figure 2-1â ‡ Incident cases of acquired immunodeficiency syndrome in United States, by year of report, 1981-1992. The full height of a bar represents the number of incident cases of AIDS in a given year. The darkened portion of a bar represents the number of patients in whom AIDS was diagnosed in a given year, but who were known to be dead by the end of 1992. The clear portion represents the number of patients who had AIDS diagnosed in a given year and were still living at the end of 1992. Statistics include cases from Guam, Puerto Rico, the U.S. Pacific Islands, and the U.S. Virgin Islands.â ‡ (From Centers for Disease Control and Prevention: Summary of notifiable diseases—United States, 1992. MMWR 41:55, 1993.) in Figure 2-2, based on a method devised in 1957.1 Figure 2-2 provides data concerning eight persons who have a given t1 t2 disease in a defined population in which there is no emigra- tion or immigration. Each person is assigned a case number (case no. 1 through case no. 8). A line begins when a person becomes ill and ends when that person either recovers or 1 dies. The symbol t1 signifies the beginning of the study period (e.g., a calendar year) and t2 signifies the end. 2 In case no. 1, the patient was already ill when the year began and was still alive and ill when it ended. In case nos. 3 2, 6, and 8, the patients were already ill when the year began, 4 but recovered or died during the year. In case nos. 3 and 5, the patients became ill during the year and were still alive 5 and ill when the year ended. In case nos. 4 and 7, the patients became ill during the year and either recovered or died 6 during the year. On the basis of Figure 2-2, the following calculations can be made. There were four incident cases 7 during the year (case nos. 3, 4, 5, and 7). The point preva- lence at t1 was four (the prevalent cases were nos. 1, 2, 6, and 8 8). The point prevalence at t2 was three (case nos. 1, 3, and 5). The period prevalence is equal to the point prevalence at t1 plus the incidence between t1 and t2, or in this example, 4 + 4 = 8. Although a person can be an incident case only once, he or she could be considered a prevalent case at many points in time, including the beginning and end of the study period (as with case no. 1). Jan 1 Dec 31 D.â ‡ Relationship between Incidence Figure 2-2â ‡ Illustration of several concepts in morbidity. Lines and Prevalence indicate when eight persons became ill (start of a line) and when they recovered or died (end of a line) between the beginning of a year (t1) and Figure 2-1 provides data from the U.S. Centers for Disease the end of the same year (± t2). Each person is assigned a case number, Control and Prevention (CDC) to illustrate the complex which is circled in this figure. Point prevalence: t1 = 4 and t2 = 3; period relationship between incidence and prevalence. It uses the prevalence = 8.â ‡ (Based on Dorn HF: A classification system for morbidity example of AIDS in the United States from 1981, when it was concepts. Public Health Rep 72:1043–1048, 1957.) 18 S e c t i o nâ … 1â … Epidemiology first recognized, through 1992, after which the definition of data for illustrating the relationship between incidence and AIDS underwent a major change. Because AIDS is a clinical prevalence. Nevertheless, Figure 2-3 provides a vivid illustra- syndrome, the present discussion addresses the prevalence of tion of the importance of a consistent definition of a disease AIDS, rather than the prevalence of its causal agent, human in making accurate comparisons of trends in rates over time. immunodeficiency virus (HIV) infection. Prevalence is the result of many factors: the periodic In Figure 2-1, the full height of each year’s bar shows the (annual) number of new cases; the immigration and emigra- total number of new AIDS cases reported to the CDC for tion of persons with the disease; and the average duration of that year. The darkened part of each bar shows the number the disease, which is defined as the time from its onset until of people in whom AIDS was diagnosed in that year, and death or healing. The following is an approximate general who were known to be dead by December 31, 1992. The clear formula for prevalence that cannot be used for detailed sci- space in each bar represents the number of people in whom entific estimation, but that is conceptually important for AIDS was diagnosed in that year, and who presumably were understanding and predicting the burden of disease on a still alive on December 31, 1992. The sum of the clear areas society or population: represents the prevalent cases of AIDS as of the last day of 1992. Of the people in whom AIDS was diagnosed between Prevalence = Incidence × (average) Duration 1990 and 1992 and who had had the condition for a relatively short time, a fairly high proportion were still alive at the This conceptual formula works only if the incidence of the cutoff date. Their survival resulted from the recency of their disease and its duration in individuals are stable for an infection and from improved treatment. However, almost all extended time. The formula implies that the prevalence of a people in whom AIDS was diagnosed during the first 6 years disease can increase as a result of an increase in the of the epidemic had died by that date. following: The total number of cases of an epidemic disease reported n Yearly numbers of new cases over time is its cumulative incidence. According to the CDC, or the cumulative incidence of AIDS in the United States n Length of time that symptomatic patients survive before through December 31, 1991, was 206,392, and the number dying (or recovering, if that is possible) known to have died was 133,232.2 At the close of 1991, there were 73,160 prevalent cases of AIDS (206,392 − 133,232). If In the specific case of AIDS, its incidence in the United these people with AIDS died in subsequent years, they would States is declining, whereas the duration of life for people be removed from the category of prevalent cases. with AIDS is increasing as a result of antiviral agents and On January 1, 1993, the CDC made a major change in the other methods of treatment and prophylaxis. These methods criteria for defining AIDS. A backlog of patients whose have increased the length of survival proportionately more disease manifestations met the new criteria was included in than the decline in incidence, so that prevalent cases of AIDS the counts for the first time in 1993, and this resulted in a continue to increase in the United States. This increase in sudden, huge spike in the number of reported AIDS cases prevalence has led to an increase in the burden of patient (Fig. 2-3). Because of this change in criteria and reporting, care in terms of demand on the health care system and dollar the more recent AIDS data are not as satisfactory as the older cost to society. 40,000 Expansion of surveillance 35,000 case definition 30,000 25,000 Reported cases 20,000 15,000 10,000 5,000 0 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Year (quarter) Figure 2-3â ‡ Incident cases of AIDS in United States, by quarter of report, 1987-1999. Statistics include cases from Guam, Puerto Rico, the U.S. Pacific Islands, and the U.S. Virgin Islands. On January 1, 1993, the CDC changed the criteria for defining AIDS. The expansion of the surveillance case definition resulted in a huge spike in the number of reported cases.â ‡ (From Centers for Disease Control and Prevention: Summary of notifiable diseases—United States, 1998. MMWR 47:20, 1999.)  C h a p t e râ … 2â … Epidemiologic Data Measurements 19 A similar situation exists with regard to cardiovascular disease. Its age-specific incidence has been declining in the Number of dead United States in recent decades, but its prevalence has not. As advances in technology and pharmacotherapy forestall death, people live longer with disease. Number of ill II.â ‡ RISK Number of infected A.â ‡ Definition Number of exposed In epidemiology, risk is defined as the proportion of persons who are unaffected at the beginning of a study period, but who experience a risk event during the study period. The Number of susceptible risk event may be death, disease, or injury, and the people at risk for the event at the beginning of the study period con- stitute a cohort. If an investigator follows everyone in a Total population cohort for several years, the denominator for the risk of an event does not change (unless people are lost to follow-up). In a cohort, the denominator for a 5-year risk of death or Figure 2-4â ‡ Graphic representation of why the death rate from an disease is the same as for a 1-year risk, because in both situ- infectious disease is the product of many proportions. The formula ations the denominator is the number of persons counted at may be viewed as follows: the beginning of the study. Care is needed when applying actual risk estimates (which Number Number Number Number of dead of dead of ill of infected are derived from populations) to individuals. If death, = × × disease, or injury occurs in an individual, the person’s risk is Total Number Number Number 100%. As an example, the best way to approach patients’ population of ill of infected of exposed questions regarding the risk related to surgery is probably Number Number of exposed of susceptible not to give them a number (e.g., “Your chances of survival × × are 99%”). They might then worry whether they would be Number Total in the 1% group or the 99% group. Rather, it is better to put of susceptible population the risk of surgery in the context of the many other risks they may take frequently, such as the risks involved in a long If each of the five fractions to the right of the equal sign were 0.5, the persons who were dead would represent 50% of those who were ill, 25% of automobile trip. those who were infected, 12.5% of those who were exposed, 6.25% of those who were susceptible, and 3.125% of the total population. B.â ‡ Limitations of the Concept of Risk Often it is difficult to be sure of the correct denominator for a measure of risk. Who is truly at risk? Only women are at risk for becoming pregnant, but even this statement must be The proportion of clinically ill persons who die is the case modified, because for practical purposes, only women aged fatality ratio; the higher this ratio, the more virulent the 15 to 44 years are likely to become pregnant. Even in this infection. The proportion of infected persons who are clini- group, some proportion is not at risk because they use birth cally ill is often called the pathogenicity of the organism. control, do not engage in heterosexual relations, have had a The proportion of exposed persons who become infected is hysterectomy, or are sterile for other reasons. sometimes called the infectiousness of the organism, but Ideally, for risk related to infectious disease, only the sus- infectiousness is also influenced by the conditions of expo- ceptible population—that is, people without antibody pro- sure. A full understanding of the epidemiology of an infec- tection—would be counted in the denominator. However, tious disease would require knowledge of all the ratios shown antibody levels are usually unknown. As a practical compro- in Figure 2-4. Analogous characterizations may be applied mise, the denominator usually consists of either the total to noninfectious disease. population of an area or the people in an age group who The concept of risk has other limitations, which can be probably lack antibodies. understood through the following thought experiment. Expressing the risk of death from an infectious disease, Assume that three different populations of the same size and although seemingly simple, is quite complex. This is because age distribution (e.g., three nursing homes with no new such a risk is the product of many different proportions, as patients during the study period) have the same overall risk can be seen in Figure 2-4. Numerous subsets of the popula- of death (e.g., 10%) in the same year (e.g., from January 1 tion must be considered. People who die of an infectious to December 31 in year X). Despite their similarity in risk, disease are a subset of people who are ill from the disease, the deaths in the three populations may occur in very differ- who are a subset of the people who are infected by the ent patterns over time. Suppose that population A suffered disease agent, who are a subset of the people who are exposed a serious influenza epidemic in January (the beginning of to the infection, who are a subset of the people who are the study year), and that most of those who died that year susceptible to the infection, who are a subset of the total did so in the first month of the year. Suppose that the influ- population. enza epidemic did not hit population B until December (the 20 S e c t i o nâ … 1â … Epidemiology end of the study year), so that most of the deaths in that A rate, as with a velocity, also can be understood as population occurred during the last month of the year. describing reality at an instant in time, in which case the Finally, suppose that population C did not experience death rate can be expressed as an instantaneous death rate the epidemic, and that its deaths occurred (as usual) or hazard rate. Because death is a discrete event rather than evenly throughout the year. The 1-year risk of death (10%) a continuous function, however, instantaneous rates cannot would be the same in all three populations, but the force actually be measured; they can only be estimated. (Note that of mortality would not be the same. The force of mortality the rates discussed in this book are average rates unless oth- would be greatest in population A, least in population B, erwise stated.) and intermediate in population C. Because the measure of risk cannot distinguish between these three patterns in the B.â ‡ Relationship between Risk and Rate timing of deaths, a more precise measure—the rate—may be used instead. In an example presented in section II.B, populations A, B, and C were similar in size, and each had a 10% overall risk of death in the same year, but their patterns of death differed III.â ‡ RATES greatly. Figure 2-5 shows the three different patterns and illustrates how, in this example, the concept of rate is supe- A.â ‡ Definition rior to the concept of risk in showing differences in the force of mortality. A rate is the number of events that occur in a defined time Because most of the deaths in population A occurred period, divided by the average number of people at risk for before July 1, the midyear population of this cohort would the event during the period under study. Because the popula- be the smallest of the three, and the resulting death tion at the middle of the period can usually be considered a rate would be the highest (because the denominator is the good estimate of the average number of people at risk during smallest and the numerator is the same size for all three that period, the midperiod population is often used as the populations). In contrast, because most of the deaths in denominator of a rate. The formal structure of a rate is population B occurred at the end of the year, the midyear described in the following equation: population of this cohort would be the largest of the three, and the death rate would be the lowest. For population C, Numerator both the number of deaths before July 1 and the death rate Rate = × Constant multiplier Denominator would be intermediate between those of A and B. Although the 1-year risk for these three populations did not show dif- Risks and rates usually have values less than 1 unless the ferences in the force of mortality, cohort-specific rates did so event of interest can occur repeatedly, as with colds or asthma by reflecting more accurately the timing of the deaths in the attacks. However, decimal fractions are awkward to think three populations. This quantitative result agrees with the about and discuss, especially if we try to imagine fractions graph and with intuition, because if we assume that the of a death (e.g., “one one-thousandth of a death per year”). quality of life was reasonably good, most people would Rates are usually multiplied by a constant multiplier—100, prefer to be in population B. More days of life are lived by 1000, 10,000, or 100,000—to make the numerator larger those in population B during the year, because of the lower than 1 and thus easier to discuss (e.g., “one death per thou- force of mortality. sand people per year”). When a constant multiplier is used, Rates are often used to estimate risk. A rate is a good the numerator and the denominator are multiplied by the approximation of risk if the: same number, so the value of the ratio is not changed. n Event in the numerator occurs only once per individual The crude death rate illustrates why a constant multiplier during the study interval. is used. In 2011, this rate for the United States was estimated n Proportion of the population affected by the event is as 0.00838 per year. However, most people find it easier to small (e.g.,