EPIDEMIOLOGY: THE MEASUREMENT OF HEALTH-RELATED OUTCOMES AND THEIR DETERMINANTS IN POPULATIONS

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1 OCW Epidemiology and Biostatistics, 2010 J. Forrester, PhD Tufts University School of Medicine September 28, 2010 EPIDEMIOLOGY: THE MEASUREMENT OF HEALTH-RELATED OUTCOMES AND THEIR DETERMINANTS IN POPULATIONS Learning objectives for this session: 1) Understand in general terms how the discipline of epidemiology is applied to medical sciences and its uses in clinical and community medicine, and public health. 2) Understand what is meant by, the "population perspective" and its relevance to clinical practice. 3) Understand what is meant by the following terms: a. risk factor/exposure/predictor/determinant b. outcome/endpoint/event c. association vs. causal association 4) Have a general understanding of the natural history of disease and why it is important to consider this in evaluating epidemiologic studies seen in medical journal articles. 5) Describe the types of epidemiologic study designs seen in the medical literature in the following terms: - observational vs. intervention - analytic vs. descriptive 6) Know which study designs fit into each of the above categories 7) Define, calculate and contrast prevalence and incidence rates 8) Know how to calculate cumulative incidence and incidence density 9) Understand the relations among prevalence, incidence and disease duration 10) Know what a cross-sectional study design is, as well as its strengths and limitations Outside preparation Gordis, Chapter 1; Chapter 3; Chapter 4 (pages 59-64) What is epidemiology? Epidemiology is the study of the distribution and determinants of disease and health-related events in human populations, and the application of this study to the prevention and control of health problems. Basically... Who gets sick? What causes them to get sick? How can we prevent it? Key points about epidemiology - The focus is on understanding the causes (etiology) of disease 1

2 - The perspective is on the population of patients, not the individual patient - It is an applied discipline the ultimate goal of which is to prevent and control disease - It involves observation as well as experimentation - It is a fusion of clinical sciences, lab science and statistics Some uses of epidemiology 1) Surveillance of disease epidemics for public health programs e.g. Swine flu HIV surveillance West Nile virus outbreaks 2) Research to identify the causes of disease e.g. C-reactive protein as a cause of heart disease Red wine to prevent heart disease 3) Evaluation of new treatments for disease e.g. Evaluation of new antiretrovirals for HIV disease 4) Program evaluation e.g. Evaluation of a program to treat common sexually transmitted diseases and its impact on HIV transmission Evaluation of a mammography screening program for breast cancer The terminology: What do we mean by the terms outcome or endpoint or "event"? The terms outcome or endpoint or event (synonymous) are used to describe the disease or health-related condition that we are interested in. This could be death, the development of new disease, improvement in mobility following hip surgery, reduction in wait times for surgery or any other event of interest to the researcher or health professional. What do we mean by the terms risk factor, exposure, predictor or "determinant"? A risk factor (exposure, predictor, or determinant are all synonyms) is a treatment, an aspect of personal behavior or lifestyle, an environmental factor or an inborn or inherited characteristic, that is associated with a health-related condition(s). The term risk factor is loosely defined to mean an attribute or exposure that is associated with an increased or decreased probability of a specified health-related outcome. A risk factor is not necessarily a direct cause of disease; it can also be a risk marker or surrogate marker for the true cause of disease. For example, low socio-economic status is associated with a variety of poor health outcomes. However, it is likely a risk marker for some other, more proximal risk factor, such a poor access to health care. Vaccines are an example of a risk factor that may prevent disease. What do we mean by the term "association"? An association is a statistical dependency of two variables. High levels of exposure X are found with high rates of disease Y or, alternatively, high levels of exposure X are found with low rates of disease Y. For example, more hours of sleep are associated with fewer colds. 2

3 Not all associations are causal associations. A statistical association between an exposure X and an outcome Y will usually not be deemed a causal association by the scientific/ medical community until there is a solid body of research evidence that supports a causal association. This evidence usually consists of many studies of different design done in different populations that show the same result. For example, it was not until many studies had shown an association between cigarette smoking and lung cancer that the scientific/medical communities came to the conclusion that cigarette smoking is a cause of lung cancer (i.e. that the association between cigarette smoking and lung cancer is a causal association). The role of C-reactive protein (CRP) in cardio-vascular disease is an example for which the research is still in progress. The current position of the American Heart Association is that, although many studies suggest that CRP may be a useful predictor of cardiovascular disease risk, it is not known whether this is a causal association. Since a plausible mechanism explaining a role of CRP in cardiovascular disease has not yet been identified, it is possible that CRP is an innocent bystander and not active participant in the disease process. More research will need to be done to determine the role of CRP in cardiovascular disease. The concept of a causal web Rarely do risk factors have a 1:1 relationship with disease, except for some genetic disorders. Most often the causes of disease are multi-factorial. For example, established factors that increase or decrease the risk of cardiovascular disease include age, weight, smoking habits, exercise, and diet. Your patients will have many different combinations of these risk factors. Furthermore, these risk factors interact with one another. For example, smoking raises low density lipoprotein (LDL) or "bad" cholesterol. This means that smoking promotes heart disease through mechanisms that include raising LDL as well as through mechanisms that do not involve LDL. However in research, we study risk factors as though they have a linear relationship with the outcome (risk factor outcome) which simplifies the research question. The causes of disease are best thought of as a web of risk factors that are inter-related with each other as well as with health outcomes. The natural history of disease All diseases follow this generalized temporal sequence. The exact time frame of each stage depends on the disease. Influenza develops in a matter of hours or days, while cardiovascular disease develops over many years. It is important to consider the time relationship between the exposure and the outcome. For example, cholesterol control needs to occur before significant atherosclerosis has developed. When you look at the results of a study, ask yourself if the temporal relationship between the exposure and the outcome makes sense in terms of what is known about the natural history of the disease. 3

4 Exposure Biologic onset Detection threshold Cure Chronic condition Death Susceptible Presymptomatic Symptomatic Outcome TIME How do we measure the frequency of disease in a population? 1) Prevalence: Total # of cases Total population -Often expressed as a percentage -A measure of disease burden in the population -A snapshot in time Example: The prevalence of HIV in the USA is about 0.5%. In contrast, the estimated prevalence of HIV in South Africa is 17% among year olds. 2) Population incidence rate: # New cases in a given time period Total population at risk Measures the number of new cases in a population at risk over a given period of time Is also a measure of the risk of developing a disease within a given period of time Measures how quickly new cases develop in the population Has factor of time involved (unlike prevalence) The incidence rate is only interpretable within a stated time frame Incidence is also referred to as the absolute risk (vs. relative risk, seen later) Total population at risk" loosely means those who don't yet have the disease but might develop it. Those not at risk are not included in the denominator (e.g. men for ovarian cancer) The Massachusetts Dept. of Public Health uses the state population on June 1 as the total population at risk. As such, this is not a very precise measure of population incidence rate, but it is good enough for their purposes. CDC uses national population data as the population at risk. Again, this is not very precise, but is good enough for national estimates. 4

5 Example 1: In August, 2008, the Centers for Disease Control (CDC) published for the first time the estimated national incidence rate for HIV (i.e. estimated rate of new infections) based on 2006 data. The CDC estimated that there were 53,600 new HIV infections in the year The population of the USA reached 300 million in October of Therefore the annual HIV incidence rate was 53,600/ 300,000,000 (the approximate number of persons at risk) or 17.9 per 100,000 per year. To be absolutely correct, we should subtract out of the 300,000,000 persons at risk the persons who already have HIV since they are no longer at risk once they already have the disease. In practice, this will not change our estimate by much since the prevalence of HIV is low. Example 2: The CDC reported 66,873 confirmed cases of H1N1 flu for the season (the 40 week period ending May 22, 2010). Using the same US population estimates as above, the estimated incidence of H1N1 flu in the season was 66, 873/ 300,000,000 or 22.3 per 100,000 per year. 3) Cumulative incidence: # New cases in a specified time period Total # at risk at the start of the time period - A more precise measure of incidence often used in research - A proportion affected within a time frame usually expressed as % - Does not allow entries after the start of the time period - Does not account for loss to follow-up This is a measure of incidence often used in research studies. Cumulative incidence is a more precise measure of incidence than the population incidence rates shown above because the at risk group is more accurately defined. Cumulative incidence is calculated by starting with disease-free people in a study and following them forward in time to count how many persons develop the disease during the study period. The figure below is a simple example of a study in which six initially disease-free people were followed for five years. Three completed the study disease-free. Two developed the disease during the five years and one person was lost-to followup (for example, moved out of state) before the five years of follow-up was complete. The cumulative incidence was 2 new cases of disease out of 6 persons at risk at the start of the study period = 33%. Note, the person who was lost-to-follow-up still carries the same weight as all other persons in the study although he was only followed for 2 years. This adds some imprecision to our estimated incidence rate. This is a disadvantage of cumulative incidence that is not found using incidence density (below). Cumulative incidence was the measure of HIV incidence used in the study entitled, Effect of Herpes Simplex Suppression on Incidence of HIV among Women in Tanzania, by Watson-Jones et al. New England Journal of Medicine 2008; 358: , which is the paper we will be reviewing in our first small group. 5

6 4) Incidence density: # New cases in a specified time period # Units of person-time -Most precise measure of incidence -Accounts for partial follow-up -New cases cease to contribute person-time after diagnosis -More awkward to calculate -Used almost exclusively in research Incidence density overcomes the limitations of cumulative incidence by using as the at risk group only those persons who were followed - for the length of time they were followed. Thus, the denominator is units of person-time, often person-years. This allows investigators to account for persons who have partial follow-up, such as people who move out of the area after joining a study and are therefore lost-to-follow-up. Computers make the calculation of person-years easy for large studies. Incidence density is used in the assigned study reading entitled, A randomized trial of Low-Dose Aspirin in the Primary Prevention of Cardiovascular disease in Women, by Ridker et al. New England Journal of Medicine 2005; 352: Below is a simple example calculation of incidence density in a 5-year study of 5 subjects. The # units of person-time is 13 person-years. 6

7 Prevalence is a measure of disease burden Incidence is a measure of disease risk The relation among prevalence, incidence and disease duration Prevalence, incidence and disease duration are related by the following formula P I x D See if you can describe how each of the following scenarios will affect prevalence, incidence and disease duration. The answers are given below. Situation 1: A new treatment for AIDS prolongs survival Prevalence? Incidence? Duration? Situation 2: An effective AIDS vaccine is developed Prevalence? Incidence? Duration? 7

8 Situation 3: A more sensitive mammogram is developed allowing breast cancer detection at an earlier stage of disease Prevalence? Incidence? Duration? It is common to hear people use the term incidence when they really mean is prevalence. Don t get confused they are different concepts: - Incidence is a measure of the rate of appearance of new cases over a defined period of time. Therefore incidence is only interpretable with reference to a time frame (much like a credit card annual percentage rate of interest) - Prevalence measures the burden of disease in a population at a particular moment in time. More vocabulary: Epidemic: The occurrence of an illness in greater numbers than normally expected. The swine flu epidemic last year is a good example as we expected no or, at most, a few cases of swine flu in a given year. Pandemic: An epidemic occurring over a wide area and usually affecting a large proportion of the population. The World Health Organization declared swine flu to be a pandemic on June , three months after the epidemic was first identified because the swine flu was reported to be in many different countries at that point. There were an estimated 30,000 cases in 74 countries. Endemic: The constant presence of a disease in a population e.g. cardio-vascular disease in the USA, malaria in parts of sub-saharan Africa Case fatality rate: Number of deaths from a disease in a given period x 100 Number of diagnosed cases of the disease in the same period For example, the case fatality rate for ordinary seasonal flu is <0.05%. For the recent swine flu pandemic it was a little higher at 0.4%. The World Health Organization estimates that the case fatality rate for avian flu is 56%. This is one of the reasons why health officials are so nervous about avian flu. Mortality rate: Number of deaths during a specified period Number of people at risk of dying during the period (usually the mid-year population) 8

9 Note: The key difference between the case fatality rate and the mortality rate is the denominator. You may hear something like, "The mortality rate from acute myocardial infarction (heart attacks) is 20%." What this person really means to say is that the case fatality rate of acute myocardial infarction is 20%. Other important measures of the health of populations can be found at the website of the National Center for Health statistics: Types of Epidemiologic Studies Epidemiologic studies are of classified broadly as observational or intervention, and within these categories as analytic or descriptive. Observational studies are those in which an investigator just observes and collects data based on those observations. An intervention study is one in which the investigator applies some new treatment or intervention and examines the result of the intervention on a health-related outcome. Analytic studies are designed to address a specific research hypothesis usually about the effect of a particular exposure on a particular outcome (e.g. the effect of low-dose aspirin on cardiovascular disease). In contrast, descriptive studies measure and report data without addressing a specific hypothesis. For example, a group of Med 13 students conducted a study in Haiti this past summer to examine mothers knowledge of childhood vaccinations. They reported what they found, but they did not have a specific hypothesis in mind when they did the study. Therefore, their data are considered descriptive, not analytic. In this course we will focus on the three most common types of analytic study: randomized controlled trials, cohort studies, and case-control studies. 9

10 TYPES OF EPIDEMIOLOGIC STUDIES OBSERVATIONAL INTERVENTION STUDIES STUDIES DESCRIPTIVE ANALYTIC DESCRIPTIVE ANALYTIC STUDIES STUDIES STUDIES STUDIES Case report Cohort studies Case report Randomized Case series Case-control studies Case series controlled Cross-sectional trial Correlational studies (ecologic studies) Descriptive case series differ from cross-sectional studies in that there is no comparison group Intervention because exposure was chosen by the investigator, case series because there is no comparison group 10

11 Cross-sectional study designs These are studies that examine the exposure and outcome at the same point in time. In some studies of this design it is hard to know which came first, the exposure or the outcome (like the chicken vs. egg question). This means we cannot establish the temporal relationship between the exposure and outcome. For this reason, many text books describe cross-sectional studies as an inferior study design. If the exposure is a factor that does not vary, like race, then the temporal relationship between the exposure and outcome is not in question. We will not examine crosssectional studies in detail in this course, but they are very commonly seen in the medical literature because they are relatively quick and easy to do when you do not have the resources to wait a long time for outcomes to occur. Strengths: Relatively fast study design to conduct Limitations: Sometimes difficult to establish the temporal order of exposure and outcome Ask yourself whether the outcome could have preceded the exposure or whether that even makes sense. For example, in a cross-sectional study of the relation between race and hypertension, it is not plausible that hypertension alters race, so a cross sectional study demonstrating an association between hypertension and race is not inferior to a prospective study with regard to the temporal relationship between the exposure and outcome. On the other hand, a cross-sectional study of the association of exercise with arthritis might be problematic. If researchers find that people who exercise regularly have less arthritis than people who do not exercise regularly, it would not be clear whether exercise prevents arthritis or whether arthritis causes people to exercise less. We encourage you to think critically before you dismiss a potentially valuable cross-sectional study. In the next section we will look at randomized controlled trials which are used to test the impact of a new treatment. We will also learn how to measure the impact of the new treatment by comparing the incidence rates of disease in a group receiving the new treatment vs. a group treated with a placebo or a different medication. It is important before you move on to the next section that you have a firm understanding of the concept of incidence as a measure of the rate of disease development. 11