2019 GATE Notes PDF

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The GATE Notes: a Graphic Approach To Epidemiology “Epidemiology with Pictures” PREFACE The “Graphic Approach To Epidemiology” (GATE) uses a triangle, circle, square and arrows to graphically represent the design of all epidemiological studies, including experimental (mainly randomised trials) and observational studies. Every epidemiological study can be hung on the GATE frame, which was developed so users could visualise key epidemiological principles, study design features, and analytical and appraisal concepts. GATE illustrates how every epidemiological study shares the same basic structure and how every epidemiological study is designed with the same objective - to measure how much dis-ease occurs in different groups (or populations). Comparing the occurrence of dis-ease in different groups provides insights into causes and predictors of dis-ease, and how to prevent, diagnose and treat it. The GATE Notes are not a comprehensive epidemiology textbook but a visually aided guide to epidemiological principles, measures, analyses, errors and study design. The GATE approach is equally applicable in clinical, health services or public health practice. GATE started life as a ‘Graphic Appraisal Tool for Epidemiological studies’ to help clinical students develop critical appraisal skills, but the GATE approach is also relevant to teaching epidemiological study design (‘Graphic Architectural Tool for Epidemiology’), which is the ‘flip-side’ of the critical appraisal of epidemiological studies. GATE was inspired by Professor Ken Rothman, a US epidemiologist, who elegantly dissected epidemiological study designs into their component parts (1) and by the Evidence Based Medicine Working Group (2,3), who developed structured guides for critiquing the clinical epidemiological literature. Further inspiration came from the late Professor Jerry Morris, a British epidemiologist who defined epidemiology as ‘numerator ÷ denominator’ (4) (i.e. the number of people with a dis-ease outcome ÷ the number of people in a population). He won me over to epidemiology’s underlying simplicity and inspired me to develop teaching approaches to make epidemiology universally accessible.

THE GATE FRAME

GATE has been ‘work in progress’ since about 1990. I thank the thousands of students and health professionals who my colleagues and I have exposed to multiple versions of GATE. We have observed their reactions, assessed their understanding and continuously modified GATE. Finally, I thank my colleagues who have supported me and helped make GATE work. Professor Rod Jackson, University of Auckland, Auckland, New Zealand: 1 March 2019.

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CHAPTER 1: EPIDEMIOLOGICAL STUDY DESIGN, MEASUREMENT & ANALYSIS: triangles, circles, squares & arrows 1. 1.

WHAT IS EPIDEMIOLOGY?

Epidemiology is the study of how much ‘dis-ease’ occurs in a population and of the factors that determine differences in dis-ease occurrence between populations. •

We use the term ‘dis-ease’ (i.e. not at ease) rather than disease, to encompass any health-related event (e.g. an injury, a heart attack, a death) or health-related state (e.g. diabetes, a disability, a raised blood pressure, or quality of life). Epidemiologists tend to study negative events or states, like death and disease, because they are easier to measure than positive states of health such as degrees of wellbeing.

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Epidemiologists count dis-ease occurrences in populations of people (or in populations of animals if they are veterinary epidemiologists). An ‘occurrence’ (of dis-ease)’ describes the transition from a ‘non-dis-eased state’ to a ‘dis-eased state’. If the transition from a non-dis-eased to a dis-eased state is an easily observable ‘event’ like a road traffic injury or crushing central chest pain caused by a heart attack, then epidemiologists usually count the occurrences as the number of events over a period of time. However, if the transition is not easily observable, like transitioning from a non-diabetic to a diabetic state, then epidemiologists count the occurrences as the number of people with the dis-ease ‘state’ at a point in time.

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Epidemiologists measure and compare dis-ease occurrences in different populations or groups of people (or other animals). We often use the terms groups and populations interchangeably. A population is any group of people who share a specified common factor. This factor could be a geographic characteristic (e.g. people living in northern or southern Europe); a demographic characteristic (e.g. an age group, gender, ethnicity or socio-economic category); a time period (e.g. 2001); a dis-ease (e.g. heart disease); a behaviour (e.g. smoking); a treatment (e.g. a blood pressure lowering drug); or a combination of several of these factors.

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Epidemiological studies are measurement exercises involving the collection of data that can be counted (i.e. quantified). Quantitative data can be classified as categorical – data that is grouped into categories (e.g. male/female, smokers/ nonsmokers, dead/alive); or numerical - data that take on numerical values (e.g. body weight, blood cholesterol levels, number of hospital visits, number of births). For simplicity, most examples in the GATE Notes use just two categories to describe groups or dis-eases (e.g. smoking – yes/no and lung cancer - yes/no).

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Measuring dis-ease occurrence in a population can inform health service planners about types of health services required for populations, including health promotion, dis-ease prevention, disease diagnosis and treatment. Measuring dis-ease occurrence in different groups (e.g. smokers and non-smokers or Māori and nonMāori) in a population can help identify causes or predictors of dis-ease occurrence. Similarly, measuring dis-ease occurrence in groups treated with, say, different drugs or surgical interventions can help determine which treatments work best.

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This chapter describes: § the epidemiological approach to measuring dis-ease occurrence § the GATE frame - the shape of all epidemiological studies § the two measures of dis-ease occurrence in groups; § ways of describing differences in dis-ease occurrence between groups; § the shared design features of all epidemiological studies.

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1.2.

EPIDEMIOLOGICAL THINKING: MEASURING DIS-EASE OCCURRENCE WITH NUMERATORS and DENOMINATORS (the hourglass)

ALL epidemiological measures of dis-ease occurrence begin by counting and describing the number of people in a study population - the Denominator - and then counting the number of people from the study population in whom dis-ease occurs - the Numerator.

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An hourglass illustrates these two essential features of all epidemiological measures of disease occurrence. The number of people in the study population at the beginning of an epidemiological study is represented by the number of grains of sand in the top bulb of the hourglass, before any sand has flowed to the lower bulb. The number of people in whom disease occurs is represented by the number of grains of sand that fall into the lower bulb.

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A key requirement of epidemiological studies is that the dis-ease outcomes counted (numerator) must all come from a defined population (denominator), just as the sand in the bottom bulb must come from the top bulb. That’s why all well-conducted epidemiological studies begin by defining the denominator.

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Most epidemiological studies measure dis-ease occurrence in several subpopulations (or groups) within a study population and the study objective is to determine if the occurrence differs between groups, and if so, why it differs.

Epidemiology is the study of the occurrence of dis-ease in populations

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Number of persons in whom dis-ease occurs (numerator) -----------------------------------------------------------------------------Number of persons in study population (denominator)

Note: see over for details of different measures of occurrence related to the timing of measurements

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What differentiates epidemiology from other health-related sciences is its starting point – the study population or denominator. While all health sciences study dis-ease, they have different starting points; perhaps a gene, a diseased cell, tissue, organ, or a person. As all epidemiological studies involve the calculation of the occurrence of dis-ease in populations, epidemiological thinking always involves asking the question: ‘who is in the denominator (i.e. the study population)?

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1.3.

THE GATE FRAME: THE SHAPE OF ALL EPIDEMIOLOGICAL STUDIES

The GATE frame (Figure 1.1) illustrates the component parts of all epidemiological studies.

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the triangle represents the Participant (or Study) Population (P) e.g. 2000 men. While the GATE Participant Population P triangle is the overall study (overall denominator, it is usually divided into DENOMINATOR) two or more study-specific subdenominators (the circle). • º» the circle represents the studyspecific sub-Denominators (we will call them Groups). In its simplest Exposure & version, the GATE circle is divided into Groups EG CG Comparison (study an Exposure Group (EG) & a DENOMINATORS) Comparison Group (CG), for example; EG = 400 men ‘exposed’ to smoking & CG = 1600 men ‘unexposed’ to smoking (i.e. non-smokers). EG & CG yes a b are the actual denominators used in the Outcomes O (NUMERATORS) no c d calculations of dis-ease occurrence T (e.g. in smokers & non-smokers). Some studies have multiple Exposure Groups. Figure 1.1. An epidemiological study with one Exposure Group (EG), a Comparison • È the square represents the Group (CG) & a categorical (yes or no) Numerators (or Dis-ease Outcomes Outcome (O)) e.g. lung cancer. The simplest GATE square is divided into 4 cells: ‘a’ are the people from EG in whom dis-ease occurs & ‘c’ are those from EG who don’t get dis-ease, while ‘b’ are people from CG in whom dis-ease occurs & ‘d’ are those from CG who don’t get dis-ease, during the time over which the study is conducted. •

the horizontal & # vertical arrows represent the Time when or during which outcomes are measured (discussed in the next section).

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Measures of dis-ease occurrence normally use ‘a’ and ‘b’ as the Numerators – the people with dis-ease. We call the occurrence of dis-ease in the Exposure Group the ‘Exposure Group Occurrence’ or EGO (EGO = a/EG) and the occurrence of disease in the Comparison Group is called the ‘Comparison Group Occurrence’ or CGO (CGO = b/CG). One could measure the occurrence of ‘no dis-ease’ in EG (= c/EG) and CG (=d/CG) and this is done in some studies, particularly diagnostic test accuracy studies (discussed later).

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Some exposures and outcomes like the smoking and lung cancer examples above naturally fit into yes/no categories but others don’t. Another study exposure might be something like ‘the amount of salt consumed’ and the study outcome might be 'blood pressure levels'. In this latter example, both the exposure (salt consumption) and outcome (blood pressure level) are known as numerical measures rather than categorical measures (described on page 2).

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Numerical measures are often converted into categorical measures. In the example above, numerical measures of salt consumption could be divided into two or more

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categories (e.g. high and low intake) in which case the Denominators could be people with high salt intake (EG in Figure 1.1) and people with low salt intake (CG). Similarly, blood pressure levels could be divided into high and low blood pressure categories, in which case the study outcome groups (i.e. the Numerators) would be people with high blood pressure (a and b in Figure 1.1) and people with low blood pressure (c and d). •

When numerical data on salt intake and blood pressure are changed into categorical measures, it is possible to calculate the occurrence of high (or low) blood pressure in people with high salt intake (EGO) and the occurrence of high (or low) blood pressure in people with low salt intake (CGO).

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When numerical outcomes are not converted into categories in order to calculate disease occurrence, we often calculate the mean or median level of the outcome (e.g. average blood pressure) in EG and CG. In the salt and blood pressure example, salt intake could still be categorically classified into high and low intake categories, but the blood pressure levels can remain as numerical data and the average (or mean) blood pressure can be calculated in each group. The calculated average or mean blood pressure (based on numerical outcomes) has many similarities to measures of dis-ease occurrence using categorical outcomes and simply involves adding together (i.e. summing) the outcome measure (e.g. a blood pressure level) for every person in EG (e.g. people with a high salt intake), then dividing by the total number of people in EG to determine the average blood pressure level. The same calculation is done for CG (e.g. people with low salt intake). So EGO = Σa/EG and CGO = Σb/CG. There are conflicting views as to whether these calculations of the average value of a numerical outcome in a group of people can be described as measures of occurrence. Measures of occurrence usually only refer to categorical outcomes, but we think the similarities in the calculations are sufficient to also refer to calculated averages of numerical outcomes as measures of occurrence (i.e. as EGO or CGO). Notes in purple are not required reading for POPHLTH 111.

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Many exposures and dis-ease outcomes can be classified into more than two categories such as high / moderate / low salt intake and high / moderate / low blood pressure, which would just involve adding additional vertical and horizontal dividers to the GATE frame circle and square and involve additional calculations of dis-ease occurrence. For example, there could be two exposure groups (EG1 = the group with high salt intake and EG2 = the group with moderate salt intake), while the comparison group (CG) could be the group with low salt intake.

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In the ideal study using categorical data, everyone in P – the triangle - would also be in either EG or CG – the circle - and would ultimately be classified as either having a dis-ease outcome (a or b) or not having dis-ease (c or d) – the square. Therefore, the number of people in P should equal the number of people in EG & CG; the number of people in EG should equal the number of people in a & c; and the number of people in CG should equal the number of people in b & d.

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Some studies involve both numerical exposure measures (e.g. salt intake classified numerically) and a numerical outcome measure (e.g. blood pressure levels). The association between numerical exposures and numerical outcomes is often presented as a correlation coefficient.

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1.4.

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THE TWO EPIDEMIOLOGICAL MEASURES OF DIS-EASE OCCURRENCE: INCIDENCE & PREVALENCE

It is often possible, and useful, to observe when a person transitions from a non-dis-ease to a dis-ease state (e.g. when a heart attack occurs) and epidemiologists usually count the number of these types of events that occur over a period of time. In other situations, it is only possible, and also more useful, just to determine if, not when, the transition has occurred, such as classifying someone as overweight or having diabetes. For example, it would be very difficult to observe a person transitioning from a ‘normal’ to an overweight state or from a no-diabetes to diabetes state, but it is relatively easy to observe (and count) how many people are overweight, or have diabetes at a specified point in time. •

Population Cloud

Incidence raindrops è

To address these different situations, epidemiologists use two key measures of dis-ease occurrence incidence and prevalence. They are differentiated by the timing of the measures. We illustrate this using the analogy of the ‘Incidence raindrops falling into the ‘Prevalence’ pool.

Incidence involves counting the number of onsets of dis-ease (events) occurring during a period of time (the Numerator – analogous to the number of raindrops falling into the pool, say, over one hour). The analogy of the raindrops is to illustrate that the dis-ease events (raindrops hitting the pool) could in theory be observed as they fall into Prevalence pool è the pool and so it is possible to count the number of events that occur during a specified time period. Incidence is the most appropriate measure of dis-ease occurrence for dis-eases that have an easily observable onset (e.g. the number of hospital admissions for heart attacks occurring over 5 years among 1000 smokers compared to hospital admissions for heart attacks occurring over 5 years in 1000 non-smokers). Incidence measures require the dis-ease outcome to be a categorical (e.g. yes / no) variable. Cure cloud è

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Death drips è

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We use the vertical arrow ê in the GATE frame (Figure 1.1) and the raindrops falling into the pool (diagram above) to represent incidence events.

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Incidence is usually presented as the proportion (or percentage) of people from the study population (or more commonly from the Exposure or Comparison Groups within the Study Population) in whom a dis-ease event occurs during a specified time period.

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Most epidemiological textbooks differentiate between two slightly different measures of incidence - Incidence Proportion and Incidence Rate. Incidence proportion (discussed above) is also known as cumulative incidence or more commonly simply as ‘risk.’ Incidence Proportion counts everyone who started the study in the denominator and everyone who has a dis-ease onset during the study time period in the numerator. In contrast, the Incidence Rate is a more exact measure of incidence because, rather than including everyone who started the study in the denominator, once a person has an event, they are removed from the denominator. To achieve this, participants are counted in units of person-time in the study. For example, participants remaining in a study for 10 years contribute 10 person-years each to the denominator while a person who dies 2 years into the study contributes only 2 person-years and another who decides to leave the study after 5 years contributes 5 person-years. In practice, unless the study has a very long follow-up period or a high loss to follow-up or a very high event rate (all of which are uncommon), there will be little difference between the Incidence Rate and Incidence Proportion and the terms can be used interchangeably, as long as the time period over which events are counted is specified. The incidence proportion is easier to calculate because the denominator is simply the number of persons who started in the study rather than person-time. For simplicity, the GATE Notes only use Incidence Proportion (usually known as Risk).

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Incidence is typically calculated separately in the Exposure and Comparison Groups in the Study Population (i.e. in EG and in CG; see box below). Therefore, EG and CG are considered to be separate denominators within the initial overall study population.

Number of persons in group who have dis-ease outcomes (Numerator) Incidence = ----------------------------------------------------------------------------------------------Total number of persons in group (Denominator) during study time T Incidence in EG* (EGO**) = (number of dis-ease events from EG ÷ number in EG) during study time T or = [a ÷ EG] or = [a ÷ (a + c)] during time T Incidence in CG* (CGO**) = (number of dis-ease events from CG ÷ number in CG) during time T or = [b ÷ CG] o...