Epidemiology and Public Health Final Exam Revision Guide PDF (2022)

Epidemiology and Public Health Final Exam Revision Guide (2022) Carolyn Gates [email protected] ________________________________________________________________________________________________________________________________________________________________________ Overview Veterinary Epidemiology Learning Outcomes According to the Competency-Based Veterinary Education (CBVE) framework, Day-One competent veterinarians are expected to achieve the following competencies in Epidemiology: Domain 3: Animal Population Care and Management The graduate designs and implements programs in herd/flock health, disease prevention, and control to improve the health, welfare, and productivity of animal populations 3.2 – Recommends and evaluates protocols for biosecurity o Develops isolation protocols o Selects disinfection protocols o Recommends protocols for animal movement Domain 9: Scholarship The graduate demonstrates the systematic identification, evaluation, integration, and adaptation of evidence and experience to formulate questions and solutions, and educate others 9.1 – Evaluates health-related information o Retrieves and evaluates information based on research principles o Analyses information for accuracy, reliability, validity, and applicability 9.2 – Integrates, adapts, and applies knowledge and skills o Formulates questions and customizes solutions, drawing on personal experience and available evidence o Applies literature to solve clinical or scientific problems (e.g. evidence-based practice) o Applies creativity to develop innovative solutions 9.3 – Disseminates knowledge and practices to stakeholders o Develops and disseminates educational materials o Explains evidence-based recommendations EPA 5: Formulate relevant questions and retrieve evidence to advance care Formulate focused pertinent questions based on situation evaluation Appraise sources of information to evaluate the quality of the content Assess applicability and generalizability of published studies to specific clinical situations Identify resources and use information technology to assess accurate and reliable online medical information and retrieve animal/herd information Evaluate animal/herd response to interventions and use available evidence to adjust care plan ________________________________________________________________________________________________________________________________________________________________________ This document contains a detailed study guide with all the material you are required to know to meet the following learning outcomes: Define, calculate, and interpret basic descriptive measures of animal health used in epidemiology (prevalence, incidence, incidence rate, and odds) - Know which research study designs can be used to estimate these in a population Define, calculate, and interpret basic measures of association used in epidemiology (prevalence ratio, incidence risk ratio, incidence rate ratio, and odds ratio) - Know which measures of association are appropriate to use for each of the research study designs Understand the difference between statistical significance and clinical significance Identify the different study designs from a brief description of the methods and describe the advantages and disadvantages of each - Expert opinion and editorials - Case reports and case series - Cross-sectional studies - Case-control studies - Cohort studies - Randomised-controlled trials - Systematic reviews and meta-analysis Distinguish between random error and bias Understand which types of selection bias are most likely to influence each study design Describe the five different ways we can control for confounding in research studies and under what circumstances it is appropriate to use each of them Estimate how much we can potentially reduce disease occurrence by eliminating a risk factor (population attributable fraction) Determine whether the different types of bias (selection, information, and confounding) may be leading to overestimates or underestimates of a risk factor’s effect Understand the difference between internal validity and external validity for a research study Calculate and interpret basic measures used to evaluate diagnostic test performance - Sensitivity and Specificity - False Positive Rate and False Negative Rate - Positive Predictive Value and Negative Predictive Value - True Prevalence and Apparent Prevalence ________________________________________________________________________________________________________________________________________________________________________ Differentiate between accuracy and precision in diagnostic test measurement for data on a numerical scale Understand the difference between screening tests and confirmation tests Understand what the two basic categories of infectious disease diagnostic tests (tests that detect pathogens and tests that detect antibodies against pathogens) imply about the patient's epidemiological status Describe how changing the cut-off values for diagnostic tests measured on a numerical scale will affect test performance Describe the mechanisms through which outbreaks are commonly detected Describe the steps involved in conducting an outbreak investigation Verify the outbreak Step 1: Define the illness or problem Step 2: Establish if there is a true excess of disease Investigate the outbreak Step 3: Establish a formal case definition Step 4: Enhance surveillance to identify new cases Step 5: Describe the outbreak (individual, place, time) Step 6: Develop hypotheses about the cause of the outbreak Step 7: Conduct analytical studies Implement interventions Step 8: Implement control and prevention measures Step 9: Initiate or maintain ongoing surveillance Step 10: Communicate with relevant stakeholders Define the terminology and approaches for characterising the levels of disease in a population Interpret a simple epidemic curve to determine the most likely type of exposure and disease transmission mechanisms Describe how the basic reproduction number (R0) is estimated and used to make inferences about the effectiveness of disease control strategies Outline the different basic tools we have to control infectious and describe how they work to reduce R0 to meet disease control objectives Understand the role of research and disease simulation modelling in supporting decisions around controlling infectious disease outbreaks Identify strategies that can be used to mitigate infectious disease outbreaks even when the cause is unknown Describe the principles behind herd immunity and how we can achieve this through vaccination. ________________________________________________________________________________________________________________________________________________________________________ Public Health Learning Outcomes According to the Competency-Based Veterinary Education (CBVE) framework, Day-One competent veterinarians are expected to achieve the following competencies in Public Health: Domain 4: Public health The graduate responds to issues at the interface of animals, humans, and the environment, utilizing a global perspective and sensitivity to local cultures 4.1 – Recognizes zoonotic diseases and responds accordingly o Identifies the clinical signs, clinical course, transmission potential, and pathogens associated with zoonotic diseases o Responds to zoonotic disease diagnosis through owner education, reporting, quarantine, and disinfection 4.2 – Promotes the health and safety of people and the environment o Makes recommendations for management of animal waste, carcasses, and by-products o Implements safety and infection control practices o Advises on disaster/emergency preparedness and response o Practices responsible use of antimicrobial agents o Describes the role of the veterinarian in food safety You may be tested on any of the topics relevant to achieving those competencies. Here is a list of the zoonotic diseases you are responsible for knowing: Endemic Diseases Exotic Diseases Ringworm Leptospirosis Q fever Campylobacter Roundworm Rickettsial diseases Giardiasis Hookworm Rabies Salmonellosis Pasturellosis Brucellosis Cryptosporidiosis Listeriosis Anthrax Yersiniosis Chlamydiosis Hendra Tapeworm Cryptococcosis Hydatidosis/Echinococcosis Orf Bartonellosis Toxoplasmosis Tuberculosis VTEC/STEC infections For each zoonotic disease, students should describe the:  Causative agent  Range of species affected o Main reservoir host(s) o Hosts most likely to transmit infection to humans  Relative prevalence/incidence of disease o Rare, uncommon, common, frequent o Geographic regions where disease is present  Transmission mechanisms/pathways ________________________________________________________________________________________________________________________________________________________________________ o Most likely source of human cases, including through the food chain  High risk populations o Human risk factors for getting disease (who is most susceptible) o Animal risk factors for having disease (who is most susceptible)  Primary clinical signs in humans and animals o Most common/identifiable clinical signs o How long after exposure would you expect to see signs  Diagnostic tests available for animals o Type of test o Sensitivity and specificity and related issues  Preventative measures in humans and animals o Behavioural measures o Vaccines or prophylactic treatments  Availability of treatments in humans and animals o Can the disease be cured or only managed  Prognosis in humans and animals o Long term sequelae and clinical outcomes  Legislative requirements to notify MPI or public health authorities You will need to consult your class notes and other resources to review the appropriate information. I would strongly recommend making yourself a table. Veterinary Epidemiology Carolyn Gates Acknowledgements The Epidemiology content for this study guide was developed in collaboration with Naomi Cogger and Emilie Vallee in EpiCentre and the Infection Control content was adapted from previous lecture materials from Janelle Wierenga. ___________________________________________________________________________________________________________________ 7 ________________________________________________________________________________________________________________________ 1. Introduction ________________________________________________________________________________________________________________________________________________________________________ Contents 1. Introduction..................................................................................................................................................... 12 Evidence-based Decisions............................................................................................................................... 12 Epidemiological Mindset................................................................................................................................. 15 2. Measuring Animal Health................................................................................................................................ 17 Prevalence....................................................................................................................................................... 18 Incidence Risk.................................................................................................................................................. 18 Incidence Rate................................................................................................................................................. 19 Odds................................................................................................................................................................. 20 3. Exploring Epidemiological Relationships......................................................................................................... 21 Disease Causation............................................................................................................................................ 21 Measures of Association.................................................................................................................................. 24 Measures of Effect........................................................................................................................................... 26 4. Diagnostic Tests............................................................................................................................................... 30 Clinical Measurements........................................................................................................................................ 30 Assessing Test Performance................................................................................................................................ 32 Reference Ranges................................................................................................................................................ 33 Testing for infectious diseases............................................................................................................................ 34 Screening for the pathogen............................................................................................................................. 34 Screening for an immune response................................................................................................................. 34 Disease pathogenesis.......................................................................................................................................... 35 Test Performance................................................................................................................................................ 37 Factors influencing test performance................................................................................................................. 40 Understanding Cut-off values.............................................................................................................................. 41 Receiver Operating Characteristic Curves........................................................................................................... 43 Improving test performance............................................................................................................................... 44 Screening versus confirmation tests................................................................................................................... 45 When Should I Perform Diagnostic Tests?.......................................................................................................... 46 5. Epidemiological Study Designs........................................................................................................................ 47 Expert Opinion..................................................................................................................................................... 48 Case Reports and Case Series.............................................................................................................................. 48 Cross-Sectional Studies........................................................................................................................................ 49 Case-Control Studies........................................................................................................................................... 50 Cohort Studies..................................................................................................................................................... 51 ___________________________________________________________________________________________________________________ 8 ________________________________________________________________________________________________________________________ 1. Introduction ________________________________________________________________________________________________________________________________________________________________________ Randomised Controlled Trials............................................................................................................................. 52 Systematic Reviews and Meta-Analysis.............................................................................................................. 53 Study Design Algorithm....................................................................................................................................... 54 6. Error and Bias.................................................................................................................................................. 55 Random Error...................................................................................................................................................... 55 Bias...................................................................................................................................................................... 55 Selection Bias....................................................................................................................................................... 56 Information Bias.................................................................................................................................................. 58 Confounding Bias................................................................................................................................................. 59 1. Restriction.................................................................................................................................................... 62 2. Matching...................................................................................................................................................... 62 3. Randomization............................................................................................................................................. 63 4. Stratification................................................................................................................................................ 63 5. Multivariate methods.................................................................................................................................. 64 7. Critical Evaluation............................................................................................................................................ 65 Prevalence & Incidence Studies.......................................................................................................................... 65 Risk Factor Studies............................................................................................................................................... 65 Clinical Impact Studies......................................................................................................................................... 66 Diagnostic Test Studies........................................................................................................................................ 66 Intervention Studies............................................................................................................................................ 66 Modelling Studies................................................................................................................................................ 67 Publication Process.............................................................................................................................................. 67 What are scientific journals?........................................................................................................................... 67 What happens when a journal article is submitted for publication?.............................................................. 68 What are the main limitations of the peer-review process?.......................................................................... 68 How do I access published journal articles?.................................................................................................... 68 Anatomy of a Journal Article............................................................................................................................... 69 1. Abstract....................................................................................................................................................... 69 2. Introduction................................................................................................................................................. 69 3. Materials and Methods............................................................................................................................... 69 4. Results......................................................................................................................................................... 71 5. Discussion.................................................................................................................................................... 72 6. Conclusions.................................................................................................................................................. 72 7. References................................................................................................................................................... 72 Evaluating a journal article.................................................................................................................................. 72 ___________________________________________________________________________________________________________________ 9 ________________________________________________________________________________________________________________________ 1. Introduction ________________________________________________________________________________________________________________________________________________________________________ External validity............................................................................................................................................... 73 Systematic reviews.............................................................................................................................................. 73 Critical Appraisal Worksheet............................................................................................................................... 74 8. Infection Control.................................................................................................................................................. 78 Introduction......................................................................................................................................................... 78 Why Pathogens Persist........................................................................................................................................ 78 Mechanisms of Disease Transmission................................................................................................................. 80 Preventing Disease Transmission........................................................................................................................ 81 Developing Hospital Infection Control Plans....................................................................................................... 83 Summary.............................................................................................................................................................. 84 9. Outbreak Investigation – Herd Level................................................................................................................... 85 What is an outbreak?.......................................................................................................................................... 85 How do we detect outbreaks?............................................................................................................................ 86 How we conduct an outbreak investigation?...................................................................................................... 86 Steps for Investigating Outbreaks....................................................................................................................... 87 Verifying the Outbreak........................................................................................................................................ 89 Step 1: Define the illness or problem.............................................................................................................. 89 Step 2: Establish if there is a true excess of disease....................................................................................... 89 Investigating the outbreak.................................................................................................................................. 90 Step 3: Establishing a formal case definition................................................................................................... 90 Step 4: Enhancing surveillance to identify new cases..................................................................................... 92 Step 5: Performing descriptive epidemiology to describe the outbreak (individual, place, and time).......... 92 Step 6: Developing hypotheses about the cause of the outbreak.................................................................. 94 Step 7: Conducting analytical studies to test the hypothesis.......................................................................... 95 Implementing Interventions................................................................................................................................ 96 Step 8: Implement control and prevention measures.................................................................................... 96 Step 9: Initiate or maintain ongoing surveillance............................................................................................ 97 Step 10: Communicate with relevant stakeholders........................................................................................ 98 Summary.............................................................................................................................................................. 98 10. Outbreak Investigation – National Level......................................................................................................... 100 Defining Outbreaks............................................................................................................................................ 100 Contact tracing.................................................................................................................................................. 102 Epidemic curves................................................................................................................................................. 103 Disease transmission......................................................................................................................................... 105 Pathogenesis of disease.................................................................................................................................... 105 ___________________________________________________________________________________________________________________ 10 ________________________________________________________________________________________________________________________ 1. Introduction ________________________________________________________________________________________________________________________________________________________________________ Why do epidemics take-off?.......................................................................................................................... 108 Risk factors for clinical outcomes.................................................................................................................. 110 Controlling Diseases.......................................................................................................................................... 110 1. Reducing the number of susceptible individuals....................................................................................... 111 2. Preventing disease transmission from infected individuals...................................................................... 112 3. Removing infected individuals from the population................................................................................. 113 4. Increasing the rate of recovery from infection......................................................................................... 114 Epidemiology in action: the measles case example.......................................................................................... 115 Making Decisions............................................................................................................................................... 116 Choosing intervention strategies.................................................................................................................. 116 Defining an objective for the control programme........................................................................................ 117 Monitoring disease outcomes....................................................................................................................... 118 Summary............................................................................................................................................................ 119 Answer Key............................................................................................................................................................ 120 Section 4: Diagnostic Tests................................................................................................................................ 120 Section 9: Outbreak Investigation..................................................................................................................... 121 Section 10: Outbreak Investigation................................................................................................................... 123 ___________________________________________________________________________________________________________________ 11 ________________________________________________________________________________________________________________________ 1. Introduction ________________________________________________________________________________________________________________________________________________________________________ 1. Introduction Over the past few years, you have spent a lot of time learning about the different diseases that affect our veterinary patients as well as the different diagnostic tests, medications, and surgical procedures that we have at our disposal to treat them. Veterinary epidemiology is the speciality field in veterinary medicine that is dedicated towards studying populations to understand what causes disease and what steps we can take as animal health decision-makers to better prevent, manage, and cure disease. In other words, epidemiology is the science underpinning the information we use to make evidence-based clinical decisions in practice. The study of the frequency, distribution, and determinants of diseases and other health-related conditions in populations and the application of this study to the prevention of disease and promotion of health. Evidence-based Decisions In the previous introductory lecture, we reviewed all the different steps involved in the veterinary consultation process from the animal first displaying clinical signs at home through collecting information during the consult to help develop a treatment plan and monitoring at home to make sure the animal has responded well to your interventions. We will now explore these steps again through an epidemiological and research lens. ___________________________________________________________________________________________________________________ 12 ________________________________________________________________________________________________________________________ 1. Introduction ________________________________________________________________________________________________________________________________________________________________________ Presenting Complaint Most consults start with the client ringing the clinic to book a consult because they have noticed something wrong with their animal. This requires the client to have some knowledge of what is considered normal for that animal or species and then some criteria around when the problem has become severe enough to seek advice from a veterinarian. For example, an owner might notice their dog wanting to go outside to pee 6 or 7 times per day compared to the normal 3 to 4 times and decide to call the veterinarian when the dog starts having accidents in the house. In epidemiology and population medicine, we call this process of comparing a patient against normal values for either itself or its species to identify when things are going wrong benchmarking. If disease in a population is happening more frequently than we expect, we may be dealing with an outbreak situation that we need to investigate. Signalment You will generally have some basic information in the practice management software including the appointment date as well as the species, breed, age, sex, and reproductive status of the animal. This information is referred to as the patient signalment. This is important information to know because certain diseases are more common in different time periods, different locations, and different patient demographics. For example, your list of differential diagnoses for a dog presenting for increased urination would be very different for an 8-week-old male puppy compared with an 8-year-old spayed female. In epidemiology, we use the term prevalence to describe how common disease currently is in a particular population and the term incidence to describe the risk of an individual without the disease getting sick over a set time period. These are also collectively called measures of disease frequency. Clinical History At the beginning of each consult, we typically ask the client a series of questions about progression of clinical signs associated with the current problem as well as questions about the animal’s routine diet, management, environment, historical medical problems, and recent events. This helps us to identify if patients have been exposed to anything that may have increased their chances of acquiring a particular disease. For example, if an animal has a history of recurrent urinary tract infections, we would be more suspicious of the current urinary issues being another infection. We also know that female dogs who have been spayed are at greater risk of developing urinary incontinence due to progressive loss of tone in the urethral sphincter when there is no longer oestrogen being produced by the ovaries. For routine vaccine consults, we might ask owners if their dogs will be boarded at a kennel since we know this increases the risk of getting kennel cough (Bordetella bronchiseptica) and we can preventively give a kennel cough vaccine to protect them against future infections. In epidemiology, we use the term exposures to describe anything that could influence the likelihood of an animal having or developing disease. These can either be a risk factor like being boarded at a kennel that increases the likelihood of disease or a protective factor like vaccination that decrease the likelihood of disease. We can compare the frequency of disease in a group of animals that have risk factor and a group of animals that do not have the risk factor using measures of association to determine how strongly a risk factor influences the disease outcome. ___________________________________________________________________________________________________________________ 13 ________________________________________________________________________________________________________________________ 1. Introduction ________________________________________________________________________________________________________________________________________________________________________ Physical Examination The next step in the consult process is to conduct a nose-to-tail physical examination where we document all the observable clinical impacts of disease. We may also collect data on normal physiological parameters such as body temperature, heart rate, respiratory rate, capillary refill time, and mucous membrane colour. For example, we would not expect to see any significant systemic changes in our patient if it just has a simple urinary tract infection causing the increased frequency of urination. However, if our patient also had progressive weight loss, nausea, retinal detachment, and ulcers in the mouth, we may be more suspicious that we are seeing secondary effects from chronic kidney disease. In epidemiology, we use the terms pathogenesis or natural history of disease to describe the biological progression of disease in animals after they first become sick. We can study groups of individuals with the disease to figure out which clinical signs are most commonly present. We can also calculate reference ranges or confidence intervals to determine the range of values for physiological measurements that we would expect in most normal healthy patients. Differential Diagnosis After integrating data we have collected from the signalment, history, and physical examination, we sometimes get lucky and can establish a definitive diagnosis based on the presence of a pathognomonic clinical sign (a clinical finding that is unique to a particular disease and therefore allows us to make a confident diagnosis). More often than not, however, we usually have some theories about what is most likely causing the problem and need to do more investigative work. For example, we may suspect that our dog has a urinary tract infection, but want to do some more testing to confirm the diagnosis and guide our treatment decisions. In epidemiology, we call our theory about what could be causing disease a hypothesis and this often forms a basis for designing epidemiological research studies in which we use different study design frameworks to observe and collect data about individuals in a population to look for patterns and trends that provide insight about the causes for disease and what we do to better manage them. This is different experimental research studies, which are you more traditional laboratory-based studies that involve experimental manipulations. Diagnostic Tests Diagnostic tests are any tools that we use to classify an animal as being diseased or non-diseased. These could include physical examination findings, biochemical tests, serological assays, microbiology, radiographs, and other diagnostic imaging. Unfortunately, these tests are usually not completely perfect and there is a chance we could end up missing or misdiagnosing a disease. In epidemiology, we have tools to measure diagnostic test performance including the sensitivity (probability that the test will detect disease in an animal that truly has disease) and specificity (probability that the test will fail to detect disease in an animal that truly does not have disease). We can combine this with our knowledge of disease prevalence to work out how much we should trust positive and negative test results (predictive values). We frequently use diagnostic tests in epidemiological research studies to assign animals into outcome groups – usually those that have disease (cases) and those that do not (controls) - so we can look for differences between the two groups that could explain why cases got sick. ___________________________________________________________________________________________________________________ 14 ________________________________________________________________________________________________________________________ 1. Introduction ________________________________________________________________________________________________________________________________________________________________________ Interventions Interventions refer to any medications, surgical procedures, or husbandry practices that we use to help manage, cure, or prevent disease in our patients. It should be noted that even without a definitive diagnosis, we can still manage disease for the our patients by providing basic supportive care such as giving intravenous fluids to correct dehydration or prescribing medications such as non-steroidal anti-inflammatories (NSAIDS) to treat pain secondary to inflammation or reduce fever while the body does the hard work of fighting off disease. In epidemiology, we often do studies to compare the efficacy of different interventions and calculate measures of effect to determine the expected clinical impacts of implementing them in practice. Although some research findings may be statistically significant, they may not have clinically significant effects for our patients. Monitoring Our job in clinical practice does not end when the patient walks out the door. We usually need to follow-up with the client to make sure that our recommendations have been working to resolve the disease and if they haven’t been working, try to figure out why so that we can change our current course of action for this patient and make better decisions for other patients in the future. In epidemiology, we use the term critical evaluation to describe the process of reflecting on our findings to identify errors, biases, and confounding factors that could be leading to unexpected or incorrect inferences from our research. COMMON CLINICAL RESEARCH QUESTIONS ▪ How common are each of the diseases on my differential diagnosis list? ▪ Does my patient have any risk factors for a particular disease? ▪ What clinical signs or symptoms are likely to accompany a particular disease that I might need to treat? ▪ Which diagnostic test(s) should I choose to confirm my diagnosis and how do I interpret the results? ▪ How effective is treatment A compared with treatment B (or even doing nothing)? ▪ What is the average survival time for patients after diagnosis and/or treatment? Epidemiological Mindset Although epidemiologists and veterinarians working in practice are both concerned with disease and the control of disease, there are some important differences in mindsets for how both groups approach the problem. One illustration of these different approaches can be seen when considering an animal with diarrhoeal disease. Clinicians are focused on treating and caring for the sick individual. However, the epidemiologist will want to answer the following additional questions: What factors has the animal been exposed to? How many susceptible individuals are there in the population? For some diseases, this will only include one species but for others we may need to consider other animal species and humans. These zoonotic diseases can be transferred from animals to humans. Is there potential for the agent to spread to other people or animals? What interventions can prevent additional cases or recurrence? ___________________________________________________________________________________________________________________ 15 ________________________________________________________________________________________________________________________ 1. Introduction ________________________________________________________________________________________________________________________________________________________________________ We can summarize these differences by saying that veterinarians who work as clinicians are concerned with the health of individuals, while epidemiologists are concerned with the health of the groups of individuals. This is a particularly important mindset for infectious diseases where the decisions made about an individual patient will have wider impacts on the population due to the potential for disease to spread from the infected individual to other susceptible individuals. In epidemiology, we use measures like the basic reproduction number (R0) to estimate how many susceptible individuals a single infected individual is expected to infect and then we look at how measures like infection control or the level of vaccination coverage to achieve herd immunity can slow or stop the rate of disease spread. Infection control is also particularly important in veterinary medicine because many infectious diseases are zoonotic meaning that they can spread between animals and people. Practicing good hand hygiene and wearing appropriate personal protective equipment (PPE) can help prevent us from getting sick as well as preventing our hospitalised patients from acquiring nosocomial infections from coming into contact with other infected animals or fomites (objects that have been contaminated by infected animals). We will learn a lot more about controlling infectious diseases and outbreak investigation in further sections. ___________________________________________________________________________________________________________________ 16 ________________________________________________________________________________________________________________________ 2. Measuring Animal Health ________________________________________________________________________________________________________________________________________________________________________ 2. Measuring Animal Health A fundamental task in epidemiology is to quantify how commonly or frequently disease occurs in a population (measures of disease frequency). This is important since it allows animal health decision-makers to: determine which diseases are of meet reporting requirements of international economic importance organizations set priorities for the use of resources for demonstrate disease freedom to trading partners disease control activities narrow down differential diagnoses lists to manage plan, implement, and evaluate disease cases of disease control programs Measuring disease frequency can be done by simply counting the number of affected individuals in a population. However, in order to compare levels of disease across different groups of individuals, time frames and locations, we also need to consider counts of cases in context of the size of the population from which those cases arose. For example, 10 cases of disease in a population of 1,000 is a very different story than 10 cases in a population of 1,000,000. It is also important to note that the level and distribution of disease in a population depends on the interplay of individual, spatial and temporal factors. Individual factors: what types of individuals tend to develop disease and who tends to be spared Spatial factors: where is the disease especially common or rare and what is different about these places Temporal factors: how does disease frequency change over time, and what other factors are associated with these changes The figure on the right illustrate changes in the frequency and distribution of bovine tuberculosis (bTB) cases in the United Kingdom from 1986 to 2010. The growing clusters in south east of England and Wales are related to a high density of badger populations which act as wildlife reservoirs that continuously spread the disease to cattle populations. Scotland has remained virtually bTB free since 2013 thanks to requirements for cattle imported from high-risk regions to be tested for bTB before and after moving across the border. Before discussing the methods for quantifying disease frequency any further, we first need to define a few key terms: A proportion is a fraction in which the numerator is included in the denominator. Say we have a herd of 100 cattle and over a 12-month period we identify 58 diseased animals. The proportion of diseased animals is 58 ÷ 100 = 0.58 = 58%. A ratio defines the relative size of two quantities expressed by dividing one (numerator) by the other (denominator). A commonly used ratio in epidemiology is the odds of disease. If we return to our herd of 100 cattle, then the odds of disease is 58:42 which reduces down to 1.4:1. The term morbidity is used to refer to the extent of disease or disease frequency within a defined population. Morbidity can be expressed as either prevalence or incidence. ___________________________________________________________________________________________________________________ 17 ________________________________________________________________________________________________________________________ 2. Measuring Animal Health ________________________________________________________________________________________________________________________________________________________________________ Prevalence Prevalence measures existing cases of disease in a population and is calculated as the proportion of individuals in a defined population that has disease at a specified point in time (point prevalence) or during a specified period of time (period prevalence). When we measure prevalence, we are essentially ‘stopping the clock’ and determining how many individuals have disease at that point in time. However, the point in time is not necessarily a single point in calendar time. To illustrate this, consider a study research in which we want to estimate the prevalence of dental decay in a sample of 400 cats. It is not feasible to examine every single cat on the same day or even in the same week and therefore, the visits for the study will have distributed over several weeks throughout the year. We are still examining each cat only once, but the time point for measurement becomes the office visit during the year rather than a specific calendar date. In the example to the right, we have a population of 18 cats with 5 found to have dental decay. The prevalence of dental decay in this population is therefore 5 / 18 or 27.7% Incidence Risk Incidence risk measures new cases of disease occurring in a population and is calculated as the proportion of initially disease-free (susceptible) individuals in a population who become new cases during a defined follow-up period. The key here is that you are observing individuals at two or more time points – at the first time point you are checking to make sure the individual does not have disease and then you follow the individual over a period of time to see how many of them develop disease. In the example to the right, let’s pretend we want to investigate the risk that healthy cats will get a cat bite abscess over a 10-week period. We take a group of 5 cats who do not currently have a cat bite abscess and follow them for a period of 10-weeks. Cat 1 develops a cat bite abscess in Week 5 and Cat 3 develops a cat bite abscess in Week 8, while the rest of the animals in our study remain puncture free. The incidence of cat bites is 2 / 5 cats or 40% over a 10-week period. It is always important to specify the time period when you are reporting incidence because 2 / 5 cats in a 10- week period present a very different level of concern compared with 2 / 5 cats in a 10-year period. ___________________________________________________________________________________________________________________ 18 ________________________________________________________________________________________________________________________ 2. Measuring Animal Health ________________________________________________________________________________________________________________________________________________________________________ Incidence Rate Incidence rate is the number of new cases of disease that occur per unit of individual time at risk during a defined follow-up period. Incidence rate is expressed in terms of events per unit of time-at-risk and is calculated as follows: Incidence rate is used when the population is dynamic, that is individuals are entering and leaving the population over the course of the study and this allows for inclusion of individuals with varying duration of follow up. The key to the calculation of incidence rate is the concept ‘time-at-risk’. Individuals contribute time-at-risk for as long as they are in the study or until they experience the event of interest. Consider the example below where we have some cats that were recruited into the study late (Cat 2 and Cat 3) and some cats that left early either because they developed disease (Cat 1 and Cat 3) or dropped out from our study for another reason (Cat 2). Even though we intended to observe all cats for a 10-week period, Cats 1, 2, and 3 were only observed for 5 weeks each. In this case, we would count up the total number of cats that developed disease (2 cats) and divide that by the total weeks that cats were observed (35 weeks) to get our final incidence rate of 2 cases per 35 cat-weeks or 0.06 cases per cat-week. You can get some very strange units here like dog-days, cow-months or horse years. Both incidence risk and incidence rate are useful for counselling animal owners in preventative care discussions about the likelihood of their currently healthy animal developing disease in the future. Another way of thinking about prevalence and incidence is imagining a bucket filling and emptying with water. The bucket represents the total number of individuals in your population and the level of water in your bucket represents prevalence of disease. The water flowing from the tap into the bucket represents incidence or new cases that will increase the water level. The hole in the bottom of the bucket represents recovery (or possibly death!) from disease and will act to lower the water level in the bucket. The balance between rate in and rate out determines your disease prevalence. An interesting example of this is when they introduced anti-viral agents for the treatment of HIV in people. Even though the incidence of new HIV cases remained roughly the same, the prevalence of HIV actually increased because people were living longer with the disease (i.e. the hole at the bottom of the bucket was plugged so the water levels rise). ___________________________________________________________________________________________________________________ 19 ________________________________________________________________________________________________________________________ 2. Measuring Animal Health ________________________________________________________________________________________________________________________________________________________________________ Odds The odds of disease is a slightly strange measure of the likelihood that an event will take place and is calculated as the number of diseased individuals (events) divided by the number of non-diseased individuals (events). You have probably heard the term most commonly used in the context of placing bets for things like horse races. In the example below, we have 3 diseased cats and 2 non-diseased cats which would make our prevalence 3 / 5 or 60% and our odds 3:2 (which reduces to 1.5:1). Summary The following table summarises the key take home messages about the measures of disease frequency. Item Prevalence Incidence risk Incidence rate Odds Numerator All cases New cases New cases occurring All cases counted counted on a occurring during a during a specified follow- on a single single specified follow-up up period occasion occasion period Denominator All individuals All susceptible Sum of time periods All non-cases examined individuals present during which all (cases and at the start of the individuals could have non-cases) study developed disease Time Single point or Defined period Measured for each Single point or period individual from beginning period of study until disease event Interpretation Probability of Probability of How quickly new cases The odds of having having disease developing disease develop over a specified a disease at a at a given over a specified period given point in point in time period time ___________________________________________________________________________________________________________________ 20 ________________________________________________________________________________________________________________________ 3. Exploring Epidemiological Relationships ________________________________________________________________________________________________________________________________________________________________________ 3. Exploring Epidemiological Relationships Disease Causation Many of the scientific research questions in veterinary and human medicine addresses causal relationships (i.e. knowing what causes individuals to develop a particular disease or health outcome). We need to understand these causal relationships to prevent disease and improve diagnosis and treatment. For example: Will exposure to a particular environmental toxin cause disease? Will a treatment cause an improvement in the patient’s quality of life? Does the chemical castration of dogs cause a reduction in the number of stray dogs? Does the vaccination of birds prevent the spread of avian influenza? In order to answer these and other questions, we need to be aware of the difference between association and causation. Association is a statistical measure of the strength of the relationship between exposure and outcome. We can say, for example, that there is an association between the presence of antibodies to Toxoplasma gondii in people and mental illnesses, but we can’t say definitively that toxoplasmosis causes people to become crazy cat ladies. Causation is not straightforward and is the subject of many philosophical debates. A useful working definition is that from Rothman and Greenland (2005, P S144): Rothman and Greenland’s definition is useful as it allows for events or conditions that are neither sufficient nor necessary. A cause is said to be sufficient if the factor will always produce the outcome, and a necessary cause is defined as one without which the outcome cannot occur. To illustrate these concepts, consider tuberculosis in humans. In order for clinical tuberculosis to occur, the person must be infected with the tubercle bacteria. However, infection alone is not a sufficient cause of clinical tuberculosis because not all people infected with the tubercle bacteria will develop clinical tuberculosis due to other underlying factors. Whether or not disease occurs in an individual depends on an interplay of three factors (the epidemiological triad): the host the exposure the environment The host is the person or animal that may contract a disease. Age, sex, species, genetic makeup, level of exposure, immune status and state of health all influence a host’s susceptibility to developing disease. The exposure is the factor that causes the disease. When we are studying infectious disease, the exposure is the agent that causes disease (for example, bacteria, virus, fungus or parasite) and infection in the host can be altered by virulence (pathogenicity), genetic subtype and infective dose. However, not all diseases are infectious. For example, skin cancer is caused by exposure to the sun. The environment includes the surroundings and conditions that influence whether disease occurs as well as the severity of the disease. The environment may weaken the host and increase its susceptibility to disease or provide conditions that favour the survival of the agent. It is worth noting that the environment can include natural features as well as man-made ones such as the housing we create for production animals. ___________________________________________________________________________________________________________________ 21 ________________________________________________________________________________________________________________________ 3. Exploring Epidemiological Relationships ________________________________________________________________________________________________________________________________________________________________________ With all this controversy around causation, it is not surprising that epidemiologists have turned to checklists for making causal inference. The most widely used list of causal criteria in epidemiology is based on a paper by Sir Austin Bradford-Hill that was published in 1965. The criteria, often called ‘Hill’s Criteria’, are frequently used as a checklist for causation and as such, they warrant further attention. However, to be fair to Hill, he never called them criteria nor did he want them to be thought of as a checklist. The first factor Hill considered was the strength of association. This is often incorrectly interpreted as saying weak associations are less likely to be causal. What Hill actually meant was that when the association is stronger, it is more difficult to explain this away by bias or confounding. However, this should not be taken to mean that small measures of association are automatically biased or non-causal. Hill’s second viewpoint was that the observed association should have consistency across different types of studies and in different populations. For example, at the time of Hill’s paper being written, the relationship between smoking and lung cancer had been shown in at least 29 retrospective studies and seven prospective studies. Hill argued that the consistency allowed one to infer that the association was unlikely to be due to a consistent error. However, he was quick to point out that different results from different studies do not invalidate the first. Furthermore, one should not consider a set of results as inconsistent when some results are statistically significant and others are not. It is entirely possible for studies to get the same estimate for the strength of the association, but differ in the level of statistical significance due to difference in the standard errors or size of the study sample. Hill then proposed that specificity should be the third characteristic that was considered. Specificity means that if an association is limited to a specific group of people or specific diseases, this adds weight to the argument of causation. Clearly, this cannot be considered as a general rule. For example, it would be ridiculous to suggest that the evidence that smoking causes lung cancer is weakened by the fact we have found smoking is associated with other diseases. The fourth characteristic was the temporal relationship between the factor and outcome; that is, the cause comes before the effect in time. A temporal relationship is the only criterion that can be considered essential as it is implied in the definition of causation. However, Hill’s point was more a warning that it can be difficult to determine the temporal relationship when there is a long lag time between exposure and disease. Further, when undertaking a cross-sectional study, we do not know if the associated factor caused disease or was the case of disease. For example, if severe angina due to coronary heart disease led to reduced physical activity and a sedentary lifestyle in a previously active person, such inactivity although associated with coronary heart disease in a cross-sectional context could not be held accountable for it. Fifthly, an association might be causal if one could demonstrate a biological gradient; that is, the likelihood or severity of the outcome is greater with a higher dose of exposure. For example, the death- rate from lung cancer increases with the number of cigarettes smoked daily. Hill did concede that in observational studies it is difficult to demonstrate a dose-response relationship because, for example, the number of cigarettes smoked daily likely varies over time. Furthermore, some relationships are not linear meaning that there may be threshold effects or inconsistent patterns in the relationship. Another factor we should consider when deciding if an association is causal is plausibility or does it make ‘biological sense’. Biological plausibility provides a strong argument for causation if it is present. However, when there is absence of biological plausibility should not be seen as a reason to exclude a causal relationship as it may be a new one to science or medicine (something Hill himself acknowledges). Despite Hill acknowledging that we should not dismiss an association as non-causal because it seems implausible the association should not be in conflict with the general knowledge. That is, the association should be coherent with the other evidence. The requirement for coherence is vague and, in many ways, not ___________________________________________________________________________________________________________________ 22 ________________________________________________________________________________________________________________________ 3. Exploring Epidemiological Relationships ________________________________________________________________________________________________________________________________________________________________________ substantially different to biological plausibility. Furthermore, the whole process of science is frequently challenging what we previously thought of as facts. Do not forget that people once thought the world was flat and the sun circled the earth! Hill also noted that, on occasion, it is possible to find experimental or semi-experimental evidence for a causal association. Therefore, evidence from controlled randomized trials of interventions provides the best evidence for a causal relationship. However, it is not generally possible to perform experiments on humans in which possible disease-producing agents are administered or risk behaviours encouraged. There is good evidence that alcohol consumption during pregnancy harms the fetus, but it would be considered highly unethical to run an experiment where you asked groups of pregnant women to drink. The final point made by Hill was that in some circumstances an association might be considered causal by analogy. This means that a similar relationship could have been observed with another exposure and/or disease. Hill gave the example that the known effects of the drug thalidomide would accept evidence that another drug could cause birth defects. In the veterinary sphere, bovine spongiform encephalopathy (BSE) and scrapie/transmissible mink encephalopathy make us more willing to accept the existence of similar diseases in other species like variant Creutzfeldt-Jakob’s disease in people. If we cannot use a defined checklist, then how can we assess causation between an exposure and an outcome? To answer this question, consider the following quote from Rothman and Greenland (2005): Summary of Hill’s Criteria ___________________________________________________________________________________________________________________ 23 ________________________________________________________________________________________________________________________ 3. Exploring Epidemiological Relationships ________________________________________________________________________________________________________________________________________________________________________ Measures of Association The first step towards understanding causal relationships is to establish whether the ‘amount’ of disease in a group of individuals who are exposed to the potential risk factor is different the occurrence of disease in a group that were not exposed to the potential risk factor. To express this formally, we can ask ‘Does the frequency of disease occurrence (measured using prevalence, incidence, or odds) differ between the exposed group and the non-exposed group?” The ‘exposure’ that we are interested in is called a risk factor if we believe it increases disease or a protective factor if we believe it decreases disease. For example: Worn tyres are a risk factor for motor vehicle accidents. In humans, high blood pressure is a risk factor for coronary heart disease. The MMR vaccine is a protective factor against measles, mumps and rubella. If we identify risk factors that are causally associated with an increased likelihood of disease and those that are causally associated with a decreased likelihood of disease, then we are in a good position to make recommendations about health management. Much of epidemiological research is concerned with identifying and quantifying the effect of risk factors on the likelihood of disease. It is important that we have a group of individuals without the exposure for comparison because diseases are usually multifactorial and so we would expect at least some of the unexposed population to develop disease. For example, we have pretty good evidence that smoking causes lung cancer and we would therefore expect the frequency of lung cancer to be higher in smokers. However, people can still develop lung cancer for other reasons like exposure to high levels of air pollution in crowded cities or working in buildings with asbestos. As epidemiologists, we are interested in how much greater the disease occurrence is in the exposed versus unexposed. In epidemiology, we often relate disease occurrence with exposure to a particular agent. If both disease status and exposure status are binary variables (yes or no), we can construct a table that reports the number of individuals in each of the four exposure-disease categories (a, b, c and d cells). In this 'standard' format of a two-by-two table, disease status is presented in the columns and exposure status in the rows. Letters a, b, c and d represent the number of individuals exposed and diseased, exposed and non- diseased, non-exposed and diseased, and non-exposed and non-diseased, respectively. In some textbooks, you may find the disease status in the rows and exposure in the columns; therefore, it is not important to memorize what a, b, c and d mean, but to understand the logic behind a two-by-two table. ___________________________________________________________________________________________________________________ 24 ________________________________________________________________________________________________________________________ 3. Exploring Epidemiological Relationships ________________________________________________________________________________________________________________________________________________________________________ To illustrate how this works, consider a study looking to determine if consumption of raw milk is associated with brucellosis. The study involved 5000 people; 2,200 drank raw milk and 2,800 did not. During the study period, there were 1,800 cases of brucellosis, of which 1,000 occurred in people who had consumed raw milk. To assess whether there is an association between raw milk consumption and brucellosis, we have to determine whether there is an increased risk of disease in the group exposed to a particular agent (in this case raw milk consumption) compared to a non-exposed group. We can calculate the risk of disease in both exposed and non- exposed individuals using incidence risk. The incidence risk in the exposed group (Re) is: And, the incidence risk in the non-exposed group (R0) is: For reasons that will become apparent later, it is also good to calculate the incidence risk in the study population. The incidence risk in the study population was: Having calculated these three values, we will now calculate measures of association to determine the strength of the relationship between consumption of raw milk and brucellosis. The next step is to calculate our risk ratio which is simply dividing the two values. In this case, because we are using incidence risk as our measure of disease frequency in the exposed and non-exposed groups, the risk ratio we calculate will be the incidence risk ratio. For our example, this would be 0.45 / 0.29 = 1.55. In other words, the incidence risk of disease in individuals who consumed raw milk was 1.55 times greater than for individuals who did not. If our study had instead measured prevalence, incidence rate, or odds, then we could calculate the prevalence risk ratio, incidence rate ratio, and odds ratio, respectively. The risk ratio provides an estimate of how many times more likely exposed individuals are to experience disease compared with non-exposed individuals. If the incidence risk ratio equals one, then the risk of disease in the exposed and non-exposed groups are equal and there is no association between them. If the risk ratio is greater than one, then exposure increases the risk of disease, with greater departures from one indicative of a stronger effect. If the risk ratio is less than one, exposure reduces the risk of disease and exposure is said to be protective; the greater the departure from one, the stronger the protective effect. ___________________________________________________________________________________________________________________ 25 ________________________________________________________________________________________________________________________ 3. Exploring Epidemiological Relationships ________________________________________________________________________________________________________________________________________________________________________ We also often calculate the 95% confidence interval around the risk ratio value, which is basically a measure of how confident we are that our estimate of the risk ratio accurately reflects the true value for the population. For example, if we had a risk ratio of 2.05 with a 95% confidence interval of 0.39 to 3.71, that basically means that we are 95% confident the true point value of the risk ratio in the population falls somewhere within that range. We would consider this risk ratio to be statistically insignificant because the ranges spans the values for protective effect (0 to 1 to ∞) and so we can’t make any robust conclusion about the relationship between the exposure and the outcome. However, if the 95% confidence interval had a narrower range at 1.74 to 2.36, then we are pretty confident that is indeed a risk factor since the entire range of potential values lies in the risk factor range (>1 to ∞). We would say that this is a statistically significant risk factor. It is also important to note that the farther away the risk ratio is from 1 in either direction, the stronger the association. Summary RR > 1: Occurrence of disease in the exposed group is greater than in the unexposed group. The risk factor is associated with an increased risk of disease RR = 1: Occurrence of disease in the exposed and unexposed groups is identical. There is no association between the disease and the risk factor RR < 1: Occurrence of disease in the exposed group is less than in the unexposed group. The risk (protective) factor is associated with a decreased risk of disease Measures of Effect Measures of effect aim to quantify how much of the disease can be attributed to a certain exposure or, in other words, how much a disease could be prevented in a population if the exposure is eliminated (assuming of course that the relationship between exposure and disease is causal). In the association between exposure and disease there is generally a risk of disease (incidence risk) in the total population, in the exposed group and in the non- exposed group. If we want to know how much of the disease could be prevented by controlling for a given exposure, we have to subtract the risk observed in the non-exposed group because it represents the baseline or background risk in the population. There are two main types of measures i) measures of effect in the exposed group; and ii) measures of effect in the whole population. Attributable risk tells us how much disease can be attributed to exposure in the exposed group and, therefore, preventable if we could control for exposure. Imagine that exposure to an agent is causing disease, but disease is also occurring in the non-exposed group. You want to know how much of the disease in the exposed group is due to exposure. We calculate the attributable risk (risk difference) as follows: ___________________________________________________________________________________________________________________ 26 ________________________________________________________________________________________________________________________ 3. Exploring Epidemiological Relationships ________________________________________________________________________________________________________________________________________________________________________ The result is interpreted as the incidence risk in the exposed individuals that is due to exposure. In other words, it represents the amount of disease that we can hope to reduce in the exposed group if exposure were eliminated completely. We can also express the attributable risk as a proportion of the risk in the exposed group. The measure is known as the attributable fraction and is calculated as follows: This result is interpreted as the proportion of the disease risk in the exposed group that is due to exposure. In other words, it represents the proportion of disease that we can hope to reduce in the exposed group if exposure were eliminated completely. When the exposure of interest prevents disease, such as vaccination, it makes more sense to talk about the ‘number needed to treat’ rather than the risk difference. The number needed to treat is defined as follows: For example, if sheep vaccinated against Salmonella have 5/100 (0.05) fewer deaths than the untreated group (risk difference). The number of sheep that need to be vaccinated in order to prevent one death is 20 (1/0.05). If it costs $5 per sheep to vaccinate and the cost of a death is only $60 then it would not be economically viable for a farmer to implement a vaccination programme. The population attributable risk tells us how much disease can be attributed to exposure in the population (not only in the exposed group). Our interest now is not to assess the effect that controlling for exposure would have on the exposed group, but the effect that controlling for exposure would have on the entire population. To calculate the population attributable risk, we need to subtract the observed risk of disease in the non-exposed group to the risk of disease observed in the total population: The result is interpreted as the incidence risk in the population that is due to exposure. In other words, it represents the amount of disease that we can hope to reduce or prevent in the population if the exposure were eliminated completely. The graphical representation is very similar, but the difference is that instead of the risk of disease in the exposed group, the total risk of disease (risk in exposed + risk in non-exposed) is used. ___________________________________________________________________________________________________________________ 27 ________________________________________________________________________________________________________________________ 3. Exploring Epidemiological Relationships ________________________________________________________________________________________________________________________________________________________________________ We can also express this measure as a proportion, which is called a population attributable fraction (PAF). The PAF is calculated as follows: This result is interpreted as the proportion of the disease risk in the population that is due to exposure. In other words, it represents the proportion of disease that we can hope to reduce or prevent in the population if exposure were eliminated completely. The following is an extreme example to show the difference between the attributable risk for the exposed group and the attributable risk for the total population. A study investigated the association between people exposed to atomic bomb radiation and the development of cancer. In total, there were 50 people exposed to radiation: 45 of them developed cancer; while from 10,000 people non-exposed to this radiation, 500 developed cancer. The two-by-two table would look like this: Incidence Risk of disease in the exposed (Re) = 45 / 50 = 90% Incidence Risk of disease in the non-exposed (R0) = 500 / 10,000 = 5% Incidence Risk of disease in the non-exposed (RT) = 545 / 10,050 = 5.4% Attributable risk: Assuming causation 85 excess cases per 100 people (i.e. 90% - 5%) in the exposed group that can be attributed to those exposed to the atomic bomb when compared to those that were not exposed. Attributable fraction: If we want to express the attributable risk as a proportion of the observed incidence in the exposed group, we can say assume causation 94% of risk of cancer in the group exposed to the atomic bomb can be associated with the exposure. Population attributable risk: Assuming causation there was 0.4 excess cases per 100 people in the population due to exposure to the atomic bomb. The reason this number is so small, relative to the attributable risk in the exposed population, is because very few people in the population are exposed. ___________________________________________________________________________________________________________________ 28 ________________________________________________________________________________________________________________________ 3. Exploring Epidemiological Relationships ________________________________________________________________________________________________________________________________________________________________________ Population attributable fraction: If we want to express the population attributable risk as a proportion of the observed incidence in the population, we can say assuming causation 7.8% of the disease incidence in the population can be attributed to exposure to the atomic bomb. This shows that if we could prevent exposure to atomic bomb radiation, we could eli

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