Ebook Veterinary epidemiology (4/E): Part 2

Part 2 book “Veterinary epidemiology” has contents: Demonstrating association, observational studies, design considerations for observational studies, clinical trials, validity in epidemiological studies, systematic reviews, diagnostic testing, surveillance, statistical modeling, mathematical modeling,… and other contents.

383 18 Validity in epidemiological studies The goal of epidemiology is to generate and interpret information about disease and health in populations in order to aid decision making In an ideal world each research question would be addressed by a particular study, the study would provide an exact representation of the relevant domain, and the study results would provide the information needed to truthfully answer the question Unfortunately, this is never the case In truth, all studies provide flawed depictions of ‘reality’ Maclure and Schneeweiss (2001) imagine epidemiological studies of causation to be like a telescope used to observe populations – they call this the Episcope The Episcope is made up of a number of filters and lenses, each of which is imperfect and therefore distorts the image to a greater or lesser extent A simplified version of Maclure and Schneeweiss’ Episcope is shown in Figure 18.1 This has eight lenses representing the key issues affecting the validity of epidemiological studies (although these could be thought of as compound lenses, each containing an array of imperfect lenses): background factors; interpretation biases; selection biases; statistical interaction and effect-measure modification; information biases; confounding; errors in analysis; communication biases Each of these lenses will induce an amount of error and the resulting image of reality will be imperfect An unavoidable problem is that we can only compare this image with other imperfect images, and hence we cannot say exactly where, how and to what extent the image differs from reality The role of the epidemiologist is to minimize the error associated with each of these lenses as best as possible and then to understand where residual errors remain and the potential impact of these on the results By doing this we can get closer to knowing the true situation Although it is often useful to think about these main sources of error in isolation, in reality they are an interconnected complex that may independently impact estimates of association between an exposure and an outcome, but also may so by impacting one another Hence, information biases may lead to selection biases and may affect measurement of, and therefore the ability to control for the effect of, confounders Examples are described below Types of epidemiological error Broadly, error can be defined as any belief, conclusion or action that is not correct In epidemiology, the term error may be used in numerous ways It is common to distinguish between random error (Box 18.1) and nonrandom error Random error is variation that is due to chance That is, random errors in one variable are not associated with other variables In contrast nonrandom errors vary systematically between groups and are often called biases (The concept of bias was introduced in Chapters 10 and 15.) Hence, bias occurs when an error affects some groups more than others The term bias, when used in epidemiology, does not necessarily suggest prejudice on the part of the experimenter; many forms of bias may be inadvertent Conceptualization of random error depends, to some extent, on one’s worldview Some people view chance variation as an intrinsic part of reality that prevents full prediction, whilst others take a more deterministic view in which full understanding of all relevant variables can enable prediction without error, with random error simply resulting from incomplete knowledge However, even with this latter view, in most situations it is unlikely that we will acquire sufficient knowledge to enable full prediction, and the unpredictability that results from incomplete knowledge has the same effect as that due to random variation Veterinary Epidemiology, Fourth Edition © 2018 John Wiley & Sons Ltd Published 2018 by John Wiley & Sons Ltd Companion website: www.wiley.com/go/veterinaryepidemiology Communication Analysis Confounding Information Interaction Selection Interpretation 18 Validity in epidemiological studies Background 384 Fig 18.1 Maclure and Schneeweiss (2001) imagine epidemiological studies of causation to be like a telescope (an Episcope) made up of imperfect lenses The observer (epidemiologist) makes observations through this series of lenses, within each of which particular biases and errors may occur Box 18.1 An example of random error, selection bias and information bias A survey is carried out to assess the prevalence of lameness in a dairy herd of 100 milking cows Because of time constraints, only a sample of the herd can be examined If the entire herd were examined, it would have been found that 10 were lame In other words, the true prevalence of lameness in this herd is 10% and, on average, if we examine a random sample of 10 cows, we may expect to find one lame This last statement implies that, if we took many random samples of 10 cows, the average number of lame cows observed in the samples would be one Imagine a veterinarian examines a random sample of 20 cows from this herd and finds four lame; the prevalence estimate is therefore 20% If another survey was conducted choosing another random sample of 20 animals, the prevalence estimate may have been 5% If the study is repeated many times, the average prevalence estimate will be very similar to the true prevalence The differences in prevalence estimates between studies occur because, by chance, more or fewer lame cows may appear in each selected group This is known as The results or interpretation of an epidemiological study may, therefore, be ‘wrong’ due to random error (chance) and systematic error (bias) When conducting studies, epidemiologists should attempt to reduce both sources of error When interpreting studies, a reader should, ideally, be aware of both types of error, be able to identify potential sources and impacts of these errors, and assess whether they have been adequately addressed in the study Accuracy, precision and validity in epidemiological studies The terms accuracy, precision and validity are often applied to measurements (see Chapter 10) However, random error The larger the sample size, the lower the error and the more precise the estimate of prevalence However, this assumes there is no systematic error (bias) in lameness assessment Selection bias may have occurred if the cows selected were the first 20 to enter the yard This method of selection may underestimate the prevalence of lameness, as the lame cows may tend to be toward the back of the herd Importantly, repeating the study, with the same selection procedure, would lead to similarly biased estimates of the prevalence of lameness, with some variation due to random error There may also be measurement bias For example, the investigator may be inexperienced and not identify subtle lameness In this situation, the prevalence may be underestimated Alternatively, the inspection may be carried out on very rough ground, resulting in the overdiagnosis of lameness and an overestimation of the prevalence Again, repeating the study under these same circumstances would lead to repeated estimation of biased prevalence estimates these terms can equally be applied to epidemiological studies A study that is able to estimate a parameter (e.g., prevalence or relative risk) with little error is said to have a high level of accuracy In such cases the parameter estimates will be close approximations of the true values Just like error, accuracy can be divided into two component parts Studies in which parameters are estimated with little random error are said to have high precision, whereas studies with little systematic error (bias) are said to have high validity (see Chapter 10) Hence, for a study to be accurate, it must be both precise and valid Validity may further divided in to two types: internal validity and external validity (see Chapter 17) A study is said to be internally valid if the study results provide unbiased information about the individuals included in the study (i.e., the study sample or Interpretation bias Target population For practicality, only certain (groups of) animals may be included Study population Some (groups of) animals may not be present on available lists Sampling frame Only a proportion of the (groups of) animals might be sampled Study sample Complete data may not be collected on some animals Sample available for analysis Fig 18.2 Schematic representation of the process of selection of the sample available to the analyst from the target At each stage only a sub-set may proceed to the subsequent stage The aim of the selection process should be to ensure that all study units present at higher levels have equal probability of selection into the subsequent level, and failure to achieve this may result in selection bias experimental population) (Figure 18.2) Much of the practice of epidemiology is concerned with minimizing bias in order to maximize internal validity External validity refers to the ability to extrapolate from the results of a study to a target population In designing a study there may be tension between attempts to maximize internal validity and external validity Selection of a relatively narrow study population may increase the potential to sample representatively from that population, but may make the study population unrepresentative of the target population Although such situations may not be ideal, internal validity should be preferred over external validity, as there is little value in generalizing incorrect (biased) results Background factors Random variation can be thought of as part of the ‘background noise’ within which an epidemiological study takes place Random error is the resultant distortion in the results of a study Random error often occurs because we usually study a sample of the population, rather than the entire population, but it can occur at any stage in an epidemiological study In the absence of bias, increasing the size of the sample will reduce the random error When the prevalence, relative risk or other measure is calculated, what is really reported is an estimate, based on the available data Ideally, this will approximate the true value, and confidence intervals (see Chapters 12 and 15) provide a guide to the precision of the estimate Wide confidence intervals suggest poor precision (large random error), whereas narrow confidence intervals suggest high precision (small random error) Epidemiological studies are conducted against the background of current scientific knowledge Although science (and epidemiology) is often portrayed as an empirical endeavour in which the scientist is an impassive observer, in reality scientists must make a multitude of decisions and assumptions during the scientific process (e.g., Gilbert and Mulkay, 1984) These actions are taken, at least in part, on the basis of cultural understandings and within the existing scientific paradigm (see Chapter 1) That is, scientific research is less objective and more contingent than may be suggested in formal reports of research Error and bias in current scientific knowledge may lead to error and bias in choice of research question and hypotheses Similarly, incomplete scientific knowledge (the normal state of affairs) may restrict the range of possible research questions available to researchers For example, prior to recognition, in 1984, of the association between Helicobacter pylori and peptic ulcers, hypotheses relating to management of this infection in order to treat gastric ulcers were simply not postulated Subsequently, the discovery of this link has led to studies that have also identified a range of other effects of H pylori, including as risk factors for gastric malignancies (Fock et al., 2013) Interpretation bias Interpretation of scientific data is never completely independent of scientists’ pre-existing beliefs and expectations Errors resulting from the effects of scientists’ (or other users of scientific results) pre-existing beliefs and expectations are called interpretation biases, and Kaptchuk (2003) defines six forms of these biases Most interpretation biases occur after data are collected or during review of other scientists’ work Confirmation bias arises when evidence that supports a scientist’s preconceptions is interpreted differently (more favourably) to that which challenges these notions This also may involve rescue bias, in which evidence at odds with the scientist’s convictions is discounted through selectively applied critical scrutiny 385 386 18 Validity in epidemiological studies Similarly, mechanism bias may result in reduced scepticism of the quality of evidence which is supported by the scientist’s beliefs about the underlying processes Where unanticipated evidence cannot be explained in such ways, auxiliary hypothesis bias may lead the scientist to suggest ad hoc modifications to the original hypothesis in order to explain why the unexpected results have occurred Scientists also vary in the level of evidence required to make a judgement – so-called time-will-tell bias Some may rapidly accept new data as evidence or proof (particularly where they have a vested interest, such as when it is they who have conducted the study), whereas others remain unconvinced by the data (perhaps employing one of the previous forms of interpretation bias) Orientation bias occurs prior to data collection and results in researchers’ preconceptions affecting the design of the study or the collection of data in a way that influences the results This may include decisions concerning which exposures to study, and how these and the outcome(s) are defined It may also result in information biases, such as expectation bias (see information biases, later in this chapter) Other forms of interpretative bias may be defined For example, Gilbert and Mulkay (1984) describe the truth-will-out device in which scientists, faced with evidence that challenges their preconceptions, propose that despite such evidence their own beliefs will be shown to be correct in the long run Cognitive dissonance bias (Sackett, 1979) occurs when belief in a mechanism increases in the face of contradictory evidence Interpretative biases can result from the heuristics (that is, ‘rules of thumb’) used when making decisions under uncertainty Tversky and Kahneman (1974) provide a fascinating description of many such heuristics that may influence judgements by scientists and non-scientists alike Selection bias Selection bias (introduced in Chapter 15) results from systematic differences between characteristics of the subjects available for analysis and the population from which they were drawn Selection bias may arise between the study population and the study subjects during the selection process itself, during the period when the study is being conducted, or after collection of the data Hence, there are many potential sources of selection bias and many types of selection bias have been named Some of these are discussed in more detail below In any study, the aim is to draw conclusions about the target population based on the sample available for analysis Therefore, the aim of any study is to ensure the latter group is truly representative of the former Ideally, the study sample would be randomly drawn from all subjects in the target population and all sampled subjects would have complete data available for analysis However, this is rarely the case and individuals with certain characteristics may be more or less likely to be present in the sample available for analysis It is useful to think of the sampling process in terms of a flow diagram in which individuals may be more or less likely to leave at certain stages (Figure 18.2) The different levels of the sampling hierarchy were introduced in Chapter 13, but are repeated here because a clear understanding of the sampling process is needed in order to appreciate the range of potential sources of selection bias The target population is the population about which the study is designed to make conclusions The study population (also called the study base) is the population that the researchers aim to include in the study and from which a sample is drawn Ideally, the target and study population should be the same However, it may be impractical to sample from the entire target population The sampling (or study) frame is a list of study units (often individual animals) that the researchers believe are in the study population The study sample includes those study units selected from the study frame The sample available for analysis consists of those study units about which sufficient data are collected for them to be included in the study analysis At each stage of the selection process only a sub-set of study units may be included into the subsequent level The aim of the selection process should be to ensure that all study units present at higher levels have equal probability of selection into the subsequent level Selection bias may arise due to the way in which study units are selected from the sample population and/or due to selective loss from the sample population prior to analysis Examples of selection bias arising at each stage of the selection process are described here Selection of the study population from the target population Often it is not possible to select all the animals in the target population for inclusion in a study, so the study population includes only a sub-set of the target population Consider a study that aims to make conclusions about the dogs in a particular geographical area, and that defines dogs attending small-animal veterinary practices in that area as the study population (i.e., the veterinary practices are the source of the study population) It is possible that the dogs treated at these Selection bias practices may not be representative of the general dog population in the region (i.e., the target population) For example, these practices may see disproportionally more of certain breeds or types of animals This may be a consequence of dealing with particular breeders, having a specialist interest in racing greyhounds, or being associated with a rescue shelter and seeing many young animals for vaccination and neutering, and so on Alternatively, they may have a limited geographical scope (e.g., all urban dogs) In such cases the study population is unlikely to be representative of the proposed target population and thought should be given to developing a more representative study population or to redefining the target population For example, consider a study that aims to determine the national prevalence of a particular disease If, for practical reasons, the study only can select animals from just one region of the country it may be best to redefine the target population to include only that region In this way it is clear to the researchers and to the readers that the study results apply directly to this region, and that extrapolation to the rest of the country relies on assumptions about the generalizability of the results to these other areas Identification of the sampling frame In some situations the study population and the study sampling frame may be identical, such as when the study population is the dogs attending a clinic and the sampling frame is the patient records of the clinic However, in other circumstances, an accurate sampling frame does not exist or cannot be accessed or constructed, such as when the study population is the dogs in a particular town and the sampling frame is the patient records of one clinic in that town (which, even if the only clinic in the town, may be unlikely to have records for all dogs in the town) In such situations researchers must rely on alternative sampling methods and, although probability sampling methods (Chapter 13) may be applied, a random sample may not be generated as not all units within the study population have an equal chance of being selected into the study sample Selection of the study sample from the sampling frame A complete sampling frame (i.e., one that includes every individual identified with a unique identifier) often permits selection of the study sample by probability sampling methods The absence of a suitable sampling frame, or the use of a non-probability method to select from a sampling frame, may result in biased selection into the study samples For example, biased selection is likely to occur when only a portion of animal owners asked to volunteer to be involved in a study agree to so, even where those owners have been selected by probability sampling methods Omission of study subjects from the analysis Not all individuals selected for study may provide data for the analysis Hence, selection bias may occur not only when the study sample is selected, but also due the presence of ‘missing’ data from the study sample (see Chapter 11) This type of selection bias occurs due to events during the implementation of the study The most common causes include non-response bias, losses to follow-up and missing data in multivariable analyses Examples of selection biases There are very many types of selection bias defined Some are described in the section below This list, which is adapted from Sackett (1979), is not exhaustive, but serves to illustrate some of the wide variety of ways in which selection bias can occur Competing risks bias may arise when there is ‘competition’ between mutually exclusive events and is most frequent when dealing with causes of death; as an animal can only die once, the risk of a specific cause of death can be affected by the risk of an earlier cause of death As euthanasia and culling are widespread in veterinary medicine, this bias greatly impacts upon estimates of death due to particular disease For example, a veterinary practice with a tendency towards early (pre-operative) euthanasia of horses with severe colic may have a higher post-operative survival rate compared with a practice that tends to operate irrespective of severity Interpretation of the results from such studies should take into account the competing causes of death Healthcare access bias occurs when the study animals are those that are attended by an institution or clinic and they not represent the general population This may arise because: (1) personnel at the institution may have a special interest towards particular cases (popularity bias); for example, an orthopaedic surgeon may have a particular interest in greyhounds and attract a high proportion of this breed to his or her practice, hence conditions affecting greyhounds may be over-represented; (2) difficult or unusual cases are referred to referral centres (referral filter bias); for example, recurrent airway obstruction in horses often can be diagnosed and managed by field clinicians and only difficult, protracted or atypical cases tend to be referred; (3) geographical, cultural or economic factors may limit access by some subjects to diagnostic 387 388 18 Validity in epidemiological studies facilities (diagnostic access bias); for example, owners of pleasure horses may be more or less likely to seek veterinary attention for particular types of health problems in their horses, compared with owners of performance horses Incidence-prevalence bias (also called survival bias or Neyman bias) may occur if ‘survivors’ of a disease are studied, and the exposure is related to prognostic factors or to survival Such exposures will affect the probability that an individual will survive long enough to participate in a study This bias can occur in cross-sectional and case-control studies using prevalent cases For example, a study may wish to examine the association between exposure to a particular factor and the level of milk production in dairy cows However, in a cross-sectional study, the cows most affected by the exposure may have already been culled from the herd prior to the survey, due to low milk production Case ascertainment bias arises when there is a systematic difference in the probability that particular types of cases will be detected and included in a study For example, in order to be included as a case in a hospital-based study, an animal must attend a veterinary clinic However, this may depend on the affluence of the owner, which may be related to a number of risk factors If the control population is not selected from the equivalent population from which the cases arose (i.e., among those animals that would have attended a clinic if ill), bias could result This example could also be considered an example of healthcare access bias Case ascertainment bias may occur due to surveillance bias when there is more intensive surveillance or screening among those individuals or groups known to be exposed compared with those unexposed Exclusion bias arises when individuals in one group are excluded if they have conditions related to an exposure, whereas they are included in the other group For example, an intervention study was designed to assess the effect of post-partum administration of a non-steroidal anti-inflammatory drug (NSAID) on calving-to-conception interval in cattle One group of post-partum cows was randomly selected to receive the NSAID (i.e., the treatment group) However, if animals in the non-treatment group required treatment with NSAIDs for another reason (lameness, mastitis, etc.), they were excluded from the study Hence, the treatment group was more likely to include cows with a range of other diseases, which may increase the calving-to-conception interval and alter the apparent efficacy of the NSAIDs This is often dealt with during the analysis by ‘intention-totreat’ (i.e., non-exclusion) (see Chapter 17) Exclusion bias may be a particular problem in some case-control studies For example, cases may be selected from all animals with a particular condition, whereas controls are selected only from the healthy population (i.e., those without the case disease or other diseases) As the healthy population may have a lower exposure to a range of risk factors that also relate to the disease in question, the effect of these risk factors may be overestimated The healthy-worker effect may cause a lower rate of disease in individuals in particular population sub-groups compared with the general population as a result of biased recruitment or retention of individuals into those sub-groups Classically, this effect is observed in human occupational epidemiology where active workers are typically healthier than the general population This may occur because very unhealthy people may be less likely to be employed or more likely to leave employment Hence, real increased disease due to, say, exposure to toxins at work, may be partly or wholly masked This effect is not well described in veterinary epidemiology but may arise whenever subgroup membership is related to health For example, performance horses and show animals may be healthier than the general populations from which they arise Samples obtained by non-probability sampling (see Chapter 13) may not be representative of the target population and the use of such methods may induce a range of biases The use of volunteers may introduce volunteer bias as these people may wish to participate for reasons associated with exposure or outcome For example, farmers with a herd mastitis problem may be more willing to participate in a study of mastitis, as they may perceive it will benefit them in some way Conversely, farmers with poor management may be less willing to be involved in studies if they perceive they will be criticized Volunteers arising from advertisements, for example in a dog-fancier magazine, may not represent the general population of dog owners Membership lists are often attractive sources for sampling as they may be accessible and well maintained, and the people listed may be enthusiastic participants However, membership bias may arise when samples are drawn from groups whose members are systematically different to the target population This may include, for example, breed societies and interest groups (such as farmer groups) Telephone random sampling bias arises because not everyone has a telephone or is listed in telephone directories and may spend differing amounts of time at or near the phone This has also become a problem because of a greater reliance on mobile phones, which tend not to be listed, and other modern modes of Selection bias communication In intervention trials, selection bias may occur if non-random allocation methods are used For example, French and others (1994) report a study investigating the effect of tail amputation on lamb health and productivity In this study, lambs were allocated to the treatment or control groups on the basis of odd or even ear tag number The research team carried out ear tagging on all but one farm, where the farmer performed this procedure On this farm more female lambs were docked than male lambs; as only female lambs were retained for breeding on this farm, the authors suggested that the farmer might have preferentially allocated ear tags to females that would have ensured that only docked adult ewes remained on the farm Procedure selection bias occurs when certain treatments or procedures are preferentially applied to different risk groups For example, medical management of a disease may be offered in preference to surgical treatment to milder cases Inclusion bias occurs in hospital-based casecontrol studies (or case-other disease studies) when one or more diseases in the controls are related to the exposure in question For example, a study of colic in horses admitted to veterinary hospitals compared these cases with controls drawn from horses presented for all other problems (Reeves et al., 1996a) Many of the control horses were racehorses and performance horses, with almost half of the control group presenting for orthopaedic problems Hence, the apparent reduction in risk of colic associated with consumption of concentrate feed may have arisen because horses undertaking performance-level physical activity were more likely to receive concentrate and more likely to suffer orthopaedic injury Such situations can lead to admission rate (Berkson’s) bias, which arises in hospital-based (and similar) case-control studies when the rate of hospitalization (or of veterinary attention), and hence the probability of inclusion in the study, differs between the cases and controls and is also influenced by exposure Berkson suggested that the relative frequency of disease in a group of patients that has entered a hospital (or otherwise been identified within a health system) is inherently biased when compared with the whole population served by the hospital (Roberts et al., 1978) Jelinski and others (1996) identified that an observed association between abomasal hairballs and abomasal perforating ulcers in calves was most likely spurious due to a Berkson’s bias affecting control selection The authors postulated that two factors, age at death and cause of death, may have been involved in inducing the bias Among the controls (non-ulcer group), 55% died in the first two weeks of life, compared with just 12.5% of the cases Hence, the control calves had less time in which to develop an abomasal hairball compared with the general population Also, most control calves died of enteritis or septic processes Both conditions have a long clinical duration, compared with fatal ulcers, during which time the lethargic and sick calves may be less likely to engage in normal (self- and allo-) grooming and nursing (that involves licking the udder and under belly) – behaviours that may encourage ingestion of hair Diagnostic suspicion bias usually induces information bias, but in case-control studies, knowledge of putative causal exposures may influence identification of cases and controls Matching bias may arise in matched case-control studies Matched controls are selected to be similar to cases with respect to the matching variables and hence, if these variables are truly confounders, the matching variables and the exposure variable will be associated Therefore, the exposure frequency in the controls will be systematically different to that of the underlying population and, consequently, matching has introduced a selection bias, unless the matching is accounted for in the analysis Omission of study subjects from the analysis can result in selection bias Loss to follow-up bias may occur in both cohort and intervention studies if losses are associated with exposure and outcome For example, owners may withdraw their animals from the study and refuse further participation Alternatively, animals may be moved (livestock between farms, or pets with their owners) so that they are difficult to trace For example, performance horses that not recover from a particular injury sufficiently to race again may be either more difficult to trace (if they are sold for other purposes) or more readily traced, if they are repeatedly seen by their veterinary surgeon, compared with those that recover Non-response bias occurs when those people that respond to requests to participate in a study are different to non-responders For example, people whose animals not suffer from a particular disease may be less interested in participating in a study of that disease In such instances, data will be missing for some study subjects Multivariable analyses only include subjects with complete records for all of the variables included in the analysis; unless data are imputed (see later) those with missing data in any of these variables will be excluded If the individuals with complete records not represent the target population, selection bias may occur For example, complete data may be more likely to be obtained from serious cases and the sample analysed may under-represent less serious cases In each of these three situations (loss to follow-up, non-response and missing data) bias only occurs if the risk of missing 389 390 18 Validity in epidemiological studies data is associated with both the exposure and the outcome; for example, missing data may be more or less likely in exposed cases Multiple imputation methods may be used during analysis in an effort to account for the effects of missing data (Sterne et al., 2009) Information bias Gathering of information is essential to all epidemiological studies Data must be collected on the exposures and outcomes of interest, as well as on potential confounders and effect-measure modifiers Information bias arises when errors in measurement result in biased estimates of the parameters of interest, such as measures of frequency or association Information bias may occur if errors in the measurement of an exposure, outcome or confounder are associated with the value of that variable, the value of other variables, or to the errors in the measurements of other variables (Rothman et al., 2008) Information bias is often referred to as misclassification bias when the error is in a categorical variable and measurement bias when the error is in a quantitative variable Misclassification Misclassification (introduced in Chapter 15) is a type of information bias due to error in the measurement of a categorical exposure or outcome variable As noted in Chapter 15, two types of misclassification can be defined: differential and non-differential Differential misclassification occurs when the magnitude or direction of misclassification is different between the two groups that are being compared Non-differential misclassification occurs if the magnitude and direction of misclassification are similar in the two groups that are being compared (i.e., either cases and controls, or exposed and unexposed individuals) Emphasis is often placed on minimizing unpredictability due to differential bias through steps designed to ensure misclassification occurs non-differentially, for example through blinding However, Rothman and others (2008) point out that these approaches not rule out the possibility of unpredictable effects on estimates of relative risks or odds ratios A reduction of continuous variables measured with non-differential error to categories may often result in differential misclassification (Flegal et al., 1991) Similarly, collapsing of categorical exposure variables into fewer levels may change non-differential misclassification to differential misclassification, and Wacholder and others (1991) argue that this may occur regardless of whether the categories are collapsed at the analysis stage or at the exposure assessment stage Dosemeci and colleagues (1990) illustrate that non-differential misclassification of polychotomous variables (i.e., categorical variables with more than two levels) can induce bias both towards and away from the null, depending on the form of the non-differential error Bias towards or away from the null may also occur when both an exposure and outcome are measured with non-differential error when these errors are correlated (Chavance et al., 1992; Kristensen, 1992) An example of where this may occur is when the threshold for reporting both the exposure and the outcome varies between subjects Finally, if the non-differential misclassification is of a confounder, the ability to control for the effect of the confounder is reduced and the observed results for each stratum will lie somewhere between the uncorrected and the corrected measurement of effect, and may misleadingly suggest effect-measure modification However, if the confounder is measured with differential error, the estimate of the effect may lie outside the range of the corrected and uncorrected estimates This effect is further discussed in Rothman and others (2008) The widespread belief that non-differential misclassification predictably results in biased estimation of effect towards the null has led to conclusions that detection of a (statistically and clinically) significant effect in the presence of non-differential bias is sufficient to claim that a larger effect could be expected in the absence of misclassification However, as noted above, non-differential misclassification only consistently results in bias towards the null under quite specific circumstances Hence, even when steps have been taken to try to ensure non-differential misclassification, the effect of misclassification can be unpredictable Measurement Measurement error occurs when a quantitative outcome, exposure or confounder is measured with error Where this occurs randomly, the precision of the measurement is reduced Measurement bias occurs when this error is systematic and this affects the validity of the study As with misclassification, measurement error may be differential or non-differential Examples of information biases As with selection bias, many types of information bias have been identified Some important sources of information bias are described here (adapted from Sackett, Information bias 1979; and Choi and Pak, 2005) The list is not exhaustive and different classifications exist, but it does illustrates some of the many ways in which information bias can arise Outcome identification bias and exposure identification bias may occur due to many types of bias that result in error in measurement of an outcome or exposure, respectively Expectation bias results from systematic errors leading to measurement or recording of information in the direction of the observer’s prior expectations Interviewer bias may arise when interviewers’ conscious or subconscious preconceptions affect the way in which an interview is conducted (see also Chapter 11) Interviewers may phrase questions to individuals from different groups (cases vs non-cases, exposed vs not exposed) sufficiently differently to systematically elicit different results, thus resulting in differential information bias Even when the interviewer only uses exactly worded questions, the question may tend to be repeated more often to one group than another Where possible, interviewer bias may be controlled by blinding the interviewer to the status of the interviewee If the location of an interview can influence the information observed, interview setting bias may occur (e.g., if owners of cases are interviewed in a veterinary consulting room and owners of non-cases are interviewed in the community) Where interviewees respond differently due to knowledge of exposure or outcome status, responder bias occurs Recall bias is a form of responder bias that may occur when information gathering relies on the recollection of the study subjects (or, in the case of animals, their owners or keepers) For example, in a casecontrol study investigating risk factors for horse falls in the cross-country phase of eventing, Murray and others (2004b) found that riders’ ability to recall dressage penalty scores was influenced by the time between the event and the administration of the questionnaire, and by their level of performance, with riders who performed well reporting more accurately their scores compared with those who performed poorly Recall bias is a particular issue in case-control studies because the recall of cases and controls may vary (both in amount and in accuracy) due to their knowledge of their outcome status Hence, recall bias is usually differential Recall bias may also arise in other types of studies (e.g., cross-sectional studies) when they rely on recall Obsequiousness bias may result from subjects altering their responses to better match those they perceive to be desired by the researcher Unacceptability bias (social undesirability bias) may arise when collection of information results in discomfort or embarrassment and hence may result in such measurements being avoided or under-reported This may be further classified as unacceptable disease (or exposure) bias where the disease (or exposure) being measured may have social (such as embarrassment or stigmatization) or legal consequences (such as illegal activities or notifiable diseases) Such effects also may be called faking good bias where socially undesirable responses are incorrectly reported or under-reported Faking bad bias may occur if respondents report greater levels of disease in order to appear worthy of assistance or support Faking bad bias may occur prior to treatment only to be replaced by faking good bias following treatment The behaviour of study subjects (be they human or animal) may alter when they are aware they are being observed, resulting in attention bias Where an individual’s past exposure is known, diagnostic suspicion bias (also called diagnostic bias) may result in differential application, intensity or outcome of diagnostic procedures Similarly, exposure suspicion bias may occur when knowledge of an individual’s outcome status affects the application, intensity or outcome of ascertainment of exposure information Measurements using instruments may be affected by instrument bias if incorrect calibration or maintenance results in systematic error, or by apprehension bias if stress associated with the procedure affects the measurement (e.g., measurement of heart rate) When measurement requires the use of scales (e.g., Likert scales: see Chapter 11) respondents may avoid the extremes and tend to provide answers towards the middle of the scale (end aversion bias) Long questionnaires may result in response fatigue bias leading to inaccurate or incomplete completion of (often the latter) parts of the questionnaire Data capture bias and data entry bias may occur when practices for the acquisition or database entry of data vary between different locations This may result in spurious differences between these locations For example, different countries may have different systems to capture or enter national data relating to the occurrence of certain diseases which may influence the assessment of the relative occurrence of diseases between countries Controlling information bias Many forms of information bias may be prevented through careful planning, or their affects may be measured and accounted for when conclusions are drawn from a study The diversity of sources of potential information biases highlights the need for their identification and consideration as part of study planning and design Useful strategies include: blinding of 391 392 18 Validity in epidemiological studies interviewers and observers to the subjects’ exposure or outcome status (see Chapter 16); the use of standardized questionnaires and measurements (see Chapter 11); ensuring use of explicit and objective criteria of exposure and outcome assessment, which are equally applied across all subjects and all study sites; and validation of data (or sub-sets of data), particularly where they may be affected by biased responses by study subjects (such as recall bias or unacceptability bias) or instruments It may be possible to correct for the effects of information bias following data collection if information on the probabilities of misclassification are available (e.g., sensitivities and specificities) or if validation studies have been performed Hill and Kleinbaum (2005) and Dohoo and others (2009) summarize some methods to correct for misclassification Approaches have been also described to attempt to correct for the effects of measurement error and are described in more detail in Dohoo and others (2009) Statistical interaction and effectmeasure modification The term statistical interaction is often referred to simply as interaction although this may lead to confusion with biological interaction (see Chapter 5); statistical interaction also is often used interchangeably with effect-measure modification However, VanderWeele (2009) highlights a distinction between these (related) terms According to VanderWeele (2009), the definition of statistical interaction (see Chapters and 15) does not privilege one variable of interest (e.g., referred to as x in Chapter 15) over the other (referred to as y) and it is the causal effect of the two exposures together that is of interest In contrast, VanderWeele (2009) refers to effect modification when the causal effect of one exposure (say, x), within strata of the other exposure (y), is of interest That is, there is asymmetry in the roles of x and y in that only the effect of x on the outcome is evident; the role of y simply concerns whether the effect of x varies across strata of y For example, age may modify the effect of vaccination against canine parvovirus (Godsall et al., 2010) Young unvaccinated dogs have a greater risk of parvovirus infection compared with young vaccinated dogs, but this difference is less evident in older dogs, possibly due a protective effect in older dogs of past low-level environmental exposure Effect-measure modification is often also referred to as heterogeneity of effects, sub-group effects or simply as effect modification However, effect-measure modification is preferred over effect modification as it is possible to distinguish modification on two types of measures of effect: riskratio modification and risk-difference modification Importantly, VanderWeele (2009) demonstrates that effect-measure modification and interaction only co-occur under specific circumstances, such as when the effects on the outcome of the effect modifier, or one of the interacting variables of interest, are not confounded by another (measured or unmeasured) variable The validity of conclusions about the causal effects of one or more exposure variables is dependent on consideration of relevant statistical interactions and/ or effect-measure modification and clear presentation of this information However, Knol and VanderWeele (2012) contend that many authors not provide sufficient information to enable readers to adequately interpret effect modification and interaction, and they provide recommendations for the presentation of the results of these analyses Confounding Confounding (introduced in Chapters and 15) is defined as occurring when the measure of effect of an exposure on the risk of the outcome is distorted due to the association of that exposure with other, extraneous, variable(s) that influence the outcome, with such extraneous variables referred to as confounders or confounding variables (Porta, 2014) In order for the results of a study to be valid, it is therefore essential that the effects of confounding be adequately controlled for within the study and/or the analysis of the results As confounding results from the confusion of the effects of extraneous variables with those of the exposure of interest, it is a logical requirement that to confound the relationship between the exposure and the outcome, the extraneous variable must (1) be a risk factor for the outcome, and (2) be associated with the exposure in the study population A third requirement is (3) that the confounder must not be affected by the exposure or the outcome In the example provided in Chapter (see Figure 3.5b), pig herd size (the confounder) is: (1) a risk factor for respiratory disease because, for example, larger herds may be kept at higher density, with poorer biosecurity or be more likely to have pathogens introduced than in smaller herds; (2) associated with fan ventilation because larger pig herds are more likely to require ventilation due to poorer natural ventilation in larger sheds; and (3) is not affected by the exposure (having fan ventilation does not change the size of the herd) or outcome (if it is reasonable to assume that respiratory disease does not affect the size of the herd) 850 Index Food chains (cont’d) trophic cascades 152 trophic levels 152 Food, competition for 142 Food poisoning 53, 172 Food quality 21 Food webs 152–154 animal size and 153 connectance and 153 Foot-and-mouth disease airborne spread 126–128 as a plague 7–8 carrier state 99 cyclical trend in 185 epidemics in the UK 60 eradication 605 neighbourhood analysis 246 nucleotide sequencing 34, 97 outbreaks definition of 61 in Paraguay 185 in the UK 7–8, 12, 13, 60 post-epidemic serosurveillance 246, 285 serotypes 97 survival 121 topotypes 97 vaccination 606, 615 Footrot 108, 366 Forecasting systems, for fluke 526–527 Formations, of vegetation 139 Forward selection 509 Foxes chlorinated hydrocarbon poisoning in 59 movement of 160 rabies in 148, 155, 169, 523–525 Fracastorius, Hieronymous (Fracastoro, Girolamo) Fragmentation, of habitats 163 Framingham Heart Study 48 Free-roaming populations 62 Frequency polygon 253 Frequentist statistics 297 Friedrich William, King of Prussia 463 Frosch, Paul Fucosidosis 613 FUS see Feline urological syndrome g Gaia hypothesis/theory Galen, Claudius 3, 31, 142 Galileo (Galilei, Galileo) 43 ‘Sin of’ 538 Gastroenteritis, of pigs 178 Gause, Georgyi Frantsevitch 142, 149 Gauss, Carl Frederich 254 Gaussian distribution see Normal distribution Gaussian plume model of atmospheric diffusion 126 General adaptation syndrome 105 Generality, of a theory 535 Generalized additive model 514 Generalized linear mixed model 516 General linear model 499 Generation time 119 Gene-modified models see Transgenic models Genes, role of in disease pathogenesis 34 Genetic determinants in the host 92 in infectious agents 94–97 Genetic diseases, animal models of 196 Genetic epidemiology 33 Genetic markers, in animal identification 464 Genetic improvement, in disease control 612–614 selective breeding 614 transgenic biology 614 Genetic reassortment 95, 96 Genetic screening 612 candidate genes 612 linked markers 612 Genotype see Genetic determinants Geographic base maps 80–84 Geographical information systems 244–248 Geometric mean titre 422 GIS see Geographical information systems Glanders 10, 604 Glass’s delta 378 Global Health 24, 40 Global surveillance see Surveillance Global warming, as a determinant 103 Goats, domestication of Goitre, in budgerigars 77 ‘Gold standard’, in diagnostic tests 431 absence of 448–450 Gompertz’s Law 193 Google™, as a source of surveillance data 471 Gosset, William 257 Government veterinary organizations, as sources of data 464–465 Grass sickness 18, 30 Graunt, John 10, 520 Gray collie syndrome 612 Grazing, and disease control 610 Grebes, parasites in 151 Greek medicine 2, 4–6, 629 ‘Greenhouse effect’ see Global warming Grey literature 401, 478 Grey-scale figures 266 Gross margin analysis 579 Group effects, in clinical trials 368 Group behaviour, of animals 147 h Habitat 138 HACCP (Hazard Analysis Critical Control Point) 21, 549–550 Haeckel, Ernst 138 Haemaphysalis spinigera 164 Haemobartonella spp see Candidatus spp Haemonchus contortus 47 Haemophilia, canine 92 Half-life, of antibodies 424 Halley, Edmund 11 Hares, Brucella suis from 469 ‘Harking’ 379 Haygarth, John 363 Hazard ratio 322, 400 Healers, primitive 1, Healex index 586 Health definition 23 ecosystem 40 Health schemes companion animals birds 601 cats 601 dogs 600 ferrets 601 horses 602 rabbits 601 Index national schemes accredited/attested herds 597–598 health schemes 598–599 private health and productivity schemes action level 588 components 588–591 beef cattle 594–597 dairy cattle 588–591 interference level see action level pigs 591–592 sheep 592–593 justification 588 objectives 586–587 structure 586–588 targets 587–588 uptake 588, 601 Healthy-worker effect 344, 388 Heart disease, in dogs 93 Heartwater, risk of introduction into the US 126 Heartworm see Dirofilaria immitis Hedges’ g 378 Hefts, as epidemiological units 271 Hekistotherms 139 Helicobacter pylori, and gastric cancer 110, 385 Helminthiasis climate change and 103 in dogs 620 environment and 30 Henle-Koch postulates see Koch’s postulates Herd effects see Group effects Herd-level testing see Aggregate-level testing Herd management software 242 Herpesvirus, causing malignant catarrhal fever 30 Herschel, John Frederick 38, 43 Heuristics 386 Hierarchical statistical model see Mixed effects model Hierarchic codes 212–213 Hierarchic database model 230 Higgin’s & Thompson’s H heterogeneity index 334 Hill, Austin Bradford see Hill’s criteria Hill’s criteria 55–56 Hippocrates of Cos 5, 629 Histogram 252, 264–265 HIV/AIDS 35, 124, 138 as a population regulator 146 Home range 147 Homeopathic remedies, assessment of 361 Hookworm 122 Horizontal transmission 115–129 Hormonal determinants 91 Horse cultures Horses see also specific diseases of horses grass sickness 18, 30 health programmes 602 motor neuron disease 187 Hosmer-Lemeshow goodness-of-fit test 514 Host(s) aberrant see secondary accidental see incidental amplifier 116 dead-end see incidental definitive 115–116 determinants 89–94 environment within 131 final 116 hibernating 116 incidental 116 infectiousness 118 link 116 intermediate 116 maintenance see primary microbial colonization 100–101 mobility, and transmission of infection 125–126 natural see primary paratenic 116 primary 116 reservoir 116 secondary 116 susceptibility 118 Host/parasite relationship, in disease control 617 Housing, as a determinant 104 Household surveys, to estimate population sizes 62 Humanosis 29, 637 Humboldt, Alexander von 139 Hume, David 43, 56 Humoral pathology Humours, of the body Husbandry, as a determinant 86 Huxley, Thomas Henry 45 line 139 Hybridization, nucleic acid 34 Hyperendemic disease 58 Hyperkeratosis 55 Hypocupraemia 80 Hypomagnesaemia 52, 54, 98, 103, 179 Hypospadias 197 Hypotheses characteristics 536 definition 44 formulation 53–56 statistical testing of vs estimation 300 significance tests 297 i Identification of animals 62, 463 Immunity, sterile 98 Immunization history of natural 607–608 feedback 608 Immunological status, vertical transmission and 129–130 Immunosuppression, as a microbial maintenance strategy 133 Inapparent infection 98 Incantation, in disease control Incidence cumulative incidence 67–68 confidence interval 259 incidence density 70 incidence rate 68–70 confidence interval 261 relationship to prevalence 70–71 Incidence density see Incidence Incubation period 45, 118 Index case 173 Indian Ocean Dipole 102 Indicator variables see Dummy variables Indirect adjustment see Adjusted measures of disease occurrence Individual-value plots 264 Induced models see Experimental models Induction of cancer 110–112 in causal inference 43 ‘Inductivist turkey’ 44 851 852 Index Infection(s) agents 94–101 antigens on 429 biomes affecting 139–142 classification 100, 146, 521 genotypic changes in 94–97 hazards to 131–132 resistant forms 133 basic reproductive number 169–172 characteristics of pathogens 120 clinical 98 effective contact 120–121, 169, 172 emergent/emerging 14, 161 extrinsic incubation period 119 generation time 119 gradient of 97–98 group types 89 host and vector types 115–118 host/parasite relationships 134, 617 inapparent (silent) 98 incubation period 118 latent 99–100 latent period 118 maintenance of environment in host 131 extension of host range 134–135 external environment 131–132 persistence in host 133–134 ‘rapidly in, rapidly out’ strategy 133 resistance and 133 strategies 132–135 mixed 17, 106–108 novel 15–16 outcome of 98–100 subclinical 98 transmission 115–137, 147 aerial 123–124 aerosol see aerial ascending 129 biological 117–118 characteristics of hosts 118–120 characteristics of pathogens 120 coitus 124–125 congenital 129 contact 124 cyclopropagative 118 developmental 118 direct 115 droplet nuclei 126 epidemiological interference in 151–152 factors in 118–121 food webs and 153–155 germinative 129 hereditary 129 horizontal 115–129 host mobility 125–126 iatrogenic 124–125 indirect 115 ingestion 123 inhalation see aerial transmission inoculation 124 lateral see horizontal long-distance 125–129 oral route of 121 at parturition 129 percutaneous 122 propagative 118 pseudovertical 115 respiratory route of 122 salivarian 118 stercorarian 118 transovarial 130–131 trans-stadial 130–131 vectors 117–118 vertical 129–131 via embryos 129 via skin, cornea and mucous membranes 122–123 vectors 117–118 virulence 94 Infectious disease see also Infection as a regulator of populations 145–147 complex 17, 106–108 diagnosis 457 emergent/emerging 14, 161 factors in control 616–623 novel 15–16 Infectious keratoconjunctivitis 101, 123 Infectiousness, of hosts 118 Infectivity, of pathogens 120 Influential data 498–499 Influenza viruses antigenic drift in 96 antigenic shift in 96 classification 428 serological tests 209 see also Equine influenza ‘Infodemiology’ 471 Informatics 638 Information, definition of 234 Information systems see Recording schemes Inhalation, infection by see aerial transmission Inheritance Mendelian 89, 90, 92 multifactorial 89–90 polygenic 89 Inhibitors, in serological tests 429 ‘Inner perfection’, of a theory 535–536 Inoculation, infection by 124 Instrumental knowledge 43 Insurance companies, pet, as sources of demographic data 62 as sources of surveillance data 468 Intangibles, in economics 565, 567, 581 Interaction 106–112 biological 108, 638 mechanical 108 statistical 109–110, 328–330, 392, 638 Interactive knowledge 43 Interactive site segregation 150 Interference, epidemiological 151–152, 611 Interference levels see Action levels Interherd 242 Internal parasitism, niches 150 Internal rate of return 581 Internal time component 68 Internal validity 368, 384 Internet, The as a source of surveillance data 471 sites relevant to epidemiology 648–649 Interval estimation 257–262 see also specific parameters Interval scale of measurement 202 Interviews 226–227 computer-assisted 227 semi-structured 479 telephone 226–227 Index Intracellular parasitism, as a microbial maintenance strategy 150–151 Intra-class correlation coefficient 518 see also Sampling, rate of homogeneity Intrinsic determinants 86 Intuition, role of, in accepting a hypothesis 44 Investigation, epidemiological, types of 32–35 Irish Potato Famine 102 Isomorbs 83 Isomorts 83 Isoplethic maps 83–84 Iwanowsky (Ivanovsky), Dmitri j Jaagsiekte 597, 614 Jackknife residuals 496 Jaw tumours, in sheep 80, 101 Jenner, Edward 5, 30 Johne’s disease 123, 224, 290, 355 JP15 campaign 16 k K88 antigen 92, 108 KAP survey see Knowledge, Attitude and Practice survey Kaplan-Meier analysis 74 Kappa statistic 450–452 Kendall’s Threshold Theorem 168–169 Kendall’s waves 175–177 Keratoconjunctivitis, bovine 101, 123 in hens 104 superficial, in dogs (Uberreiter’s syndrome) 102 Kermack-McKendrick model 521 Ketosis 18, 47 Key-Gaskell syndrome see Feline dysautonomia Kikuyu grass poisoning 55 Kilbourne, Fred Knacker yards, as sources of data 467 Knowledge Attitude and Practice survey 479 critical 43 interactive 43 instrumental 43 Koch, Robert see also Koch’s postulates Koch’s postulates 45 Koppen, Waldimir 139 Kuhn, Thomas 5, 44 Kyasanur Forest disease, landscape epidemiology of 162–164 l Lameness in cattle, survey of 223 in pigs, assessment of 203 Lancisi, Giovanni 5, 30, 606, 614 Landscape epidemiology 158–165 Landscapes 156 Laplace, Pierre-Simon 12 Lassa fever 117, 178 Latent infection 99–100 Latent period 118 Lateral transmission see Horizontal transmission Law (s) of diminishing returns 566 scientific 536 Lebeau’s method of lifespan adjustment 194 Lee waves 126 Leibnitz, Gottfried Leptospira spp see Leptospirosis Leptospirosis epidemic 173 genetic resistance 614 hosts 30 landscape epidemiology of 161 seasonal trends 178–179 Leukaemia, in cats, spatial clustering of 186 Leverage 496 Liber de Wintonia (Book of Winchester) see Domesday survey Lice, niches of 150 Life cycles evolution of 153 of infectious agents 115 Life-table analysis 73–74 Lifespan adjustment 194 Life zones 140 Likelihood 638 ratios 436–441 ratio test 508, 510, 512, 513 Likert scale 220 Lind, James 361 Line of best fit see Regression line Linear mixed model 515 Linear regression 183, 492 Linked markers 612 Link function 511 Listeria spp see Listeriosis Listeriosis 89, 342 Live birth rate 77 Livestock health schemes accredited/attested herds 597–598 national 598–599 private 586–597 beef cattle 594–597 dairy cattle 588–591 pigs 591–592 sheep 592–593 medicine, future of 628 population of Great Britain 18 production, as an economic process 566 world populations of 17 Lobb, Thomas Location maps see Point maps Loeffler, Friedrich Logarithms 641 Logistic model, in ecology 144 Logistic regression 510 diagnostic checking 513–514 model selection 512 Logistic transformation 510 Logit transformation 510 Lognormal distribution 256 Longevity, studies of, in animals 75 Longitudinal studies 320 Longitudinal surveys 37 Lorenz, Konrad 40 Lotka, Alfred see Lotka-Volterra equations Lotka-Volterra equations 148 Louis, Pierre-Charles-Alexander 12 Louping-ill 70 Louse infestations 150 Lucké’s frog virus 148 Lucretius Carus, Titus Lymphocytic choriomeningitis 130 Lysogeny 95 Lysosomal storage disease 196, 613 m Macroclimate see Climate Macro-epidemiology 35 Macroparasites definition 146 mathematical models 521 Maedi-visna, control of 618 853 854 Index Magnesium deficiency 52, 86, 179 levels 54, 86, 179 Malignant catarrhal fever 30, 477 Malignant hyperthermia see Porcine stress syndrome Malthus, Thomas Robert 146 Management, as a determinant 105 ‘Manhattan Principles’ 24 Mannheimia spp see Pasteurella spp Mannosidosis 613 Manpower, veterinary, in disease control 617, 618 distribution of 21 Mantel-Haenszel procedure 332–333 Maps choroplethic 82–83 demographic base 80 distribution 82 geographic base 80 grid-based 245 isoplethic 83–84 participatory 480 point (dot or location) 81 proportional circle 82 proportional sphere 82 raster-based see grid-based vector-based 245 Margin of clinical equivalence 374 Marginal cost 572 Marginal analysis 577 Marisa cornuarietis 149 Masking, in clinical trials see Blinding Mass screening 37 Mastitis control 590, 597 economic study of 577–579 environmental 88 summer 107 Mastomys natalensis 116 Matching 334, 351, 395 Maternal mortality ratio 77 Mathematical models 520–539 Mathematics, notation and terms 641–642 Matrices, models using 530–532 Matrix scoring, in participatory epidemiology 479–480 Maximum likelihood 494, 510 McNemar’s change test 313 Mean 253 arithmetic 422 confidence interval for difference between two means 303–304 single sample 257–258 geometric 422 Measurement error 390 Measurements see Data, measurements Mechanistic causal approach 48 Median 253 confidence interval for difference between two medians 308–309 single sample 258 Medical records, as sources of data 231–232 Meerkats, tuberculosis in 93 Megatherms 139 Mendelian disorders 92 Merriam, Clinton Hart 140 Mesothelioma 193 Mesotherms 139 Meta-analysis 205, 375–380, 412–416 Metabolic profiles 591, 593, 594 Metadata 243 Metaphysical medicine Method of agreement 54 of analogy 54–55 of concomitant variations 54 of difference 53–54 Miasmata (miasmas) 4, 5, 6, 49, 123 Microbiome 141 Microbiota 141 Microchips, in animal identification 62, 464 Microclimate see Climate Micro-epidemiology 35 Microparasites definition 146 mathematical models 521 Microtherms 139 Military campaigns, spread of disease by Milk yields, in productivity schemes 588 Mill, John Stuart 43, 53–55 Minimal disease methods 614 Misclassification 330–331, 390 Missing values, in data analysis 228–229, 387 Mixed effects logistic regression 519 Mixed effects model 516 Mixed infections 106–108 Models and modelling 38 biological 189–199 types of 189–190 causal 50–52 mathematical 520–539 types of 521 statistical 492–519 additive 109, 502 Moduli 311, 642 Molecular epidemiology 34–35 Monitoring definition 37, 457, 638 disease 20 see also Surveillance Monte Carlo models 242, 323, 528–529 Moran effect 144–145 Moraxella bovis see Keratoconjunctivitis, bovine Morbidity definition 62 measures of 67–80 true and false changes in 180 Mortality cumulative mortality 72 confidence interval 260 definition 62 human 11 caused by the Black Death 59 mortality rate 72 Mosquito, in yellow fever, transmission 158 Mouse, Lassa virus infection in 117, 178 Movement, of animals in control of disease 609–610 as a determinant 125–126 Moving averages see Rolling averages Multifactorial causes 50–52 Multifactorial diseases 19–20, 88 Multifactorial inheritance 89–90 Multi-level model see Mixed effects model Multinomial distribution 260 Multiple linear regression see Multivariable linear regression Multiple logistic regression 511–512 Index Multivariable analysis, definition 317, 499, 638 Multivariable linear regression 499 Multivariate analysis, definition 317, 499, 638 Muscle, haemorrhages in 30 Mutation, in infectious agents 94–95 Muzzle patterns, in animal identification 464 Mycobacterium paratuberculosis (previously M johnei) see Johne’s disease Mycobacterium tuberculosis see Badgers, tuberculosis in; Opposums, tuberculosis in; Tuberculosis Mycoplasma spp 30, 46 Myxomatosis 179 n Naegleria fowleri 94 NADIS see National Animal Disease Information System NAHMS see National Animal Health Monitoring System National Animal Disease Information System 237–239 National Animal Health Monitoring System (US) 239–241 National Companion Animal Surveillance Program 244 National Scrapie Plan (GB) 612 Natural experiment 32–33 Natural history of disease 17, 30– 31, 35, 139, 237, 534, 616 Natural law, universe of 4–5 Natural models see Spontaneous models Nature, balance of 142 NCASP see National Companion Animal Surveillance Program Nebuchadnezzar 361 Negative-herd retesting 446 Negative models 189 Neisseria gonorrhoeae 96 Nematodiriasis, control by alternate grazing 610 Nematodirus spp see Nematodiriasis Neorickettsia helminthoeca 153, 154 Nested data structures 516 Net Present Value 579, 580 Network(s) database model 230–231 general 153 mathematical models using 532–534 surveillance 475 New Zealand EpiMAN decision support system, development in 242–243 leptospirosis in 54 tuberculosis in opossums in 615 Newcastle disease outbreaks 61, 94, 176 virulence 94 Niches, ecological 150–152 filling, disease control and 611 fundamental 150 realized 150 Nidality 159–161 NNT see Number needed to treat Noise, as a determinant 101 Nomenclature of disease 205–207 Nominal scale of measurement 202 Non-compensating error 271 ‘Non-naturals’ Non-parametric tests 300 Nonreactive models see Negative models Non-sampling error 271 Normal distribution approximations 257 definition 254–255 Normative economics 569 North Atlantic Oscillation 102 Nosoareas 159 Nosocomial infections 96 Nosogenic territories 159 Nosologia Methodica 211 Novel infectious diseases 15–16 Nuer 3, 479 Null hypothesis 297–298 Number needed to harm 367 Number needed to treat 366–367 Numerical rating scale see Ordinal scale of measurement Nurmi phenomenon see Interference, epidemiological Nuthatches, character displacement in 150 Nutrition, as a determinant see Diet, as a determinant Nutritional epidemiology 35 o Observational studies 319–360 bias 330–335, 344–346, 352 case-case 357 case-case-time 359 case-chaos 359 case-cohort 357 case-control 319 design 346–352 hospital-based 350 nested 356–357 case-crossover 358 case-only 358 case-other disease 358 case-time-control 358–359 cohort 319 design 340–346 comparison of types 321 confounding 331, 332–335 cross-sectional 320 design 352–354 ecological 354–355 interaction 328–330, 505–506 longitudinal 320 Mantel-Haenszel procedure 332–333 matching 334–335, 351 frequency 334, 351 individual 334, 351 misclassification 330–331 multivariable techniques 499–500 nomenclature 320 panel 355 parameters estimated in see also specific parameters aetiological fraction see attributable proportion attributable proportion 327–328 attributable risk 325–327 cross-product ratio see odds ratio odds ratio 323–325 prevalence ratio 324 rate ratio see relative risk relative odds see odds ratio relative risk 321–323 risk ratio see Relative risk power calculation 336–337 prospective 320 repeated surveys 355 retrospective 320 sample size determination 335–36 855 856 Index Observational studies (cont’d) spatial correlation 354 time series studies 355 types 319–321 upper confidence limits 337–338 Occam (Ockham), William of see ‘Occam’s razor’ ‘Occam’s (Ockham’s) razor’, 45 ‘Occupation’, animal, as a determinant 105 Odds ratio 323–325 calculation of confidence intervals for 324–325 logarithmic-based method 324 test-based method 325 calculation of upper confidence limits 337–338 definition 323 diagnostic 434 Office International des Epizooties (World Organisation for Animal Health) 23, 60, 237 OIE see Office International des Epizooties Oncogenes 111 Ondiri disease see Bovine petechial fever One Health 24–25, 40 One-tailed tests 298–299 Opossums, tuberculosis in 615 Opportunistic pathogens 19, 100–101 Opportunity cost 568 Orbiviruses, antigenic change in 96 Ordinal scale of measurement 202, 364, 430–431 Ornithodoros moubata 130 Orphan models 189–190 Origin of Species see Darwin, Charles Osteosarcoma in children 93 in dogs 93 Ostertagia spp see Ostertagiasis Ostertagiasis control of, by mixed grazing 610 models of 533 Ostler, William 24 Outbreak of disease definition 60–61 investigation of 623–628 epidemiological approach to 626–628 Outliers 498–499 Overmatching 334, 350–351 Oxytrema silicula 154 Ozone, depletion of, as a determinant 102 p Pain, assessment of 203, 204 Palliative care 604 Pandemics 59 Panel studies 355 Panel surveys 352 Papyrus of Kahun Parallel testing 444 Paramecium spp., population growth curve of 142 Parameter 252, 641 Parametric tests 299 Parasitic mange, eradication of 10 Parasitism, intracellular 150–151 Parasitocenosis 159 Paratuberculosis see Johne’s disease Parelapthostrongylus tenuis 149 Partial farm budgets 579 Pareto charts 263 Participatory appraisal see Participatory epidemiology Participatory epidemiology 34, 475–483 Participatory mapping 480 Particle:infectivity ratio 120 Parturition, infection at 129 Parvovirus, canine 59, 92 Passive data collection 235, 460, 475 Passive surveillance see Surveillance ‘Passports’, horse 64 Pasteur, Louis 5, 362 Pasteurella haemolytica 18 Pasturella spp., in mixed infections 107 Pasteurellosis 51 Patch ecological types of 156 parameters and disease dynamics 165 Patent ductus arteriosus 196 Pathogenicity 94 ‘Pathogenicity islands’ 96 Pathogens characteristics of 120 endogenous 100 exogenous 100 opportunistic 100–101 Pavlovsky, Evgeny Nikanorovich 159 PCR see Polymerase chain reaction Peirce, Charles Sanders 43 Pellagra 361 Pentateuch, dietary laws of 540 Peptide mapping 34 Percentiles see Centiles Performance-related diagnosis 20 Perkins’ tractors 363 Personalized medicine 629 Petitio principii 538 Pet-food manufacturers, as sources of data 469 Pet shops, as sources of data 469 ‘PETS’ travel scheme 605 Pharmaceutical sales, as sources of data 468 Phase variation 95 Pie charts 263 Piece-wise regression 502 Pig Health Scheme (UK) 599 PigCHAMP 242, 594 PigORACLE 242 Pigs see also specific diseases in pigs adenomatosis 17 atrophic rhinitis 450–451 disease eradication 615 enzootic pneumonia 324–325 foot lesions 104 gastric torsion 105 gastroenteritis 102, 179 health and productivity schemes 591–592 herd structure 19 leptospirosis 59 lymphosarcoma 112 parvovirus 20, 89 porcine stress syndrome 106 reproductive failure 88 respiratory disease 324–325 stillbirths 53 Placation, to remove demons Placebo/placebo effect 363 by proxy 363 Plague(s) biblical definition occurrence 49, 520 Planetary health 31, 138 Plants and animals, relationships between types 152–155 Plasmids 96 Plasmodium spp 116 ‘Play the winner’ rule 370 Index Pleurisy, in sheep, temporal occurrence 181 Pleuropneumonia 9, 30, 136 eradication 9, 604 Pneumonia acute bacterial 71 causes 52 enzootic 324–325 in sheep, temporal occurrence 181 Point (dot or location) maps 81 Point estimation 257 Poisson distribution 255–256 confidence interval for 259–260 Poisson, Siméon-Denis 12, 255 Policy briefs, evidence-based advocacy briefs 604 objective briefs 604 Poliovirus immunization, natural 152 Pollution, environmental 101, 198 Polygenic inheritance 89 Polymerase chain reaction 34, 430 Popper, Karl 44, 536 Population(s) of cats and dogs 63 competitive exclusion 149 contiguous 62–65 control of, by disease 145 control of, implications for disease 148 density-dependent competition 144 dispersal 144 distribution 139–142 biomes 139–142 vegetational zones 139 experimental 368–369 extinction 145 group behaviour 147 home range 147 of horses 64 interspecific competition 149 intraspecific competition 148 life zones 140, 141 of livestock 66 Moran effect 144–145 niches 148–150 predator/prey relationship 145, 155 pyramids 90, 153 regulation of size 142–148 at risk 61 separated 65–67 serological estimations and comparisons in 424–427 size competition for food and 142 control by competition 142–144 estimation of 62–65, 66–67 logistic equation 144 predation and 145 regulation 142–148 social dominance 147 Wynne-Edwards hypothesis 147–148 structure of 62–67 study population 270 surveys of prevalence in 270–295 sympatric species 149 synchrony 145 target population 270, 386 of wild animals 64–65 Porcine stress syndrome 106 PorkCHOP 242 Positive economics 569 Positivism 43 Postcapture myopathy syndrome 106 Posterior probability 43 Post-test probability see Posterior probability Potato Famine, Irish see Irish Potato Famine Pott, Percivall 110 Poultry, disease eradication in 615 see also specific diseases of poultry Poultry Health Scheme (UK) 599 Poultry slaughterhouses, as sources of data 467 Power, statistical calculation, in observational studies 336–337 definition 298 PPS sampling see Sampling, using probability proportional to size Precautionary principle 543 Precipitating factors 52 Precision 209, 257, 384 of a theory 535 Precision medicine 629 Predation 145, 155 Predictive value 434–436 Predisposing factors 52 Pre-emptive slaughter (culling) 606 Pregnancy rate 77 Premium Sheep and Goat Health Scheme (UK) 599 Prepatent period 118 Prescriptive screening 37 Pre-test probability see Prior probability Prevalence 67 annual 67 calculation of incidence rate from 71 confidence intervals for difference between two samples 313 one sample aggregate-level 292 cluster sampling 293–294 simple random sampling 291–292 imperfect tests 292 low prevalence 292–293 small samples 292 stratified sampling 293 systematic, sampling 293 definition of 67 detectable 285 determination of sample size to estimate difference between two samples 313–314 one sample aggregate-level 289–290 cluster sampling 277–284 simple random sampling 275–277 stratified sampling 277 systematic sampling 277 presence of disease 284–289 lifetime 67 period 67, 352 point 67 relationship to incidence rate 70–71 Prevalence models 521 Prevalence ratio 324 Prevalence studies 339 Prevention of disease 586 primary 604 secondary 604 tertiary 604 Preventive medicine programmes 586 Primary case, in epidemics 173 Primary data collection 219 Primary determinants 86 857 858 Index Principle of parsimony see ‘Occam’s razor’ Prior probability 43 Probability conditional 251 definition 251 interval 257, 639 Probing, in participatory epidemiology 479 Probit transformation 510 Production as an economic process 565 shortfalls 588 Production functions 565–567 Productivity, of cattle 18 historical improvements in 17 Professions, characteristics of 40 Pro-formas, as sources of data 232 Progressive retinal atrophy 612 Proportional circle maps 82 Proportional morbidity rate 77 Proportional morbidity ratio 192 Proportional mortality rate 77 Proportional piling, in participatory epidemiology 479 Proportions confidence interval for difference between two proportions 313–314 multinomial, proportions 260–261 single sample 258–259, 433 Agresti-Coull method 259 exact method 259 Wald’s method 259 Wilson’s method 259 sample-size determination difference between two proportions 313–314 single sample 275–276 Prospective studies 320 Prospective surveys 37 Protection zones, in disease control 610 Proteus spp., conjugation in 96 Protoporphyria 613 Pruritus, in dogs 87, 364 Pseudomonas spp iatrogenic transmission of 124 transduction in 96 Psittacines, preventive medicine in 601 Psychometric approach to measurement 205 Psychosocial epidemiology 35 Public health, veterinary 21 Pulex irritans, survival of infectious agents in 134 Pulmonary disease, chronic canine 198 Purposive sampling 272 Pyometra, canine 337 Pyramid of numbers 153 q Q fever 53, 78, 121 QQ (quantile-quantile) plot 498 Quality of life, in pets 75 Quantal assay 423–424 Quantification dangers of 12 evolution of, in medicine 12 Quantiles 253 Quarantine 6, 125, 543, 605–606 Quartiles 253 Questionnaires see Surveys Quetelet, Adolphe 12 r R0 see Basic reproductive number Rabbit haemorrhagic disease 485 Rabbit, preventive medicine in 601 Rabies in bats 48 in Chile 185 endemic occurrence 160 in foxes 148, 160, 169, 523–525 iatrogenic transmission 124 legal aspects 621 long-term (secular) trends 180 in skunks 83 sporadic cases 59 in the US 83, 180 Radiation, solar 101–102 Ramazzini, Bernardino Random-digit dialling 271, 350 Random-effects model 516 in clinical trials 378–379 in statistical modelling 515 Random intercept model 517 Random numbers 661–662 Random slope 517 Randomization in clinical trials 369–370 in controlling confounding 395 Ranks see Ordinal scale of measurement Rapid rural appraisal see Participatory epidemiology Rate ratio see Relative risk Rate of homogeneity see Sampling, rate of homogeneity Rates 76–77 Ratio scale of measurement 202 Ratio(s) 76–77 particle:infectivity 120 Rats leptospirosis and 157 plague and scrub typhus and 147 in synanthropic ecosystems 156–157 Receiver-operating characteristic curve see ROC curve Recombination 95–96 Record cards, as sources of data 231 Record database model 229 Recording schemes examples 237–244 implementation 236–237 objectives 236 scales of 232 Reductionism 48 Reed-Frost model 173–175 Reed-Muench titration 423 Refinement, definition 209 of serological tests 427–428 ‘Refutedness’, of a theory 535 Registries, as sources of data 467 Regression analysis 180–185, 492–505 Regression line 494 Reinforcing factors 52 Related samples 299 Relational database model 231 Relative odds see Odds ratio Relative risk 321–323 calculation of confidence intervals for logarithmic-based method 323 test-based method 323 definition 321 Relative risk increase 367 Relative risk reduction 365–366 Reliability 209–211, 454 of questionnaires 227 Renatus, Publius Flavius Vegetius Renaissance, The 59 Repeatability 209, 454 Reporting systems see Recording schemes Reproducibility 209, 454 Index Reproductive failure, in pig herds 88 Research laboratories, as sources of data 469 Residuals 493 empirical 496 jackknife 496 ordinary 496 standardized 496 studentised 496 theoretical 496 Residues, antibiotic 23 Response rate, in interviews and questionnaires 221 Restriction enzyme analysis 34 Restriction, in controlling confounding 394 Retinal scans, in animal identification 464 Retrospective studies 320 Retrospective surveys 37 Reverse causation 395 Rhinopneumonitis virus 118 Rhinotracheitis virus 102 Rhipicephalus spp., nidus of 159 Rift Valley fever 102, 118, 141, 152, 160, 618 Rimpuf model 127 Rinderpest control 5, 10, 16, 284, 287, 457, 463, 614, 616 economic aspects 579 eradication of 16 sample size determination for 287 excretion 119 JP15 campaign 16 military campaigns disseminating Pan-African Rinderpest Campaign 284 pandemic 59 as a plague 7–8 stimulus for founding veterinary schools 10 Ring vaccination 606 Risk analysis 39, 540–564 acceptable risk 560–563 application of diagnostic tests to 446–447 hazard identification 546 microbial 39 precautional quarantine 543 precautionary principle 543 risk assessment 546–548 consequence assessment 546 entry assessment 546 exposure assessment 546 hazard characterization 546 risk characterization 546 risk estimation 546 uncertainty 547 aleatory 547 epistemic 547 stochastic 547 variability 547 risk communication 551 risk management 548–550 HACCP (Hazard Analysis Critical Control Point) 549–550 qualitative 551–556 quantitative 556–563 probability distributions 557–558 anchoring 557 semi-quantitative 551–552 Risk-based surveillance see Surveillance Risk, definition 540 Risk-difference modification 392 Risk factors 47 Risk indicators 47 Risk markers 47 Risk ratio see Relative risk Risk-ratio modification 392 Robustness, of a diagnostic test 209 ROC curves 441–443 Rocky Mountain spotted fever 159 Rolling (moving) averages 181, 423 Ross River virus 120 Ross, Ronald 48 Rotaviruses 121 Rous, Francis Peyton Routes of transmission, of infectious agents see Infection, transmission Ruggedness, of a diagnostic test 209 Ruini, Carlo Ruminal boluses, in animal identification 464 Russell, Bertrand 44, 535–536 s Sacrifice, to remove demons Salihotriya Salivarian transmission of infection 118 Salmon poisoning 153–154 Salmonella spp see Salmonellosis Salmonellosis 53, 100, 159, 614 Sampling see also Prevalence; Statistics; Surveys capture-release-recapture 65 in clinical trials 362–364 cluster 274–275, 277–285, 293 convenience 272 design effect 279 distance 65 error 271, 639 fraction 271 frame 271, 386, 387 imperfect diagnostic tests in 276–277, 288–289 intra-class correlation coefficient see rate of homogeneity multistage 274 non-probability 272, 388 in observational studies 319–320 optimization 285–287 probability 272–275 proportional allocation 273–274 purposive sampling 272 random-digit dialling 271 random numbers 661–662 rate of homogeneity 279 sample size in clinical trials 372–373 comparing two samples 302–314 disease detection 284–290 in observational studies 335–336 prevalence 275–284 see also appropriate parameter simple random 275–277, 291–292 stratified 273–274, 277, 293 systematic 272–273, 277, 293 types of 272–275 unit 271 using probability proportional to size 283–284 variation 639 without replacement 288, 661 with replacement 288, 661 SARS 126, 620 SAVSNET see Small Animal Veterinary Surveillance Network Scanning surveillance see Surveillance Scapegoats Scatter plots 265 859 860 Index Schistosoma spp see Schistosomiasis Schistosomiasis 149, 154, 159, 462 and bladder cancer 110 Scholasticism see The Scholastics Scholastics, The 42 Schwarz’s Bayesian Information Criterion see Bayesian Information Criterion Scientific inquiry, role of, in accepting a hypothesis 45 Scientific revolutions 10 Scoring schemes/systems 203–204 Scrapie 133, 440, 464, 612 Screening 37, 431, 434, 435 genetic 612 mass 37 prescriptive 37 strategic 37 Screw-worm fly 125 cost-benefit analysis of control of 581 Scrub typhus 147, 158 Scurvy, early clinical trial 361 Seasonal calendars, in participatory epidemiology 480 Seasonal trends in disease 178–179 Secondary attack rate 70 Secondary cases, in epidemics 173 Secondary data collection 219 Secondary determinants 86 Sectioned density plots 264 Secular trends in disease 179–180 Security interval 257 Segmented regression see Piece-wise regression Selenium, plant uptake by 138 Semantic differential questions 220 Semi-interquartile range 254 Semmelweis, Ignaz 9, 53, 362 Semmelweis effect (reflex) Sensitivity analysis 537 analytical 423, 430 diagnostic 208–209, 430 aggregate-(herd-) level 443 calculation of confidence intervals for 433 parallel 444 serial 445–446 Sentinels 459–460 environmental 191, 198 Sentinel health events 460 Sentinel surveillance see Surveillance Separated populations 65–67 Sequential-design trials 371–372 Serial interval 119 Serial measurements 302 Serial testing 445–446 Serological epidemiology antibody assay titres 423–424 arithmetic mean 367, 422 geometric mean 367, 422 multiple serial dilution 423 Spearman-Kärber titration 423–424 relative analytical sensitivity of 430 single serial dilution 423 antigenic tolerance 429 definition of 421 interpreting tests 427–430 seroconversion 422 calculation of rate of 426 tests see Diagnosis, diagnostic tests Serpulina hyodysenteriae see Brachyspira hyodysenteriae Serum banks applications 470 collection of serum 470 definition 470 selection bias in 470 as sources of data 470 Severe acute respiratory syndrome see SARS Sex, as a determinant 91–92 Sex chromosomes 92 Sex-influenced inheritance 92 Sex-limited inheritance 92 Sex-linked inheritance 92 Shadow prices 580 Sheep see also specific diseases in sheep disease eradication in 615 domestication footrot 107 efficacy of vaccine against 365–367 health schemes 592–593 jaw tumours 80 pleurisy 181–185 pneumonia 181–185 Sheep pox, introduction of into the UK Sheep ticks, populations of 528–530 Shewhart charts 267 Shift, antigenic, in influenza viruses 96 SHIFT veterinary import network 464 Shigella spp 96 Shipping fever 18, 106 Significance clinical (biological) 300 statistical 297 tests 296–297 Silent infection see Inapparent infection Simplicity of a hypothesis 45 of a theory 535 Simulation models 526–530 ‘Sin of Galileo’ 538 SIR models 521 Sitta spp 150 Size of hosts, as a determinant 93 Skin cancer, coat colour and 94 Skin, infection via 122 Skunks, map of rabies in 83 Slaughter (culling), of badgers, tuberculosis and 17 as a control measure 615 Small Animal Veterinary Surveillance Network 244 Smallpox 5, 9, 11, 30, 123, 126, 170, 520, 604, 608, 617 SMEDI viruses 89 Smith, Adam see ‘Wealth of Nations’ Smith, Theobald Smog, cause of respiratory-disease epidemic 59 Smoking, cause of lung cancer 54 Snails, control of 149 SNOMED 214 SNOP 214 SNOVET 214 Snow, John 49, 50, 55, 520, 538 SNVDO 212 Social cost-benefit analysis see Economics and veterinary epidemiology Social dominance 147 Social ecology 138 Social epidemiology 35 Software, computer, veterinary epidemiological packages 643–647 Spatial correlation study 354 Southern Oscillation 102 Spatial distribution of disease 186–187 Index Spearman-Kärber titration 423–424 Species, as a determinant 92–93 Specific measures of disease occurrence 76–78 Specificity analytical 430 diagnostic 208–209, 430 aggregate-(herd-) level 443 calculation of confidence intervals for 433 parallel 444–445 serial 445–446 Spiroceria lupi, and canine oesophageal carcinoma 112 Spontaneous models 190 Sporadic disease occurrence 59–60 Squamous cell carcinoma in cats 47 in cattle 30 Stability, of pathogens 120 ‘Stamping-out policy’ 606, 609, 640 modified ‘stamping-out policy 640 Standard deviation 254 Standard error definition of 257 of specific parameter see appropriate parameter Standard Nomenclature of Veterinary Diseases and Operations see SNVDO Standardized measures of disease occurrence see Adjusted measures of disease occurrence Standardized morbidity ratio see Adjusted measures of disease occurrence Standardized mortality ratio see Adjusted measures of disease occurrence Standardized Normal deviate 254 Staphylococcus spp., conjugation in 96 State-transition models 523 Statistical packages 318, 643–647 Statistics see also Association; Observational studies; Sampling; Surveys asymptotic methods 257 basic definitions 251–252 binomial distribution 255 Normal approximation 257 Bland–Altman plot 452–454 centiles 253 Chi-squared (χ2) distribution 660 confidence intervals and limits see specific parameters constants 641 correlation 316–317 correlation coefficient 691 degrees of freedom 257 descriptive 252–254 distributions 254–257 errors of inference 297–298 generalized linear models 511–519 independent and related samples 299 interaction 109–110, 328–330, 505–506 interval estimation 257–262 see also specific parameter kappa statistic 450–452 logistic regression 510 Mantel-Haenszel procedure 332–333 mean 253 measures of position 253–254 measures of spread 254 median 253 multiple significance testing 298 Bonferonni correction 298 multivariable analysis 317 multivariate analysis 317 Normal distribution 254–255 null hypothesis 297 one-and two-tailed tests 298–299 parameter 252 parametric vs non-parametric techniques 299–300 Poisson distribution 255–256 Normal approximation 257 power 298, 336–338 probability 251 quantiles 253 quartiles 253 random numbers 661–662 regression 183, 492–499 sample sizes 300 see also Sampling, and specific parameters standard deviation 254 standard error 257 tests χ2-test 310–313 χ2-test for trend 314–315 Fisher’s exact test 311–313 McNemar’s change test 313, 452 Wilcoxon–Mann–Whitney test 304–306 Wilcoxon signed ranks test 306–308 Student’s t-test 302–303 triangular distribution 557 variables 46, 251–252 variance-ratio (F) distribution 692–693 Stercorarian transmission of infection 118 Sterile immunity 98 St Louis encephalitis virus 459 Stochastic models 521, 525, 529, 556, 559 Stochastic variation 493, 547 Strategic screening 37 Strategic vaccination 606–607 Stratification in clinical trials 370 in observational studies 334 in surveys 273 Streptobacillus spp 123 Streptococcus uberis, and environmental mastitis 88 Stepwise regression 509 Stress 105–106 cold 101 in veterinarians 105 Stressors see Stress STROBE 396 Student’s t-distribution 257, 262, 650 Student’s t-test 302–303 Studies, observational see Observational studies Subclinical disease 97 Subclinical epidemiology 35 Subclinical infection 97 Subgroup analyses 395 Suk Summa Theologica 42 Summary plots 264 Summer mastitis 107 Suppressive (dampening-down) vaccination 607 Surveillance 457–491 aquatic-animal 485–486 861 862 Index Surveillance (cont’d) assessing the performance of 486–488 companion-animal 483, 485 definition 457 goals 458–459 mechanisms 471–475 networks 475 sources of data for 464–471 systems 475 types of active 460–461 disease 20, 232, 459 early-warning 461 epidemiological 459 passive 460–461 post-authorization drug 362 risk-based 461 scanning (global) 461 sentinel 459 serological 460 syndromic 461 targeted 461 wildlife 485 ‘Surveillance committees’, in the Reign of Terror 457 Surveillance zones, in disease control 246, 285, 610 Survey(s) see also Prevalence, Sampling, Statistics 37, 270–295 cost of 290 cross-sectional 37 Domesday 66 detecting presence of disease 284–290 household, to estimate population sizes 62 longitudinal 37 prevalence 275–284 questionnaires 219–228 closed questions 219 coding 220–221 computer-assisted 227 criteria for success 227–228 design 219–226 interviews 226–227 mailed and self-completed 226 missing values 228–229 open-ended questions 219 presentation and wording 221 question structure 221 response rate 221 structure 219–220 telephone 226–227 testing 227 types of 272–275 Survival 73–75 confidence interval 260 Susceptibility, of hosts 118 Swine dysentery, eradication of 100 Swine fever, eradication 615 Sympatric species 149 ‘Symptom iceberg’, 465 Synanthropic ecosystems 156–157 Syndemics 108–109 Syndrome, definition of 461 Synergism biological 108 statistical 109 Synopsis Nosologiae Methodicae, 211 Syphacia obvelata 58 Systematic reviews 376, 397–420 clinical heterogeneity 403, 408 Cochran’s Q statistic 412 collecting data 407–409 comparison with narrative reviews 398 contextual heterogeneity 408 DerSimonian and Laird weighting method 416 effect modification 408 electronic databases 401 evidence synthesis 397 extraction of outcomes 407–408 extraction of study characteristics 408–409 forest plot 415 funnel plot 414 GRADE working group 417 heterogeneity 412–415 interpretation 419 inverse variance method see DerSimonian and Laird weighting method literature searching 399–400 Mantel-Haenszel weighting method 416 meta-analysis 412 meta-regression 416 methodological heterogeneity 408 narrative review 398 overall effect estimate 412 PECO(S) systematic review question 403 Peto weighting method 416 PICO(S) systematic review question 364, 399 PICOTT systematic review question 364, 403 PIT(S) systematic review question 403 PO(S) systematic review question 403 pooled effect estimate 412 presentation of results 416–419 PRISMA 415 Protocols 398 publication bias 411 QUADAS 411 quantitative synthesis 412 reporting bias 411 risk of bias 400, 409–412 assessment tool 411 screening 406 selection of relevant studies 406–407 STARD 411 statistical heterogeneity 408 steps in 398 summary effect measure 412 summary effect size 412 synthesis of results 412–416 Tau-squared 414 Tritrichomonas foetus 411 types of review question 399 vote counting 407 Systematized Nomenclature of Veterinary Medicine see SNOVET Systematized Nomenclature of Human and Veterinary Medicine see SNOMED Systems models 534 t Tables 262–263 Tail docking in dogs 23 in horses 23 Talmud, The Targets, in health schemes 587–588 Targeted surveillance see Surveillance Tarone’s test for homogeneity 333 Tattoos, in animal identification 62 Taxonomy, microbial 97 Temporal distribution of disease 177–186, 255 Tenacity, role of, in accepting a hypothesis 44 Territoriality 147 Tertiary cases 70 ‘Test and removal’ control strategy 606 ‘Testedness’, of a theory 535 Index Testicular neoplasia 78 Texas fever, eradication of 5, 604–605 Theileriosis 130 Theoretical epidemiology 33 Theories 535 ‘Theory of Elevation’ 49 ‘Theory of Everything’ 536 Threshold levels in diagnostic tests 422 in epidemics 168 of infectious agents 120 of multifactorial traits 90 in vaccination 608 Ticks climate change and 103 disease transmission by 5, 130, 164 in sheep 529–530 Time-invariant data 212 Time lines 266 Time-series analysis 180–186 Time-series studies 355 Time series plots see Time trend plots Time trend plots 265 Titre see also Serological epidemiology definition of 421 estimation 423–424 logarithmic transformation 256, 422 Tobacco rattle virus 96 Tolerance, immune 129, 130, 133, 429 ‘Tombstone data’ see Timeinvariant data Topley, William 32 Topotypes, of foot-and-mouth disease 97 TOST procedure see Two one-sided tests procedure Totalvet 242 Toxoplasma gondii 133 Traceability 463–464 Tracing in infectious disease outbreaks 623–624 systems 463 TRACES 464 Trade bans, economic effects of 571–575 Transboundary diseases 135–136 Transduction 96 Transference, to remove demons Transformation in bacteria 96 mathematical 256 in regression 502 Transect walks, in participatory epidemiology 480 Transgenic biology 614 Transgenic models 190 Translational medicine (research) 25 Transmission of disease see Infection, transmission Transmission rate 169 Transmission risk 169 Transposition/transposons, bacterial 95 Treatment of disease, ancient methods of 2–4 Trends in disease occurrence spatial 186–187 temporal 177–186 cyclical 178 long-term (secular) 179–180 seasonal 178–179 short-term 177 Trends in veterinary medicine 20–25 Trepanation, to remove demons Treponema hyodysenteriae see Brachyspira hyodysenteriae Triad, of determinants 88 Triangular distribution 557 Triangulation, in participatory epidemiology 478 Trichinella spiralis 98 Trophic cascades 152 Trophic levels of food chains 152 Trypanosoma spp antigenic drift in 96 transmission 118 Trypanosomiasis 16 eradication 605 tolerance to 614 translocation by cattle 126 Tsetse fly 31, 124, 480, 605 Tuberculosis see also Badgers bovine, badgers and 17, 31, 615 in cattle 247 eradication 615 negative-herd retesting 446 in opossums 31, 615 in white-tailed deer 615 Tularaemia, landscape epidemiology of 161–162 Tumour registries, as sources of data see Registries Tumour-inducing viruses 110 Tumour-suppressing genes 111 Tumours bladder 110, 191–192 cervix 33, 110 fibrosarcoma 112 gastric 110 horn 112 leucosis 110 leukaemia 110 mammary 193 mesothelioma 192 oesophageal 112 osteosarcoma 93 scrotal 110 Turkeys, importation to Europe Two one-sided tests procedure 375 Two-tailed tests 298–299 Type I and Type II error 297–298 ‘Typhoid Mary’ 99 u Uberreiter’s syndrome see Keratoconjunctivitis, superficial, in dogs Ultraviolet radiation, as a determinant see Solar radiation Unapparent infection see Inapparent infection Uncertainty in economics 580 in risk analysis 547 United Kingdom disease eradication in 615 health schemes in national 597–599 private 586–588 holdings by size of herd 66 pig herd structure 19 United States heartworm in see Dirofilaria immitis leptospirosis in 179 rabies in 83, 180 Universal constant 641 USSR, leptospirosis, epidemic in 173 Utricularia spp 154 v Vaccines and vaccination barrier 606 critical vaccination threshold early 5, clinical trial of 362 efficacy 366 608 863 864 Index Vaccines and vaccination (cont’d) emergency 606, 609, 615 frontier see barrier immune-belt see barrier inactivated (killed) 607 live 607 new technology 608 preventive 606 ring 606 routine 606 vs slaughter 615 strategic 606 suppressive (dampening down) 607, 615 use in disease control 606, 609, 615 ‘vaccination to live’ 616 Validity 209–210 construct 205, 364, 450 content 205, 364 in epidemiological studies 383–396 external 368, 384 face 205 internal 368, 384 of mathematical models 537 of questionnaires 227–228 Variables 46, 251–252, 641 dependent see response explanatory 252 independent see explanatory input see explanatory outcome see response response 252 study 252 Variance 254 Variance-ratio (F) distribution 692–693 Variant surface glycoprotein (VSG), in trypanosomes 96 Variates 251 Variolation 9, 520 Vase plots 264 Vectors, types of 117–118 Vegetational physiognomy 141 Vegetational zones 139 Velvet removal 23 VeNom 214 Venezeulan equine encephalitis virus 459 Vertical transmission 129–131 VetCompass see Veterinary Companion Animal Surveillance System Veterinarians, employment trends 21 Veterinary Companion Animal Surveillance System 244 Veterinary Investigation Diagnosis Analysis 242 Veterinary Medical Databases 191, 241–242 Veterinary medicine contemporary 12–25, 628–629 development of 1–25 Veterinary practices computerized records in 465–466, 485 as sources of data 465 Veterinary schools founding of 9–10 as sources of data 241, 469 VETSTAT 468 VIDA see Veterinary Investigation Diagnosis Analysis Violin plots 264 Virchow, Rudolf 24 Virome 141 Virulence 32, 94, 120, 146, 153, 640 evolution of, in parasites 94, 153 Viruses initiation of cancer by 110 particle:infectivity ratio 120 Visual analogue scale 203–204, 299, 364 VMDB see Veterinary Medical Databases Volterra, Vito see Lotka-Volterra equations von Willebrand’s disease see Haemophilia, canine VSG see Variant surface glycoprotein w WAHID see World Animal Health Information Database WAHIS see World Animal Health Information System Wald confidence interval 259 Wald test 512 Wales, pig herd structure in 19 Wallace, Alfred Russel see Wallace Line Wallace Line 139 Warble fly infestation 609 ‘Wealth of Nations’ 543 ‘Web of causation’ 52 Webster, Noah Whewell, William 43 Wilcoxon–Mann–Whitney test 304–306 Wilcoxon signed ranks test 306–308 Wild-animal organizations, as sources of data 469 William of Occam (Ockham) see ‘Occam’s razor’ Willingness-to-pay 567 Wilson confidence interval 259 Wind-chill index 102 Witches and witchcraft, as causes of disease Wolf, as ancestor of dogs 1, 93 ‘Wooden tongue’ see Actinobacillosis Woolf’s test for homogeneity 333 World Animal Health Information Database 237 World Animal Health Information System 237 World Organisation for Animal Health see Office International des Epizooties World Trade Organization 23, 237, 543, 574 WTO see World Trade Organization ‘Wynne-Edwards’ hypothesis 147–148 Wynne-Edwards, Vero Copner see ‘Wynne-Edwards’ hypothesis x ‘X disease’ see Hyperkeratosis Xerophiles 139 y Yearbooks, as sources of demographic data 66 Yellow fever, transmission of 55, 152, 157, 158, 199 Yersinia enterocolitica 429 Youatt, William Youden’s index 433 z Zika virus 126 Zoography 62 Zoological gardens, as sources of data 468 Zoonosis incidence ratio 77 Zoonoses 5, 29, 101, 620, 640 economic aspects 570 as opportunists 101 ... 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