Epidemiology and Biostatistics: Introduction – Epidemiology | Lecturio

[Music] hello and welcome to epidemiology one of the most exciting and fastest growing of the medical sciences of course I'm biased I am an epidemiologist but maybe after you hear a little bit about it you'll feel the same way as me I think about epidemiology that makes us so fascinating is first of all in my opinion it's the only medical science that probably doesn't involve a halt of medicine and also some epidemiologists are sort of like medical detectives there on the ground collecting data talking to people solving mysteries about what causes the diseases in a population it can be quite sexy quite exciting kind of like a police investigator except around medicine so today we're going to learn about the differences between descriptive and analytical studies in epidemiology we're a little bit about historical origins of Epidemiology where it came from and maybe even where it's going and we're also going to learn a little bit about the triumphs of FBI ology because I think it's important to brag a little bit about the things that we've done for society over the last couple of hundred years so when we talk about epidemiology is traditional to define it first it's difficult to define however this is one particularly popular definition is offered by the Centers for Disease Control in Atlanta Georgia and they say that epidemiology is the study of the distribution and determinants of disease or health status in a population okay Wikipedia offers a little more complicated definition calling it the branch of medicine that deals with incidence distribution and so forth I would add in prevalence and mortality and so forth it gets complicated pretty fast and pretty dry most tests book definitions of epidemiology land somewhere between the two poles of the CDC in Wikipedia I will offer you another definition in my opinion it's the science of science or if you want to be a little drier it's the science of looking at the health of populations rather than of individuals hopefully this will all be clear to you we're done the lecture our story begins in the early 19th century in London England when a man named John Snow whose name you might have heard of decided to investigate a cholera that was happening in London at the time back then we didn't know a lot about what caused diseases there is a few theories some were more fantastical than others but there was a lot of evidence to support many of the theories Jon Snow's innovation was that he was going to use maps and numbers to describe the epidemic of the outbreak now to us today that seems kind of rational kind of boring kind of everyday back then it was a revolutionary so what he did was he figured out that back in London in 19th century several neighbourhoods were being served by a water pumping stations and he reasoned that probably cholera was waterborne and he took the time to figure out which pumping stations were providing what water to what neighborhoods his investigation led him to conclude that one particular pump the Broad Street pump was likely responsible for most of the cholera outbreaks in that city you can go to London today and actually visit the Broad Street pump there's a plaque there that says this is the spot where epidemiology was born or something like that it's quite fascinating I encourage you to go if you can let's look a little bit at the history of disease before we continue before john snow and his brethren made their innovations and discoveries it was thought that diseases were caused by miasma what's miasma it's some kind of fantastical poisonous magical vapour or mist that would emanate from swamps of the ground and people would shut their windows at night so nothing into their houses and caused them to be sick and it had a foul smell associated with it miasma doesn't really exist we know that today because we know that diseases are caused by infectious agents microbes pathogens bacteria viruses we know this because around the same time that John Snow was making his investigations the microscope made his first appearance and we could see these microbes in action so as a revolutionary time let's look a little bit at the data that Jon Snow collected he looked at the number of houses being served by a variety of pumping companies the south work and voxel company the land of company and other companies in London he saw that each was servicing a different number of houses and he counted the number of deaths from cholera experienced by those neighborhoods to most people the number of deaths alone were sufficient to tell the story that's not where Jon Snow stopped he divided the number of deaths by the number of houses being served in each neighborhood and got a quotient a ratio a proportion again to us today that's an obvious thing to do back then it was a revolutionary it was an innovation and by looking at the quotient the proportion he discovered that a majority of deaths were being caused by one particular pumping station and he narrowed it down to one pump the Broad Street pump and today that's what we have the signs of epidemiology it's the science of using non-medical tools mathematics paper your mind your hands your feet to learn something about disease but otherwise you would not have known so today we have different kinds of epidemiologists I mentioned that Jon Snow was a kind of outbreak investigator a medical detective of some kind most physicians who are epidemiologists are clinical epidemiologists and they bring to bear a variety of perspectives they bring to bear the choices made by their patients the the clinical research and practice experience that they have and also the experiences of their mentors to make decisions for small groups of people usually in the clinical environment Public Health epidemiologists are people like Jon Snow these are the individuals who investigate outbreaks who figure out which sandwich at the picnic gave you diarrhea who figure out what's probably causing that cause of that outbreak of disease in that community over there they're also the ones who plan the vaccine schedules for a community and population epidemiologists look at large disease trends in a population the incidence and prevalence the risks factors that cause diseases and so forth obviously if there's a lot of overlap in all these different types of epidemiologists and there are newer emerging types of epidemiologists today epidemiology is forging partnerships with political science with economics with computer science and genetics it's a fast evolving and diversifying science so before I continue I want to talk about how we know what we know is important to understand where our discipline fits into the science of knowing and we call this paradigms paradigms of research paradigms of knowing when we talk about paradigms we're talking about how our discipline interacts with the world and understands knowledge and evidence and truth there are several dimensions to a paradigm and I'll suggest you that there are three main pillars philosophically of how to define a paradigm the first is ontology and that's how we how we experience reality is reality defined by my imagination your imagination or is your reality defined by some objective truth of a universe then we deal with epistemology and that's how we acquire and process knowledge do we know about the universe from interrogating it from talking to people from collecting data and then we have our methodology may be the ways in which we design our studies that determines how we understand the universe the important part about all this is that paradigms of research allow us to define how the world works and how we extract knowledge from it a psychologist a political scientists an economist an anthropologist an epidemiologist interact with the world a bit differently in defined knowledge a bit differently we define the questions that we ask a bit differently as well when we look at evidence-based medicine we're going to phrase a research question and the paradigm of knowledge from which we arise helps us define the types of questions that we can ask our paradigm of research also tells us what is publishable and what isn't because it defines what constitutes knowledge what constitutes proper evidence an epidemiology is I hope you will conclude with me all about how we rank evidence and understand truth in essence a paradigm tells us how the world is structured and tells us what determines meaning and significance epidemiology exists within something we call the etiologic paradigm which is a kind of positivism and it purports that there this external objective truth that we can access via art our methodologies our science our study designs and we can measure risk factors and things that cause outcomes smoking causes cancer this behavior will cause this other kind of outcome that's the nature of the epidemiologic etiologic paradigm I bring this up because it's important that we remember that epidemiology and any discipline for that matter is about one half to knowledge there are a variety of paths to knowledge different disciplines have their own paths it's important that we don't descend into arrogance when we consider the evidence that we choose to base our medical decision-making on so let's now change gears a bit and talk about terminology it's important that we get our our words straight before we can talk more deeply about epidemiology and how to use it when we're in mathematics versus clinical research versus laboratory research versus epidemiology some common concepts often have different names so we have clinical research and mathematical relationships and and in this domain we have independent and dependent variables so an independent variable is free to be whatever it wants but it determines the value of dependent variables in epidemiologic research a variable that predicts an outcome is an exposure you're exposed to something which may lead to an outcome you can be exposed to a contaminant a pollutant may be something in your food and the outcome could be a disease a cancer or some other kind of behavior so again traditionally we have independent variables that lead to dependent variables in epidemiology these are exposures leading to outcomes the example that I'm fond of making all the time is that smoking is an independent variable you are free you're independent to smoke if you want to or not smoke if you want to but the smoking causes a certain outcome and that outcome could be lung cancer now an exposure that increases or decreases the likelihood for developing a certain outcome or disorder commonly diseases we call that a risk laughter now go back to Jon Snow again Jon Snow discovered that water from this pumping station was likely associated with cholera he didn't know how it worked he didn't understand the biology the mechanism by which water caused the disease he just knew that this water was a risk factor for getting the disease and this really is one of the foundations for Public Health epidemiology we can measure statistically the relationship between risk factors and outcomes and thus we can control the risk factors and maybe then control the outcomes without knowing of the relationship without knowing the mechanism of how that risk factor caused the outcome or in fact if it was indeed causal for example smoking causes lung cancer we know this because there's a strong statistical association between whether or not you smoke and whether or not you are likely to get lung cancer you don't have to know the science or the biology or the mechanism of how the smoking causes the lung cancer it helps it's useful we recommend that we figure this out we don't have to know in order to in order to have a public health intervention epidemiologically I can control the risk factor and thus reduce the likelihood of the outcome sometimes when people say the epidemiology of a disease what they actually mean is the description of that disease and that brings us to talking about descriptive epidemiology or a descriptive study by the way I don't like it when we say the epidemiology of a disease it's a limiting description of the full breath and majesty and complexity of what the science of Epidemiology brings to the table but when they say descriptive epidemiology what we're talking about is the who who gets that a disease or an outcome the what what is that they're getting the where where is it happening and when when is it happening notice that there is no why there just as for w's who what where and when when we're into the why we're into a deeper kind of investigation descriptive epidemiology only cares about describing the who what where and when so let's think of an example let's just let's aside that we're going to investigate the prevalence of left handedness among students in your neighborhood who we're dealing with students what left handedness where the place where you live your neighborhood and when right now we care about measuring this prevalence this distribution right now and so the thing that I'm measuring the thing that I'm ascertaining is right now twenty three percent of students in your neighborhood are left-handed that's a very simple and relatively useless observation but a good description of descriptive epidemiology now I want to talk a little bit about the way the study designs our taxon amazed how we categorized them because a large part of Epidemiology is in designing studies to acquire the evidence that fits within our research paradigm so the first large delineation I want to draw attention to is qualitative versus quantitative studies in qualitative research that's the world of social sciences the world of political science and sometimes Health Sciences that's when we're dealing with descriptions that are qualitative words themes things like that quantitative research is in numbers statistics and that's where we're going to live we're going to live in the world of quantitative research in epidemiology amongst quantitative studies we have descriptive versus analytical studies now descriptive studies we've talked about already that's the who what where and when right and one example of a descriptive study is a cross-sectional study which we will describe in greater depth later but I just did an example left hand is my neighborhood that's a cross-sectional description of a particular phenomenon when we are looking at two different variables or more variables and how they relate to one another then we're into the world of analysis into analytical studies now descriptive studies dealt with one variable really describing left-handedness analytical studies are looking at two variables or more and a relationship between them amongst a little studies there are two categories observational or experimental amongst observational studies there are three perhaps four large categories the case control the cohort different kinds of cross-sectional studies and possibly this thing called an ecological study all of which we'll learn more about in another lecture the distinguishing characteristic of observational studies is that we observe them the universe unfolds and we observe we don't determine who does what or who gets what all we do is we watch and we marvel in the way the universe unfolds experiments on the other hand that's when we do get to interfere a bit the classical experiment in epidemiology is the randomized control trial or the RCT also called clinical trials there are other kinds of experiments as well there are quasi experimental designs or natural experiments and we'll talk about more of those in a future lecture as well now when the layperson thinks about an experiment they mean any kind of study any kind of investigation whenever you may all just talk about an experiment we mean something very particular we're talking about when we manipulate a variable so perhaps we are deciding who gets a drug versus who gets a placebo or who gets to smoke first who doesn't smoke whenever we are telling people what to do we are conducting an experiment we manipulate something we manipulate variable in observational studies we don't we left the universe unfold as it would be that people choose what they're going to do we may select them based upon what they're doing but we don't tell them what to do an experiment involves us changing a variable well now that we've covered the differences between analytical and descriptive studies and difference between observational and experimental studies let's look at some historical examples in particular I want to talk about the triumphs of Epidemiology one in particular and it's this you may recognize this photo may recognize this disease this is smallpox one of the great scourge ha's of humankind one of the great burdens of human civilization and smallpox is particular because it is the first human disease deliberately eradicated by human beings and we did this in 1980 in large part due to some innovative approaches by epidemiologists heroes I would argue so as I mentioned smallpox was defined to be eradicated by the w-h-o in 1980 but attempts to do so go back decades before then in 1975 this individual or Hema Banu I think she's from Bangladesh was the last person to contract natural smallpox and since then we don't know of any individual who has contracted natural smallpox so we're pretty confident the smallpox has been eradicated I'm sufficiently old that I still have a mark on my shoulder from my smallpox vaccine they left a large mark but young people today don't have that mark they don't have that burden because we have eliminated the need for that vaccine I love this particular quote by Thomas Jefferson in a letter to Edward Jenner in 1806 he says and the the the critical part of this letter is the last sentence future nations will know by history only that the loathsome smallpox has existed it's important because Edward Jenner devised the first workable smallpox vaccine in the early 1800s and Thomas Jefferson implies in his letter that due to that vaccine we will no longer have a smallpox problem but it took another hundred and eighty years for us to truly eradicate smallpox so clearly the solution for beating back this disease wasn't simply the technology of vaccination it was something a little more complicated more profound more interesting and that's where the science of Epidemiology came in it was a need to compute the numbers of people who need to be vaccinated to truly eradicate the disease and this was made possible because of something called herd immunity herd immunity is when individuals in a population are immune to a disease and thus prevent other individuals who are not immune from getting contact with that disease if you think about a herd of cattle those at the center of the herd will never have any contact with anyone outside of the herd they're protected by those on the periphery so long as those on periphery are immune or have been vaccinated than the ones in the center don't need to be in other words not everybody in the herd needs to be vaccinated just a certain proportion epidemiologists stepped in and computed that proportion that was needed to ensure that smallpox would be eradicated let's look at the timeline of smallpox in human civilization going back centuries if not millennia smallpox was a major scourge of humankind in the 18th century attempts at finding a treatment made some great progress with Edgar Jenner creating the first variation attempts but back then 400,000 people would die every year at least it was quite the killer a third of the survivors though became blind and so they lived on with disability so it's not just about the disease killing people it's not just about the mortality it's also about the morbidity and it's important that we keep in mind that when we're looking at the impact of disease it's not just the deaths we care about it's the effects on the quality of life that matter the people who survive also developed an immunity to smallpox this is an important consideration when we talk about eradicating the disease once you got it you're not going to get it again we can cross you off the list of someone who needs to be inoculated so Edward Jenner took interest in cow pox which was a kind of small pox at cattle had and he noticed that people who were milking cows would be exposed to cow pox and tended to be immune to smallpox as a result so he got the idea that exposure to this particular infection of cow pox may imbue an individual with a kind of immunity to smallpox so in the 1950s the world took notice that maybe it was time to have a more militaristic administrative attempt to control smallpox globally in 1967 a decision was made by the whu-oh to attempt to eradicate and remove this scourge from the human experience altogether and as I mentioned in 1980 this was accomplished w-h-o had eradicated smallpox entirely a great triumph so to review that timeline in 67 we began the eradication program process and back then 15 million people were developing smallpox every year but 2 million died every year and today no one dies anymore of the smallpox so one of the important qualities of Epidemiology is through the observational process we can determine risk factors and likely causes of disease I say likely causes because I use that word cause very carefully we'll talk later on about how we define what a causal factor is but an absence of knowing for sure that something causes something else we call it a risk factor and we talk about associations rather than causations so many times we don't know the cause of a disease but we can associate it with various exposures for example streptococcal infection is often followed by rheumatic fever and sometimes rheumatic heart disease so we can prevent traumatic heart disease by preventing spread kakaako infection even if we're not entirely sure what the causal mechanism might be we know that romantic fever is more frequent amongst army recruits than in school children so now we know the population to focus on and when an intervention is most likely lung cancer and smoking is the classic example it was fought in courts for many years about whether or not tobacco really was a cause of lung cancer and we're pretty sure that it is but in absence of solid laboratory evidence we had mountains of epidemiological observational evidence showing that people who smoked tended to have lung cancer more so than people who didn't smoke the power of observation in many cases overcomes the need for solid specific causal information observational epidemiology is also useful for understanding morbidity and mortality from diseases in the population as a whole we can associate lifestyle factors like driving without a seat belts or eating too much fat or having too many calories in our diet or being too immobile and not moving enough with other kinds of negative health outcomes we don't need to know the mechanism in order to be able to control the outcome by controlling lifestyle choices and risk factors let's talk about some of the important tasks that epidemiologists are engaged in well the first and most important thing that a population epidemiologist cares about is disease surveillance most modern countries have several complicated surveillance programs and action all the time including something called a notifiable disease registry that's when a list of key diseases are made so that anytime a health professional encounters one they must by law inform the government or whoever's in charge of that surveillance program that they saw a case in this way we have a solid idea of whether or not our country or population has a particular disease some of the key ones include tuberculosis or hiv/aids or even Ebola now has made the list in recent months in most countries disease surveillance allows us to detect whether or not an epidemic is happening it allows us to detect whether or not a disease is changing its profile in a way it allows us to predict or detect a time the population is changing its behavior with respect to certain diseases as well epidemiologists are also involved in diagnostic tests we're going to go into greater detail on diagnostic tests in a further lecture but know right now that these tests involve computing things like sensitivity and specificity deciding whether or not we can use this test in this context or whether or not a test is viable as a screening tool to identify individuals who are good candidates for further investigation further on epidemiologists are also useful in trend analysis and we're going to talk a little bit more about trend analysis in a second this is when we look at the changes in diseases over time or over populations and try to ascertain some wisdom from looking at the changes in the numbers without actually investigating individual cases and also one of the important things that clinical epidemiologists and population epidemiologists do is designing studies so very often a researcher will contract an epidemiologist to go over their study design and their protocols to make sure that everything method logically sound now let's look at some of the issues in trend analysis here's an example from us data this is the changes in mortality rates of white women and lung cancer as you can see from 1973 to 1995 the mortality rates were going up dramatically this is white women in America dying of lung cancer if you look at black women well the rates are kind of the same so there is no reason to expect white women and black women to be physiologically different so this is not surprising if you look at breast cancer white women the rates have come down slightly from 1973 to 1995 with a black women they've gone up that's interesting here's all the data in one slide we see that there are no changes or no differences between white and black women except with respect to breast cancer that's very interesting and there could be a host of reasons for this observation including something we call detection bias that's when we're looking for more cases so we find more cases and again we're talking more about detection bias in a future lecture about biases but the point I'm trying to make here is that trend analysis allows us to know what questions to ask allows us to ascertain that there's probably something happening here that I need to investigate further so what have we learned as a result of this lecture well you've learned the origins of Epidemiology you know that it began in England in the early 19th century with a bit of medical detective work by John Snow and since then it's evolved into a host of other realms including clinical epidemiology and genetic epidemiology and population epidemiology and data science and all these other categories of Epidemiology that maybe you're interested in now now you can distinct between descriptive and analytical studies descriptive epidemiology remember is when we're describing a scenario we care about the who the what the where and the when analytical studies is when we are drawing relationships between two variables and now you can identify the different types of epidemiologists the clinical epidemiologist the population epidemiologists and çal epidemiologists in this lecture we're going to learn a little bit about critical thinking and evidence-based medicine and I want to start off by telling you about my first consulting opportunity when I was a graduate student learning epidemiology I was assisting some scientists who are trying to determine if a gynecological practice was largely evidence-based and to do this we looked through the literature and and attempted to tell if the things that gynecologists were doing commonly with pregnant women like shaving their pubic hair and giving them enemas before birth was worthwhile we determined it was a not worthwhile so much of these practices weren't evidence-based the problem though is that we couldn't convince the gynecologist to stop doing these practices so lesson there is even though things may or may not be evidence-based clinical practice is still based upon values and experiences having said that today you're gonna learn about how to apply epidemiological principles to help you make evidence-based medical decisions so we're gonna learn how to apply the steps of EBM which is evidence-based medicine we're going to learn how to rank the different kinds of study designs that you're going to discover in the process of doing your EBM searches and you're going to learn how to phrase a research question using a method that we call Pico P ICO to begin with I want to show you this image this is an image that I saw on the subways of Toronto many years ago essentially it says a couple of things has two bits of information the first is that approximately three to five children in every Canadian classroom have witnessed their mother being assaulted a dire statistic little depressing and the second is as seventy percent of men in court-ordered treatment for domestic violence witnessed it as a child okay so let's think about what this is actually saying it's telling us that obviously some children are seeing domestic violence at home that's not a good thing it's further implying that those children may grow up to be abusers themselves I don't mean to minimize this issue it's a serious issue we should take it very seriously but I want you to think about those numbers through a new epidemiological lens what are the numbers actually saying to you what information is missing what additional information do you think you need to a bit more nuance and wisdom to these numbers first thing is three to five children is that a large number think about it what's the denominator how many children are in the classroom totally if there are ten children then that's thirty or fifty percent of kids in that class saw domestic abuse that's a high number we can agree that's bad but it's a hundred kids in the classroom that's three to five percent still one child is bad enough but three to five percent isn't as bad as thirty to fifty percent so the denominator matters the second bit of information is that 70 percent of men in a court-ordered treatment for domestic violence saw it as a child okay that seems like a large number but I want you to think about again what should we be comparing that to how many men who weren't in the court-ordered treatment saw it as a child I don't know the answer now think about this maybe 70% of men in a court-ordered treatment of domestic violence also ate rice pudding at some point in their lives is the implication that rice pudding causes you to be a domestic abuser again I don't mean to minimize this issue my point is the numbers are meaningless without a control group or denominator to get my point I hope so so epidemiology is a way of thinking it's a way of adding wisdom to numbers that otherwise are alone in a void without context and some of the things we do in epidemiology is to critically evaluate published studies using many of the tools that we're going to learn in the course of this lecture and other lectures we're going to identify the biases that may affect the conclusions that we draw from the published evidence again in another lecture we're going to talk more deeply about what those biases are but I want you to be aware that biases exists they always exist the question is how much do those biases interrupt your ability to make meaningful conclusions that are valid for your practice and lastly we're going to assess the qualities of different types of evidence not all evidence is the same some are ranked more highly than others and we'll talk a bit about how those rankings occur so that takes us to evidence-based medicine evidence-based medicine is not a new idea but has it's taken off in recent years become very very popular it is the attempt to integrate best research evidence with the clinicians personal experiences and the values of their patients to make again meaningful evidence-based clinically appropriate choices and decisions for their patients it's a way to use literature to help you make clinical decisions in a systematic fashion what a systemic mean systematic means there's a process there's a list there's a step-by-step procedure to follow by which we assess and collect the best quality evidence and summarize in a way that answers the clinical question that we are trying to ask so again EBM is the application of critical thinking in order to make clinical decisions so what's best research evidence we're trying to summarize the best research evidence to allow us to make clinical decisions what does that mean it's clinically relevant research sometimes it comes from basic sciences by which I mean lab sciences but typically it comes from the medical literature that is peer reviewed literature written by doctors or medical scientists to be consumed by other medical scientists and doctors was clinical expertise that's your expertise as a clinician or a doctor or a nurse or some other kind of care giver so it's your ability to identify your patient's unique needs and make the evidence that you collect relevant for this particular case that you're interested in what's patient values just because you find an answer from the literature it doesn't mean it's going to correspond to your patients needs for example maybe a patient has religious beliefs that don't allow him or her to accept the finding that you have found in your research or maybe they prefer pain alleviation or lifestyle considerations more so than lifespan elongation these are things to consider when assessing the evidence in making your final clinical decision but why is EBM so interesting all of a sudden it's been around for a long time it's been around since post-revolutionary Paris but a couple of things well four things in particular have caused it to be to gain a lot more traction in recent years first is that doctors need daily information about how to conduct their practice diagnosis prognosis therapy and mention that's always been the case but now doctors are seeing more patients than ever before and are seeing a wider variety of conditions that are before so daily information is needed second the textbook that doctors have often relied upon are now out-of-date research is coming in so fast and so furiously and so groundbreaking that very often the textbooks are simply not relevant anymore there are also too many journals to plow through you haven't got a lot of time to go through them all so what do you do your knowledge as a clinician is going to decline over time this is the nature of the world we're all getting older Rock retaining less information in our brains and renewing a little bit less we're getting wiser but we're knowing less you've got only a few seconds every day to deal with the mountain of evidence in between patients and you've got about 30 minutes a week that you can set aside to do additional reading to maintain your clinical expertise those are all some serious considerations that need a new practice and you process by which we could interrogate the literature to gain clinical expertise to answer clinical questions and that takes us to evidence-based medicine it's essentially a series of strategies for finding and appraising the best quality evidence it's the appraising part in which the epidemiology really kicks in how do we decide which evidence is good which is bad bad is not the best word here which evidence is good at which evidence perhaps is not as good as others EBM allows us to look at systematic reviews and look at summaries for ongoing research we will define our systematic reviews are in a second but follow that term away in your memory right now IBM also has spurred the development of evidence based journals these journals are now allowing us to do searches that we know are focused on good quality evidence and not just on opinion or one-off studies or things like that it really shortens the time span between having a question and finding relevant information we have new information systems now I'm talking about computers it'll allow us to search or very very quickly imagine doing the kinds of searches I'm going to show you in a second 20 30 years ago before there are computers we have to go to libraries and search through stacks of books to find five or six articles now we can find hundreds that's the tough icky touch of a key and that's going to change everything and lastly we have these new attitudes new generations of doctors have new attitudes towards lifelong learning that convinces them that they need to be abreast of current information in order to be the best possible conditions so here are the steps of vbm evidence-based medicine first is that we need to convert our need for information whatever it might be whatever your patient demands whatever clinical crisis is convincing you that you need to access literature need to convert that information into a question not just any kind of question but an answerable research question and we're gonna do this in a second as an example the second thing we need to do is we're gonna have to search the literature to find the best evidence to answer that question and again what is best evidence best evidence depends upon the rankings of studies based upon the epidemiology of those particular studies that you find the third step in EBM is to critically appraise that evidence for its validity impact and applicability we're going to decide if that evidence is in fact worthy of being included in the soup of evidence that will inform the answer to your question and lastly we're going to integrate that evidence with your particular clinical expertise what does that mean it means that you have wisdom gained from a lifetime spent treating patients or observing patients or doing research that you need to be able to bring to bear on top of all the research that you've currently conducted this is because EBM is not simply an automated mechanical computerized system that a machine can do it requires a human being to apply their skill sets plus their wisdom in order to make an appropriate clinical decision and lastly we like to evaluate whether or not we've done a good enough job to be honest very few practitioners do the evaluation phase but we encourage you to do so anyway so let's go through those three conditions that I mentioned just now validity impact and applicability what does it literally mean validity is the closeness to the true or the real world that the study that you find purports to be let me think of an example for you let's say you find a study that finds an association of connection a relationship between maternal diet and child's intelligence and they find that mothers who eat a lot of fat have children who grow to be very very intelligent but they measure intelligence via education level in other words the highest education to that child achieves is a proxy measurement for that child's intelligence now right away hope you see the problem just because you have a high education doesn't mean you're intelligent just because you have a low education doesn't mean you're not intelligent so education is an invalid measurement of intelligence so that study would fail the validity test a facetious example of a valid measure is to say that a score on an IQ test is a valid measurement of one's ability to write IQ tests a less facetious example is to say that an ultrasound scan is a very valid test for pregnancy impact is important so you may find a relationship between two factors that's relevant for your question but is the impact great enough to warrant your interest and by impact I mean effect size did the subjects in this study change by 5% 10% 20% what's relevant to your particular condition for your patient and lastly applicability you may have found a very relevant study that talks about something very similar to the questions you're asking it but is it applicable to your case maybe the studies you found were done on young men and your patient is an older woman you have to ask yourself is that distinction important for your particular circumstance so let's talk about now some of the studies that you might find and in another lecture we're gonna go further in detail into the qualities of these studies and why some might be more causal or of higher quality than others the first is the RCT or the randomized control trial this is when you've got a group of patients that have been randomly allocated into two groups one group received a treatment and the other group receives a placebo or control this is considered to be of the gold standard of evidence because we can reliably test for causal relationships to this treatment cause that outcome we like to find RCTs RCTs make us excited the next two studies are going to be cohort and case-control studies these are what we call observational studies we don't interfere in the variables of observational study and said we watch them unfold in the universe naturally the first observational study is the cohort design that's when we find some people who have been exposed to something of interest some other people who haven't been exposed and we look forward in time to see the proportions in each of those groups that determine an outcome similarly a case control is also an observational study it's backwards it's the opposite of a cohort study that's when we ascertain which patients have the outcome we care about those are the cases we find some other patients who don't have that outcome those are the controls with back in time to see who had the exposures of interest and again we're gonna go in greater detail into these designs in a future lecture k-series are a poor quality of evidence they are descriptions of individual patients and very often there is no control group involved a case report is one instance several reports make up a case series systematic reviews on the other hand are considered to be very good evidence depending upon the studies that are included in them a systematic review is a summary of literature of several studies that have been brought together to answer a larger question related to systematic review is a meta-analysis and some people confuse the two some people will use the term meta-analysis a systematic review interchangeably but they are distinct concepts a meta-analysis is when we take the summaries or the estimates from a variety of studies and mathematically synergize them all if that's a word we make one estimate from all of them mathematically a systematic review doesn't necessarily do that so a systematic review can include a meta-analysis where it doesn't have to so those are the six basic large categories of study types that we will find in our search again the RCT is the gold standard a systematic review of RCTs might even be better so what do we do with all this information now we're going to use our well phrase resource question to search for evidence to search for which studies are relevant to the question that we care about and we're going to apply what's called the pyramid of evidence to determine which studies we should perhaps give more weight to because they're probably better there are many ways to create an evidence pyramid but they're all sort of agree on several key features I want you now to guess about where certain kinds of studies may rank on evidence pyramid we're at the very top we have our best quality evidence at the bottom we have our least quality evidence so in vitro test-tube research where do you think that is at the bottom it's not very good evidence about case series already mentioned is not great evidence it's somewhere in the middle randomized control trials we think are among the gold standard the very best in the world so you know they're on top what do you think of animal research put it down here how about systematic reviews and meta-analyses well if you guessed on top you're right because a systematic review made up of randomized control trials is probably better than an individual randomized control trial about case reports well they're up there a case series cohort and case-control or observational studies and they're near the top as well and then we have opinions and they're near the bottom now in practice we tend to like systematic reviews a randomized controlled trials a lot when pressed we will include some observational studies as well we almost never go beneath the case control but sometimes we have to now they're different ways of phrasing or making an evidence pyramid I like this one this puts expert opinion at the bottom and systematic reviews and RCTs at the top now consider your personal experience whether you're watching government debates or things in the media what really wins the day it's not randomized control trials if it's not systematic reviews it's actually personal and expert opinion people's opinions carry a lot of weight in public but in science is the experiments and a systematic reviews of experience that carry weight so remember when dealing with policy we like to invert the pyramid sometimes okay now we're going to ask the research question this seems like it should be a straightforward easy thing to do but professionals who conduct systematic reviews or do EBM searches will spend a great amount of time focusing on phrasing the question as precisely as possible the more focused and precise the phraseology the more correct and targeted the result of a search will be there are many ways to phrase a research question correctly I like to use a method called the Pico method P ICO and those letters stand for certain the P stands for patient population so who is your patient population who's your patient what are his or hoar needs or cohorts the I stands for interventions what action are you considering the ste C stands for a comparison or a control group what are you comparing them to and lastly the O is outcome what is it you're trying to change or accomplish what's the disease state you're measuring so there are many different kinds of questions we could be asking as well there at least for one of them is therapeutic maybe you want to know what treatments are available that lead to certain kinds of outcomes for your patient maybe you want to know about how best to diagnose a patient's condition maybe you want to know about how likely is the patient going to have certain kinds of outcomes their prognosis or maybe you want to know what's the relationship between the disease and its possible cause otherwise a measurement of harm so let's do an example a doctor wants to research the effects of dietary fat on breast cancer risk maybe this doctor has a middle-aged female patient who has a family history of breast cancer and she wants to minimize her risk in all possible ways going forward because she knows this in her family history so she knows about the genetic portion of breast cancer now she wants to know a little bit more about the behavioral aspects of breast cancer and she wants to know whether removing dietary fat from her behavior will gain her any kind of an advantage going forward in not acquiring breast cancer like her mother did so what do you do you're going to search the literature you're going to employ EBM methods to do so you know do so by phrasing an appropriate question let's use the Pico method patient population well it's going to be adult woman because your patient is an adult woman the intervention you care about is dietary fat that's the thing that's going to make the change the comparison group you care about well there isn't really one we're comparing adult women to themselves really and the outcome we care about is breast cancer note the comparison group here is empty that's quite common the Pico method has some flexibility to it the type of question you're asking is an etiological one you're looking for relationships between exposures and outcomes that's relevant when you're looking through the studies that you find to see what their focus is is their focus a description is that idea logic it's behavioral as an opinion and you'll take any kind of study of this part even though you know at the end of the day you're going to apply the evidence pyramid and try to pick the best quality evidence so here is your question then adult women is dietary fat a risk factor for breast cancer that's the Pico outcome now you may think that you could have gotten that answer yourself without going through the Pico process and that's fine but some people need a framework to help them phrase these kinds of questions so what do you do now you actually apply the search there are lots of search engines you could go to so one of them is PubMed and that's a free search engine run by the American government and you can type in the appropriate keywords dietary fat treatment you can go to Google Scholar and type in keywords as well there are literally scores of possible search engines to try world science for example so at the end of all this what you will find is a host of studies that attempt to answer the question that you were phrase using the Pico method you're going to apply the period of evidence to establish which studies are most relevant to your case and from that you will apply your particular clinical expertise and extract a bit of wisdom that you can take to your patient so what do we learn we've looked at how to do the steps of evidence-based medicine we've looked at how to rank the kinds of studies that result from your search using EBM methods and you've used the Pico method to phrase a research question to allow you to undertake an EBM leadership search [Music] you

Channel: Lecturio Medical

Views: 48,239

Rating: 4.9356723 out of 5

Keywords: epidemiology crash course, epidemiology video lectures, epidemiology lecture, Epidemiology and Biostatistics: Introduction, biostatistics usmle step 1, biostatistics lecture

Id: hxF8i-t3pP0

Length: 51min 55sec (3115 seconds)

Published: Mon Sep 24 2018