Lecture notes ( first two weeks)
Lecture notes ( first two weeks) PUBH 3131
Popular in Epidemiology: Measuring Healht/Disease
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This 13 page Class Notes was uploaded by Gabriela Saint-Louis on Monday January 25, 2016. The Class Notes belongs to PUBH 3131 at George Washington University taught by Margaret Ulfers in Spring 2016. Since its upload, it has received 59 views. For similar materials see Epidemiology: Measuring Healht/Disease in Public Health at George Washington University.
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Date Created: 01/25/16
01122016 class lecture Learning Objectives review applicable jargon from public health distinguish between eminence and evidence based medicine what we are doing Epidemiology Measuring Health amp Disease whatgt measuring howgt whygt ultimate goal is to prevent disease or at least contol it and have an effect measure health and disease so we can do something about it Goals learn how Epidemiology fits into public health learn to apply these principles into public health ReviewgtJargon specific language of academic specialties Morbidity and Mortalitydisability and death disease distribution demographics time region classic bellcurve something of interest in terms of demographics Risk factors environmentalgenetic markers indicators determinants artifactual artifact of the way things are measured may make things appear to be an association but not real because of misinterpretation case an individual event ex one person with disease case fatality not just death but from the case incidence and prevalence risk of it how muchmany Hypothesis Etiology what causes the disease background gt how iimportforepidemiologyto help solve and prevent disease the more you know about etiology Health impact Burden of Disease Economics mortality individual vs group Evidence based vs eminence based medicine evidence tested and proven to work Eminence based respected well known subject matter expert ex observation of respected doctors medicine based on what seemed to suject matter experts that it works example back to sleep babies sleeping positions Levels of Prevention Primaryie education secondaryie early detection prevent from going to tertiary tertiarylimit morbidity and disabilitydeath Recurring themes difficulty in determining causality difficulty in studying populations human subjects gt pple are confusing public health vs personal health 011416 Class Lecture Populations Epidemiological Perspective adding dimensions changes what we see take home points epidemiology is scientific foundation of public health jargon goal is to measure data understand epidemiology perspective distinguish between medical and public health application history Epi101 The basic science of public health is a science gt scientific method Hallmarks da empirical amp measurable conclusions repeatable amp refutable What is epidemiology study of health on population levelgt good bad indifferent BTW off label uses of epidemiology the epidemiology of really refers to the results of epidemiologic studies on distribution major risk factors and etiology of disease This course is not on the epidemiology of gt but how to evaluate as opposed to incidental examples Epidemiology is a survey course surveying a moving target Epidemiology Friis s points population focus distribution determinants outcomes quantification control of health problems science more with focus on application Friis on Epidemiology is it a liberal art why would it matter has broader applications wirting aimed at undergrads push at havign it at lower level classes epidemiology is for everyone everyday etc self interest Public Health vs Medicine what distinguishes medicine from public health the way you describe population differs how you describe an individual where does Epidemiology fit often only evidence we have to make medical decisions Epidemiology vs other medical research looking at populations why Public health interest want to know on population level no other choice Human Populations by definition Populations must be a defined group defined by some common characteristics is a population always large NO Populations vs subpopulations every population is a subpopulation of the population of all humans ever born it s all relative to who it s a subpopulation of at least one characteristic differs between subgroups each distinguishing characteristic brings us closer to individual level Why does defining the population matter it s the denominator who we ve defined as the population An observational science never a completely experimental science because studying people gt observational mostly look at what happened and observe it ethicspeople no choice but to observe Drawbacks not repeatable cultural mindset amp sometimes can t Epidemiology vs Individual Epidemiology uses statistics which are group measures by definition stat always groups ex average ex your baby is an individual not a statistic you can t be having that side effect because only 1100 do Life expectancy what doe sit mean for the individual tells very little about an individual more so how s public health doing overall Uses of Epidemiology Historica gt understanding past community health gt understandingplanning Health services gt Best practices for cost now Risk assessment gt from population to individual Disease causality gt determine which associates real vs arti actual U PPONT History of Epidemiology term epidemiology didn t come into use until 1800 s epidemiologist even later only more recently really a separate field fo study pretty young as a science 1600 s Bills of mortality records in local courthouses of when people lived and died for tax purposes males to fight etc not out of public health concern John Graunt statistician true populations tudy considered age when making comparison quantified it standardizing of age to make comparisons analyzed patterns empirical data 1700 s Scurvy James Lind of groups of sailors 12 in 6 groups compared different treatments experimental approach fan early idea not an immediate success took 5060 years before the British Navy started using it 1700 s Chimney sweeps SirPercival Potts surgeon observed chimney sweeps higher rates of scrotal cancer to to the soot exposure note importance of comparisons even though not formally done still know others had lower rates 1800 s Data collection william Farr was an epidemiologist who studied medicine medical doctor by training collected data comp 1800 s Snow and Cholera cholera plague of the times couldkill you in 8 hoursminsunderstood untreatabel and deadly john snow doctor in london in mid 18003 identified cause of epidemic through descriptive and quantitative stats methods ie epidemiology Known as father of Epidemiology removed pumps so people couldn t get water made pa with lines for every case of cholera no cholera near brewery because they mostly drank what was brewed able to make convincing argument spoke to places to ask them where they got their water from 1800 s Snow and Ricketts Snow published paper on Rickets caused by Vitamin D ignored and forgotten the true dietary nature of rickets was not elucidated and accepted until 1920 s Snow amp Cholera pump handle put back on 1800 s Ignaz Philipp Semmelweis puerperal fever 1646 paris while doctor 2030 mortality rate for women giving birth in hospitals Hungarian OB working in Vienna Austria compared 2 clinics and showed deaths due to cadaverous materials on student doctors other clinic was with midwife mortality rate reduced to 1 with hand washing increased following his departure huge controversy in medical community 1900 s Noninfectious disease 1948 FHS began large studies where began following populations for diseases started studying chronic diseases and doing studies following people Biostatistics many mentioned were staticians if epi is science of public health biostats is math of science 1930 s RAFisher developed concept of null hypothesis and statistics test 1948 FHS began 1950 s BradfordHill and Richard don used statistical methods to quantify certain risks from smoking 197320005 Bogalusa heart study History is English Centric Version illustratesgtnot standard scientific progression multidisciplinary relatively young tends to be build by glacial accretion as a science epidemiology more about the tools than the results biostatistics is important tooland a lot of what we use is very new 01192016 A transition in Epidemiology judging by this history of epidemiology what was the focus in the past infections communicable diseases what did it shift to chronic diseases is that shift permanent no What are we measuring in Epi health and disease what is necessary in order to measure something some type of scale Quantifiable units gather statistics an agreed on and clear definition that allows for consistent quanitifcation Definitions HeaHh how has it been defined for you so far From 1948 WHO statement a state of complete physical mental social wellbeing and not merely the absence of disease of infirmity original root word just meant to be whole looking for what s missing The basics vital statistics refers to birth and death includes marriagedivorce records most common public records historically how long someone lived generally gives you the where who age gender race when and how can be used to calculate group descriptors average life span gt this is where we get life expectancy from birth and death rates statistics by subgroup sex age places etc Measuring health can you measure complete well being what can we measure life expectancy may be specific to subgroups or what the average lifespan was pros minimum requirement for health is to be alive fairly easy to compute cons maybe too minimal only an average so expected part is iffy based on past experience Health adjusted life expectancy subtracts expected days with disabilitiesillness pros closer to defnition of health cons expectancy on average subjectivity aspect various quality of life scales QoL Happiness indexes Hale and QoL mostly physical the happiness indexes try to look at level of functioning as well but are you happy or not this is a new area of measuring health new scales gt all scales have some issues of subjectivity Measurement issue subjectivityinconsistency easily result in inconsistencies Measuring the complement of health limited in trying to completely measure health so we often measure the complement of health scientists love this trick the easier thing to measure for example if you can t count life count deaths if we can t measure health can we measure those thigns that impinge upon it morbidity mortality Outcomes adverse health events negative parts Broad Categroeis Direct disease injury Mental healthQoL Indirect Behaviorsconditions that are known disease or injury risks Reality Check what is disease this is the one we tend to focus on the most generally thinking of a process mostly use this term in a narrow sense where there is some sort of physiological dysfunction that s part of a process General disease categories Infections disease disease resulting from the presence of a biological agent reproducing in the host ex parasites bacteria viruses prions may or may not be communicable must produce disease symptoms eventually generally acute short latency What s the difference httpemergencycdcgovpreparednessquarantine infections communicable contagious directly transferable very communicable and directly from someone where epidemiologists start to get excited Review communicable disease transmission direct transmission person to person indirect transmission vehicle transmission inanimate on somethinggttoilets doorknobs vector transmission animategt mosquitoes birds fleasticks aka vector borne Airborne bacteria virus can float in air Either What s fomite other matter General disease categories Chronic diseases generally not thought to be infectious long lasting condition gt3 months according to NCHS often with slowly worsening conditions ex cancers diabetes CVD osteoporosis asthma often unknown etiology particularly in individual less of a strict category than infectious Communicable vs Chronic Diagnosis communicable generally straightforward diagnosis requries accurate knowledge of pathogen Chronic complicatedoften must rule out infectious requires agreed on standardsdefinitions Communicable vs Chronic mortality communicable generally highest in youngvery old immune system strength higher when little access to medical care communicable tends to be treatable but no access makes it deadly may be quickly fatal chronic higher rates with age seen more in absence of other causes of death not as fatal Communicable vs chronic risk factors communicable exposure to infectious agent immunity factors suppressedweakened immune system unvaccinated not previously exposed populations chronic often lifestylebehavior factors genetics various and multiple exposures communicable vs Chronic Epidemiology research focus communicable prevention treatment chronic understanding causation pathways mostly for prevention purposes early diagnosis if not prevention catch it early Which is it cervical cancer lyme disease it is infectious food poisoning doesn t fit in either generally not communicable but not infectious AIDS Blindness there are things that result in chronic disease but caused by infection diseases don t always fit in categories We could categorize as chronic vs Acute communicable vs noncommunicable 1212016 Kkoch and causation 1 organism present in every disease case 2 it must be isolated and grown in pure culture 3 inoculation with culture 4 etc Koch and causationtoday original postulatees based on infectious disease no role of chance cant be applied to organisms that only infect humans need to find causes 0 noninectious disease need to identify causes that don t always result in disease leeds to importance of concepts risk and risk factors Defining risk risk implies the role of chance i something increases or diminishes your risk it s like tossing a dice it is the probability that something probably bad will happen Risk actors are those things that aect risk Determinants of health another word or cause and alot 0 times they are risk factors biological lifestylebehavior environment access to health care according to Friis Causal Models there are different ways to picture causality keep in mind individual vs populations issues consider how they address the role of chance there are models modesl and then models Triangles also known as epidemiological Triad Hostgt environmentgt agent generally the pathogen Wheels of causation figure 35 in a persona ll of these things genrally affect whether they have this disease or not Web of Causation how do the risks interact Direct vs Indirect Causal Models Direct A causes B Indirect C influences A which then cases B subsequent models thend to look like clear path ways A gt B gt C etc Practical exaples OrNecessary Sufficient and the Component Cause Model A is Necessary if Must have A toget B example infectious Diseases you don t have Aids without having HIV Sufficient if A alone results in B may apply to a set of causes risk factors A and C are component causes i in causal in combination gt may be different combinations that are causal gt may be combined with directindirect terms Rothman s Weels Sufficient case Sufficient cause I ll some set of things if all are present you will get the disease if you remove any one of them you don t get disease or a specific disease there are several different combinational factors these models illustrate sufficient component causes What is necessary if only and II lead to disease there is almost always a factor that is unknown does not show relationship between components each separate piece can be called a contributory cause Contriutory causes in component cause model contributory causes need to be neither necessary nor sufficient ex smoking has a causal relationship with cancer smoking is neither sufficient or necessary Most risk factors we study are contributory if something is necessary easy to study and not requrie a lot of studies to prove that Directed Acyclic Graphs graphical way to describe causal asassociations direction of causal chain indicated by arrows can not be a loop acyclic arrows do not indicate sole causes D gt Y extended to looking tthings on a group level at an individual you may not have all of these things POpulation vs Individual models do not always make clear distinctions between population and individual may be multiple causes in population but not individual may be multiple causes component cause for individual case epi studies look at causation on population level What studies find Associations how much do we know that something is truly causal association does not imply a direction says nothing about direct indirectnecessary or sufficient weasel word epidemiologists like it actually most accurate description of results How do we decide an association is a cause if we cant use Koch s postulates The scales of epidemiology criteria that researchers have come up with if we meet enough of them we say it s causal and if not we say it wasn t Dr Ulfers Quick Guide to Causal Criteria Caveats 1 STRENGTH OF ASSOCIATION Interpreted here as a large effect aproblematic if artifactual or by chance from small sample how large is strong enough Sometimes people interpret this as statistical strength or a small pvalue but this may be due to a small effect found in a very large sample so we won t this will make sense later J 2 CONSISTENCY Multiple studies addressing the same question give same results a Problematic if same artifactual associations found in all studies a Best if same results under different conditions andor with different designs 3 SPECIFICITY A risk factor produces one particular effect that is generally not an effect seen from other risk factors example vinyl chloride rare exposure is risk factor for hepatic angiosarcoma rare outcome a Problematic in that it rarely applies in noncommunicable diseases few causal associations aside from pathogens leading to infectious disease are truly specific 4 TEMPORALITY Exposure precedes disease Will disprove causal association if refuted ie if the cause did NOT occurexist before the effect it can t be the causeD a Problematic because a lot of things came before the disease a The most abused of the criteria 5 BIOLOGICAL GRADIENT aka Doseresponse relationship but response seen on a group level a Problematic if artifactual association and false factor closely associated with true cause a Can t see this if only yesno exposure status 6 PLAUSIBILITY Makes biological sense aProblematic because sometimes accepted knowledge is incomplete or even wrong 7 COHERENCE Fits in with other research findings ex Smoking causing lung cancer fits with more men die from lung cancer more men smoke carcinogens in smoke etc etc 8 ANALOGY Other disease models have similar mechanisms a Both 7 and 8 similar to plausibility with similar caveats Some comments on repeatability scientific principle that a study sresults should be confirmed byr epetition yielding the same results rarely f ever doen in medical studies consistency is as close as we generally get and it s nto quite the same thing
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