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Epidemiology Week 1 Definitions Epidemiology: study of the distribution and determinants of disease in (MacMahon and Pugh, 1970) Epidemiology: study of the distribution and determinants of states or events in specified populations, and the application of this study to control of health (Porta, 2008)...

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Epidemiology Week 1 Defini t i ons Epi demi ol ogy :’ Thes t udyoft hedi s t r i but i onanddet er mi nant sofdi seas ei nhumans ’ ( Mac MahonandPugh,1970) Epi demi ol ogy :’ Thes t udyoft hedi st r i but i onanddet er mi nant sofheal t hr el at eds t at esor ev ent si ns peci fi edpopul at i ons ,andt heappl i cat i onoft hi ss t udyt oc ont r olofheal t h pr obl ems ’( Por t a,2008) 1.t hatepi demi ol ogi s t sar e notonl yconc er nedwi t hdeat h,di s eas eanddi s abi l i t y al soi nt er es t edi ngoodheal t hs t at es So,t hi sc ov er sawi despec t r um ofheal t hr el at eds t at esandev ent s-any t hi ngt hatmi ght affec taper s on’ sheal t h,whet heri tbegoodorbad

2.t hatepi demi ol ogi s t sar ei nt er es t edi nbot h: t hedi s t r i but i onofdi seas e;t he‘ per s on,pl aceandt i me’ oft heev entunderst udy WHATdi s eas e/ condi t i oni spr esenti nex c es s ? WHO i saffect ed? WHEREi si tocc ur r i ng? WHENi si toc cur r i ng? t hedet er mi nant sorcaus esofdi s eas e/ condi t i on; WHYar es omepeopl ei l landot her snot ? 3.Thedefini t i oni sext endedt oi ncl udet heappl i cat i onoft hi si nf or mat i ont ot hecont r olof heal t hpr obl ems. TheUs eofepi demi ol ogy •Under s t andi ngt heus eofnat ur als t or yofdi seas e •Epi demi ol ogi st sconc er nedwi t ht henat ur alhi s t or y( cour s eandout c ome)ofdi s eas ei n bot hi ndi v i dual sandgr oups •Of t ens ubcl i ni cal di seas ei spr es entbef or et hes y mpt omsbecomes ev er eenoughf oran i ndi vi dual t os eekmedi calat t ent i on–Henc edi s eas edet ect i onatanear l ys t ageopenst he wayf orsc r eeni ngpr ogr amst oi mpr ov eheal t hout c omes •Nat ur alcour seofadi seases houl dbeunder s t oodbef or ei mpl ement i ngev en ex per i ment als cr eeni ngpr ogr amsandt r eat ment s-l i t t l epoi nti nsc r eeni ngandear l y i nt er v ent i oni ft hes edonoti mpr ov eout come

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2.Descr i pt i onofheal t hst at usofpopul at i ons Whati sheal t h?‘ . . . ast at eofphy s i cal ,ment alands oci al wel l bei ngofi ndi v i dual s ’ ( WHO, 1948) . Howev er ,i t sconv er s e,i l l nes s( t hes y mpt omsofi l lheal t h)anddi seas e( di agnos ed c ondi t i ons )ar ewhatar eus ual l ymeas ur edi nass es s i ngt he“ heal t h”ofacommuni t y ,eg. i nci denc e,pr ev al enc e( mor ei nf ut ur el ec t ur es )

3.Causat i on bes tr ec ogni s edus eofepi demi ol ogyi si nt heel uc i dat i onoft hecaus esofdi seas e genet i cf act or shav ebeeni dent i fi ed env i r onment aldet er mi nant sar ec r uc i al , 4.Eval uat i onofi nt er vent i ons Epi demi ol ogi calst udymet hodspl ayani mpor t antr ol ei nt heev al uat i onoft he effec t i v enes sofi nt er v ent i onst r at egi es Thi sc ani nc l ude •t heev al uat i onofdi ffer entt r eat ment sf orapar t i c ul ardi s eas eegdr ugt r i al s •di ffer entmeas ur esori nt er v ent i onst opr ev entac ci dent sordi s eas e,or •anev al uat i onoft heeffec t i v enes sandeffic i enc yofheal t hs er v i c es

Week 2 PREVELANCE s eas et hatocc uri napopul at i onatr i skt hati s •s naps hot :Ref er st onew casesofdi f ol l owedovert i me •meas ur eoft hebur denofdi s eas e •poi ntpr ev al ence •per i odpr ev al ence •f act or s :dur at i onofdi s eas e,i nci denc eofdi s eas e •i nci denc eSt eps : 1.I dent i f yapopul at i onatr i s k 2.Fol l owt hepopul at i onov ert i me 3.Rec or dnewcas esofdi s eas east heyoc cur i nci denceRat e

-Thenumberofnewcas esdev el opedperuni tofper s ont i me( e. g.per sony ear s) ac count sf orv ar yi ngout comes Cumul at i veI nci dence

= A=numberofs ubj ect sdev el opi ngdi s eas edur i ngat i meper i od/N= numberofs ubj ect s f ol l owedf ort het i meper i od =A/ N -Val uet endst oi nc r eas ewi t hi ncr eas edper i odoff ol l owup( needt ospeci f yt i meper i od) Pot ent i alPr obl em:Compet i ngRi s k s :l essofapr obl em i nshor tt i meper i ods ,s ubj ect scoul d beatr i s kduet oot herf ac t or s Pot ent i alPr obl em:Los st oFol l owup:peopl ebecomemi s s i ngordi ebyt heendoft heper i od oft i me

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•us i ngper sont i meandcal cul at i ngt hei nc i dencer at et hesec anbeav oi ded Denomi nat ori st hes um ofeac hi ndi vi dual ’ st i meatr i s k I nc i denc er at e: Numberofnewcas es /t ot alper sont i meatr i s k

Thel ear ni ngout c omesf ort hi st opi car e:

•Tobeabl et odefineepi demi ol ogyandout l i nei t sscope •Tobef ami l i arwi t ht hehi s t or i c aldev el opmentofepi demi ol ogy •Tobef ami l i arwi t ht hepur pos esofepi demi ol ogi calr es ear c h,i nc l udi ngt heappl i cat i onsof epi demi ol ogyi npubl i cheal t h •Toappr ec i at et henonex per i ment al nat ur eofmostepi demi ol ogyandt hepr obl ems i nher enti nt hi s •Tobeabl et odes c r i besomeoft hec ont r i but i onsofepi demi ol ogyt ot hei mpr ov ementof t heheal t hst at usofpopul at i ons •Tounder st andt hei mpor t anc eofus i ngr at esandpr opor t i ons ,r at hert hans i mpl ec ount sof di seas eev ent s ,f ormaki ngc ompar i s onsofdi seas ebur den. •Tobeabl et odes c r i bet hec omposi t i onofar at eandpr opor t i oni nt er msoft henumer at or anddenomi nat orandt ounder st andt her el at i ons hi pbet weent hem. •Tobeabl et odefi nepr ev al enceandi nci denc e,st at et her el at i ons hi pbet weent hem,t he us esofeac handt oi dent i f yf act or st hatmaycaus ev ar i at i oni neachmeas ur ement . •Tobeabl et ocal cul at et hef ol l owi ngmeas ur esofdi s eas ef r equenc y : • Pr ev al ence • Cumul at i v ei nc i dence • I nci dencer at e •Tounder st andt henat ur eofc ommonl yus edmeas ur esofass oc i at i on,andt hei r appl i cat i oni nas ses s i ngc ausal i t yandpubl i cheal t hi mpor t anceofanex posur e Toc al c ul at eandi nt er pr etdi ffer entr at i oanddi ffer encemeas ur esofass oc i at i onf oc us i ngon t hesampl eonl yorgoi ngbey ondt hatt oapar t i cul arpopul at i onofi nt er es t

Measures of association: -wemeas ur eas soc i at i onbet weenadi s easeandanex posur e -Twof ac t or sar eas soc i at edwhent heocc ur r enceofonei sr el at edt ot heoc cur r enceoft he ot her -Todet er mi neas s oci at i on,wemus tdet er mi newhet hert her ei sanex c es sr i s kofdi seas e r el at i ngt oc er t ai nex posur e Rat i oofr i s k s( Rel at i v er i s k) -Ri s kr at i o -Rat er at i o Di ffer encei nr i sk s( At t r i but abl er i s k) -Ri s kdi ffer ence -Rat edi ffer ence

Abs ol ut eRi s k :

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I nc i denc e/ r i s ki ngr oupofpeopl eofc er t ai n

ex posur e Nocompar i s onwi t hnonex pos ed Rel at i v eRi sk : Compar ei nc i dence/ r i s ki nex pos edwi t h i nci denc e/ r i s ki nnonexpos ed

At t r i but abl eRi sk

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At t r i but abl er i s ki sameas ur eoft he maxi mum r educt i oni nr i s kof di seas et hatwecanex pectamong t heex pos edi ft heexpos ur ewas c ompl et el yel i mi nat ed

I nacas ec ont r ols t udy ,t heodds r at i oi sdefined ast her at i ooft heoddst hatt hec aseswer e ex posedt ot heoddst hatt hecont r ol swer e ex posed. Popul at i onAt t r i but abl eRi s k Whatpr opor t i onoft hedi s eas e i nci denc ei nt het ot al popul at i oni s at t r i but abl et ot heexpos ur e Thet ot alpopul at i oni nc l udesbot h ex posedandnonexpos ed Al l owst oes t i mat et hemaxi mum i mpactofapr ev ent i onpr ogr am I ncohor ts t udywecancal c ul at er i s k : Wehav epopul at i onatr i skast hedenomi nat or Wehav ecompar i s ongr oupt ocal c ul at eex ces sr i sk Ri s kc annotbecal c ul at eddi r ec t l yi nac asecont r ols t udy Wedon’ tknowf r om whi c hpopul at i onatr i s kt hecas escome

OddsRat i oi sanal t er nat i v ewayt omeas ur et he“ r el at i v er i sk ”t hatcanbeusedi nc asecont r ol s t udi es =ad/ bc Oddst hatTeam Awi l lwi nt hemat c h OddsRat i oi naCohor tSt udy Pr obabi l i t yt hatTeam Awi l lwi nt hemat c h/ Pr obabi l i t yt hatTeam Awi l ll oset hemat ch

I nacohor ts t udy ,t heoddsr at i oi sdefinedast he r at i ooft heoddsofdev el opmentofdi s eas ei n ex posedper s onst ot heoddsofdev el opmentof di seas ei nnonexpos edper sons

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TOPIC 4: STUDY DESIGNS 

Observational

 Interventional Observational study

Interventional study Randomized controlled trials Non randomized controlled trials

Cohort Case-control

Cross-sectional Case-series Correlational studies

DESCRIPTIVE STUDIES  Person: What population or subgroup do or do not develop a disease  Place: In which geographic location it is most or least common  Time: How the frequency of occurrence of disease varies over time  Uses: - for efficient allocation of resources - to help formulate hypothesis CORRELATIONAL STUDIES (ecological study)  uses data from an entire population  compares disease frequencies and characteristic - between groups during the same period - in the same population at different points in time  can only raise a hypothesis not conclude because - Refers to whole population: Ecological fallacy - Other factors may be different between countries - Other factors may be different at different point in time in the same population CASE REPORT  Is the most basic type of descriptive study of individuals  Carefully detailed report by clinicians of the profile of a single patient CASE SERIES ▪ Case reports can be expanded to a case series

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▪ Describes the characteristics of a number of patients with a given disease ▪ Routine surveillance programs often use accumulating case reports to suggest the emergence of new diseases or epidemics

Cross-sectional studies  Snapshot  The status of an individual with respect to the presence or absence of both exposure and disease in assessed at the same point in time  Cannot usually discern the temporal relationship between an exposure and disease

Why descriptive studies cannot test hypothesis?  both Case report and Case studies lack comparison groups  As a result, they can only generate hypothesis, but they cannot test a hypothesis.  Correlational studies- data on population than individual  Case report and case series- lack comparison groups  Cross sectional studies- cannot usually discern the temporal relationship between an exposure and disease  The temporal relationship between exposure and disease can not be clearly determined.  Cross-sectional studies are useful for raising questions rather than testing hypothesis. ANALYTIC STUDIES ▪ Focuses on determinants of an outcome ▪ Test a hypothesis  Studying individuals  Collecting information backed by temporality  Using an appropriate comparison group

Types:

• Observational study Ø Investigator observes

• Interventional study Ø Investigator intervenes Ø Investigator decides who will be exposed and who will be not

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▪ Two basic types: - Case control study - Cohort study ▪ In theory: Possible to test a hypothesis using either design strategy ▪ In practice: Need to choose one - Each design offers certain unique advantages and disadvantages

Case Control Study • A group of individuals

• A group of individuals

who have the outcome of

who do not have the

interest

outcome of interest

• Cases

• Controls

The proportion with the exposure of interest in each group compared

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COHORT STUDY

 The two groups are followed for a specified period of time  The duration of follow-up depends on how common or rare the disease is

TOPIC 5: ROLES OF CHANCE AND BIAS Evaluation of epidemiologic associations

• Have the association been observed by chance? • Use statistical tests to evaluate the role of chance • Could the association be due to bias? • Evaluate the effects of systematic errors in the design, conduct and analysis of the study • Could other factors have accounted for the observed relationship? • Control confounding in the design or analysis of the study

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Target population

Source population/

study population Study sample    

Parameters and association

Estimation and inference

Statistics

Research question relates to a target population Identify a source population or study population – represent target population Cannot include everyone of your source population or study population Take a sample - represent source or study population

Validity ▪ Internal validity - Lack of random errors (precision) - Lack of systematic errors or bias (accurate) - Lack of confounding ▪ External validity - Ability to generalize study findings to the source, study or target population - Are study groups representative of the study population?

Accuracy (lack of systematic error) and precision (lack of random error)

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Bias •

•

•

Systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on disease Selection Bias – Errors in selection of study participants – Virtually all studies select/sample subjects from a larger population – Selecting subjects for a study from a larger referent population is not the same as selection bias – The process of selecting subjects for a study from a larger population potentially affects external validity or generalizability – Selection bias affects the comparisons within the study or internal validity – Information Bias – Errors in procedures for gathering relevant information – Systematic error in obtaining exposure or outcome information regarding subjects in the study Types and Sources of Information Bias - Recall bias - Reporting bias - Surveillance bias - Bias in interviewing - Bias from surrogate interviews

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-

Bias in abstracting records Losses to follow-up

Evaluating Bias • Why did it occur? • What effect does it have on the observed association? • What can be done to control for bias in this study, and to prevent it in future studies? Preventing Bias • Careful attention to design and sampling • Minimize non-response, loss to follow-up • Standardization of measurements • Training and quality control • Blinding

TOPIC 6: COUNFOUNDERS o Mixing of effects when the exposure we are interested in is mixed up with the effects of some other factor o Exists when an association between an exposure and an outcome is observed because of a third variable o Third variable: confounding variable or confounder o Example: Urban and CHD incidence  Can be affected by the ages of the urban population, increasing the incidence of CHD  ages are a confounding variable

o General rule for confounder: In a study of whether factor A is a known risk factor for disease B, factor X is a confounder if factor X is associated with factor A but is not a result of factor A AND if factor X is known risk factor for disease B o Confounder is not a confounder If it is a result to the exposure

TOPIC 7A: ASSOCATION AND CAUSATION Introduction • Measures of association in epidemiological studies are used to investigate the etiology, treatment and prevention of disease. 12

Epidemiological measures of association are calculated by categorising the population into two or more groups. The rate of disease in each category is compare with that of a single reference • An association is identified if the exposure increase or decrease the risk of disease in one or more groups. • A positive association is said to exist if the exposure tends to increase risk. A negative association is said to exist if the exposure tends to decrease risk. • Epidemiological studies are aimed at quantifying the level of increased risk when exposed to a particular factor. The effect measure can be obtain to quantify the strength of the association. • However, most diseases do not have a single identifiable cause. It appears that most diseases are multifactorial and represent the effects of a combination of different factors. • Therefore, “association” should not be confused with the “causation”. That is, the discovery of a statistical association between an exposure and disease should not immediately be construed as causal. • Associations can provide an important link in determining cause and effect. What is a cause?  A single condition or event that inevitably leads to a particular effect or outcome.  A one-to-one relationship so that wherever or whenever the cause occurs, the effect will also be found. •

Example: What is required to produce light from a lamp? → activate the switch and the light will come on. But, what if the bulb has blown, if the wiring is faulty or there is no power supply? To ‘cause’ the light to come on, need combination of power, wiring and a viable light bulb. Separate requirements known as component causes, as they are all components of the sufficient cause that will inevitably lead to the light coming on, the ‘effect’. General Mode of Causality:  Cause is a result of interactions between the host and his/her genetic make up,  and the surrounding physical, biological and social environments. Defintions of causes 1. ‘an event, condition, characteristic or combination of these factors that, when it changes in frequency or quality, results in a corresponding change in the frequency of disease or the outcome of interest’. 2. a sufficient cause will inevitably produce or initiate disease 3. a necessary cause is any agent or component of a sufficient cause that is required for the development of the disease 4. a component cause is any component of a sufficient cause that is not an absolute requirement for the development of disease Casual Pies

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I

A B

E C

  

D

II

III

A

A

B

H F

G

C

J F

I

I, II & III are three different sufficient causes for a disease A is a necessary cause as it is present in all three sufficient causes A, B, C, D, E F, G, H, I and J are component causes

Removable or Modifiable Causes  Not necessary to address all components to eliminate a sufficient cause to reduce the occurrence of disease.  An important concept:  Causes of many diseases are complex; unlikely to be able to identify all components.  By just identifying one or two, could still prevent large proportion of disease  Potential outcomes from Results in a Study  No Statistical Association eg RR=1  Statistical Association: could be: - due to chance or bias - due to uncontrolled confounding - causal. Chance, Bias and Confounding Must rule them out to infer cause Internal Validity MUST be established first before external validity or causation can be considered From association to causation • If the study has shown that an association exists, the a question will be asked: “Does the association have a causal significance?” • Statistical methods cannot establish proof of a causal relationship in an association. The causal significance of an association is a matter of judgment which goes beyond any statistical probability. • There are a number of criteria must be used to judge or evaluate causal significance

Bradford ihlls Criteria for Causality 14

1. . Strength of association Weak *: RR or OR< 1.5 Modest: RR or OR 1.5 – 2.0 Moderate: RR or OR 2.0 – 3.0 Fairly strong: RR or OR 3.0 – 4.0 Strong *: RR or OR > 4.0 * The strongest measures of association are less likely to be affected by bias or confounding * Weaker effect does not mean it cannot be causal – only harder to eliminate error as a possible explanation 2. Consistency of results  with other studies in different settings (eg animal experiments), times and populations  If inconsistency can’t be explained by variation in design or conduct – can’t make a judgement about causation 3. Dose-response pattern  If factor does cause disease then risk of getting disease is r...