What qualities should a disease have to make it worthwhile to investigate?

Disease investigations should have some public health significanceThe disease is important in terms of the number of individuals it affectsThe disease is important in terms of the types of populations it affectsThe disease is important in terms of its causal pathway or risk characteristics

Research Questions/Hypotheses

Is there an association between Exposure (E) & Disease (D)?Hypothesis: Do persons with exposure have higher levels of disease than persons without exposure?Is the association real, i.e. causal?Sever

Big PictureLook for links between exposure & diseaseto intervene and prevent disease

Look to identify what may cause diseaseBasic definition of causeexposure that leads to new cases of diseaseremove exposure and most cases do not occur

Big PictureOn a population basisAn increase in the level of a causal factor will be accompanied by an increase in the incidence of disease (all other things being equal).If the causal factor is eliminated or reduced, the frequency of disease will decline

Infectious Disease EpidemiologyInvestigations/studies are undertaken to demonstrate a link [relationship or association] between an agent (or a vector or vehicle carrying the agent) and diseaseExposureDisease[ Agent ][ Vector/Vehicle ]

Injury EpidemiologyStudies are undertaken to demonstrate a link [association] between an agent / condition and an injury outcomeExposureDisease[ Agent Energy Transfer ][ Vehicle carrying the agent automobile ][ Condition Risk taking behaviour ]

Chronic Disease EpidemiologyStudies are undertaken to demonstrate a link [relationship or association] between a condition/agent and diseaseExposureDisease[ Condition e.g. gene, environment ]

Issues to consider

Etiology (cause) of chronic disease is often difficult to determineMany exposures cause more than one outcome Outcomes may be due to a multiple exposures or continual exposure over time Causes may differ by individual

ExposureORGenetic BackgroundORCombination of BothDisease or Other Outcome? Causation ?AssociationSuppose we determine that an exposure is associated with disease.How do we know if the observed association reflects a causal relationship?

First step in determining causation: Understanding disease etiologyExperimental studiesin vitro systemsanimal studies in controlled environmentsAllows forcontrol of precise dosecontrol of environmental conditionsloss to follow up kept to a minimumProblems withextrapolating data to human populationshuman diseases with no good animal modelsClinical pathologies

Second step in determining causation: Conducting Studies in Human Populations

Heres where Epidemiology is important.

Epidemiology capitalizes on natural or unplanned experiments. We take advantage of groups who have been exposed for non-study purposes.

All of the study designs are important here and provide different evidence for or against a causal hypothesis.

Figure 14-3 A frequent sequence of studies in human populations.Downloaded from: StudentConsult (on 11 September 2009 07:00 PM) 2005 Elsevier

Causation and AssociationEpidemiology does not determine the cause of a disease in a given individual

Instead, it determines the relationship or association between a given exposure and frequency of disease in populations

We infer causation based upon the association and several other factors

Association vs. CausationAssociation - an identifiable relationship between an exposure and diseaseimplies that exposure might cause diseaseexposures associated with a difference in disease risk are often called risk factors

Most often, we design interventions based upon associations

Association vs. CausationCausation - implies that there is a true mechanism that leads from exposure to disease

Finding an association does not make it causal

ASOSIASI VS KAUSASIUntuk memutuskan apakah eksposur A menyebabkabn penyakit B, kita pertama kali harus menemukan apakah dua variabel itu berasosiasi, misal apakah satu ditemukan lebih umum pada adanya yang lain.

CAUSAL ASSOCIATIONADALAH SUATU ASOSIASI ANTARA 2 KONDISI ATAU KARAKTERISTIK DIMANA BILA TERJADI PERUBAHAN (MIS: PERUBAHAN JUMLAH ATAU PERUBAHAN KUALITAS) PADA 1 KONDISI, MAKA AKAN DIIKUTI OLEH PERUBAHAN PADA KONDISI YANG LAINNYA

CAUSALITY OR CAUSAL ASSOCIATIONBERHUBUNGAN DENGAN CAUSE EFFECT RELATIONSHIPS UNTUK MENENTUKAN SEBERAPA BERBEDA SUATU KONDISI (MIS: FAKTOR RISIKO) BERHUBUNGAN DENGAN KONDISI LAINNYA (MIS: PENYAKIT) MENENTUKAN PENYEBAB ATAU ETIOLOGI

Two step process to carry out studies and evaluate evidence1.Determine if an association is presentEcologic studies: studies of group characteristicsCross-sectional studies: studies at one particular timeCase-control or cohort studies: studies of individual characteristics.2. If an association is demonstrated, determine whether the observed association is likely to be a causal one using pre-determined criteria.

General Models of CausationCause: event or condition that plays an role in producing occurrence of a diseaseHow do we establish cause in situationsthat involve multiple factors/conditions?For example, there is the view thatmost diseases are caused by theinterplay of genetic and Environmental factors.

How do we establish cause?General Models of CausationAdditional Factors

Web of CausationThere is no single causeCauses of disease are interactingIllustrates the interconnectedness of possible causesRS Bhopal

Understanding CausalityTypes of AssociationcausalnoncausalTypes of Causal relationshipsdirectindirectTypes of causal factorssufficientnecessary

Jenis-jenis hubungan sebab-akibat

Tidak berhubungan secara statistik

Berhubungan secara statistik kausal langsung kausal tidak langsung

TYPES OF ASSOCIATIONA. Not statistically associated (independent)B. Statistically associated1. Noncasually (secondarily associated)2. Causially associated a. Indirectly associated b. Directly casual

Hampir semua statistik berusaha menemukan apakah dua variabel berhubungan, dan jika demikian, seberapa kuat, dan apakah chance (kebetulan) dapat menjelaskan asosiasi yang diamati.

Statistik terutama dirancang untuk menilai peranan chance dalam asosiasi itu.

Suatu nilai p hanya menceritakan kepada kita seberapa mungkin asosiasi itu mempunyai peningkatan secara kebetulan.Oleh sebab itu, Analisis statistis sendiri tidak dapat membangun bukti hubungan kausal.

Figure 14-5 Types of associations.Downloaded from: StudentConsult (on 11 September 2009 07:00 PM) 2005 Elsevier

The relationship between coffee consumption and pancreatic cancerIn 1981, MacMahon et al. reported results from a case-control study of cancer of the pancreas.

There was an apparent dose response relationship between coffee consumption and cancer of the pancreas, particularly in women.

Was the disease caused by coffee consumption or by some factor closely related to coffee consumption?MacMahon B, et al. N Engl J Med 1981 304:630 - 33

The relationship between coffee consumption and pancreatic cancerSmoking is closely associated with both pancreatic cancer and coffee consumption.

There were many issues with control selection and measurement of exposure levels in cases and controls.

Subsequent studies were unable to reproduce the result.

MacMahon B, et al. N Engl J Med 1981 304:630 - 33

Interpreting Associations - Causal and Non-CausalCausal Non-Causal (due to confounding)Coffee ConsumptionCoffee ConsumptionPancreatic CancerSmokingPancreatic CancerSpuriousAssociation

Real Association

Real Association

Two Types of Association: Real and SpuriousA real association is present if the probability of occurrence of an event orthe quantity of a variabledepends upon the occurrence of one or more other events, characteristics or variables.

Spurious associations refer to non-causal associations due to chance, bias, failure to control for extraneous variables (confounding), etc.

Why is it important to distinguish between causal and non-causal associations?Causal relationships are used to make public health decisions and design interventions.

In our example, if smoking was indeed causal, it would be irresponsible to target coffee drinking as an intervention.

Very important to consider all confounders.

Hubungan faktor dg penyakit1. Hubungan statistik KAUSAL:a. Langsung/DIRECT* Dua arah* Searahb. Tak langsung/INDIRECT2. Hubungan substantif

HUBUNGAN KAUSALDIRECT vs INDIRECT

DIRECT (LANGSUNG):FAKTOR OUTCOME

INDIRECT (TIDAK LANGSUNG):FAKTOROUTCOMEStep1 Step2

Figure 14-12 Direct versus indirect causes of disease.Downloaded from: StudentConsult (on 11 September 2009 07:00 PM) 2005 Elsevier

Types of Causal Relationships: Direct vs IndirectFactorFactor 1DiseaseFactor 2Factor 3Factor 4DiseaseDirectIndirect

Types of Causal Relationships: Direct vs IndirectF508 PolymorphismHigh cholesterolCystic FibrosisArtery thickeningHemostatic factorsMyocardial infarctionDirectIndirect

Steps in causalityNeed to answer 2 major questions

Is there actually an association?If there is an association, is it likely to be causal?

Steps in causing causality Is there actually an association?

Association actually exists and is statistically meaningfulAssociation is not due to chance so is statistically significantThe association occurs at individual level and not on ecological level (aggregate or geographical unit)Based on appropriate population based rates eg odds ratio or relative risks

Steps in causing causalityIf there is an association, is it likely to be causal

Association is not due to biasSelection biasInformation or measurement biasConfounding bias

Confirmatory criteria for causality is satisfiedBased on specific qualities of association between risk factor and disease

Pembuatan kesimpulan kausalPenggunaan kriteria kausal dalam pembuatan kesimpulan dari data.

Evaluasi Hubungan KausalPostulat Koch:Hanya berlaku untuk penyakit infeksi, tidak berlaku untuk penyakit non-infeksi

Kriteria Bradford Hill:Berlaku pada seluruh kondisis

Postulat Koch-Henle berlaku pada penyakit-penyakit infeksi, tetapi tidak berlaku pada penyakit non-infeksi

Pada penyakit non-infeksi: pada media kultur tidak akan tumbuh kuman

Understanding CausalityLets say you have determined: there is a real association, you believe it to be causal (ruled out confounding), figured out that it is a direct causal factorsorted out the necessary vs. sufficient factor issueNOW have your proven CAUSALITY?

Nine guidelines for judging whether an association is causalTemporal relationship

Strength of association

Dose response relationship

Replication of the findings

Biologic plausibilityConsideration of alternate explanations

Cessation of exposure

Specificity of the association

Consistency with other knowledge

Bila melihat data dari studi epidemiologis, kita sering menggunakan kriteria kasual untuk membantu dalam pembobotan bukti.

Hal yang paling umum digunakan adalah sebagai berikut, dinyatakan pertama kali dari kerja Ahli statistik dari Inggris Austin Bradford Hill, dan kemudian dikembangkan lebih lanjut oleh Surgeon General's Office, Amerika Serikat, dalam laporannya tahun 1964 tentang merokok dan kanker.

16.3 Hills FrameworkStrength ConsistencySpecificityTemporalityBiological gradientPlausibilityCoherenceExperimentationAnalogyHill, A. B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58, 295-300.

Epidemiology (Schneider)

Hills PostulatesStrength of Association the stronger the association, the less likely the relationship is due to chance or a confounding variableConsistency of the Observed Association has the association been observed by different persons, in different places, circumstances, and times? (similar to the replication of laboratory experiments)Specificity if an association is limited to specific persons, sites and types of disease, and if there is no association between the exposure and other modes of dying, then the relationship supports causationTemporality the exposure of interest must precede the outcome by a period of time consistent with any proposed biologic mechanismBiologic Gradient there is a gradient of risk associated with the degree of exposure (dose-response relationship)

Epidemiology (Schneider)

Biologic Plausibility there is a known or postulated mechanism by which the exposure might reasonably alter the risk of developing the diseaseCoherence the observed data should not conflict with known facts about the natural history and biology of the diseaseExperiment the strongest support for causation may be obtained through controlled experiments (clinical trials, intervention studies, animal experiments)Analogy in some cases, it is fair to judge cause-effect relationships by analogy With the effects of thalidomide and rubella before us, it is fair to accept slighter but similar evidence with another drug or another viral disease in pregnancyHills Postulates (cont)

Criteria 1: Strength Relative risk as a measure of strengthStronger RRs carry more weight More difficult to explain away by confoundersBut, do not dismiss small RRs some causal relations will by nature have a weak association e.g., smoking and cardiovascular disease

KRITERIA KAUSALPostulat Hill (1)Kekuatan asosiasiasosiasi yang lebih kuat, kurang mungkin berhubungan secara kebetulan atau suatu variabel perancu (confounding)

KEKUATANApakah asosiasi itu kuat? Perokok berat berasosiasi dengan duapuluh kali lipat lebih tinggi tingkat kanker paru-paru, dan dua kali lipat tingkat penyakit jantung.Asosiasi merokok dengan kanker paru karena itu lebih kuat dari pada asosiasi dengan penyakit jantung. Asosiasi yang lebih kuat lebih memungkin, itu adalah jadi sebab sebenarnya.

Criterion 1: Strength Stronger associations are less easily explained by confounding than weak associationsRatio measures (like RR, SMR, OR) are the best way to quantify the strength of an associationExample: An RR of 10 is much stronger evidence for causality than an RR of 2

Strength of associationWhich odds ratio would you be more likely to infer causation from?

OR#1: OR = 1.4 95% CI = (1.2 - 1.7)

OR#2:OR = 9.8 95% CI = (1.8 - 12.3)

OR#3:OR = 6.695% CI = (5.9 - 8.1)

Criteria 2: ConsistencySimilar findingsusing diverse methods in different populations under a variety of circumstancesConsistency alone does not prove causalityYou can have consistently biased studies

KRITERIA KAUSALPostulat Hill

2. Konsistensi Asosiasi yang diamatiMempunyai asosiasi yang diamati oleh orang yang berbeda, tempat, persoalan dan waktu yang berbeda? (mirip dengan replikasi eksperimen laboratorium)

KONSISTENSIKonsistensi dapat juga berarti :Replikasi pasti, sebagai ilmu laboratorium, atau Replikasi dalam banyak persoalan yang berbeda.Dalam epidemiologi, replikasi pasti adalah tidak mungkin (impossible)

Criterion 2: ConsistencyConsistency: studies using diverse methods in different populations under a variety of circumstances lead to similar conclusionsExample: Ecological, cohort, and case-control done by independent researchers studying different populations all showed a strong association between smoking and lung cancer.

KRITERIA KAUSALPostulat Hill (2)SpesifisitasJika suatu asosiasi terbatas pada orang, tempat dan tipe penyakit tertentu (spesifik), dan jika tidak ada asosiasi antara ekposur dan model lain kematian, kemudian hubungan itu mendukung kausasi

Criteria 3: SpecificityFactor leads to a specific diseaseRequires knowledge at cellular levelConverse is NOT trueSome casual relations are non-specifice.g., smoking causes multiple diseases

SPESIFISITASKausalitas diperkuat jika eksposur diasosiasikan dengan suatu penyakit spesifik, dan bukan dengan keseluruhan varitas penyakit-penyakit

Contoh 1.Asbestos sebab penyakit paru-paru spesifik, asbestosis, dapat dibedakan dari berbagai penyakit paru-paru lainnya. Tetapi eksposure timbal pada tingkat rendah dihubungkan dengan IQ (Intelligent Quotient) yang lebih rendah daripada suatu sindrom otak yang dapat dibedakan. Jadi timbal (Pb = Plumbum = timah hitam) lebih tidak tentu sebagai sebab karena kemungkinan rancu dengan sebab-sebab yang lain, ini bukan efek yang spesifik, IQ rendah (misal SES = Social Economic Status).

Criterion 3: SpecificityThe factor is linked to a specific causal mechanismExample: Smoking is linked to physical and chemical carcinogenesis of epithelial cellsComment: Mechanisms are difficult to establish when there is a vacuum of knowledge

Aristotle (384 322 BCE)

KRITERIA KAUSALPostulat Hill4. Temporalitas/Urutan waktuEksposur yang menjadi perhatian harus mendahului outcome (penyakit) menurut periode waktu yang konsisten dengan berbagai usulan mekanisme biologik

URUTAN WAKTUIni kriteria yang sangat penting secara sederhana menyatakan bahwa orang harus mengetahui pasti bahwa sebab mendahului akibat dalam waktu. Kadang-kadang ini sulit mengetahui , terutama dalam studi kroseksional (penelitian survei).

Contoh 1. Studi telah menemukan hubungan terbalik antara tekanan darah seseorang dengan kadar kalsium serum. Tetapi yang mana sebab dan yang mana akibat?Urutan waktu dapat juga menjadi tidak tentu bila penyakit mempunyai periode laten yang panjang, dan bila eksposur mungkin juga mewakili efek durasi yang panjang.

GerstmanChapter 16*Criterion 4: TemporalityExposure precedes disease by a reasonable amount of time

KRITERIA KAUSALPostulat Hill (3)Gradien biologikAda suatu gradien risiko berhubungan dengan derajat eksposur (hubungan dosis-respons)

Hubungan Dosis-responsJika suatu gradien teratur risiko penyakit ditemukan paralel terhadap gradien eksposur (misal: perokok ringan mendapat kanker paru pada tingkat menengah antara bukan perokok dengan perokok berat) kemungkinan hubungan kausal diperjelas. Dosis-respons umumnya dipikirkan sebagai suatu sub-kategori kekuatan.

Contoh:

Untuk setiap peningkatan jumlah rokok yang dihisap, risiko kanker paru meningkat.

Cohort study: Tobacco smoking and lung cancer, England & Wales, 1951Source: Doll & Hill

Cigarettes smoked/d

Person-years at risk

Cases

Rate per

1000 p-y

Rate

ratio

> 25

25,100

57

2.27

32.4

15 - 24

38,900

54

1.39

19.8

1 - 14

38,600

22

0.57

8.1

none

42,800

3

0.07

Ref.

Biological GradientThere is evidence of a dose-response relationshipChanges in exposure are related to a trend in relative risk

GerstmanChapter 16*CRITERION 5: Biological GradientIncreases in exposure dose dose-response in risk

KRITERIA KAUSALPostulat Hill (3)

Plausibilitas biologikDiketahui atau ada mekanisme yang dipostulasikan menurut ekposur yang mungkin beralasan setelah risiko perkembangan penyakit

Criteria 6: PlausibilityPlausible = makes sense in face of known biological and and other factsBut what of new previously unexplained associations?Where does new knowledge come from?

GerstmanChapter 16*Criterion 6: PlausibilityPlausible mechanism in face of known biological factsPlausibility (defined): appearing worthy of belief Comment: All that is plausible is not always true

KRITERIA KAUSALPostulat Hill (4)KoherensData yang diamati tidak harus konflik dengan fakta yang diketahui tentang riwayat alamiah dan biologi penyakit

KOHERENSApakah asosiasi sesuai (cocok) dengan pengetahuan biologis? Seseorang harus mencari dukungan pemeriksaan laboratorium, atau dari aspek kondisi biologi yang lain.

Criteria 7: CoherenceDo the facts cohere (i.e., to stick together)?

GerstmanChapter 16*Criterion 7: CoherenceAll facts stick together to form a coherent whole.Example: Epidemiologic, pharmacokinetic, laboratory, clinical, and biological data create a cohesive picture about the smoking and lung cancer.

KRITERIA KAUSALPostulat Hill (4)Eksperimen Dukungan yang paling kuat untuk mendukung penyebab mungkin dapat diperoleh melalui ekperimen yang dikontrol (percobaan klinis, studi intervensi, percobaan hewan)

Criteria 8: ExperimentationExperimental evidence should support observational studiesTypes of experimentsEpidemiologic (trials) In vitroAnimal modelsNatural experiments

GerstmanChapter 16*Criterion 8: ExperimentationExperimental evidence supports the epidemiologic evidenceIn vitro and in vivo experiments Experimentation is often not possible in humans Animal models of human disease

KRITERIA KAUSALPostulat Hill (5)AnalogiPada beberapa kasus, adalah wajar menilai hubungan sebab akibat menurut analogi. Dengan efek talidomid dan rubella sebelum kita, adalah wajar bersikap menerima tetapi pembuktian yang mirip dengan obat atau virus yang menyebabkan penyakit pada kehamilan

CONTOH:

Adanya penanda (marker) serologis infeksi Hepatitis B dihubungkan dengan laju peningkatan yang besar kanker hati. Bahwa infeksi Hepatitis B adalah sebab yang benar dari kanker hati, juga ditunjang oleh penemuan genom viral dalam berbagai kanker hati.

Sebaliknya, Reserpine (suatu obat anti- hipertensif) dipikirkan menjadi suatu sebab kanker payudara berdasarkan atas studi yang dilakukan awal tahun 1970an. Tetapi tidak ada informasi biologis yang menunjang, atau berbagai mekanisme biologis yang dapat dijelaskan secara benar. Rangkaian studi yang lebih besar gagal mendukung hubungan ini..

Criteria 9: AnalogySimilarities among things that are otherwise differente.g., before HIV was discovered, epidemiologists noticed that AIDS and Hepatitis B had analogous risk groupsEvidence of similar transmissionNote: analogy is a weak form of evidence

GerstmanChapter 16*Criterion 9: AnalogySimilarities among things that are otherwise differentWeak form of evidenceExample: Before the HIV was discovered, epidemiologists noticed that AIDS and Hepatitis B had analogous risk groups, suggesting similar types of agents and transmission

Criteria for Causation:Smoking and Lung CancerTemporal relationshipBiologic plausibilityConsistencyAlternativesCessation effectsSpecificity of associationStrength of Association

Dose-response

Smoking before CaYes> 36 studies?YesPoint of attack25 x > 25+ cigarettes /day*Yes*.Estimated that 80% of all Lung cancer due to Cigarette smoking

Why was it relatively easy to determine that smoking was a cause of lung cancer?History of exposure to cigarettes can be assessed with reasonable accuracy.Cigarette smoking is common and present in persons whose environment is otherwise similar to that of nonsmokers.Lung cancer incidence in smokers is much greater than in nonsmokers.Lung cancer is uncommon in nonsmokers.

Why will it be relatively hard to determine if community air pollution is a cause of lung cancer?Difficult to measure pertinent exposureLong latent period of diseaseMigrationLittle variation in exposure among individuals within a communityLung cancer is common, even among persons not exposed to pollution.

NOASSOCIATION

DIRECTASSOCIATIONNO POSSIBILITY FOR ASSOCIATIONPOSSIBILITY ASSOCIATEDASSOCIATEDDIRECT CAUSE-EFFECT-Physically not possible -Scientifically/ medically not probable-Statistically not associated-Remote cause-effect a possibility -Not statistically associated at an acceptable level (50%- 60%)-Secondarily association-Cause-effect associated exists -Causally associated but an indirect association -Scientifically connectedStatistically associated but not causative association-Causality assurance-Association affirmed-Scientifically and biomedically proven-Physically possible-Statistically proven Figure 10.4 Continuum of association

Associations are observedCausation is inferred

It is important to remember that these criteria provide evidence for causal relationships.

All of the evidence must be considered and the criteria weighed against each other to infer the causal relationship.

Causal Inference: RealitiesNo single study is sufficient for causal inferenceCausal inference is not a simple processconsider weight of evidencerequires judgment and interpretationNo way to prove causal associations for most chronic diseases and conditions

Judging CausalityRS BhopalWeigh weaknessesin data and otherexplanationsWeigh qualityof science andresults of causalmodels

GerstmanChapter 16*Epidemiology Kept SimpleChapter 16From Association to Causation (Causal Inference)

ReferencesPorta M. A dictionary of epidemiology. New York, Oxford: Oxford University Press, 2008. Susser MW. What is a cause and how do we know one ? A grammar for pragmatic epidemiology. American Journal of Epidemiology 1991; 133: 635-648. Rothman J, Greenland S. Modern epidemiology. third edition. Lippincott - Raven Publishers, 2008.Gordis L, Epidemiology .fourth edition .SaundersElsevier,2009

*-- Whereas a physician tries to determine presence of disease and causes in individuals, epidemiologists focus on populations

-- Unlike microorganisms (like a bacteria) which can be linked to a given disease (disease is defined as exposure to that microorganism) - few chemical/physical factors have a unique effect on health - for example exposure to asbestos - causes lung cancer, but other things may also cause lung cancer

-- Also, outcomes may be due to a combination of factors - e.g., genetics + environmental exposure = disease, so env. exposure is a component cause

-- Different individuals within population with the disease may have gotten it through different causal pathways - one person through env. exposure another through personal factor, etc.*****-- Therefore, an epidemiologic study cannot predict the exact cause of the disease in every individual

-- It looks at a population and tries to determine whether exposure is significantly associated to the disease on average - uses statistical techniques to make conclusions about the strength of these relationships

-- Often these relationships are more strongly supported/concluded when a plausible biological mechanism exists for the effect

-- In general, epidemiologic studies are not experimental - cant expose humans deliberately to something that may affect their health, instead often look at populations that were inadvertently exposure to an agent due to job or where they live (clinical trials is exception)**************Source: Hill, Sir Austin Bradford (1965)

***1. Chapter 16*From Association to Causation*