This document discusses association and causation in epidemiology. It defines association as a statistical relationship between two or more variables. There are different types of association including positive, negative, direct, indirect, causal, and non-causal. Causation requires satisfying criteria such as strength of association, temporality, consistency, biological gradient, plausibility, and consideration of alternative explanations. Methods for proving association include choosing an appropriate study design, measuring variables to address bias and confounding, and using statistical tests. Multiple factors may influence outcomes, so studies must consider confounding and apply techniques like stratification and multivariable analysis. Not all associations are causal, so the criteria for causation help determine if an observed relationship is likely due
EpidemiologyUnit 3Bias, Error, Confounding and Effect Modification4hrs
Radha Maharjan
MN(WHD)
Contents
3.1 Bias and Error in Epidemiology
3.1.1 Bias (Researcher and Respondent)
Recall Bias
Information Bias ( sponsor bias, social desirability bias, acquiescence Bias)
Selection Bias
Confirmation Bias
The halo effect.
Contents
3.1.2 Error
Systematic Error
Random Error
Confounding & Effect Modification
Definition of Error
A measure of the estimated difference between the observed or calculated value of a quantity and its true value.
Random error or Chance
It is the by-chance error
It makes observed value different from the true value
May occur through sampling variability or random fluctuation of the event of interest due to
biological variability, sampling error and measurement error (not due to machine)
lack of precision in the measurement of an association
Biological variability:
The natural variability in a lab parameter due to physiologic differences among subjects and within the same subject over time.
Differences between subjects due to differences in diet, genetics or immune status.
Sampling error:
Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data.
Measurement error:
Measurement Error (also called Observational Error) is the difference between a measured quantity and its true value.
Random error or Chance
Random error can never be completely eliminated since we can study only a sample of the population.
Random error can be reduced by
careful measurement of exposure and outcome
Proper selection of study
Taking larger sample- increase the size of the study.
Systematic error or Bias
Systematic error (or bias) occurs in epidemiology when results differ in a systematic manner from the true values.
Bias is any difference between the true value and observed value due to all causes other than random fluctuation and sampling variability.
This type of error is generally more insidious and hard to detect.
Systematic error or Bias
For example over-estimate of blood sugar of every subject by 0.05 mmol/l resulted from using inaccurate analyser.
The possible sources of systematic error are many and varied but the important biases are selection bias, measurement bias, confounding, information bias, recall (respondent) bias, etc..
Sources of error in epidemiological study
Common sources of error are
selection bias
absence or inadequacy of controls
unwarranted conclusions
improper interpretation of associations
mixing of non-comparable records
errors of measurement (intra-observer variation, inter-observer variation), etc.
The error can be minimised through
study design (by randomisation, restriction & matching) and
during analysis of the results (by stratification and statistical modelling) ..
Selection bias
EpidemiologyUnit 3Bias, Error, Confounding and Effect Modification4hrs
Radha Maharjan
MN(WHD)
Contents
3.1 Bias and Error in Epidemiology
3.1.1 Bias (Researcher and Respondent)
Recall Bias
Information Bias ( sponsor bias, social desirability bias, acquiescence Bias)
Selection Bias
Confirmation Bias
The halo effect.
Contents
3.1.2 Error
Systematic Error
Random Error
Confounding & Effect Modification
Definition of Error
A measure of the estimated difference between the observed or calculated value of a quantity and its true value.
Random error or Chance
It is the by-chance error
It makes observed value different from the true value
May occur through sampling variability or random fluctuation of the event of interest due to
biological variability, sampling error and measurement error (not due to machine)
lack of precision in the measurement of an association
Biological variability:
The natural variability in a lab parameter due to physiologic differences among subjects and within the same subject over time.
Differences between subjects due to differences in diet, genetics or immune status.
Sampling error:
Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data.
Measurement error:
Measurement Error (also called Observational Error) is the difference between a measured quantity and its true value.
Random error or Chance
Random error can never be completely eliminated since we can study only a sample of the population.
Random error can be reduced by
careful measurement of exposure and outcome
Proper selection of study
Taking larger sample- increase the size of the study.
Systematic error or Bias
Systematic error (or bias) occurs in epidemiology when results differ in a systematic manner from the true values.
Bias is any difference between the true value and observed value due to all causes other than random fluctuation and sampling variability.
This type of error is generally more insidious and hard to detect.
Systematic error or Bias
For example over-estimate of blood sugar of every subject by 0.05 mmol/l resulted from using inaccurate analyser.
The possible sources of systematic error are many and varied but the important biases are selection bias, measurement bias, confounding, information bias, recall (respondent) bias, etc..
Sources of error in epidemiological study
Common sources of error are
selection bias
absence or inadequacy of controls
unwarranted conclusions
improper interpretation of associations
mixing of non-comparable records
errors of measurement (intra-observer variation, inter-observer variation), etc.
The error can be minimised through
study design (by randomisation, restriction & matching) and
during analysis of the results (by stratification and statistical modelling) ..
Selection bias
Study of the distribution and determinants of
health-related states or events in specified populations and the application of this study to control health problems.
John M. Last, Dictionary of Epidemiology
P-values the gold measure of statistical validity are not as reliable as many...David Pratap
This is an article that appeared in the NATURE as News Feature dated 12-February-2014. This article was presented in the journal club at Oman Medical College , Bowshar Campus on December, 17, 2015. This article was presented by Pratap David , Biostatistics Lecturer.
Systematic (non-random) error that results in an incorrect estimate of the association between exposure and risk of disease.
Can occur in all stages of a study
Not affected by study sample size
Difficult to adjust for afterwards, but can be reduced by adequate study design.
•Can never be totally avoided, but we must be aware of it and interpret our results accordingly
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Study of the distribution and determinants of
health-related states or events in specified populations and the application of this study to control health problems.
John M. Last, Dictionary of Epidemiology
P-values the gold measure of statistical validity are not as reliable as many...David Pratap
This is an article that appeared in the NATURE as News Feature dated 12-February-2014. This article was presented in the journal club at Oman Medical College , Bowshar Campus on December, 17, 2015. This article was presented by Pratap David , Biostatistics Lecturer.
Systematic (non-random) error that results in an incorrect estimate of the association between exposure and risk of disease.
Can occur in all stages of a study
Not affected by study sample size
Difficult to adjust for afterwards, but can be reduced by adequate study design.
•Can never be totally avoided, but we must be aware of it and interpret our results accordingly
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Are There Any Natural Remedies To Treat Syphilis.pdf
Association_and_Causation.pptx
1. Association and Causation
Lt Col Ayon Gupta
MBBS, MD (CM), AFMC,
PhD Epidemiology (AIIMS-ND)
Dept of Community Medicine,
Armed Forces Medical College, Pune
2. Outline of presentation
• Association
▫ Definition
▫ Types of association
▫ Proving association
• Causation
▫ Criteria for causation
3. Association- definition
• Synonyms: correlation, statistical dependence,
relationship
• Statistical dependence between two or more
events, characteristics or variables.
• An association is present if probability of
occurrence of a variable depends upon one or
more variable.
(A dictionary of Epidemiology by John M. Last)
4. Examples
• Thunder versus Lightning
• Cholera and Source of Water
• Childbed fever and clinics by doctors/midwives
6. Births, deaths, and mortality rates (%) for all
patients at the two clinics 1841-1846
First clinic Second clinic
Births Deaths Rate Births Deaths Rate
20042 1989 9.92 17791 691 3.38
8. Association- types
• Positive – occurrence of higher value of a
variable is associated with the occurrence of
higher value of another variable.
▫ Ex – education and suicide
• Negative – occurrence of higher value of a
variable is associated with lower value of
another variable.
▫ Ex – female literacy and IMR
9. Association types
• Direct – directly associated i.e. not via a known
third variable
Direct
Indirect
Salt intake Hypertension CAD
Salt intake Hypertension
11. Association- types
• Causal – independent variable must cause
change in dependent variable.
▫ Ex – salt intake and hypertension
• Non-causal – non-directional association
between two variables.
▫ Ex – alcohol use and smoking
▫ Oral Pill use and number of sex partners
12. Causal and non-causal associations
Can we think of examples of non-causal association shown above
13. Why do we measure association/
causation?
• If causal –
▫ Identifying people at risk – risk prediction models.
▫ Use the term risk factors
▫ Interventions can be identified for prevention
• If non-causal – markers of risk
▫ Could be non-specific (CRP), or mediator or
byproduct of the disease
▫ Schizophrenia, Alzheimer's Disease
14. Let us list some
questions/hypothesis of
associations
16. Steps in proving an association
• Choose appropriate study design
• Measure variables addressing bias and
confounding
• Statistical analysis & significance testing
17. What study design to choose?
• Descriptive/analytical/interventional
• Cross-sectional/longitudinal
• Case-control/Cohort
Examples
19. Algorithm for studying Association
Could it be due to bias?
Could it be due to
Confounding?
Could it be a result of
Chance?
Could it be causal?
OBSERVED
ASSOCIATION
NO
Probably not
Apply Hill’s
guidelines
True Association
Significance testing
Multi-variate analysis
Association Persists
Critical review
Causal Association
NO
Probably Yes
20. Measuring association
• Depends on
• Type of variables
▫ Continuous - Correlation coefficient, Z or t Test
▫ Categorical - Chi square test/ p value
▫ Non-normal distribution- Non-parametric tests
• Type of Study Design
▫ Case Control - Odds ratio
▫ Cohort - Relative risk/Risk ratio/Risk Difference
21. Correlation Co-efficient
Salt intake
Blood Pressure
Correlation coefficient (r) = 0.85
Negative association
•Pearson’s Correlation Coefficient
•The correlation coefficient
ranges from −1 to 1.
•A value of 1 implies that a linear
equation describes the
relationship between X and Y
perfectly.
•A value of −1 implies that all
data points lie on a line for which
Y decreases as X increases.
•A value of 0 implies that there is
no linear correlation between the
variables.
22. Chi-square test
40 80
60 20
Obese Non-obese
Heart
disease
Present
Absent
100 100
80
120
200
Chi square = 33.33, p value < 0.001
23. Other tests of association
• Comparing means using Z test or t Test
• Nonparametric tests
▫ Spearman rank correlation
▫ Kendall’s Tau
24. Measures of Association - Odds Ratio
40 80
60 20
Obese Non-obese
Heart
disease
Present
Absent
100 100
80
120
200
Odds ratio = ad/bc
= 60*80/40*20 = 48/8
=6.00 (95% CI - 3.05-11.9)
25. Measures of Association - Risk Ratio
•The table shows results
of a study examining
the risk of wound
infections when an
incidental
appendectomy was
done during a staging
laparotomy for
Hodgkin's disease.
Appende
ctomy
Wound Infection Total Incidenc
e (%)
Yes No
Yes 7 124 131 7/132 =
5.34
No 1 78 79 1/79 =
1.27
Risk Ratio = 5.34/1.27 = 4.2
95% CI = 0.53 – 95.0
26. Role of Chance ( p –value)
• Probability of a finding occurring by chance.
• Conventionally kept at 0.05 or 5%.
• If the probability is low ( <5%) it is considered
sufficiently unlikely to have occurred by chance
to justify the designation “statistically
significant.”
• Or if 95% CI does not cross
▫ 1 for OR/RR
▫ 0 for correlation coefficient
27. Determinants of Significance
• Sample Size
▫ Formula for Difference in proportions or means
▫ Large sample size everything can be significant
▫ Power of a test or Beta error
• Clinically significant difference
▫ Hypothesis formulation
29. Rule out Confounding
• Two criteria
▫ Independent risk factor or its determinant for disease
▫ Associated with factor under investigation
• Examples
▫ Oral pills and cervical cancer – number of sex partners
▫ Alcohol & CAD - smoking
• Handling Confounding
▫ Restriction of the study population
▫ Matching
▫ Stratification
▫ Multivariable analysis
31. 31
Properties of confounder
• Causally associated with the outcome
• Can be causally or non-causally associated with
the exposure
• Is not an intermediate variable in the causal
pathway between exposure and outcome
32. Algorithm for studying Association
Could it be due to bias?
Could it be due to
Confounding?
Could it be a result of
Chance?
Could it be causal?
OBSERVED
ASSOCIATION
NO
Probably not
Apply Hill’s
guidelines
True Association
Significance testing
Multi-variate analysis
Association Persists
Critical review
Causal Association
NO
Probably Yes
33. Rule out Bias
• A Systematic deviation from truth
• Occurs due to limitations in methodology
• Usually of two types
▫ Selection Bias
▫ Measurement Bias
• Examples
▫ Recall bias in a case-control study of congenital defects
▫ Comparison of absenteeism between participants and
non-participants of workplace intervention
34. 34
Bias
• Any systematic error in a study resulting in
incorrect estimate of association
• Evaluation of role of bias as an alternative
explanation is necessary before interpreting
study results
• When evaluating a study for presence of bias,
▫ Identify its source
▫ Estimate its magnitude: small, moderate or large
▫ Assess its direction: Either towards or away from
the null
35. 35
Types of Bias
• Selection bias:
▫ Occurs when identification of subjects for study is
influenced by some other axis of interest
▫ More important in case-control and retrospective
cohort studies
• Observation/Information bias:
▫ Occurs due to systematic differences in data collection
procedure between different study groups
▫ Affects all study designs equally
36. 36
Types…
Selection Bias
• Control selection bias
• Differential surveillance
errors , Diagnosis errors
or Errors in referral of
individuals into study
• Self-selection bias
• Loss to follow up
• Healthy worker effect
Information Bias
• Recall bias
• Interviewer bias
• Misclassification bias
▫ Differential
▫ Non-differential
37.
38. Would you agree that Catholics commit less suicide than protestants?
40. Evidence for causal relation
Koch’s postulates
organism is always found with
the disease
organism must be isolated and
grown in pure culture
organism must cause a specific
disease when inoculated into an
animal.
organism can be recovered from
lesions in the animal and
identified.
41. Guidelines for judging whether an
association is causal
1. Strength of Association
2. Biological gradient (dose-response)
3. Temporality
4. Consistency
5. Specificity
6. Biological plausibility
7. Effect of removing the exposure
8. Extent to which alternate explanations have
been considered
42. Strength of Association
• Measures of the association
▫ Relative risk; Odds ratio
• „
Stronger association is more likely to be
causal, but a weak association can also be
causal
• Examples
▫ RR for lung cancer and cigarette smoking from
various studies are around 10
▫ RR for breast cancer and cigarette smoking from
various studies are between 1–1.5
▫ Environmental risk factors
43. Biological Gradient
• If risk increases with increasing exposure, it supports
the notion of a causal association.
• However, the absence of dose-response does not
preclude causal association
▫ There is almost always a dose below which no
response occurs or can be measured
▫ There is also a dose above which any further
increases in the dose will not result in any
increased effect
• For some substances, some dose levels may be
beneficial “The right dose differentiates a poison
from a remedy” (Paracelsus)
44. Temporality
• Exposure precedes outcome
• If factor "A" is believed to cause a disease, then it
is clear that factor "A" must necessarily
always precede the occurrence of the disease.
• Depression and Alcohol Use?
• This is the only absolutely essential criterion.
• Does not automatically mean it causes it.
• Not possible in Case-control studies
45. Consistency
• It is supportive of causal association if the same
finding can be replicated in/by
▫ different populations
▫ using various study designs
▫ different researchers
▫ different places and times
46. Specificity
• Specificity of the association suggests that one exposure
is specific to one disease
• This criterion is not applicable to all exposure-disease
associations because a disease may be caused by several
exposures, and an exposure may cause several diseases.
• An exposure is likely to have a deleterious effect on a
specific mechanism (at a cellular or molecular level) that
may then lead to one or more diseases).
• Example: tobacco use causes many diseases other than
lung cancer and lung cancer is caused by many
substances other than tobacco.
47. Biological Plausibility
• Not always possible.
• May actually follow the discovery
• Not in conflict with existing theories
48. Others
• Alternate Explanations
▫ Extent to which alternate explanations have been
considered including confounders
• Coherence
▫ This implies that a cause-and-effect interpretation for
an association does not conflict with what is known of
the natural history and biology of the disease.
▫ If we claim that a newly introduced exposure of high
prevalence greatly increased the incidence of a disease,
there should be an increased incidence of that disease
in the population at large.
49. Effect of removing the exposure
• Experimental studies or natural studies
• Similar to the dose-response relationship, the
presence of this criterion supports the notion of
causal association. However, the absence does
not preclude it.
• Example: after quitting smoking, the amount of
specific-DNA adducts decreases in blood
• Vaccine Probe Studies
52. Algorithm for studying Association
Could it be due to bias?
Could it be due to
Confounding?
Could it be a result of
Chance?
Could it be causal?
OBSERVED
ASSOCIATION
NO
Probably not
Apply Hill’s
guidelines
True Association
Significance testing
Multi-variate analysis
Association Persists
Critical review
Causal Association
NO
Probably Yes
54. Example
• The effects of prenatal exposure to diethylstilbestrol were studied by
a prospective cohort investigation of 110 exposed and 82 unexposed
females.
• The general health characteristics of mothers and daughters in both
groups were similar.
• Among the exposed, there were striking benign alterations of the
genital tract, which included transverse ridges (22 per cent),
abnormal vaginal mucosa (56 per cent), and biopsy-proved adenosis
(35 per cent). Among the unexposed there were no ridges and one
case of vaginal mucosal abnormality including adenosis (p < 0.0001).
• Abnormal cervical epithelium occurred in almost all exposed
subjects but in only half the unexposed (p < 0.0001).
• The incidence of vaginal adenosis was highest when
diethylstilbestrol was begun in early pregnancy. It was not detected
when treatment was initiated in the 18th week or later.
• Oral contraceptive use and prior pregnancy were associated with less
adenosis and erosion, respectively (p <0.05). No cases of cancer
were observed.
N Engl J Med 292:334–339, 1975)
55. Summary
• Make an hypothesis for association – choose
appropriate design and sample size
• Test for Hypothesis by ruling out chance
• Rule out the possibility of bias and confounding.
• Apply criteria of causation to judge causality
• “All that glitters are not gold”. Not all
associations are causal.