This document discusses the concepts of association and causation in epidemiology. It defines association as the occurrence of two variables more often than expected by chance. Causation requires that one factor leads to a change in another factor. Several types of associations are described, including direct, indirect, spurious and causal relationships. Guidelines for determining if an association is likely causal include temporal relationship, strength of association, dose-response relationship, replication of findings, biological plausibility and consideration of alternative explanations. Models of causation like the epidemiological triad, web of causation and Rothman's component causes model are also summarized.
A principal aim of epidemiology is to assess the cause of disease. However, since most epidemiological studies are by nature observational rather than experimental, a number of possible explanations for an observed association need to be considered before we can infer a cause-effect relationship exists.
this presentation takes you through the concept of association observed between variables in a study and how could it become a causative association in step-wise manner.Exemplify using Bradford hill criteria. slides after references are extra slides not covered in the presentation.
At the end of this session, the students shall be able to, Define Cause
Define Association
Define Correlation
Types of association
Additional criteria for judging causality
Differentiate between association and causation
A principal aim of epidemiology is to assess the cause of disease. However, since most epidemiological studies are by nature observational rather than experimental, a number of possible explanations for an observed association need to be considered before we can infer a cause-effect relationship exists.
this presentation takes you through the concept of association observed between variables in a study and how could it become a causative association in step-wise manner.Exemplify using Bradford hill criteria. slides after references are extra slides not covered in the presentation.
At the end of this session, the students shall be able to, Define Cause
Define Association
Define Correlation
Types of association
Additional criteria for judging causality
Differentiate between association and causation
Concept of Association, Causation and Correlation
Association - Spurious, Indirect & Direct
Multi-factorial causation
Guidelines for Judging causality
Additional Criteria for Judging causality
The STUDY of the DISTRIBUTION and DETERMINANTS of HEALTH-RELATED STATES in specified POPULATIONS, and the application of this study to CONTROL of health problems."
Study designs, Epidemiological study design, Types of studiesDr Lipilekha Patnaik
Study design, Epidemiological study designA study design is a specific plan or protocol
for conducting the study, which allows the investigator to translate the conceptual hypothesis into an operational one.
Observingthedistributionofdiseaseorhealth related events in human population.
• Identify the characteristics with which the disease is associated.
• Basically 3 questions are asked who, when and where.
• Who means the person affected, where means the place and when is the time distribution.
Concept of Association, Causation and Correlation
Association - Spurious, Indirect & Direct
Multi-factorial causation
Guidelines for Judging causality
Additional Criteria for Judging causality
The STUDY of the DISTRIBUTION and DETERMINANTS of HEALTH-RELATED STATES in specified POPULATIONS, and the application of this study to CONTROL of health problems."
Study designs, Epidemiological study design, Types of studiesDr Lipilekha Patnaik
Study design, Epidemiological study designA study design is a specific plan or protocol
for conducting the study, which allows the investigator to translate the conceptual hypothesis into an operational one.
Observingthedistributionofdiseaseorhealth related events in human population.
• Identify the characteristics with which the disease is associated.
• Basically 3 questions are asked who, when and where.
• Who means the person affected, where means the place and when is the time distribution.
Causation. A number of models of disease causation have been proposed. Among the simplest of these is the epidemiologic triad or triangle, the traditional model for infectious disease. The triad consists of an external agent, a susceptible host, and an environment that brings the host and agent together.
This section loois back to some gourd breaking centributions to pubic (1).pdfaslachennain
This section loois back to some gourd breaking centributions to pubic health, reprodxing then in
their original fome and adding a commentary on their sighilicance from a medesp disy
pecupective. Robyn M Lucas and Artiony L. Mrakichaed retiew the enyinonmert and diceise:
association or ciusation by Sir Austin Bradiord th on estaldiching selationshios betiven ineni and
conditions of work or lwing. The original paper is reproduced by permission of The Royal
Society of Medicine fies linited (impilwwa.ismarg) Association or causation: evaluating links
between "environment and disease" Robyn M. Lucas! \& Anthony I MdMichael?
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brtwesa enpoiate and oescome in the nounce populacion, caual nature of an observed association.
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and quickly became a mainstay of epidcmiolopical tesbooks tescarch, requite onc to procerl
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conceptualization of cheir causarion varies by discipline. While ascociarions can remonoubly he
assessed. it is soirntifically utisfying to ducidate the many component Note. though. that
particular phraie: "eaual nature". causs of an illnces, in pubtic health tcvcurch the more impot-
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neceskary or nuffcicnt reifind. The 18ch-centary Scoetinh phinomopher David Hurne causes that
afe amenable is ineerveation. Eytm sov over the pointrd our thar canarion in induced fogleally,
aot absennd four decudes since Bradford Hill' paper appeared, the range empinically (3).
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observed conjanction of methods and their engerement in wider-ranging intendisci- rwo
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ikentify a complex of interretased and often interactine fac- Around the mil-20ich cenrar. the
jhi.
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.
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
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
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
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Title: Sense of Taste
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 structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
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
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
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
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
2. CONTENTS INTRODUCTION
APPROACHES FOR STUDYING DISEASE ETIOLOGY
HISTORY
WHAT IS ASSOCIATION
TYPES OF ASSOCIATION
WHAT IS CAUSE
GENERAL MODELS OF CAUSATION
TYPES OF CAUSAL RELATIONSHIP
CRITERIA FOR A CAUSAL RELATIONSHIP
GUIDELINES FOR JUDGING WHETHER THE ASSOCIATION IS CAUSAL
EVIDENCE FOR A CAUSAL RELATIONSHIP
DERIVING CAUSAL INFERENCES: EXAMPLE
MODIFIED GUIDELINES FOR EVALUATING THE EVIDENCE OF A CAUSAL RELATIONSHIP
MEASURES OF ASSOCIATION
CONCLUSION
REFERENCES
2
4. In The Magic Years, Fraiberg (1959) characterized every toddler as a
scientist, busily fulfilling an earnest mission to develop a logical
structure for the strange objects and events that make up the world
that he or she inhabits.
Each person develops and tests an inventory of causal explanations
that brings meaning to the events that are perceived and ultimately
leads to increasing power to control those events.
The fruit of such scientific labours is a working knowledge of the
essential system of causal relations that enables each of us to
navigate our complex world.
4
5. In epidemiological studies, ascertainment of cause-effect relationships
is one of the central and most difficult tasks of all scientific activities.
Epidemiological principles stand on two basic assumptions:
Human disease does not occur at random.
The disease and its cause as well as preventive factors can be
identified by a thorough investigation of population.
Hence, identification of causal relationship between a disease and
suspected risk factors forms part of epidemiological research.
5
7. Strength of evidence of studies
Systematic review or meta-analysis of RCTs
Double-blind RCTs
Single-blind RCTs
Randomized, controlled trials (RCTs)
Non-randomized / uncontrolled experimental studies
cohort studies
Case-control studies
Ecological studies
Cross-sectional studies
Expert opinions, anecdotal reports
7
Approach for studying
disease etiology
8. Conceptually, a two-step process is followed in carrying out studies and
evaluating evidence:
1. Determine whether there is an association between an exposure or
characteristic and the risk of a disease. To do so, we use:
a. Studies of group characteristics: ecologic studies
b. Studies of individual characteristics: case-control and cohort studies
2. If an association is demonstrated, we determine whether the observed
association is likely to be a causal one or not.
8
9. Ecologic Studies
The first approach in determining whether an association exists might
be to conduct studies of group characteristics, called ecologic
studies.
ECOLOGICAL FALLACY : Eg.relationship between breast cancer
incidence and average dietary fat consumption in each country
ECOLOGICAL INFERENCE FALLACY: Eg.areas with high concentrations
of farm animals are also the areas with lowest concentrations of
childhood asthma.
It’s a fallacy to then assume that a child who has asthma must not live
near any farm animals
9
10. So? Do You Have Enough Info
To Inform The Patient?
10
11. Recognizing the limitations discussed above of ecologic studies that
use only group data, we turn next to studies of individual
characteristics: case-control and cohort studies.
In case-control or cohort studies, for each subject we have information
on both exposure (whether or not and, often, how much exposure
occurred) and disease outcome (whether or not the person
developed the disease in question).
11
13. Historical Theories of
disease causation
• “Supernatural causes”& Karma
• Theory of humors (humor means fluid)
• The miasmatic theory of disease
• Theory of contagion
• Germ theory
• Koch’s postulates
13
14. EVIDENCE FOR A CAUSAL
RELATIONSHIP
In 1840, Henle proposed postulates for causation that were expanded by
Koch in the 1880s.The postulates for causation were as follows:
1. The organism is always found with the disease.
2. The organism is not found with any other disease.
3. The organism, isolated from one who has the disease, and cultured
through several generations, produces the disease (in experimental
animals).
Koch added that “Even when an infectious disease cannot be transmitted to
animals, the ‘regular’ and ‘exclusive’ presence of the organism
[postulates 1 and 2] proves a causal relationship.”
14
15. These postulates, though not perfect, proved very useful for
infectious diseases
However, as apparently noninfectious diseases assumed
increasing importance toward the middle of the 20th century,
The issue arose as to what would represent strong evidence of
causation in diseases that were generally not of infectious origin.
15
17. Association
Syn: Correlation, Covariation, Statistical dependence, Relationship
Defined as occurrence of two variables more often than would be
expected by chance.
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)
17
18. If two attributes say A and B are found to co-exit more often than an
ordinary chance.
It is useful to consider the concept of correlation.
Correlation indicates the degree of association between two variables
Causal association: when cause and effect relation is seen.
18
19. Pyramid Of Associations
19
Raj Bhopal : Cause and effect: the epidemiological approach
Causal
Non-causal
Confounded
Spurious
Positive /negative
20. Positive: Occurrence of higher value of a predictor variable is
associated with occurrence of higher value of another dependent
variable. Ex- education and suicide.
Negative: Occurrence of higher value of a predictor variable is
associated with lower value of another dependent variable.
Ex - Female literacy and IMR
20
21. Causal: Independent variable must cause change in dependent
variable.
Definite condition of causal associations are time and direction
Ex – salt intake and hypertension
Non-causal: Non-directional association between two variables.
Ex – alcohol use and smoking
21
22. Spurious Association
(Spurious= not real, artificial, fortuitous, false, non-causal associations due to
chance, bias or confounding)
Observed association between a disease and suspected factor may not
be real.
This is due to selection bias
Eg: Increased water intake and crime rate in summer.
The ringing of alarm clocks and rising of the sun.
Cock’s crow causes sun to rise.
22
23. Ex : Neonatal mortality was observed to be more in the newborns born in
a hospital than those born at home. This is likely to lead to a conclusion
that home delivery is better for the health of newborn.
However, this conclusion was not drawn in the study because the
proportion of “high risk” deliveries was found to be higher in the
hospital than in home.
23
24. Indirect Association
It is a statistical association between a characteristic of interest and
a disease due to the presence of another factor i.e. common
factor (confounding variable).
So the association is due to the presence of another factor which is
common to both, known as CONFOUNDING factor.
Ex:
1.Rahul is a friend with Suma, and Suma is Shoba’s friend, so Shoba
is Rahul ’s friend too but indirectly. The common friend is Suma.
24
25. 2. Altitude and endemic goiter confounding factor is iodine
deficiency.
3. Glucose and CHD ,confounding factor is cigarette smoking(it
increase the of cups of coffee and amount of sugar u consume)
25
26. Direct Association
The association between the two attributes is not through the third
attributes.
When the disease is present, the factor must also be present.
26
27. Direct (Causal) association:
1. One –to- one causal association
2. Multifactorial causation
Sufficient & necessary cause
Web of causation (Interaction)
27
28. One-to-one Casual Relationship
The variables are stated to be casual related (AB) if a change in A is
followed by a change in B.
When the disease is present, the factor must also be present.
A single factor (cause) may lead to more than one outcome.
But its not always that simple , as some causes can cause more than 1
disease like streptococci
28
Hemolytic
Streptococci
Streptococcal tonsillitis
Scarlet fever
Erysipelas
29. Multifactorial causation
Multiple factor leads to the disease.
Common in non-communicable diseases
Alternative causal factors each acting independently.
Ex: In lung cancer more than one factor (e.g. air pollution, smoking,
heredity) can produce the disease independently.
Either the causes are acting
Independently OR Cumulatively
29
30. 30
Smoking
Air pollution Reaction at cellular level Lung cancer
Exposure to asbestos
Smoking
+
Air pollution Reaction at cellular level Lung cancer
+
Exposure to asbestos
Independently
Cumulatively
32. WHAT IS CAUSE
The word cause is the one in general usage in connection with matters
considered in this study, and it is capable of conveying the notion of a
significant, effectual relationship between an agent and an
associated disorder or disease in the host.”
1964 Surgeon General Report
32
33. General Models of Causation
The most widely applied models are:
– The epidemiological triad (triangle),
– The web
– The wheel and
– The sufficient cause and component causes models
(Rothman’s component causes model)
33
38. Sufficient & Necessary Cause
NECESSARY cause - causal factor whose presence is required for the
occurrence of the effect. If disease does not develop without the
factor being present, then we term the causative factor “necessary”.
Ex: Agent in Malaria: Plasmodium falciparum parasite is necessary factor-
always present.
SUFFICIENT cause - “minimum set of conditions, factors or events
needed to produce a given outcome. Usually there’s no sufficient
factor “rare”.
The factors or conditions that form a sufficient cause are called
component causes.
Necessary causes + Component causes = Sufficient cause
38
39. Rothman’s Component Causes and
Causal Pies Model
• Rothman's model has emphasised that the causes of disease comprise
a collection of factors.
• These factors represent pieces of a pie, the whole pie (combinations of
factors) are the sufficient causes for a disease.
• It shows that a disease may have more that one sufficient cause, with
each sufficient cause being composed of several factors
39
40. • The factors represented by the pieces of the pie in this model are called
component causes.
• Each single component cause is rarely a sufficient cause by itself, But may
be necessary cause.
• Control of the disease could be achieved by removing one of the
components in each "pie" and if there were a factor common to all "pies“
(necessary cause) the disease would be eliminated by removing that
alone.
40
A
U B
C
N
41. 41
A
U B
C
N
Known components (causes) – A, B,
C
Unknown component (cause) - U
N – Necessary cause
Known components causes
+
Unknown component cause = Sufficient cause
+
Necessary cause
44. If a relationship is causal, four types of causal relationships are possible:
(1) Necessary And Sufficient
(2) Necessary, But Not Sufficient
(3) Sufficient, But Not Necessary
(4) Neither Sufficient Nor Necessary
44
45. Necessary and Sufficient
A factor is both necessary and sufficient for producing the disease.
Without that factor, the disease never develops and in the
presence of that factor, the disease always develops
Types of causal relationships I:
Each factor is both necessary and sufficient
45
FACTOR A DISEASE
46. Necessary, But Not Sufficient
Each factor is necessary, but not, in itself, sufficient to cause the disease .
Thus, multiple factors are required, often in a specific temporal sequence.
Ex: Carcinogenesis is considered to be a multistage process involving both
initiation and promotion. A promoter must act after an initiator has acted.
Action of an initiator or a promoter alone will not produce a cancer
46
47. Types of causal relationships:
Each factor is necessary, but not sufficient
47
48. Sufficient But Not Necessary
The factor alone can produce the disease, but so can other factors that are acting
alone
Either radiation or benzene exposure can each produce leukemia without the
presence of the other.
Even in this situation, however, cancer does not develop in everyone who has
experienced radiation or benzene exposure, so although both factors are not
needed, other cofactors probably are. Thus, the criterion of sufficient is rarely met by
a single factor.
48
50. Neither Sufficient Nor Necessary
A factor by itself, is neither sufficient nor necessary to produce disease
This is a more complex model, which probably most accurately represents
the causal relationships that operate in most chronic diseases.
Types of causal relationships: IV.
Each factor is neither sufficient nor necessary
50
51. When we can say that this association is
likely to be causation??
We have certain criteria that should be present:
– Temporal association
– Strength of association
– Specificity of association
– Consistency of association
– Biological plausibility
– Coherence of association
51
53. Guidelines for Judging Whether an
Association Is Causal (Leon Gordis)
1. Temporal relationship
2. Strength of the association
3. Dose-response relationship
4. Replication of the findings
5. Biologic plausibility
6. Consideration of alternate explanations
7. Cessation of exposure
8. Consistency with other knowledge
9. Specificity of the association
53
54. Temporal association
The causal attribute must precede the disease or unfavorable outcome.
Exposure to the factor must have occurred before the disease
developed.
Length of interval between exposure and disease very important .
Its more obvious in acute disease more than in chronic disease
54
55. Temporal relationship (Relationship with
time)
• Cause must precede the effect.
Drinking contaminated water occurrence of diarrhea
However in many chronic cases, because of insidious onset
and ignorance of precise induction period, it become hard
to establish a temporal sequence as which comes
first -the suspected agent or disease.
55
56. Strength Of The Association
Relationship between cause and outcome could be strong or
weak.
With increasing level of exposure to the risk factor an increase in
incidence of the disease is found.
Strong associations are more likely to be causal than weak.
Weaker associations are more likely to be explained by
undetected bias.
But weaker association does not rule out causation.
56
57. • Strength of association can be estimated by relative risk, attributable
risk etc.
• Relative risks/Odds ratio greater than 2 can be considered strong
57
58. Dose-Response Relationship
( The Biological gradient )
As the dose of exposure increases, the risk of disease also increases
If a dose-response relationship is present, it is strong evidence for a
causal relationship.
However, the absence of a dose-response relationship does not
necessarily rule out a causal relationship.
In some cases in which a threshold may exist, no disease may develop
up to a certain level of exposure (a threshold); above this level, disease
may develop
58
59. Death rates from lung cancer (per 1000) by
number of cigarettes smoked, British male
doctors, 1951 –1961
59
60. Biologic Plausibility Of The Association
The association must be consistent with the other knowledge (viz
mechanism of action, evidence from animal experiments etc).
Sometimes the lack of plausibility may simply be due to the lack of
sufficient knowledge about the pathogenesis of a disease.
It is too often not based on logic or data but only on prior beliefs.
It is difficult to demonstrate where the confounder itself exhibits a
biological gradient in relation to the outcome.
60
61. Consideration of Alternate Explanations
Interprets an observed association in regard to whether a
relationship is causal or is the result of confounding.
In judging whether a reported association is causal, the extent to
which the investigators have taken other possible explanations
into account and the extent to which they have ruled out such
explanations are important considerations.
61
62. Cessation of Exposure
If a factor is a cause of a disease, we would expect
the risk of the disease to decline when exposure to
the factor is reduced or eliminated
62
63. Consistency Of The Association
Consistency is the occurrence of the association at some other time
and place repeatedly unless there is a clear reason to expect
different results.
If a relationship is causal, the findings should be consistent with other
data. Lack of consistency however does not rule out a causal
association.
Repeated observation of an association in different populations
under different circumstances.
63
64. Specificity Of The Association
The weakest of the criteria. (should probably be eliminated)
Specific exposure is associated with only one disease.
Specificity implies a one to one relationship between the cause and effect.
It’s the most difficult to occur for 2 reasons:
Single cause or factor can give rise to more than 1 disease
Most diseases are due to multiple factors.
Ex: Smoking is associated with many diseases.
• Not everyone who smokes develops cancer
• Not every one who develop cancer has smoke
64
65. Analogy (Similarity, reasoning from
parallel cases)
• Provides a source of more elaborate hypotheses about the associations
under study.
• Absence of such analogies only reflects lack of imagination or
experience , not falsity of the hypothesis.
Ex: Known effect of drug Thalidomide & Rubella in pregnancy
• Accepting slighter but similar evidence with another drug or another
viral disease
65
66. Coherence of the association and
judging the evidence
Based on available evidence or should be coherence with known facts
that are thought to be relevant: uncertainty always remains.
Correct temporal relationship is essential; then greatest weight may be
given to plausibility, consistency and the dose–response relationship. The
likelihood of a causal association is heightened when many different
types of evidence lead to the same conclusion.
66
67. Deriving causal inferences: example
Assessment of the Evidence Suggesting Helicobacter pylori Ulcers as a
Causative Agent of Duodenal
1. Temporal relationship.
• Helicobacter pylori is clearly linked to chronic gastritis. About 11% of
chronic gastritis patients will go on to have duodenal ulcers over a 10-
year period.
2. Strength of the relationship.
• Helicobacter pylori is found in at least 90% of patients with duodenal
ulcer.
67
68. 3. Dose-response relationship.
• Density of Helicobacter pylori per square millimeter of gastric mucosa is
higher in patients with duodenal ulcer than in patients without duodenal
ulcer
4. Replication of the findings.(consistency)
• Many of the observations regarding Helicobacter pylori have been
replicated repeatedly
5. Consideration of alternate explanations.
• Data suggest that smoking can increase the risk of duodenal ulcer in
Helicobacter pylori-infected patients but is not a risk factor in patients in
whom Helicobacter pylori has been eradicated
68
69. 6. Biologic plausibility.
• Originally it was difficult to envision a bacterium that infects the stomach
antrum causing ulcers in the duodenum, but is now recognized that
Helicobacter pylori has binding sites on antral cells and can follow these
cells into the duodenum.
• Helicobacter pylori also induces mediators of inflammation.
• Helicobacter pylori-infected mucosa is weakened and is susceptible to the
damaging effects of acid.
7. Cessation of exposure.
• Eradication of Helicobacter pylori heals duodenal ulcers at the same rate
as histamine receptor antagonists.
• Long-term ulcer recurrence rates were zero after Helicobacter pylori was
eradicated using triple-antimicrobial therapy,.
69
70. 8. Specificity of the association.
• Prevalence of Helicobacter pylori in patients with duodenal ulcers is
90% to 100%.
9. Consistency with other knowledge.
• Prevalence of Helicobacter pylori infection is the same in men as in
women. The incidence of duodenal ulcer, which in earlier years was
believed to be higher in men than in women, has been equal in recent
years.
• The prevalence of ulcer disease is believed to have peaked in the
latter part of the 19th century, and the prevalence of Helicobacter
pylori may have been much higher at that time because of poor living
conditions.
70
71. Modified Guidelines for Evaluating the Evidence
of a Causal Relationship. (In each category,
studies are listed in descending priority order.)
1990
1. Major criteria
a. Temporal relationship: An intervention can be considered evidence of
a reduction in risk of disease or abnormality only if the intervention was
applied before the time the disease or abnormality would have
developed.
b. Biological plausibility: A biologically plausible mechanism should be
able to explain why such a relationship would be expected to occur.
71
72. c. Consistency:
Single studies are rarely definitive. Study findings that are replicated in
different populations and by different investigators carry more weight
than those that are not. If the findings of studies are inconsistent, the
inconsistency must be explained.
d. Alternative explanations (confounding):
The extent to which alternative explanations have been explored is
an important criterion in judging causality
72
73. 2. Other considerations
a. Dose-response relationship:
If a factor is the cause of a disease, usually the greater the exposure to
the factor, the greater the risk of the disease. Such a dose-response
relationship may not always be seen because many important biologic
relationships are dichotomous, and reach a threshold level for
observed effects.
b. Strength of the association:
Usually measured by the extent to which the relative risk or
odds depart from unity.
c. Cessation effects:
If an intervention has a beneficial effect, then the benefit should cease
when it is removed from a population.
73
75. Counterfactual model (Potential outcome
model)
When we are interested to measure effect of a particular cause, we
measure effect in a population who are exposed.
• We calculate risk ratios & risk differences based on this model
• The difference of the two effect measures is the effect due the cause
we are interested in.
75
76. Causal Diagram
• Confounding is complex phenomenon.
• Useful for analysis of confounders
• Conceptual definition of variable involved
• Directionality of causal association
• Need some level of understanding (Knowledge & hypothetical) – relation between risk
factor, confounders & outcome.
• Directed Acyclic Graph (DAG)
76
78. Analytical Methods
• Measures of association /strength of association
• Testing hypothesis of association
• Controlling confounders
78
79. Measures of association
Ratio measures
Measures of association in which relative differences between groups
being compared
Difference measures
Difference measures are measures of association in which absolute
differences between groups being compared .
79
80. Absolute differences:(difference measures )
Main goal is often an absolute reduction in the risk of an undesirable
outcome.
When outcome of interest is continuous, the assessment of mean
absolute differences between exposed and unexposed individuals may
be an appropriate method for the determination of association.
Relative differences: ( ratio measures)
Can be assessed for discrete outcomes.
To assess causal associations
80
82. Relative risk
If an association exist, then how strong is it?
What is the ratio of the risk of disease in exposed individuals to the risk of
disease in unexposed individual?
Incidence among exposed
Relative risk =
Incidence among unexposed
It is direct measure of the strength of association.
82
84. Relative risk of developing the disease is expressed as the ratio of
the risk(incidence) in exposed individuals (q+) to that in
unexposed individual(q-)
Total
exposed = a+b
Total
unexposed = c+d
84
86. 86Odds ratio in a cohort study
• Odds that an exposed person
develop disease = a/b
• Odds that an unexposed person
develop disease = c/d
Odds ratio = (a/b ) / (c/d) = ad/bc
Develop
disease
Do not
develop
disease
Exposed a b
Unexposed c d
What are the odds that the disease will develop in an exposed person?
87. 87
Relationship between OR and RR
OR is a valid measure of association in its own right and it is
often used as an approximation of the relative risk’.
Use of OR as an estimate of the relative risk biases it in a
direction opposite to the null hypothesis, i.e. it tends to
exaggerate the magnitude of the association.
88. 88
ATTRIBUTABLE RISK (AR)
AR is defined as the amount of proportion of disease incidence (or
disease risk) that can be attributed to a specific exposure.
Based on the absolute difference between two risk estimates.
Used to imply a cause-effect relationship and should be interpreted
as a true etiologic fraction only when there is a reasonable certainty
of a causal connection between exposure and outcome.
89. 89
AR in exposed individuals
• It is merely a difference between the risk estimates of different
exposure levels and a reference exposure level.
• If q+ = risk in exposed individual.
q- = risk in unexposed individual.
• ARexp = q+ - q-
• It measures the excess risk for a given exposure category
associated with the exposure
90. 90
Percent AR exposure
When AR is expressed as a percentage
Interpretation:
The percentage of the total risk in the exposed attributable to the
exposure.
100
q
qq
91. 91
POPULATION ATTRIBUTABLE RISK
What proportion of the disease incidence can be attributed
to a specific exposure in a total population .
To know the PAR , we need to know incidence in total
population =a
incidence in unexposed group(background risk)=b
PAR= a-b ÷ a
92. 92
Various correlation tests
• Pearsson’s product-moment correlation
• Spearmans rank order correlation
• Kendall correlation
• Point biserial correlation
• Tetrachoric correlation
• Phi correlation
94. 94
Based on direction of correlation
Positive correlation:
As X increases ,Y also increases,
ex: As height increases, so does weight.
Negative correlation:
As X increases ,Y decreases.
ex: As time of watching TV increases , grade scores decreases.
95. Perfect positive
Moderately positive
Zero correlation
Moderately negative
Perfectly Negative
95
Based on degree of correlation
99. REGRESSION
It can also be used in measuring association.
They are the measure of the mean changes to be expected in the
dependent variable for a unit change in the value of the
independent variable.
When more than 1 independent variable is associated with the
dependent variable, multiple regression analysis will indicate how
much of the variation observed in the dependent variable can be
accounted for, by one or a combination of independent variables.
99
100. PROBLEMS IN ESTABLISHING
CAUSALITY
The existence of correlation/ association does not necessarily imply
causation.
Concept of single cause concept of multiple causation
Koch’s postulates cannot be used for non-infectious diseases.
The period between exposure to a factor and appearance of
clinical diseases is long in non-infectious diseases.
Specificity established in one disease does not apply on others.
100
101. Confounders associated with disease tend to distort relationship
with the suspected factors.
Systematics errors/ bias can produce spurious association.
No statistical method can differentiate between causal and non-
causal.
Because of these many uncertainties, the terms : Causal
inference, causal possibility, or likelihood are preferred to causal
conclusion.
This helps in formulating policy rather than waiting for the
unequivocal proof ( Unattainable in several disease conditions)
101
102. 102
Results from epidemiological studies are often used as inputs for policy and
judicial decisions.
It is thus important for public health and policy makers to understand the
fundamentals of causal inference.
Association does not imply causation.
Apart from outbreak investigations, no single study is capable of establishing
a causal relation or fully informing either individual or policy decisions.
Those decisions should be based on a carefull consideration of the entire
relevant scientific and policy literature
Conclusion
103. 103
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ed.
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[3]Roger Detels et al. Oxford Text Book of Public Health. 5th
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