Associations..Epidemiology

Variables

A variable is any observable event that can vary;

Examples:

• Weight
• Age of an animal 
• Number of cases of disease

Study variable

A study variable is any variable that is being considered in an investigation

Response and explanatory variables

A response variable is one that is affected by another (explanatory) variable

Example:

Effects of dry cat food on the occurrence of urolithiasis, cat food is the explanatory variable and urolithiasis is the response variable

Types of association

Association is the degree of dependence or independence between two variables.

Main types of association

• Non-statistical association
• Statistical association

Causal network

Non-statistical association

• A non-statistical association between a disease and a hypothesized causal factor is an association that arises by chance
• Frequency of joint occurrence of the disease and factor is no greater than would be expected by chance

Example

• Mycoplasma felis has been isolated from the eyes of some cats with conjunctivitis.
• This represents an association between the Mycoplasma and conjunctivitis in these cats
• Surveys have shown that M. felis also can be recovered from the conjunctivae of 80% of apparently normal

Analysis:

Association between conjunctivitis and the presence of M. felis arose by chance

Statistical association

• Variables are positively statistically associated when they occur together more frequently than would be expected by chance
• Negatively statistically associated when they occur together less frequently than would be expected by chance

Path diagrams

• Indicating the paradigm
• An example of causal and non-causal statistical associations
• A = Cause of disease (explanatory variable); B and C = manifestations of disease
• (Response variables)         causal association;          non-causal association

Infection of cattle with Haemonchus contortus

If infection of cattle with Haemonchus contortus were being investigated, then the following positive statistical associations could be found:

• Between the presence of the parasite and Abomasal mucosal hyperplasia;
• Between the presence of the parasite and anaemia;
• Between Abomasal mucosal hyperplasia and anemia

The first two associations are causal and the third non-causal

Abomasal mucosal hyperplasia and infection with H. contortus are risk factors for anemia, that is, their presence increases the risk of anemia

Confounding

Confounding (Latin: confundere = to mix together) is the effect of an extraneous variable that can wholly or partly account for an apparent association between variables

Confounder

• A variable that confounds is called a confounding variable or confounder
• A confounding variable is distributed non-randomly (i.e., positively or negatively correlated with the explanatory and response variables that are being studied)
• A confounding variable generally must:
• Be a risk factor for the disease that is being studied;
• Be associated with the explanatory variable, but not be a consequence of exposure to it.

The association between coffee drinking and pancreatic cancer

Schematic representation of the issue of potential confounding

Causal models

The associations and interactions between direct and indirect causes can be viewed in two ways, producing two causal 'models.

Causal model 1

The relationship of causes to their effects allows classification of causes into two types:

Sufficient

Necessary

Sufficient cause

• A cause is sufficient if it inevitably produces an effect (assuming that nothing happens that interrupts the development of the effect, such as death or prophylaxis).
• A sufficient cause virtually always comprises a range of component causes; disease therefore is multifactorial.

Example

Distemper virus-cause of distemper, although the sufficient cause actually involves

• Exposure to the virus
• Lack of immunity
• Other components

It is not necessary to identify all components of a sufficient cause to prevent disease because removal of one component may render the cause insufficient

Necessary cause

If a cause is a component of every sufficient cause, then it is necessary

A necessary cause must always be present to produce an effect

Example

Cause that is necessary but not sufficient is infection with Actinobacillus ligneresi, which must occur before actinobacillosis ('wooden tongue') can develop

Component causes therefore include factors that have been classified as

Predisposing factors:

Which increase the level of susceptibility in the host (e.g., age and immune status?)

Enabling factors:

Which facilitate manifestation of a disease (e.g., housing and nutrition?)

Precipitating factors:

Which are associated with the definitive onset of disease (e.g., many toxic and infectious agents)

Reinforcing factors:

Which tend to aggravate the presence of a disease (e.g., repeated exposure to an infectious agent in the absence of an immune response).

Example

• Pneumonia is a disease that has sufficient causes, none of which has a necessary component.
• Pneumonia may have been produced in one case by heat stress where a dry, dusty environment allowed microscopic particulate matter to reach the alveoli.
• Cold stress could produce a clinically similar result

Formulating a causal hypothesis

First step in any epidemiological investigation of cause is descriptive.

A description of time, place, and population is useful initially.

Time

• Associations with year, season, month, day, or even hour in the case of food poisoning investigations, should be considered.
• Information on climatic influences, incubation periods and sources of infection

Example

An outbreak of Salmonellosis in a group of cattle may be associated with the introduction of infected cattle feed

Place

• The geographical distribution of a disease may indicate an association with local geological, management or ecological factors
• Epidemiological maps are a valuable aid to identifying geographical associations

Population

• The type of animal that is affected often is of considerable importance.
• Hereford cattle are more susceptible to squamous cell carcinoma of the eye than other breeds, suggesting that the cause may be partly genetic
• An epidemiological investigation is similar to any detective novel that unfolds a list of 'suspects' (possible causal factors), some of which may be non-statistically associated with a disease, and some statistically associated with the disease, either causally or non-causally.

Principles for establishing cause: Hill's criteria

The British medical statistician, Austin Bradford Hill, proposed several criteria for establishing a causal association including

1. The time sequence of the events
2. The strength of the association
3. Biological gradient
4. Consistency
5. Compatibility with existing knowledge

Time sequence

• Cause must precede effect
• Unless bacterial infections were present before the mares became infertile (incorrect to infer the bacterial infections)

Strength of association

If a factor is causal, then there will be a strong positive statistical association between the factor and the disease.

Biological gradient

• If a dose-response relationship can be found between a factor and a disease, the plausibility of a factor being causal is increased.
• This is the basis of reasoning by the method of concomitant variation

Examples

• Frequency of milking in relation to Leptospirosis
• Smoking in relation to lung cancer

Consistency

• If an association exists in a number of different circumstances, then a causal relationship is probable.
• This is the basis of reasoning by the method of agreement.

An example is bovine hyperkeratosis

The disease was called 'X disease' because initially the cause was unknown. It occurred in different circumstances:

• In cattle that were fed sliced bread;
• In calves that had been licking lubricating oil;
• In cattle that were in contact with wood preservative.

The bread slicing machine was lubricated with similar oil to that which had been licked by the calves. The lubricating oil and the wood preservative both contained chlorinated naphthalene. This chemical was common to the different circumstances and subsequently was shown to cause hyperkeratosis

Compatibility with existing knowledge

It is more reasonable to infer that a factor causes a disease if a plausible biological mechanism has been identified than if such a mechanism is not known

Example

Smoking can be suggested as a likely cause of lung cancer because other chemical and environmental pollutants are known to have a carcinogenic effect on laboratory animals

Cohort Study..Epidemiology

• Cohort is a group of individuals having some common characteristics. These characteristics may be birth date, age, marriage date or smoking.
• Cohort study is called as prospective study which means “looking forward”.
• You know the risk and disease comes later.
• It is also called incidence study because healthy individuals become diseased after few years and become a new case.

Indications

• Cohort study is conducted only when you have evidence about the exposure and disease.
• When exposure is rare but incidence is higher in exposed e.g. exposure to uranium is rare but if exposed, cancer chances are very higher.
• When sufficient funds are available, cohort study is conducted.
• When follow up is easy or possible, cohort study is conducted. I.e. completion of study is necessary (at least for 25 years).
• It starts with people free of disease.
• Exposure Status is known at baseline.
• Outcome occurs after follow up.

Components:

• Cohort
• Exposure status

Considerations while selecting Group

• Exposed and non-exposed groups should be free of disease.
• Both groups should be comparable.
• Both groups should be equally susceptible.
• Diagnostic criteria for disease

Elements of Cohort Study

Selection

• Selection of study i.e. subjects

Obtaining data

• Obtaining data from study subjects (obtain exposure status through interview, questionnaire and through mails). Medical tests should be performed to check some disease in these subjects.

Comparison group

• Comparison group is selected.
• Non-exposed group is your comparison group. It is called as internal comparison. It is difficult.
• External comparison is easy because you already know that who are smokers and who are non-smokers. Non-smokers are your comparison group.
• Compare with general population. We will take two groups. E.g. cancer in uranium miners and cancer in general population. In this, uranium miners are study group and general population is comparison group.

Follow up

• You will follow the exposed and non-exposed for a particular period depending upon the period of the development of the disease. e.g. 10, 20, 25 years
• Drawbacks of follow up include death of investigator, deaths from study group, loss of follow up or non-availability of funds.

Analyses

Two factors are calculated;

• Incidence Rate (IR)
• Relative Risk (RR)

Causality..Epidemiology

Philosophical background

Causation

Deals with the relationship between cause and effect

The Classical period

Aristotle-doctrine of four causes

Material cause

• What 'stuff' (matter) a thing comprises-some combination of the four elements:
• Earth
• Air
• Fire
• Water

Formal cause

• The 'form and pattern' of a thing, or those properties without which a thing would not exist as it does

Efficient cause

• Which is the maker of a thing (and by which the formal cause is therefore explained)

Final cause

• Which is the purpose of the thing (and therefore, in the case of natural things, usually synonymous with the formal cause)

The Scholastics

• Christian mediaeval philosophers (termed the 'Scholastics') generally endorsed Aristotle's ideas, but focused on God as the efficient cause of all things
• Individuals were secondary efficient causes of things
• God as the primary cause was a subject of debate during this period.

The 'Modern' period

Explain events mathematically in terms of

• ‘How' (description)
• ‘Why' (explanation)

Causal inference

Scientific conclusions are derived by two methods of reasoning:

• Deduction
• Induction

Deduction

• Deduction is arguing from the general to the particular; that is, a general case is established, from which all dependent events are argued to be true
• Example: Thus, if one posits the truth of the general proposition 'all dogs are mammals', it follows by deduction that any particular example of a dog will be a mammal

Induction

• Arguing from the particular to the general
• Example: A dog may be vaccinated against distemper virus, and shown to be immune to challenge with the agent, from which the conclusion is drawn that the vaccine prevents distemper in all dogs.

Why to identify cause?

• Epidemiological studies are undertaken to identify causes of disease so that preventive measures can be developed and implemented, and their subsequent effectiveness identified
• Investigations of cause are usually based on inductive reasoning

Methods of acceptance of hypotheses

Accept (or reject) a causal hypothesis by four methods

• Tenacity-disregards opinion of others
• Authority
• Intuition
• Scientific inquiry

Koch's postulates

Koch in the late 19th century formulated postulates about cause of infectious disease

These postulates state that an organism is causal if:

• It is present in all cases of the disease
• It does not occur in another disease as a fortuitous and non-pathogenic parasite
• It is isolated in pure culture from an animal, is repeatedly passaged, and induces the same disease in other animals.

Koch's postulates brought a necessary degree of order and discipline to the study of infectious disease

Evans' rules

Alfred Evans (1976) has produced a set of rules that are consistent with modern concepts of causality:

• The proportion of individuals with the disease should be significantly higher in those exposed to the supposed cause than in those who are not
• Exposure to the supposed cause should be present more commonly in those exposed to the supposed cause than those without the disease, when all other risk factors are held constant
• The number of new cases of disease should be significantly higher in those exposed to the supposed cause than in those not so exposed
• Temporally, the disease should follow exposure to the supposed cause with a distribution of incubation periods on a bell-shaped curve
• A spectrum of host responses, from mild to severe, should follow exposure to the supposed cause along a logical biological gradient
• A measurable host response (e.g., antibody, cancer cells) should appear regularly following exposure to the supposed cause in those lacking this response before exposure, or should increase in magnitude if present before exposure; this pattern should not occur in individuals not so exposed
• Experimental reproduction of the disease should occur with greater frequency in animals or man appropriately exposed to the supposed cause than in those not so exposed
• Elimination (e.g., removal of a specific infectious agent) or modification (e.g., alteration of a deficient diet) of the supposed cause should decrease the frequency of occurrence of the disease
• Prevention or modification of the host's response (e.g., by immunization or use of specific lymphocyte transfer factor in cancer) should decrease or eliminate the disease that normally occurs on exposure to the supposed cause

Variables

A variable is any observable event that can vary:

Examples:

• Weight
• Age of an animal 
• Number of cases of disease

All relationships and associations should be biologically and epidemiologically credible

Scope of Epidemiology-2

Types of Epidemiological Investigations:

There are four approaches to epidemiological investigation

• Descriptive epidemiology
• Analytical epidemiology
• Experimental epidemiology
• Theoretical epidemiology

Descriptive epidemiology

Descriptive epidemiology involves observing and recording diseases and possible causal factors. “First part of an investigation” Observations may generate hypotheses that can be tested more rigorously later.

Analytical epidemiology

Analytical epidemiology is the analysis of observations using suitable diagnostic and statistical procedures.

Experimental epidemiology

• Experimental epidemiologists observe and analyze data from groups of animals from which they can select, and in which they can alter, the factors associated with the groups
• An important component of the experimental approach is the control of the groups
• Use of laboratory animals whose short lifespan enable events to be observed more rapidly than in humans

Theoretical epidemiology

Theoretical epidemiology consists of the representation of disease using mathematical ‘models’ that attempt to simulate natural patterns of disease occurrence. 

Epidemiological subdisciplines:

There are various epidemiological subdisciplines

Clinical epidemiology

Clinical epidemiology is the use of epidemiological principles, methods and findings in the care of individuals, with particular reference to diagnosis and prognosis.

Computational epidemiology

• Application of computer science to epidemiological studies
• Representation of disease by mathematical models
• Use of expert systems (Formulating disease control strategies, predicting animals productivity Supporting management decisions)

Genetic epidemiology

• Genetic epidemiology is the study of the cause, distribution and control of disease in related individuals, and of inherited defects in populations. Also explore Interactions between genetic and non-genetic factors.

Field epidemiology

• Field epidemiology is a timely, judgmental process based on description, analysis, common sense and the need to design practical control policies
• When outbreaks of foot-and –mouth disease occur, field epidemiologists promptly trace potential sources of infection in an attempt to limit spread of the disease.

Participatory epidemiology

• 1980 with development of veterinary services, animals were economically and socially important, use of local knowledge to gain information, with the main goal of improving animal health
• Techniques that are employed evolved in the social science, and consist of simple visual methods and interviews to generate qualitative data
• “Participatory epidemiology” it is a tool for the field epidemiologist, which is increasingly used in developing counties. Is concerned with local knowledge of, and practices relating to, the health of animals

Molecular epidemiology

• Study of small genetic and antigenic differences between viruses and other microorganisms at a higher level of discrimination
• The methods include peptide mapping, nucleic acid ‘fingerprinting’ and hybridization, restriction enzyme analysis, monoclonal antibodies and the polymerase chain reaction
• Nucleotide sequencing of foot-and–mouth  that some outbreaks of the disease involved vicinal strains, suggesting that improper inactivation or escape of virus from vaccine production plants. Unrestricted animals movement is a major factor in dissemination of the disease in West Africa.

Environmental Epidemiology:

• Concerned with the relationship between disease and environmental factors such as industrial pollution and occupational hazards

Micro-Epidemiology:

• Study of disease in a small group of individuals with respect to factors that influence its occurrence in larger segments of the population

Macro-Epidemiology:

Study of national patterns of disease, and the social, economic and political factors that influence them

Chronic disease epidemiology

Nutritional epidemiology

Subclinical epidemiology

Social epidemiology

Psychosocial epidemiology

Components of epidemiology:

The first stage in any investigation is the collection of relevant data. Investigations can be either qualitative or quantitative or a combination of these two approaches.

• Qualitative investigations
• Quantitative investigations

Qualitative investigations

• The natural history of disease
• The ecology of diseases, including the distribution, mode of transmission and maintenance of infectious diseases, is investigated by field observation. Field observations also may reveal information about factors that may directly or indirectly cause disease.
• Causal hypothesis testing
• If filed observations suggest that certain factors may be causally associated with a disease, than the association must be assessed by formulation a causal hypothesis

Quantitative investigations

• Quantitative investigations involve measurement (e.g., the number of cases of disease), and therefore expression and analysis of numerical values. Quantitative investigations include surveys, monitoring and surveillance, studies, modeling, and the biological and economic evaluation of disease control. Walls of the research organization- ‘armchair epidemiology’

Surveys

• A survey is an examination of an aggregate of units.       
• Surveys can be undertaken on a sample of the population.
• A census, a cross-sectional survey records events occurring at a particular point in time.
• A longitudinal survey records events over a period of time. Prospectively form the present into the future; on may be a retrospective record of past events. 
• In epidemiological surveys, characteristics might include the presence of particular diseases, or production parameters such as milk yield.

Screening

• A particular type of diagnostic survey is screening. Identification of undiagnosed cases of disease using rapid tests or examination
• Screening tests are not intended to be diagnostic; individuals with positive test results (i.e., that are classified as diseased by the screening test) usually require further investigation for definite diagnosis. They therefore differ from diagnostic tests.
• Screening involves investigation of the total population (mass screening). Targeted at animals only in areas where there have been cases of disease (strategic screening). Prescriptive screening aims at early identification of diseases that can be controlled better if they are detected early in their pathogenesis.

Monitoring and surveillance

• Monitoring is the making of routine observations on health, productivity and environmental factors and the recording and transmission of these observations.
• Recording of milk yields or routine recording of meat inspection findings at abattoirs
• Surveillance is a more intensive form of data recording than monitoring. Used to describe the tracing and observation of people who were in contact with cases of infectious disease Include all types of disease – infectious and non-infectious 
• It is normally part of control programmes for specific diseases.

Studies

There are four main types of epidemiological study:

• Experimental studies;
• Cross-sectional studies;
• Case-control studies;
• Cohort studies

Modelling

Using mathematical equations ‘Modelling’

Risk assessment

Evaluation of the risk of the occurrence of adverse, development of formal methods of quantitative and quantitative risk assessment e.g. Microbiological risk assessment with food safely risks, and estimation of the magnitude of microbial exposure at various stages in the production chain (rearing on the farm; transport and processing; retail and storage; preparation) to campylobacter spp and salmonella spp.

Disease control

The goal of epidemiology is to improve the veterinarian's knowledge so that diseases can be controlled effectively, and productivity thereby optimized. This can be fulfilled by treatment, prevention or eradication