CHAPTER NINE:

EPIDEMIOLOGY

Dadi-Mamud Naomi John & Usman-Yamman Hadijah

9.1        Introduction

Epidemiology can be referred to a branch of medical science that deals with the study of the distribution and patterns of health-events, health-characteristics and their causes or influences in a well-defined population mainly by the use of statistical tools; and as well as the application of such study results to control health problems. Epidemiology, however, is the cornerstone method of public health research and practice, and helps inform policy decisions and evidence-based medicine by identifying risk factors for disease(s) and targets for preventive medicine and public policies. Epidemiologists are involved in the design of studies, collection and statistical analysis of data, and Interpretation and dissemination of the results. They use a collection of data and a broad range of biomedical and psychosocial theories in a repeated manner to generate or expand theory, test hypotheses, and try to make educated, informed assertions about which relationships are causal, and how exactly they are able to cause the disease or illness.

In recent years, epidemiology has significantly contributed to improve methods used in clinical research, however, it is nearly impossible to assume with precision how even the most simple physical systems behave beyond the immediate future, much less the complex field of epidemiology; for epidemiologists, the key is in the term inference. Major areas of epidemiological study include investigation of outbreak, disease surveillance and screening, biomonitoring, and clinical trials to compare treatment effects. Epidemiologists rely on a number of other scientific disciplines such as biology (to better understand disease processes), biostatistics (to make efficient use of the data and draw appropriate conclusions), and exposure assessment and social science disciplines (to better understand proximate and distal risk factors, and their measurement).

9.1.1   Etymology

Epidemiology is derived from Greek epi, meaning "upon, among", demos, meaning "people, district", and logos, meaning "study", which literally means "the study of what is upon the people"(Nutter,1999).

The distinction between 'epidemic' and 'endemic' was first drawn by Hippocrates, to distinguish between diseases that are 'visited upon' a population (epidemic) from those that 'reside within' a population (endemic). The term 'epidemiology' appears to have first been used to describe the study of epidemics in 1802 by the Spanish physician Villalba in Epidemiologia, Espanola (Carol, et al., 1998). Epidemiologists also study the interaction of diseases in a population, a condition known as a syndemic. However, the term epidemiology is now widely applied to cover the description and causation of not only epidemic disease, but of disease in general, and even many non-disease health-related conditions, such as high blood pressure and obesity.

9.1.2   History

Ancient Era

The first epidemiologist was Hippocrates, a Greek physician who isreferred to as the father of medicine. He first examined the relationships between the occurrence of disease and environmental influences and he framed the terms endemic (for diseases usually found in some places but not in others) and epidemic (for diseases that are seen at sometimes but not others) (Alfredo, 2004). One of the earliest theories on the origin of diseases was that it was primarily the fault of human luxury. This was expressed by philosophers such as Plato and Rousseau, and social critics like Jonathan Swift.

In the middle of the 16th century, Girolamo Fracastoro, a doctor from Verona was known as the first to propose a theory that diseases are caused by tiny, microscopic particles and such particles were alive. According to him, those particles were considered to be able to spread by air, multiply by themselves and to be destroyable by fire. In this way he refuted Galen’s miasma theory of poison gas in sick people. He then wrote a book in 1543, titled De contagioneet contagiosis morbis, in which he promoted personal and environmental hygiene to prevent diseases. The development of a sufficiently powerful microscope by Anton van Leeuwenhoek in 1675 provided visual evidence of living particles consistent with a germ theory of disease.

Modern Era

In the 19^th century, Dr. John Snow referred to as the father of modem epidemiology was famous for his investigations into the causes of cholera epidemics.His work demonstrates the classic sequence from descriptive epidemiology to hypothesis generation to hypothesis testing (analytic epidemiology) to application (Snow, 1963). He began with noticing the significantly higher death rates in two areas supplied by Southwark Company. His identification of the Broad Street pump as the cause of the Soho epidemic is considered the classic example of epidemiology. He used chlorine in an attempt to clean the water and had the handle of the pump removed, thus ending the outbreak (Snow, 1963). This has been perceived as a major event in the history of public health and regarded as the founding event of the science of epidemiology, having helped shape public health policies around the world. Other pioneers include Danish physician Peter Anton Schleisner, who in 1849 related his work on the prevention of the epidemic of neonatal tetanus on the Vestmanna Islands in Iceland. Another important pioneer was Hungarian physician IgnazSemmelweis, who in 1847 brought down infant mortality at a Vienna hospital by instituting a disinfection procedure. His findings were published in 1850. Disinfection did not become widely practices until British surgeon Joseph Lister ‘discovered’ antiseptics in 1865 in light of the work of Louis Pasteur.

In the early 20th century, mathematical methods were introduced into epidemiology by Ronald Ross, Anderson GrayMckendrick and others. Another breakthrough was the 1954 publication of the results of a British Doctor’s study, led by Richard Doll and Austin Bradford Hill, which lent very strong statistical support to the suspicion that tobacco smoking was linked to lung cancer.

The Profession

Some epidemiologists work 'in the field'; i.e., in the community, commonly in a public health/health protection service and are often at the forefront of investigating and controlling disease outbreaks. Others work for non-profit organizations, Universities, Hospitals and larger government entities such as the Centers for Disease Control and Prevention (CDC), the Health Protection Agency, The World Health Organization (WHO), or National Agency for Food and Drug Administration and Control NAFDAC, Standard Organization of Nigeria. Epidemiologists can also work in for-profit organizations such as pharmaceutical and medical device companies in groups such as market research or clinical development.

The Practice

Epidemiologists employ a range of study designs from observational to experimental and epidemiological studies are aimed, where possible, at revealing unbiased relationships between exposures, biological agents, stress, or chemicals to morbidity or mortality. The identification of causal relationships between these exposures and outcomes is an important aspect of epidemiology. Modern epidemiologists use informatics as a tool.

The term 'epidemiologic triad' is used to describe the intersection of Host, Agent, and Environment in analyzing an outbreak. Diseases occur when host, agent and environment are not balanceddue to new agent, change in existing agent, change in number of susceptibilities in the population,and environmental changes that effect the agent or growth of agent. Therefore, there must be a unique combination of events, i.e. a harmful agent that comes into with a susceptible host in a proper environment.

Figure 1: The "epidemiologic triad" of infectious disease summarizes the factors that influence an infection, and the measures you might take to combat the infection. Source: Ian McDowell In: Johnson Y. J. (2018)

 

Epidemiologists tend to use synonyms for the 5 W's: diagnosis or health event (what), person (who), place (where), time (when), and causes, risk factors, and modes of transmission (why/how) (Kobayashi, 2020).

Advocacy
Epidemiologic evidence is often used to advocate both personal measures like diet change and corporate measures like removal of junk food advertisement, with study findings disseminated to the general public to help people to make informed decisions about their health, often the uncertainties about these findings are not well communicated; news articles often prominently report the latest result of one study with little mention of its limitations, bias, warnings, or context. The Epidemiology Forum (IEF) guidelines suggests that advocacy is appropriate, it recommend separating the roles of scientist and advocate, and the Council of International Organizations of Medical Sciences (CIOMS) guidelines recommend advocacy dependent on the quality of epidemiologic research and on causal interpretations of the data (Weed, 1994). 

Epidemiology Terminologies

Agents - are biological, physical, or chemical factors that contribute to the occurrence of a disease. Biological agents such as viruses and bacteria are often necessary causes for (infectious) diseases. Chemical agents such as poisons or allergens, or physical agents such a

radiation, noise, or heat, are all non-biological agents that are frequently not necessary causes for a disease but contributing factors.

Attributable risk – is a measure of association in cohort studies and experimental studies. The attributable risk is a difference measure and calculated as the difference between the incidence of the outcome in the exposed group (or intervention) and the incidence of the outcome in the unexposed group (or control).

Bar Chart (univariate) -is a graphical display of a categorical variable. A barchart consists of separate disjointed rectangles representing the frequencies of observations of the different categories. In a bar chart, the heights of the bars directly reflect the frequencies.

Basic reproductive rate - is the average number of people directly infected by an infectious case during its infectious period, when the case enters a completely susceptible population. The basic reproductive rate is the theoretical potential of an infection to spread in an entirely susceptible population.
Categorical variable - is a characteristic with defined categories, such as gender (categories: male and female) or blood group (categories: A, B, AB, 0). Categorical data have to be recorded in exhaustive and exclusive categories that is, there must be enough categories so that each observation fits into a category (exhaustive), and one category only (exclusive).

Chronic disease - a long-lasting, persistent or recurrent disease. Chronic diseases often lead to a loss of function, impairment, and long-term disabilities. Typical chronic diseases include cardiovascular diseases, cancer, diabetes mellitus, asthma, and musculoskeletal diseases. These are diseases with complex aetiologies.

Acute disease - Acute diseases come on rapidly, and are accompanied by distinct symptoms that require urgent or short-term care, and get better once they are treated.

Classical public health epidemiology – generally aims to investigate distributions and causes of diseases in populations.

Clinical epidemiology - studies the diagnosis, prognosis, and therapies of patients. Clinical epidemiology is conducted in a clinical setting, usually by clinicians, with patients as the participants.

Clinical equipoise - Clinical trials comparing two different treatments can only be ethically justified if there is no convincing evidence that one treatment is better than the other. This prerequisite has been called clinical equipoise.

Closed cohort - is a cohort in which membership begins at a defined time or with a defining event and ends only with observed the study outcome, the end of eligibility for membership, or the end of the study period.

Cluster sampling - is a form of probability sampling that involves sampling in naturally occurring clusters such as schools, households, or suburbs. In single-stage cluster sampling, a random sample of clusters is drawn and within each selected cluster either all units of analysis or a random sample of units are observed. An example of a two-stage cluster sampling is randomly sampling schools in Australia, randomly sampling classes within selected school, and within each selected class all students are invited to participate.

Conceptual research hypothesis – is another word for an initial research idea.
Confidence interval – is part of inferential statistics. A confidence interval allows the following statement about the unknown population parameter by taking exclusively information from a sample into account: The true but unknown population parameter lies within a (1-α)-confidence interval with a probability of 1-α. In most cases α is set to 5% and as a consequence 95% confidence intervals are calculated.

Determinant-centred epidemiology - is epidemiological research that investigates the effect of a specific determinant or exposure on health outcomes. For example, nutritional epidemiology investigates the effect of diet on health.

Diagnostic test – is a test applied to a person in order to determine the health status of the person. In contrast to a screening test, a diagnostic test is usually applied to symptomatic persons. Diagnostic tests are often used to confirm diseases suggested by symptoms and other circumstantial evidence.

Directionality – is the inner logic of an analytical study design. A study follows a forward directionality when first the exposure groups are defined based on the study factor and then the participants are followed-up to detect the outcome. Studies of “backward” directionality first define groups based on the outcome and then look backwards to exposure status of

participants. “Non-directional” means that both exposure and outcome are observed simultaneously in one group of participants.

Disease-centred epidemiology – is epidemiological research that focuses on only one disease or defined group of diseases and investigates distribution and determinants of this disease or group of diseases. For example, cancer epidemiology investigates distribution and determinants of cancers.

Dynamic cohort - is a cohort that gains and loses members throughout its existence. Most cohorts in epidemiology are dynamic.

Endemic – means the occurrence of a disease in a population or region at ‘normally’ expected levels. Endemic implies that the disease is able to maintain itself in a population or region without cases entering the population or region from outside.

Environment – in an epidemiological context refers to the habitat in which the biological agent and the host exist, survive or originate.

Epidemic – means the occurrence of a disease in a population or geographical region at clearly higher levels than ‘normally’ expected.

Epidemic curve – is a graphical display of the distribution of cases of an outbreak by time of onset.

Epidemiological (demographic) transition – is the transition from high mortality rates, usually caused by infectious diseases, to lower mortality rates mainly caused by chronic diseases in a country or region. It is usually accompanied by a transition from high to low fertility rates. The theory of demographic transition evolved to explain the rapid changes in population structure as observed during industrialization of western countries in the 19th and 20th century.

Epidemiological process – is an idealised concept on how to conduct epidemiological research. It is a cyclic process governed by the scientific method. Current theory and knowledge inform of a research idea. A study design is chosen to investigate the research idea. An operational, falsifiable, research hypothesis is formulated. Tools are developed to collect data in a standardised way. Data is collected, collated and statistically analysed. The results of this analysis confirm or reject the operational research hypothesis. The results of the study are published and thereby integrated into the current theory and knowledge.

Ethics - is the part of philosophy that deals with moral issues such as good and evil, right and wrong, what is just, etc.

Evidence-based practice - is an approach to health care where health professionals use the best currently available evidence possible. Evidence-based practice uses the most appropriate and most current information available to make optimal clinical decisions for individual patients.

Health – is a state of complete physical, mental, and social well-being; not merely the absence of disease or infirmity

Host – is a person or other animal that harbours an infectious agent.

Incidence - is a measure of disease frequency. Incidence quantifies the number of new cases (incident cases; i.e. people newly acquiring a disease or an attribute) in a population at risk of developing the disease over a given period of time.

Incidence rate - is the number of new cases (i.e. people newly acquiring a disease or an attribute) developing during a specific period of time divided by the total disease-free person-time of observation seen in the population at risk.

Incubation period - is the time interval between exposure to a sufficient cause of the disease and the onset of symptoms. The incubation period = Induction period + Latency period.

Infectious disease (Communicable disease) - is an illness caused by transmission of a specific infectious biological agent or its toxic products (= necessary cause) from an infected person, animal, or reservoir to a susceptible host. The transmission can occur directly or indirectly from a plant or animal host, vector, or the inanimate environment.

Infectious disease epidemiology – is the part of epidemiology that focuses on infectious diseases. Infectious disease epidemiology raises very specific questions about agents, transmission routes, and immunization. Infectious disease epidemiology provides models explaining occurrence and development of infectious disease outbreaks.

Point prevalence – is the total number of people with a disease or an attribute divided by the total number of people in the population at a given point in time.

Population at risk - are all people under observation who initially do not have the disease or the attribute but are “at risk” of acquiring the disease or the attribute.

Positive predictive value - of a diagnostic or screening test is the probability that a person with a positive test result will actually have the disease.

Prevalence - is a measure of disease frequency. Prevalence quantifies the number of existing cases (prevalent cases; i.e. people with a disease or an attribute) in a population at a point in time or during a period of time. Prevalence is also the number of existing cases of a disease in a population at a given time.

Prevalence odds-ratio – is a measure of association used in cross-sectional studies. The prevalence odds-ratio compares the odds of the prevalence of the outcome in the exposed group with the odds of the prevalence of the outcome in the unexposed group.

Primary prevention – are public health efforts that are directed towards the stage of susceptibility of a disease. Primary prevention aims to prevent or reduce “exposure” and thus the possibility of the disease occurring. An example of primary prevention is the “Slip, Slop, Slap” campaign to reduce sun exposure and hence prevent skin cancer.

Relative risk – is a ratio measure of association in cohort studies and experimental studies. The relative risk is the incidence of the outcome in the exposed (or intervention) group divided by the incidence of the outcome in the not-exposed (or control) group.

Reliability (Consistency, repeatability, precision, or reproducibility) – of ‘measurements’ means that if “measurements” were repeated with the same participants by the same or a different health professional, the results of the repeated “measurements” would be very similar or even identical to the first findings. The “measurements” might be responses to questions, results of diagnostic tests, or physical measurements such as height or weight. Also important is the reliability of the overall results of an epidemiological study (i.e. the amount of random error involved) which is assessed with statistical techniques such as confidence intervals and statistical hypothesis testing.

Representative uniformity – is a pre-requisite for the internal validity of an epidemiological study. Representative uniformity means that the sample(s) represent the target population. Representative uniformity implies that there is no selection bias.

Target population - is the population about which one wants to draw conclusions. The actual population, and with that, the sample may or may not be representative of the target population. The target population is partly defined by the exclusion and inclusion criteria of a study. When conducting a study, it is most important to define the target population first to ensure appropriate sampling.

Tertiary prevention - are public health efforts that are directed towards the clinical stage of a disease. Tertiary prevention aims preventing or minimising the progression of a disease or its consequences. A randomised controlled trial that aims to identify best treatment for a disease is an example of tertiary prevention.

9.2        Types of Epidemiologic Studies

The three major epidemiologic studies are case series/cross-sectional studies, case-control, and cohort

a.      Case series or cross-sectional studies

Case-series may refer to the qualitative study of the experience of a single patient, or group of patients with a similar diagnosis, or to a statistical technique comparing periods during which patients are exposed to some factor with the potential to produce illness with periods when they are unexposed. Case series identify unusual features of a disease or of individuals and may lead for example to the formulation of new aetiological hypotheses(Hennekens and Buring 1987), the identification of a new disease, or the identification of adverse effects to a certain exposure.

b.      Case control studies

A case-control study starts by categorizing groups according to the outcome (e.g. disease present or absent) and then looks back to establish the study factor (e.g. exposure present or absent). In case-control studies, individuals suffering from the studied disease are compared with controls who do not have the disease and exposure is recorded retrospectively (Ressing, 2010). Results of a case-control study are often expressed as exposure odds-ratios OR and is unable to estimate relative risk RR. Case control studies, however, select subjects based on their disease status. A group of individuals that are disease positive (the "case" group) is compared with a group of disease negative individuals (the "control" group). The control group should ideally come from the same population that gave rise to the cases. The case control study looks back through time at potential exposures that both groups (cases and controls) may have encountered. A 2x2 contingency table is constructed; displaying exposed cases (A), exposed controls (B), unexposed cases (C) and unexposed controls (D). The statistics generated to measure association is the odds ratio (OR), which is the ratio of the odds of exposure in the cases (A/C) to the odds of exposure in the controls (B/D), i.e. OR = (AD/BC).

Cases Controls
Expose d          A        B
Unexposed C  C         D

If the OR is clearly greater than 1, then the conclusion is "those with the disease are more likely to have been exposed," whereas if it is close to 1, then the exposure and disease are not likely associated. If the OR is far less than one, then this suggests that the exposure is a protective factor in the causation of the disease. Case control studies are usually faster and more cost effective than cohort studies, but are sensitive to bias (such as recall bias and selection bias).

c.       Cohort studies

A cohort study starts by defining groups by the study factor (e.g. exposure present or absent) and then follows-up these exposure groups to detect the outcome (e.g. disease present or absent).Individuals exposed to specific risk factors are compared with individuals not exposed to these factors in a cohort study, and the incidence of diseases or mortality in these two groups is observed (Ressing, 2010).  A cohort study is able to estimate relative risk since incidences are observed. The data from a cohort study allow the estimation of incidence rate and mortality rate as simple descriptive measures of frequency, as well as relative risk (RR) or hazard ratio (HR) as comparative effect measures. Standardized incidence ratios (SIR) or standardized mortality ratios (SMR) are used for comparison with the general population (Ressing, 2010).The RR compares the risk of health event among one group with the risk among other group. It is calculated by dividing the risk of disease for an exposed individual by the risk of disease for a non-exposed individual(Sauerbrei, 2009).

Prospective studies have many benefits over case control studies. The RR is a more powerful effect measure than the OR, as the OR is just an estimation of the RR, since true incidence cannot be calculated in a case control study where subjects are selected based on disease status. Temporality can be established in a prospective study, and confounders are more easily controlled for. However, they are more costly, and there is a greater chance of losing subjects to follow-up based on the long time period over which the cohort is followed.

 

9.3        Validity and Error

9.3.1   Validity

A diagnostic test is valid if the results of the diagnostic test are correct, that is, if the test is able to differentiate correctly between diseased people and people free of the disease. Validity, often refers to the overall result of an epidemiological study. The results of an epidemiological study are called valid if no bias (i.e. no systematic error that distorts the results) is present. Different fields in epidemiology have different levels of validity. One way to assess the validity of findings is the ratio of false-positives (claimed effects that are not correct) to false-negatives (studies which fail to support a true effect). Validity is usually separated into two components:

Internal validity is dependent on the amount of error in measurements, including exposure, disease, and the associations between these variables. In other words,internal validity refers to the inner workings of a study i.e. the design used, variables measured, correct analysis (Bovbjerg, 2020). Good internal validity implies a lack of error in measurement and suggests that inferences may be drawn at least as they pertain to the subjects under study.

External validity pertains to the process of generalizing the findings of the study to the population from which the sample was drawn (or even beyond that population to a more universal statement). External validity is however, truth beyond a study. A study is external valid if the study conclusions represent the fact for the population to which the results will be applied because both the study population and the reader’s population are similar enough in important characteristics (Gay, 2022).External validity can therefore, only occur if the study is first internally valid.

9.3.2   Error

Error, on the other hand, is the difference recorded between the calculated or measured value and the true outcome. This type of error is thus explained:

Random Error

Random error exists in all studies, because to some extent, it exists in all measurements. It is a type of error that is governed by chance, and is the result of fluctuations around a true value because of sampling variability. The smaller the random error in a study the more reliable are the results of the study. Standard statistical methods are used to quantify random error and the role it may or may not have played in the interpretation of a study’s results.Random error occurs in every epidemiological study due to natural or biological variation. It can occur during data collection, coding, transfer, or analysis. Random errors cannot be eliminated entirely, and by correctly interpreting p-values and confidence intervals (CIs), we can place our results in the appropriate context (Bovbjerg, 2020).

Examples of random error include: question not properly structured, poor interpretation of an individual response from a particular respondent, or a typographical error during coding. Random error affects measurement in a transient, inconsistent manner and it is impossible to correct for random error. Random error is assessed during the statistical analysis of the data collected in a study.

Precision is inversely related to random errorand is a measure of random error. Therefore, to reduce random error is to increase precision. Confidence intervals are computed to demonstrate the precision of relative risk estimates. The narrower the confidence interval, the more precise the relative risk estimate.

There are two basic ways to reduce random error in an epidemiological study. The first is to increase the sample size of the study. In other words, add more subjects to your study. The

second is to reduce the variability in measurement in the study. This might be accomplished by using a more precise measuring device or by increasing the number of measurements.

Systematic Error

Systemic error is a type of error which acts on the results of a study in a systematic way. It is on the other hand, consistent, repeatable error associated with faulty equipment or a flawed experiment design (Glen, 2022).The smaller the systematic error in a study the more valid are the results of the study. Like random error, systematic error occurs in every epidemiological study. Study design features such as randomization, blinding and matching are used to minimize systematic error. An example is a plastic tape measure that becomes slightly stretched over the years, resulting in measurements that are slightly too high. The validity of a study is dependent on the degree of systematic error.

It is quite difficult to detect and prevent systematic error. So, in order to avoid this error, the researcher should know the limitations of the equipment in use and understand how the experiment works. This can help identify areas that may be prone to systematic errors.

 

9.3.3   Bias

Bias, refers to systematic errors, meaning that they disproportionately affect the data in one direction only. Bias can be minimized with correct study design and measurement techniques, but it can never be omitted entirely. All studies have bias because humans are involved, and humans are inherently biased.

Selection Bias

Selection bias refers to a distortion in the effect measure resulting from the manner in which the people are selected for the sample. In other word, selection bias occurs when study subjects are selected or become part of the study as a result of a third, unmeasured variable which is associated with both the exposure and outcome of interest. If it occurs, the sample(s) do not represent the target population and it can threaten the internal validity of a study.Selection bias adversely affecting internal validity occurs when the exposed and unexposed groups (for a cohort study) or the diseased and non-diseased groups (for a case-control study) are not drawn from the same population (Beckmann and Beckmann, 1990). Examples of selection bias are volunteer bias (the opposite of which is non-response bias)in which participants and non-participants differ in terms of exposure and outcome.

Confounding Bias

Confounding bias may occur if the effect of the study factor on the outcome is mixed in the data with the effect of another variable (confounder). Whether confounding truly exists in a study can only be assessed during data analysis. Confounding has traditionally been defined as bias arising from the co-occurrence or mixing of effects of extraneous factors, referred to as confounders, with the main effect(s) of Interest. A confounder is thus a third variable—not the exposure, and not the outcome (Goldstein, 2016)—that biases the measure of association we calculate for the particular exposure/outcome pair.

Information Bias

Information bias refers to a distortion in the effect measure, due to measurement error or misclassification of participants for one or more variables. It occurs when the measurement of either the study factor or the outcome is systematically inaccurate. Information bias most times arises from systematic error in the assessment of a variable (Rothman, 2002).

 

9.4        Guidelines for Assessing Causality

Causality (Disease aetiology) – is about relating causes to their effects. In the context of epidemiology, causality is about identifying the causes of disease. Sir Austin Bradford Hill in 1965 detailed criteria for assessing evidence of causation (Hill, 1965). These guidelines are sometimes referred to as the Bradford-Hill criteria.

1.      Strength: A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal.

2.      Consistency: Consistent findings observed by different persons in different places with different samples strengthen the likelihood of an effect.

2.      Specificity: Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.

3.      Temporality: The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay).Biological gradient: Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an Inverse proportion is observed: greater exposure leads to lower incidence.

4.      Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).

5.      Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect. However, Hill noted that “.... lack of such [laboratory] evidence cannot nullify the epidemiological effect on associations Experiment: Occasionally it is possible to appeal to experimental evidence “.

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