In medical practice, the noun diagnosis
refers to a named medical condition or disease. The most widely used diagnostic classification system is the International Classification of Diseases (ICD), maintained by the World Health Organization. It is currently in its 11th edition.
To make a diagnosis, a clinician systematically analyses the available clinical findings:
The distinction between a diagnosis and a clinical finding is not always clear cut; some terms are both, for example, anaemia is:
Differential diagnosis (DDx) is a process listing possible diagnoses based on a systematic analysis of a patient's clinical findings. Expanding the set of relevant clinical findings narrows the differential diagnoses leading to the definitive diagnosis (Dx).
Abbreviations for differential diagnosis include DDx, DD, ddx, D/Dx, and ΔΔ.
Causation and root cause analysis relate to a structured approach to diagnosis based on the fundamental determinants of disease:
Causation and Differential Diagnosis (DDx) are related concepts.
Also known as exposures.
Medicine has always been an evidence based discipline, however, in 1979, a UK epidemiologist, Archie Cochrane identified that there were deficiencies in this aspect of health science. He stated:
It is surely a great criticism of our profession that we have not organised a critical summary, by speciality or subspeciality, adapted periodically, of all relevant randomised controlled trials.
These databases evolved from work originally done by the UK National Health Service. In 1993 it became obvious that the project was far to large to be maintained by a single group or country and the International Cochrane Collaboration was formed at a meeting held in Oxford. This meeting has become an annual event and is known as the Cochrane Colloquium.
A systematic approach to rating the quality of medical evidence means better and more consistent quality of health practice. A number of bodies, in addition to the Cochrane Collaboration, have produced guidelines for rating the quality of evidence; one of the best known of these is the NHMRC (Australia) system. The salient features of these rating systems are summarised below.
In general, evidence receives greater weighting based on the following characteristics:
Samples should include representative subjects and should avoid including non-representative subjects
The Cochrane Collaboration's mission statement states:
The Cochrane Collaboration is an international organisation that aims to User Guide people make well-informed decisions about healthcare by preparing, maintaining and promoting the accessibility of systematic reviews of the effects of healthcare interventions. It is a not-for-profit organisation, established as a company, limited by guarantee, and registered as a charity in the UK (number 1045921).
The Cochrane Collaboration evidence rating system is as follows:
Consistent evidence obtained from more than two independent, randomised and controlled studies or from two independent, population-based epidemiological studies. Studies included here are characterised by sufficient statistical power, rigorous methodologies and inclusion of representative patient samples. Alternatively, a meta-analysis of smaller, well characterised studies may support key findings.
Consistent evidence from two randomised controlled studies from independent centres, a single multi-centre randomised controlled study or a population-based epidemiological study. Data included here have sufficient statistical power, rigorous methodologies and the inclusion of representative patient samples.
Consistent evidence obtained from more than one study, but where such studies have methodological constraints, such as limited statistical power, or the inclusion of patient samples which may be non-representative.
Evidence obtained from a single case-control study or a selected cohort study.
Conflicting evidence obtained from two or more well-designed and controlled studies.
Consensus opinions of respected authorities, based on clinical experience and/or descriptive reports.
Consistent evidence obtained from:
The studies included must be characterised by:
Consistent evidence from:
The studies included must be characterised by sufficient statistical power, rigourous methods and inclusion of representative patient samples.
Consistent evidence from obtained from two or more well-designed and controlled studies performed by a single research group.
Consistent evidence obtained from more than one study, but where such studies have methodological constraints, such as limited statistical power, or the inclusion of patient samples which may be non-representative.
Evidence obtained from:
Conflicting evidence obtained from two or more well-designed and controlled studies.
Consensus opinions of respected authorities, based on clinical experience, descriptive studies, or reports of expert committees.
NOTE: In 1999 the NHMRC removed level of evidence IV although it continues to be referred to in the literature.
Hill's Criteria of Causation outlines the minimal conditions needed to establish a causal relationship between two items. These criteria were originally presented by Austin Bradford Hill (1897-1991), a British medical statistician, as a way of determining the causal link between a specific factor (e.g., cigarette smoking) and a disease (such as emphysema or lung cancer). Hill's Criteria form the basis of modern epidemiological research, which attempts to establish scientifically valid causal connections between potential disease agents and the many diseases that afflict humankind.
While the criteria established by Hill (and elaborated by others) were developed as a research tool in epidemiology, they are equally applicable to sociology, anthropology and other social sciences, which attempt to establish causal relationships among social phenomena. Indeed, the principles set forth by Hill form the basis of the evaluation used in all modern scientific research. While it is quite easy to claim that agent "A" (e.g., smoking) causes disease "B" (lung cancer), it is quite another matter to establish a meaningful, statistically valid connection between the two events.
It is just as necessary to ask if the claims made within the social and behavioural sciences live up to Hill's Criteria as it is to ask the question in epidemiology (which is also a social and behavioural science). While it is quite easy to claim that population growth causes poverty or that globalisation causes underdevelopment in Third World countries, it is quite another thing to demonstrate scientifically that such causal relationships exist. Hill's Criteria simply provides an additional valuable measure by which to evaluate the many theories and hypotheses proposed within the social sciences.
Hill's criteria are used as the basis of assessing medical evidence in many courts. In many instances, defendants will use the tactics of "there are no epidemiological studies", or "there is no statistical evidence to show causation for a certain chemical".
The United States Supreme Court, in its Daubert decision addresses that issue and concludes that the expert has to provide a scientific opinion, based on methods and techniques generally used, and/or relying on peer-reviewed publications for testability of his opinion that causation is more likely than not due to the toxin in question (51% probability). The expert does not necessarily need to rely on epidemiology, or scientific certainty, or provide scientific proof", but rather follow the below-quoted guidelines applied in the U.S. Supreme Court Daubert decision. The expert should know the accepted methodology to establish causation described in the scientific and medical literature, and established originally by Sir Bradford-Hill.
These criteria are the cornerstone of establishing causation, and are described as follows:
Exposure always precedes the outcome. If factor "A" is believed to cause a disease, then it is clear that factor "A" must necessarily always precede the occurrence of the disease. This is the only absolutely essential criterion.
This is defined by the size of the association as measured by appropriate statistical tests. The stronger the association, the more likely it is that the relation is causal. For example, the more highly correlated hypertension is with a high sodium diet, the stronger is the relation between sodium and hypertension. Similarly, the higher the correlation between patrilocal residence and the practice of male circumcision, the stronger is the relation between the two social practices.
An increasing amount of exposure increases the risk. If a dose-response relationship is present, it is strong evidence for a causal relationship. However, as with specificity, the absence of a dose-response relationship does not rule out a causal relationship. A threshold may exist above which a relationship may develop. At the same time, if a specific factor is the cause of a disease, the incidence of the disease should decline when exposure to the factor is reduced or eliminated. An anthropological example of this would be the relationship between population growth and agricultural intensification. Other things being equal, as population growth increases within a given area, we should see a commensurate increase in the amount of energy and resources invested in agricultural production.
Conversely, when a population decrease occurs, we should see a commensurate reduction in the investment of energy and resources per acre. The same analogy can be applied to the current debate on global warming. If increasing levels of CO2 in the atmosphere are the cause of increasing global temperatures, then "other things being equal", we should see both a commensurate increase and a commensurate decrease in global temperatures following an increase or decrease, respectively in CO2 levels in the atmosphere.
The association is consistent when results are replicated in studies in different settings using different methods. That is, if a relationship is causal, we would expect to find it consistently in different studies and in different populations. This is why numerous experiments have to be done before meaningful statements can be made about the causal relationship between two or more items. For example, it has taken thousands of highly technical studies of the relationship between cigarette smoking and cancer before a definitive conclusion can be made that cigarette smoking increases the risk of (but does not cause) cancer. Similarly, it would require numerous studies of the difference between male and female performance of a specific behaviour (e.g., cognitive tasks, domestic violence, nurturing activities, etc.) by a number of different researchers and under a variety of different circumstances before a conclusion could be made regarding whether a gender difference exists in the performance of such behaviours.
The association agrees with the currently accepted understanding of pathological processes. However, studies that disagree with the established understanding of biological processes may force a reevaluation of accepted beliefs. In other words, there needs to be some theoretical basis for making an association between a vector and a disease, or one social phenomenon and another. One may, by chance, discover a correlation between the price of bananas and the election of dog catchers in a particular community, but there is not likely to be any logical connection between the two phenomena. On the other hand, the discovery of a correlation between population growth and the incidence of warfare among Yanomamo villages would fit well with ecological theories of conflict under conditions of increasing competition over resources.
In judging whether a reported association is causal, it is necessary to determine the extent to which researchers have taken other possible explanations into account and have effectively ruled out such alternate explanations. In other words, it is always necessary to consider multiple hypotheses before making conclusions about the causal relationship between any two items under investigation.
The condition can be altered (prevented or ameliorated) by an appropriate experimental regimen.
This is established when a single putative cause produces a specific effect. This is considered by some to be the weakest of all the criteria. The diseases attributed to cigarette smoking, for example, do not meet this criterion. When the specificity of an association is found, it provides additional support for a causal relationship. However, the absence of specificity in no way negates a causal relationship. Because outcomes (be they the spread of a disease, the incidence of a specific human social behaviour or changes in global temperature) are likely to have multiple factors influencing them, it is highly unlikely that we will find a one-to-one cause-and-effect relationship between two phenomena.
Causality is most often multiple. Therefore, it is necessary to examine specific causal relationships within a larger systemic perspective.
The association should be compatible with existing theory and knowledge. In other words, it is necessary to evaluate claims of causality within the context of the current state of knowledge within a given field. What do we have to sacrifice about what we currently know in a given area in order to accept a particular claim of causality.
What, for example, do we have to reject of our current knowledge in geography, physics, biology and anthropology to accept the Creationist claim that the world was created as specified in the Bible a few thousand years ago? Similarly, how consistent are racist and sexist theories of intelligence with our current understanding of how genes work and how they are inherited from one generation to the next? However, as with the issue of plausibility, research that disagrees with established theory and knowledge is not automatically false. They may, in fact, force a reconsideration of accepted beliefs and principles. All currently accepted theories, including Evolution, Relativity and non-Malthusian population ecology, were at one time new ideas that challenged orthodoxy. Thomas Kuhn has referred to such changes in accepted theories as "Paradigm Shifts".