Ebola Outbreak: Epidemic ‘Out of Control’
2014 Ebola Outbreak in West Africa (Guinea, Liberia, Sierra Leone, and Nigeria)
Defining and diagnosing diabetes
Diabetes ‘mellitus’ (honey-sweet) takes its name from the loss of sugar that makes urine sweet. It occurs in two forms. Type 1, or juvenile, diabetes starts most commonly before the age of 30, while type 2, or adult, diabetes mostly begins after the age of 30. Both types, which differ in their underlying lesions and response to treatment, have in common an impairment of the metabolism of sugars leading to abnormal levels of glucose in the blood.
Among the consequences are the passage of glucose in the urine and a high level of glucose in the body tissues, inducing a series of complications in the cardiovascular and nervous systems. It is the progression of these complications that makes diabetes a potentially very serious disease.
The diagnosis of diabetes may be suspected by the presence of symptoms such as excessive urination and thirst, recurrent infections, and unexplained weight loss (particularly in type 1, or from previous overweight in type 2). It becomes established if the level of glucose in plasma (the liquid fraction of blood) in a person fasting for 8 to 12 hours equals or exceeds 126 milligrams per deciliter. Many diabetes cases, particularly of type 2, present no symptoms for several years and are recognized only through a routine blood test done for other reasons: in this situation, the diagnosis is regarded as established only if a repeated blood test in a fasting condition confirms the result of the first.
Within an epidemiological survey carried out for research purposes, the best single diagnostic tool is to have the fasting subjects drink a concentrated solution of 75 grams of glucose and measure the glucose plasma level after 2 hours. A level of 200 milligrams per deciliter or above is diagnostic for diabetes, while values between 140 and 199 milligrams indicate an impaired regulation of glucose, a condition that increases the likelihood of developing diabetes.
Reference disease definitions are found in medical textbooks and collections of definitions have been developed, the best known and most widely used being the International Classification of Diseases and Related Health Problems (abridged as ICD) of the World Health Organization. As the name indicates, ICD is not a mere collection of disease definitions but a `nosological’ (from the Greek nosos, disease) system of ordering and grouping diseases. The grouping scheme has evolved out of that proposed in the early phases of international discussions on disease classification some 150 years ago. It reflects the same compromise between two main criteria of classification, one based on the site of the disease in the body and one based on the nature and origin of the disease. It covers five broad areas: communicable diseases of infectious origin; constitutional or general diseases (blood diseases, metabolic diseases like diabetes, cancers, mental disorders); diseases of specific organs or systems (cardiovascular, digestive, etc.); diseases related to pregnancy, birth, and development; diseases arising from injuries and poisons.
The first edition of the ICD was adopted in 1900 during an international conference in Paris at which 26 countries were represented. Revisions took place at 10-yearly intervals, and in 1948 the newly established World Health Organization took charge of the 6th revision and became responsible for all subsequent developments. Currently the 10th revision (ICD-10), which has been updated annually since 1996 rather than being completely revised, is in use.
ICD-10 is organized into 22 disease categories, each being denoted by a three-character code composed of one letter and two numbers. The different types of diabetes mellitus have codes E10 to E14 and are included in category IV, `Endocrine, nutritional, and metabolic diseases’. Myocardial infarction is coded 121, within the category IX, `Diseases of the circulatory system’; a fourth digit, to be used optionally, makes it possible to specify the part of the heart wall affected by the infarction. The three-character code is used in all countries that keep some system of health statistics to code the disease regarded as the cause of death. In many countries, it is also used in its standard form or with extensions and modifications for coding diagnoses in hospital discharge or other health services’ records. Tables allowing conversion of codes between different versions of ICD have been developed. Death is an unequivocal event and mortality statistics are an established yardstick for the description of the health conditions of a population.
Causes of death, as recorded in death certificates that use ICD-10, are subject to the problems of diagnosis already mentioned and require translating a doctor’s diagnosis into ICD codes. The internationally adopted death certificate provides a simple standard format to facilitate the task of the doctor in identifying the `underlying’ cause of death among the several ailments that may affect a patient. A full set of rules, today often in computerized form, is then available to the coders who have to convert the death certificate information into ICD codes. Notwithstanding these procedures, the accuracy of coded causes of death is still imperfect and variable even in developed countries. In developing countries, where more than three quarters of the world population live and die, reaching a minimally acceptable level of accuracy in the absence of adequate medical services may demand a’verbal autopsy’, i.e. a systematic retrospective enquiry of family members about the symptoms of illness prior to death. These limitations need to be kept in mind when dealing with mortality statistics, and more generally with statistics based on disease diagnoses. Yet as the British medical statistician Major Greenwood remarked: `The scientific purist, who will wait for medical statistics until they are nosologically exact, is no wiser than Horace’s rustic waiting for the river to flow away.’
Measuring disease
Three elements are always needed to measure the occurrence of a disease in a population or in a group within the population: the number of cases of the disease, the number of people in the population, and an indication of time. The finding that in the adult (age 15 and over) male population of Flower City, 5,875 cases of type 2 diabetes mellitus have been observed has a very different significance in a male population of Flower City of 10,000 than in 100,000 or 1,000,000 men. To make this relation explicit, a first measure of occurrence can be computed, the prevalence proportion’ or simply prevalence!:
Prevalence proportion = Number of diseased persons/Number of persons in the population
If the number of persons (men) in the population is actually 45,193, the prevalence proportion is: 5,875 / 45,193 = 0.13. In percentage form, 13% of all adult males in Flower City are diabetic. Now, or some time in the past? If the count was made in 1908 or 1921, it may be only of historical interest; if in a recent year, it is of current interest and carries practical implications. The point in time at which the census of the cases and of the population was taken needs to be specified, say 1 January 2014. This completes our measure and makes it unambiguous as an instant picture of diabetes in the Flower City male population. The information is useful, for instance, for the planning of health services. In general prevalence figures for all health conditions and in the different sections of the population, males and females, young or old, are required to plan an adequate provision of health services for diagnosis and treatment. It takes just a little reflection to realize that the prevalence of diabetes reflects in fact the balance between two opposite processes: the appearance of new cases and the disappearance of existing cases who die (or if they were completely cured, which happens for some diseases, such as pneumonia, but not for established diabetes). Both processes develop in time, hence time should now be taken not as a simple indication of the point at which the measurement was made (as for prevalence) but as time intervals within which new cases and deaths occur.
Before a soccer match, the referee tosses a coin to assign ‘at random’ a side to each team in the playing field. The procedure is fair to the two teams by assuming that a perfect coin will not fall preferentially on heads or tails. The results of an experiment in which the results of 10,000 tosses were recorded show that this is a tenable assumption. As portrayed in the figure, the proportion of heads varies widely when the number of spins is small, but the variation decreases and the proportion becomes gradually more stable as the number of tosses increases. The coin does not compensate in some mysterious way for any series of consecutive heads that may have occurred with an equal number of tails: simply, any imbalance between heads and tails will be diluted as the number of tosses increases and the value of the proportion will tend to stabilize more and more closely around the value 0.50 (or if you prefer 50%). This value – hypothetical, as in principle the series of tosses should carry on indefinitely – can be taken as the probability of a head. In general, the probability of an event is the proportion of occasions the event occurs in an indefinitely long series of occasions’. Being a proportion, it ranges between zero and one, or, in percentages, from 0 to 100%.
The notion of risk relates probability to time. ‘Risk is the probability of an event in a specified interval of time’, for instance of breaking a leg within the next five years. Risk should not be confused, as often happens in common parlance, with a risk factor, also called hazard, entailing the risk of some harmful effect: for fractures of the leg bones, skiing is a risk factor. As here defined, risk is simply the probability of any effect, harmful or beneficial, for example recovery from a disease.
Disease risk and disease incidence rates
The risk of a disease is the probability that a person becomes diseased during the time of observation:
Risk = Number of persons who become diseased during a time period/Number of persons at the beginning of time period
If in the population of Flower City (no. 45,193), 226 new cases of diabetes were diagnosed in the period between 1 January and 31 December 2013, the risk is 226/45,193 = 0.005 or 5.0 per 1,000 persons. This simple measure provides an estimate of the risk for a male living in Flower City to become diabetic if the population of the town is `closed’, with no individuals entering or leaving for whatever reason. Clearly a real natural population is never closed: people die, people move out and come in. Even in an artificially formed population such as a group of people identified for long-term follow-up and study of health, with no further entries permitted into the group, there will be deaths and some people will inevitably become untraceable. In short, risk seems a too crude measure in most circumstances except when the time interval of observation is so short, not a year but a week or a day, that entries into and exits from the population are minimal and can be ignored.
These shortcomings are not shared by a related measure of disease occurrence, the incidence rate, which is of general use but requires a more subtle formulation and the availability of more detailed data than just the number of people present at the beginning of the time of observation and the number of subjects who have developed diabetes by the end of that time. The incidence rate can be regarded as the probability of developing the disease in a time interval so tiny, just an instant, that no two events (death, arrival of an immigrant, new case of a disease, etc.) can take place within it. It is an instantaneous rate of occurrence, called instantaneous death rate when the event is death (the expressive term force of mortality is also used) and instantaneous morbidity rate when the event is the occurrence of a new case of a disease. The incidence rate can be derived as:
Incident rate = Number of persons who become diseased while observed/Sum of individual observation times of all persons.
The `observation period’ of a person is the length of time from the start of the observation to the moment he/she develops the disease or dies or is no longer under observation because he/she is lost from sight or the study has come to an end. The individual times of observation are summed and form the denominator of the rate. If, while the population of Flower City was observed during the 365 days between 1 January and 31 December 2013, a subject has migrated out on 31 March, after 90 days, he/she should be counted not as one person but only as 90/365 = 0.25 person years; if somebody died on 29 August, he/she should be counted for 241/365 = 0.66 person-years. The measurement unit person year captures the key concept that each person should count not as one but as an amount equal to the time he or she has been actually exposed to the risk of developing the disease. For the male population of Flower City, the incidence rate of diabetes, properly calculated in this way, turned out to be 5.2 per 1,000 person-years, or – in a less accurate but often-used expression – 5.2 per 1,000 per year. In plain words, some 5 men out of 1,000 become diabetic every year. Time is usually and arbitrarily specified as year, but week or even day may be more convenient when dealing with acute outbreaks of diseases such as influenza or SARS. In these instances, the measurement unit becomes person-week or person-day.
Intuitively, the incidence rate must have a positive relation to the prevalence proportion. More new cases of diabetes feed a higher prevalence of diabetes in the population if the average duration of the disease, which depends on how soon death intervenes after the disease onset, does not change in time.
For the Flower City male population, in stable conditions, it is sufficient to multiply the incidence rate of 5.2 per 1,000 person-years by an average duration of diabetes of roughly 25 years to obtain the prevalence of 13% described before.
A rate, in epidemiology as in all sciences, is a measure incorporating time as the reference. A rate of interest is how much you gain per year out of a capital, a rate of progression in space or velocity (speed) is how long a distance you cover in one minute or one hour. The term `rate’ should be confined to this use and not extended generically to other types of ratios. If you hear of `prevalence rate’, it is a wrong expression simply meaning prevalence proportion. Crude rates of incidence or mortality refer to a whole population, while specific rates, e.g. age-specific or sex-specific, refer to population subgroups defined by age or by sex.
The arithmetic of incidence rate
The incidence rate is a fundamental measure in epidemiology and demography. When computed for a healthy population, it expresses the probability of new cases of a disease per unit of time. When referring to a population of diseased persons, it expresses the probability of dying, or of recovering, per unit of time.
Imagine that a mini-population of 10 people has been observed for a winter trimester, i.e. 13 weeks after 1 January 2014, in a medical practice. Two new cases of influenza have been diagnosed, Blondine at week 6 and Frank at week 10, giving a risk of influenza of 2/10 = 0.2, or 20% in the trimester. Andrew, a foreign traveller, has come under observation on week 2 and left on week 4; George moved out for his job on week 9; and Ian unfortunately died in an accident in week 11. For simplicity, all events (arrival, departure, death, diagnosis of influenza) are regarded as occurring at the mid-point of a week. Andrew was observed for only 2 weeks, hence his time at risk of developing influenza is 2 weeks; Blondine developed influenza during the 6th week, hence her time at risk is 5.5 weeks, because the subsequent time of observation until week 13 no longer presents risk of influenza from the same strain of virus.
The incidence rate can now be computed as 2 / (2 + 5.5 + 13 + .. . 10.5 + 13) = 2/101 = 0.0198 per person-week or 1.98 per 100 person weeks. 2 persons out of every 100 falling ill in the short interval of one week is a high rate.
Ostensibly, in 52 weeks (a year) 104 persons (2 x 52), out of a total of 100 would fall ill, a plainly absurd result! In fact, a correct calculation, which involves more than simple arithmetic, would show that if this rate had continued for the whole year (which by good fortune does not usually happen with seasonal influenza), only 3 or perhaps 4 of the 10 persons in our mini-population would have escaped it.
Disease causes and exposures
We use the word `cause’ frequently in everyday parlance. The concept seems intuitively simple, yet it proves logically problematic and has been subject to continuous debate ever since Greek philosophers, in particular Aristotle, started to define it in the 5th century BC. Causes of disease do not escape this difficulty. When a few hours after a club dinner several members fall sick with gastroenteritis, what is the cause? The dinner, without which the intestinal trouble would have not occurred? The `tiramisu ‘dessert, as only those who ate it fell sick? The bacterium Staphylococcus aureus which, as a subsequent laboratory investigation showed, had found its way into the `tiramisu’ through a lapse in hygiene in the kitchen? The toxin produced by the bacterium that attacks the cells of the intestinal lining? The biologically active part of the toxin molecule that binds to some molecules of the cell membrane?
It could be tempting to take the latter as the ultimate, hence `real’, cause, but our understanding of the world would fast dissolve if only relationships between molecules could qualify as causal. For instance, it would be impossible to describe and analyse the circulation of the blood in terms of individual molecules. It is only when molecules join to form higher-order, complex structures such as blood cells, arteries, veins, the heart muscle, that new properties emerge permitting explanation of the working of the circulatory system. In fact, in our club dinner example each factor, from the dinner itself
to the active part of the molecule, can be regarded legitimately, at a different level of observation and detail, as a cause. Without any one of them there would have been no gastroenteritis. In general, we can consider as a cause a factor without which an effect, adverse such as disease or favourable like the protection against it, would not have happened.
Most of the epidemiologist’s investigative work consists in trying to identify the `factors without which’ a disease would or would not arise. In terms of the actual disease measurements, this means to identify factors of any nature – social, biological, chemical, physical – whose presence can be shown to be constantly associated with an increase or a decrease in a disease incidence rate or risk. There are scores of candidates for this role, from stress at work to inherited genes, from fatty foods to physical exercise, from drugs to air pollutants. They can all be designated with the generic label of factor or (in epidemiological jargon) exposure, neutral enough not to prejudice whether the candidate will in the end come out as a cause of disease or not.
