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(10) Epidemiology

Climate Change Might Increase Risk Of Malaria

Climate change and infectious diseases - World Health Organization

The connection between climate change and malaria


Clinical medicine

Systematic reviews form an important part of clinical epidemiology, but more generally the quantitative and probabilistic traits of epidemiology pervade clinical medicine. It is common to find today in standard textbooks of medicine references to `NNTs' and schemes of `diagnostic decision trees'. Comparing treatment options is helped by computing the NNT, or number needed to treat. In severe hypertensive subjects, the risk of a major adverse outcome (such as death or stroke) in the coming three years may be as high as 20%. A treatment may, however, reduce it to 15%. The risk reduction obtained with the treatment is 20 - 15 = 5%, which means that out of 100 subjects treated, 5 avoid the major adverse outcome they would have otherwise suffered. This is the same as saying that for one subject to avoid a major adverse event, the number needing treatment is 100/5 = 20. Should a new treatment reduce the risk to 4%, it would be necessary to treat only 6 x=100 /(20 - 4) patients to avoid one adverse event. Comparing the number of people who need to be treated for the two treatments, 20 against 6, conveys tangible information on the merits of the two treatments, the second being clearly superior (provided all other aspects are the same, for instance the frequency of side effects, but these can be dealt with in terms similar to NNT).

A diagnostic decision tree is designed to assist the physician in formulating a diagnosis. If a young man presents with a sudden vague but aching and recurrent pain in the left chest, one diagnostic possibility is coronary artery disease, the narrowing of the coronary arteries that supply blood to the heart. Given the young age of the patient and the absence of any other sign, this condition appears a priori unlikely, but being very serious it could be disastrous to miss it. The patient can thus undergo an exercise stress test whereby his electrocardiogram is monitored during controlled physical effort. A negative test would be reassuring; unfortunately the test is not perfect and sometimes it turns out falsely negative even in presence of the disease, in the same way that it can be falsely positive in its absence. If narrative terms like `a priori unlikely, `sometimes falsely positive', `sometimes falsely negative' are replaced with figures of probabilities (derived from specific studies), a map, or decision tree, can be built of all possible courses of diagnostic actions. One course maybe to dismiss straight away the diagnosis of coronary artery disease because the type of pain found in an otherwise healthy and young man makes the diagnosis less than 5% probable. The alternative course is to proceed to the stress test knowing, however, that it has a 30% probability of false negative results (i.e. it has a sensitivity of 100 - 30 = 70%) and a 10% probability of false positive results (i.e. it has a specificity of 100 - 10 = 90%). Combining these figures makes it possible to calculate the probability, or predictive power, that each alternative will correctly identify the disease if present or dismiss it if absent. A comparison of these probabilities, and of the penalties involved in a wrong diagnosis, helps the physician to analyze the diagnostic process, which often involves not just one but many possible tests, and to choose an optimal diagnostic strategy (these calculations are based on Bayes' theorem, a fundamental tool for drawing inferences of probabilistic nature from empirical observations, established as early as the mid-18th century by the Reverend Thomas Bayes).

Prevention and early diagnosis

In a strict technical sense, `prevention' denotes the activities aimed at directly modifying the root determinants of disease, which fall only into two broad categories: genes and environment, or in more archaic wording `nature and nurture'. Early diagnosis, on the other hand, aims at detecting and treating diseases before they become manifest through symptoms. These two neatly separated activities, both organized at the level of the whole population, have, however, a major bridge in the diagnosis of host risk factors, like high blood cholesterol or high blood pressure, that are not yet `diseases' but increase the chance of disease occurrence; on the one side, the host risk factors share this property with a person's genes predisposing to disease, while on the other they are themselves the result, like early disease, of a complex interplay of genes and environment. Some early disease diagnosis tests are carried out as `opportunistic screening tests' by individual doctors when they examine a patient: for instance, the PSA test for prostate cancer discussed in Chapter 5 has become, rightly or wrongly, popular in several developed countries even in the absence of firm evidence of net benefit. Only screenings for which this evidence exists do, however, qualify for systematic adoption in the population in the form of `organized screening programmes', such as those for colon cancer or for cervical and breast cancer in women, now implemented on a substantial scale in many countries. Screening programmes aimed at early diagnosis in apparently healthy populations are evaluated in the same ways as the diagnostic procedures in symptomatic patients previously discussed.

Programmes for different diseases can be compared or different alternatives of a programme, for instance screening for cervical cancer using either the cytological `Pap test' or the assay detecting the human papilloma virus. For this purpose, indexes such as the predictive power and the number needed to screen (NNS) are calculated. The latter is closely similar to the number needed to treat (NNT) and tells how many subjects one needs to test in order to avoid one death or other major adverse event within a period of time. It depends not only, as NNT does, from the probability that a treatment successfully avoids death but also from the probability that an apparently healthy subject turns out to have the disease without symptoms. NNS are usually in the range from several hundreds to, more often, several thousands.

Screening for host factors, genetic or acquired, that may predispose to a disease stands on the basic assumption that subjects who will develop the disease can be distinguished from subjects who will not, so that any preventive intervention, for example a change in diet, can be concentrated on the former (should the distinction prove impossible, there would be no point in screening and any intervention would simply need to be applied to everybody). Looking closely at one of these risk factors, blood cholesterol, throws light on how far the basic assumption is justified and illustrates at the same time some general principles of prevention, taken in the wide and generic sense of any measure able to prevent at any point the progression from health to disease and death. Today, few will be surprised if a heavy smoker comes down with lung cancer. Many may be surprised, however, if told that avoiding heavy smoking will not wipe out the burden of lung cancer in the population because a substantial number of cases occur in fact in people who regularly smoke only moderately. People with frankly anomalous cholesterol levels, say above 6.5 mill moles per liter, represent 6 + 3 + 2 =11% of the population in which it has been found that 13 + 9 + 8 = 30% of the deaths from heart attacks occur (in case you feel more comfortable with milligrams per 100 milliliters, 6.5 mill moles is about 250 milligrams). Intervening on this `high-risk' fraction of the population, about one-tenth of the total would prevent - assuming an intervention that is 100% effective - just one-third of the deaths, leaving untouched the other two-thirds. Why these disappointing results? Because the risk is not concentrated solely in people `at high risk', with cholesterol levels above 6.5 mill moles, but involves everybody to some degree. As cholesterol levels increase over the very lowest levels (category 0-3.9), the risk of disease increases by small increments, with no abrupt jumps.

As a consequence, the many people with only modest elevations in cholesterol who are also at a modestly increased risk produce more cases of heart attacks than the minority of people at high risk. This `paradox of prevention' implies that the bulk of cases could be prevented by moderately reducing the cholesterol level, hence the risk, of everybody. Abating cholesterol only in people with high levels is certainly beneficial to them but cannot does the public health job of preventing the mass of cases in the population. Many disease determinants have been found to increase the risk of some diseases in a smooth, continuous way like cholesterol, for example blood pressure for heart attacks, hydraulic pressure in the eye for glaucoma, or alcohol consumption for cancer of the oesophagus or liver cirrhosis. The graded distribution over the whole population of risk generated by these determinants, rather than its exclusive concentration in some groups, stresses their role as population disease determinants in contrast to individual determinants. The susceptibility of each person, rooted in their genetic make-up, plays - as does chance - a role in determining who becomes diseased, but the number affected will depend to a major extent on the population determinants. For example, there are no known populations with a high frequency of heart attacks without also an average (over the whole population) high level of cholesterol. The next question then becomes: why do population determinants differ from one population to another? Cholesterol level is diet dependent and, like alcohol consumption, is conditioned by available foods (or alcoholic drinks), traditional tastes, and behavior influenced by marketing and by economic constraints. For infectious diseases, the proportion of people vaccinated is a typical population determinant of how often a disease will occur, because vaccinated people do not fall ill and at the same time they interrupt the chain of transmission of the contagion. For most diseases, multiple, rather than single, determinants are recognized. For example, blood cholesterol level, blood pressure, tobacco smoking, diabetes, and obesity are main population determinants of heart attacks. Interventions acting in turn on these determinants aim at promoting healthy habits, behaviors, foods, and to limit the availability of harmful products. This population strategy of prevention, based on a variable mix of incentives, education, and regulation, is beneficial to everybody, whatever one's known or unknown susceptibility or level of risk. It can be complemented by specific preventive actions, often involving the use of drugs (e.g. to lower cholesterol or blood pressure) for people known to be at definitely high risk. Recently the idea has dawned that a combination in a single pill ('polypill') of low doses of several drugs controlling cholesterol level, blood pressure, and blood clotting propensity could be used in a population prevention strategy by offering it to most or all middle-aged and older people. Whether this is an effective, safe, and realistic possibility remains to be explored. The general principle is that before being launched on a grand scale, a preventive measure must have been clearly shown to work. This involves research covering a large number of disease determinants, from proximate biological and genetic factors, to personal behavior traits, and to the `determinants of the determinants' operating at the level of the social or of the global environment.

Attention to the global environment has markedly increased in recent years. Localized `heat waves' have caused clearly documented excesses of mortality and fluctuations in urban air pollutants, especially fine particulates, which have been shown to increase hospital admissions for respiratory and cardiovascular ailments and to precipitate deaths from a variety of causes. Protocols to prevent these adverse effects affecting in particular vulnerable, already sick people have been put in place in a number of countries. In contrast to these meteorological episodes, the health consequences of the foreseen global climatic change are a completely new chapter for epidemiological investigation. A likely temperature increase of anything between 2°C and 5°C by the end of this century may be reflected in a sea-level elevation of 20 centimeters to 60 centimeters, involving a change in coastlines with consequent exposure of populations to flooding, already regularly experienced in a country like Bangladesh. Tropical cyclones, to which more than 300 million people are currently exposed, are expected to become more intense. The biological cycles of parasites are sensitive to climate changes, so that hundreds of millions of additional people will be infected by diseases like malaria. A further likely consequence is increased under-nutrition caused by droughts and rural poverty that, like the other sequels of climatic change, will induce mass migrations, themselves a source of severe health problems (as just one example, keeping well controlled a serious case of diabetes, a delicate but everyday routine task in developed countries, may become hopeless in a moving refugee population). Today, these effects can be identified but their probable impact on health (currently quite modest) remains to be quantified  through research that combines available epidemiological data, for example on malaria in different regions, with models simulating how the disease may evolve under various hypotheses of temperature and other environmental changes.




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