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Long-term legacies for health may also occur as a result of sensitization and predisposition of individuals to the effects of exposure in early life. Sensitization to house dust mite and other allergens both in the diet and the indoor environment during the first few months of life, for example, appears to increase risks of allergic airway disease later in childhood Similarly, inverse associations have been found between birth weight and the incidence of a range of diseases including hypertension, type-2 diabetes and cardiovascular disease in adults 32— Environmental exposures, such as air pollution, that contribute to these predisposing conditions may thus have long-term and in some cases lifelong implications for health.

Health outcomes may also be more or less specific to exposures to particular pollutants.

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Very few diseases are, in practice, pollutant-specific. Far more commonly, individual health effects may arise as a result of exposures to a number of different risk factors, either individually or in combination, whereas individual exposures can give rise to a range of different health effects. Environmental health is thus characterized by many-to-many relationships; understanding these is, again, a major challenge for epidemiology. Partly for this reason, it is often extremely difficult to assess the health burden attributable to an individual pollutant.

Over-estimation may occur due to double-counting or multiple attribution of health effects; under-estimation may arise due to the failure to recognize some contributions to the disease burden as a result of masking by other risk factors. In addition, of course, all epidemiological studies—and other studies that contribute to the establishment of dose—response relationships, such as laboratory experiments and clinical trials—are subject to error and uncertainty.

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These arise for many different reasons: because of errors in exposure assessment or classification, because of errors in diagnosis or reporting of health outcome, because of inadequate sample size, because of inadequate adjustment for confounding or effect modification by other factors, because of biases in sampling and statistical analysis, and because of the underlying indeterminacy of some of the associations of interest.

As a result, dose—response reported by different studies often shows substantial differences, and many separate studies may be needed before a clear pattern of association emerges. Even then, problems may be encountered in deriving reliable dose—response relationships e. Most dose—response relationships are thus accompanied by a relatively large degree of uncertainty.

As the discussion above has indicated, the relationships between pollution and health are both complex and often indirect. Considerable difficulties are thus encountered in quantifying the associations involved. It is largely for this reason that many of the health effects of environmental pollution are still uncertain, and that problems arise in attempting to attribute health outcome to environmental causes—for example, when trying to confirm or explain apparent spatial clusters in health.

These subtleties and complexities highlight the importance of examining critically any hypothesis about a relationship between a pollutant and an apparent health effect, and of setting such hypotheses within a wider environmental context. Assumptions about simple, singular cause—effect relationships often need to be eschewed; in their place we need to recognize the possibility for multifactorial effects in which single health outcomes are attributable to a wide variety of possibly inter-related environmental and other risk factors; and in which individual exposures may contribute to a range of different health effects.

The contingent and historical nature of many of these associations also needs to be appreciated: health effects seen now in many cases owe their existence to exposures, sensitization or some process of predisposition far in the past.

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Because environmental conditions, and even the very nature of the risk factors involved, may change quite considerably over time, uniformitarianist principles may not hold true, i. Against this background, the use of models to conceptualize the possible interplay of different risk factors and exposure pathways, and how they might have evolved over time, represents an important tool for attempts to understand associations between pollution and health.

One example is illustrated in Figure 6 , which shows possible sources and pathways of exposure of environmental pollution associated with landfill sites.

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Several important lessons can be drawn from this example. First, it is evident that the pathways of exposure are highly varied and complex. Which is the most important may well differ from one situation to another. The possibility of contributions from each and all of them needs to be allowed. Second, it is evident that landfill sites leave a legacy which may persist long after they are no longer operational.

Present-day land use and activity may therefore not account for current exposures.

Third, related to this, sources and pathways of exposure change markedly over time—and, indeed, many of the risks associated with what are now landfill sites may well predate the sites themselves e. Perhaps it is for this reason that several studies of health risks around landfill sites have found that raised levels of risk existed before the landfill sites were opened 35, For all the reasons outlined above, estimating the contribution of environmental pollution to the burden of disease is far from easy.

In general, too little is known either about the causal links between environmental pollution and health, or about the levels of exposure across the population, to make reliable assessments of the proportion of disease or mortality attributable to pollution. These difficulties are severe in developed countries, where disease surveillance, reporting of mortality, environmental monitoring and population data are all relatively well established.

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In most developing countries they become all but insurmountable, because of the generally impoverished state of routine monitoring and reporting. Given that controls on emissions and exposures in the developing world are often limited, it is in these countries that risks from environmental pollution are likely to be greatest.

Such uncertainties thus render any attempt to quantify the environmental burden of disease highly approximate at best.

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  • Assessments of the disease burden attributable to different forms and sources of pollution are nevertheless worth the effort. They are needed, for example, to raise awareness about some of the risks associated with environmental pollution, and as a basis for advocacy—to ensure that those most in need have a voice.

    They are needed to help motivate and prioritize action to protect human health, and to evaluate and monitor the success of interventions. They provide the foundation, therefore, for extremely powerful indicators for policy support, and a means of pricking the global conscience about inequalities in health. Over recent years, therefore, many attempts have been made to assess the health status of the population, both nationally and globally, and to deduce the contribution made by pollution and other environmental factors.

    In Europe, for example, more than 50 national environmental health action plans have been developed, following the Helsinki Conference in June , setting out strategies to tackle problems of environmental health Although these differ substantially in terms of their content and scope, many have involved attempts to make formal assessments of the disease burden attributable to different environmental hazards, and to rank these in terms of their public health significance 38, Various methods were used for this purpose, though most relied on some form of expert judgement, informed where available by quantitative data on mortality or disease rates.

    Whatever the weaknesses of these assessments, their practical importance is evident, for they have contributed directly to policy prioritization and development in the countries concerned. The same need has arisen to support the development of environmental health indicators. Since the early s, largely motivated by WHO, increasing attention has been given to constructing indicators on environmental health at all levels from the local to the global scale 40— 43 , and a number of indicator sets have been created and to a lesser extent used 44— Environmental pollution is, inevitably, a major focus of concern in these indicator sets.

    By definition, also, environmental health indicators provide measures that link environmental hazards and health effect In both contexts, the ability to make at least semi-quantitative interpretations of the link between pollution and health, and thus to assess the contribution to the burden of disease, is assumed.

    The most explicit attempts to quantify these links, however, have come in recent years through work to estimate the global and to some extent regional burden of disease. Earlier efforts in this direction were targeted specifically at making broad-scale enumerations of the total disease burden across the world 47, The traditional measure used for such assessments was mortality, both because data on deaths tended to be more reliable and widely available, and because mortality is directly comparable in terms of health outcome, unlike morbidity which implies differences in severity of effect.

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    Even so, results from the various efforts differed somewhat, largely because of the ways in which gaps and uncertainties in the available data were dealt with Crude estimates of the number and proportion of deaths due to different diseases, of this nature, obviously give only a distorted picture of the true burden of disease, for they take no account of the age of death or the duration of any preceding illness and disability, nor the amount of suffering involved. Years of life lost are estimated as the difference between age at death and the life expectancy in the absence of the disease, based on an advanced developed country DALYs also incorporate an allowance for the number of years lived with a disability due to disease or injury, weighted according to its severity based on expert assessments of the relative impact of some different conditions and disease sequelae.

    The years of disability or life lost are also discounted according to the age of onset since it is assumed that future years of life lost contribute less to the burden of disease than current ones. Results of these calculations are summarized and discussed by WHO Estimates of YYL and DALYs provide a somewhat different ranking of disease compared to crude mortality, since they give additional weight to early-onset diseases and chronic illness.

    Cardiovascular diseases are thus seen as somewhat less important making up ca. By whichever method they are computed, marked variations are evident in the burden of disease between different sectors of the population.

    Children are seen to be especially at risk—and young children most of all. When measured in terms of DALYs, the overwhelming burden of all these diseases falls on children Similar inequalities occur both socially and geographically. Clear differences were shown.

    Whereas ischaemic heart disease, for example, was responsible for Conversely, respiratory infections and diarrhoeal diseases accounted for As these examples show, generalizations about the burden of disease thus need to be interpreted with care. Beneath the often stark global figures lie even starker indications of health inequalities that cry out to be addressed. Whilst the original estimates of the global burden of disease made by Murray and Lopez and WHO during the mid s were a major step forward in terms of providing comparable data on health status across the world, they gave information only on health outcomes and did not for the most part attempt to attribute these outcomes to specific causes.

    Murray and Lopez 52 also made preliminary assessments of the relative importance of different risk factors for the global burden of disease, based on their data. Malnutrition stood out as the most important factor considered, accounting for ca. Poor water and sanitation was estimated to be responsible for ca. Based on available information, they estimated that these environmental factors were responsible for ca. These estimates are somewhat lower than those implied by the original Global burden of disease study 52 , partly perhaps because of differences in methodology and partly because of a decline in mortality in the intervening years.

    All these attempts to partition the global burden of disease by causative risk factor have faced, and admitted, a number of major difficulties. These relate not only to uncertainties in the available data on health outcome, but also to problems of how to attribute any single death to a single cause or risk factor.

    Two main approaches have been proposed for disease attribution Categorical attribution assigns each death to a specific disease or risk factor, according to a defined set of rules e. The advantage of this approach is that it is relatively straightforward and consistent, and avoids double-counting; the disadvantage is that it ignores the multi-factorial nature of many diseases and still leaves unresolved the problem of how to define appropriate rules.

    Counterfactual attribution involves comparing the current level of disease or mortality with that which might be expected to occur in the absence of the risk factor or at some other reference level. One of the main difficulties with this approach is how to define this reference level. Several possibilities exist: for example, the complete absence of the risk factor, the level of risk in some reference population or area, or the achievable level of risk with current technologies. Each will tend to give a rather different measure of the attributable burden of disease. In this context, another difficulty also arises, i.