1 World Health Organization European Centre for Environment and Health Via F. Crispi 10 00187 Rome, Italy
Künzli et al. (1) developed a useful framework for understanding how air pollution affects mortality. They considered four cases of a person's life experience, labeled A through D, giving the combinations of suffering or not suffering from long- and short-term air pollution health effects. This formulation, albeit useful, might be somewhat oversimplistic. While it is true that there are four relevant combinations of exposure (i.e., both long- and short-term (A), long-term only (B), short-term only (C), and none (D)), there might be more than four relevant patterns of a person's lifetime experience.
An episode of acute air pollution exposure can trigger death only among those persons whose frailty is high; even under this restrictive, uncertain assumption, this high-frailty group includes people whose death is not far off anyway and people who could live much longer were it not for a polluted day. For example, a person's frailty can be very high and proximal to death due to severe illness, and such frailty can subsequently decrease, even substantially, if the person recovers. If this person is exposed to high air pollution concentrations during this period of transitory elevated frailty (case E), he can then die as a result of the relatively small increase in frailty due to air pollution. This death is therefore attributable to air pollution, the person-time loss can be substantial, and, crucially, the death would not be captured by risk coefficients from long-term cohort studieshigh long-term exposure is not required. If the uncertain assumption above does not hold and there are healthy persons who can die as a result of an acute air pollution episode (case F), then the situation becomes more complicated. In any case, there are deaths caused by short-term air pollution exposure that have nothing to do with long-term exposure.
Concluding that "cohort-based estimates include the total number of cases [of attributable deaths]" (1, p. 1054) is equivalent to assuming that the risk described in time-series studies merely reflects harvesting, which, as the authors acknowledge, has been demonstrated to be incorrect (2
). A possible explanation of this apparent contradiction is that the authors make, implicitly, the assumption that frailty increases monotonically throughout a person's adult life through the accumulation of the effects of all risk factors, whereas this is not necessarily the case. What matters, however, is that the part of the excess of short-term mortality that is not explained by harvesting (i.e., at least all cases E and F and, perhaps, other relevant patterns) has a separate impact on mortality, which adds (perhaps partially) to that associated with long-term mortality. The short-term impact is certainly smaller than the long-term one (coefficients are some five times smaller), but it is not included in long-term effects, as the authors imply. It is correct that relative risks from time series studies would underestimate, on their own, the impact on mortality. On the other hand, the consistency of the findings of time series studies and the realization that harvesting is not the only underlying phenomenon suggest that short-term effects of air pollution on mortality might result in a sizable impact in terms of lost years of life in addition to the impact associated with long-term effects. It is presently unclear how such short-term impact can be estimated, and indeed, the inappropriate use of available risk estimates has been pointed out (3
). However, methodological difficulties should not be taken to imply that the impact is negligible.
Short-term effects of air pollution involve large numbers of deaths every year worldwide, and many studies found, almost invariably, significant effects. We do not know how to estimate the impact, but it might be safer to make the hypothesis that reduction in the concentrations reached at peak episodes could result in nonnegligible mortality gains (in principle, even in the unlikely situation in which long-term average concentrations remain stable). It is desirable to develop methods to verify this hypothesis and move toward health impact assessments that are as comprehensive as possible.
REFERENCES
Institute of Social and Preventive Medicine University of Basel Basel, Switzerland
Environmental Health Department National Institute for Public Health Surveillance Saint-Maurice, France
Martuzzi (1) makes a helpful contribution to a better understanding of the entire dynamic of air pollution and health. We explain why our model (2
) also includes his well- thought-out cases that may, however, not be captured either by time-series or cohort studies.
A major issue in our paper relates to the question of what we really measure in these two types of air pollution mortality studies. Our cases in categories A, B, C, and D offer a framework to a posteriori categorize the contribution of air pollution to death. From an posterior perspective, our "2 x 2 model" is complete, and there are no other theoretical cases. That is, for every death, one category must be true: A death might occur in which lifetime air pollution experience contributed to a shortening of life and air quality shortly before death was, in addition, of relevance (cases in category A). For cases in category B, we claim that the lifetime air pollution experience contributed in some way to shortening of survival time, but that the air quality in the last days before death was not relevant. For deaths of cases in category C, we claim that lifetime air pollution experience of any kind was irrelevant, but pollution concentration during the last days prior to death contributed to the event. Case E in the letter by Martuzzi (1) may be of this type if the underlying illnesses are unrelated to air pollution. Otherwise, our model would consider it as a case A. Case F in the letter by Martuzzi might again be of type C if we assume that the underlying asymptomatic condition of this "healthy" subject is unrelated to air pollution. However, a major simplification of our model is that there are only two strata of "exposure." On one side, we distinguish the short period of exposure before death. This is the time window typically used in time-series studies. On the other side, we consider the "long-term," i.e., the past exposure experience, typically used in cohort studies. This last exposure metric fails to distinguish between short-term "peak" exposure periods that occurred in the past (i.e., not shortly before death) and the long-term mean condition. Although our model allows categorizing of all deaths, we agree with Martuzzi that the cohort studies may also be an incomplete assessment of the effects of air pollution on survival time. Lifetime lost due to short air pollution peaks may not be sufficiently captured in cohort studies because long-term mean concentrations are insensitive to such "hidden" peaks. In many regions, the day-to-day variation of air pollution (e.g., PM10) may, however, be sufficiently well reflected in mean values. Cohort studies also have limited power to detect very small effects on lifetime lost, such as a few days. However, from the perspective of public health impact, one may argue that extremely short advancement may not be relevant.
To demonstrate the complexity further, we add a case B that would be missed in both time-series mortality and cohort studies; thus, such a death would be misclassified as a case D. A subject may have an increased frailty for heart attacks; an extreme air pollution episode with no influence on the long-term mean concentration may trigger a nonlethal infarction. Given this further increased frailty, the subject may suffer a second, but lethal, heart attack, which was unrelated to air pollution. Thus, although air pollution contributed to shortening of life, this case would not affect the cohort study estimate or be captured in the time-series mortality study. In summary, none of the available study designs can fully assess the contribution of air pollution to life experience. Cohort studies, however, give a more complete assessment and may become more complete for case attribution if long-term exposure assignments are used that also reflect past peak exposures.
REFERENCES