In the following sections of this paper, I consider the impact of these characteristics on study results through a series of tables. Table 2 lists results pertaining to all cardiovascular admissions with respect to PM₁₀ exposure without adjustment for co-pollutants. Succeeding tables provide results for specific cardiovascular diagnoses, results after adjustment for co-pollutants, and effects associated with subfractions of PM₁₀. This analysis provides some insight into how study results are influenced by the specific relationships being characterized and the methods used to define those relationships.

The published studies have presented results in a variety of formats and present results with respect to many different exposure ranges. Conversion to a standard range is essential to any meaningful comparison of results. For this reason, all results presented in this review have been converted to indicate the estimated percentage increase in hospital admissions associated with a 10-µg/m³ increase in particulate matter.

Studies of the Relationship between PM₁₀ Exposure and All Cardiovascular Admissions

The reasons for this variability in effect size must involve some combination of chance variation and the impact of differences among the study designs. Study design could influence results either through actual differences in the effect being measured or through apparent differences arising as an artifact of the methods used in data collection and analysis. The underlying effect of exposure to particulate matter on cardiovascular morbidity may differ among locations because of local differences in the pollutant mixture, factors influencing individual exposure, or the characteristics of the population at risk. Even if the underlying effect were constant, the estimated effect could vary because of the modeling approach including the adjustment for potential confounders and the consideration of time lags.

The major differences in consideration of confounders involved the evaluation of co-pollutants as discussed below. Approaches to considering time lags included evaluation of a range of values, evaluation of multiday averages of different durations and starting points, some combination of these approaches, or no consideration of time lags at all (i.e., a time lag of zero days).

Each approach has potential limitations. Studies in which a single time lag is evaluated without a clear prior reason for doing so will not capture effects that occur at other times. Conversely, in those studies that consider multiple time lags the researchers often select the one with the strongest effect. This approach will tend to overestimate the effect of exposure to the particles to the extent that this effect is associated with a particular lag. Of particular concern, when there is no underlying effect, the theory of extreme value distributions shows that this approach can be expected to yield estimates of effect size with a magnitude similar to those routinely reported for particles (13). To test this relationship empirically, I performed a simulation to estimate the average effect size that would be reported from studies that considered time lags of 0-5 days and picked the maximum. A simulation based on 1,000 repetitions of the case in which the underlying effect had a true mean of 0 and a standard deviation of 1 yields an average predicted effect size of 1.3%.

The variability in these results makes it difficult to draw any meaningful conclusions from the aggregated cardiovascular disease data. The results suggest instead that we must look at groups of studies with greater homogeneity in health outcomes and modeling approaches. Therefore, as a first step, I considered studies that examined specific cardiovascular diagnoses.

Evaluation of Specific Cardiovascular Outcomes

The validity of considering specific diagnostic groups could be diminished by substantial inaccuracy or inconsistency in disease coding. Although there may be some regional and international variability in disease coding, the accuracy of these codes is relatively high for cardiovascular disease (14,15). Furthermore, misclassification would be likely to alter the effect size substantially only if the variability in coding resulted in a pattern of excluding cases related to patients' susceptibility to the cardiovascular effects of exposure to particulate air pollutants. Such misclassification is plausible if, for example, presence of associated respiratory disease resulted in coding the respiratory disease as the primary diagnosis in some areas but not in others. This could result in different effect sizes in different cities. However, there is no evidence to suggest that such a systemic misclassification occurred.

Table 3 lists the estimates of the effect of PM₁₀ with respect to four categories of cardiovascular disease: dysrhythmia (DSR), congestive heart failure (CHF), ischemic heart disease (IHD), and cerebrovascular accident (CVA). Two approaches were used to pool results. A random-effects model was used to pool all relevant studies. Then, the single study making the greatest contribution to heterogeneity among studies (as determined by its contribution to Cochran's Q statistic) was eliminated and the remaining studies were pooled using a fixed-effects model.

If we pool the results for CHF, admissions decrease by 1.6% [95% confidence interval (CI): 0.4, 2.8%] per 10 µg/m³ increase in PM₁₀. After elimination of the anomalous result from Wong et al. (7), the pooled effect drops to 0.8% (95% CI: 0.5, 1.2%), similar to the effect observed for IHD [0.7% (95% CI: 0.4, 1.0%)].

The four studies that considered CVA as an outcome provide little support for the underlying existence of substantial risk of CVA associated with PM₁₀ (7,9-11). The pooled effect from the three studies that provided data was 0.7% (95% CI: -0.2, 1.6%) and this effect dropped to 0.2% (95% CI: -0.4, 0.8%) when the outlying result from Moolgavkar's Cook County data was dropped (10). Furthermore, Burnett et al. (9) found a protective effect for CVA associated with PM₁₀ exposure but did not publish numerical results. Inclusion of these results would further reduce the pooled effect. Although this indicates that PM₁₀ exposure is not associated with an increase in overall hospital admissions for CVA, it is possible that this is too broad a category, as it includes hypertensive as well as occlusive strokes. When Linn et al. (12) limited their analysis to occlusive strokes, they found a 1.3% (95% CI: 0.7, 1.9%) increase in admissions per 10 µg/m³ increase in PM₁₀. This appears consistent with the observed increase in IHD and suggests that studies including hypertensive strokes may underestimate of the effect of PM₁₀ exposure on CVA.

DSR was examined in only two studies, with substantially different results. Because the point estimates of the two studies do not fall within each other's confidence limits, pooling them seemed inappropriate. These studies do not provide sufficient basis for any conclusion as to the effects of PM₁₀ on conductive disorders of the heart.

In summary, CHF and IHD admissions appeared to be associated with PM₁₀ exposure with an increase of approximately 0.7% for an increase of 10 µg/m³ in the PM₁₀. The effect for DSR may have been of similar magnitude, but the data are too limited to draw firm conclusions. There appears to have been no relationship between CVA admissions and PM₁₀ exposure, but the conclusion might have been different if analyses were limited to occlusive strokes. The observed associations must be interpreted with caution. None of the results considered to this point included a full adjustment for covariates, particularly gaseous co-pollutants. No conclusions relevant to causality should be drawn without careful consideration of these confounders.

Consideration of Potential Confounders

Inclusion of gaseous pollutants in models of exposure to particles and development of cardiovascular disease is controversial because of the co-linearity inherent among air pollutants and the concern that multipollutant models will artificially diminish the true effect of exposure to particulates. Conversely, to the extent to which the observed association of cardiovascular disease and exposure to particles is simply a reflection of the correlation between particles and these gaseous pollutants, failure to consider these pollutants will lead to inaccurate estimates of effect size.

In the published studies, adjusting for co-pollutants consistently reduced the PM₁₀ effect, as summarized in Table 4. This reduction ranged from 10 to 320%, depending on the study, the outcome, and the co-pollutants considered. The reduction ranges from 10 to 25% with an average of 18% in the four analyses by Schwartz and co-workers (1,4) whereas the adjustments performed by three groups of researchers (2,3,9,10) produced reductions of at least 35% with an average of 82% (after excluding the extreme value of 320%).

The reasons for this variation are not clear. Possibly, it reflects differences between the analytical methods used by Schwartz and co-workers (1,4) and those used by the others. None of Schwartz's analyses includes a full set of gaseous criteria pollutants. In his study of eight cities, Schwartz (8) did not perform adjustments but instead presented the magnitude of the PM₁₀ effect as a function of the correlation of PM₁₀ with different co-pollutants. He argued that the consistency of the PM₁₀ effect among cities with differing correlations between PM₁₀ and co-pollutants demonstrates low likelihood that those pollutants would drive the PM₁₀ effect. This somewhat unorthodox approach to assessing confounding yields an interesting observation, but it should be interpreted in light of the relatively small magnitude of documented PM₁₀ effects together with the complex, nonlinear relationships among pollutants, meteorologic variables and cardiovascular disease. For example, the correlation between carbon monoxide and PM₁₀ during cold weather (conditions when the association of carbon monoxide with cardiovascular disease appears to be strongest) (2) may be far more important than the annual correlation. This seasonal correlation may be lost in a simple correlation for the entire study period.

In contrast to Schwartz's assertions about co-pollutants, Burnett et al. (3,9), Morris and Naumova (2), and Moolgavkar (10) all reported substantial reductions in the PM₁₀ effect when gaseous pollutants were considered. Morris and Naumova included all co-pollutants, while Moolgavkar evaluated two pollutant models. Burnett used two different approaches to considering the impact of co-pollutants on the observed effect of exposure to particles. One set of models used stepwise regression to select a set of gaseous pollutants for each health outcome using the Akaike information criteria, then generated a model based on those pollutants together with specific particulate matter metrics (9). Those results are presented in Table 5. In a second set of analyses, he allowed all the pollutants to compete equally in a stepwise model (9). In the second approach, none of the particulate measures was even included in models for CHF or IHD.

Other researchers considered co-pollutants but did not present comparable numerical results. Atkinson et al. (6) stated that the PM₁₀ effect was reduced in all two-pollutant models, particularly for sulfur dioxide but did not provide any numerical results. Wong's consideration of co-pollutants was limited to estimating the PM₁₀ effect when ozone and nitrogen dioxide were elevated. The PM₁₀ effect was higher in both cases [1.7% (95% CI: 0.7, 2.8%) during high ozone and 0.7% (95% CI: -0.5, 2.0%) during high nitrogen dioxide compared to 0.6% in the baseline model], suggesting a possible interaction between PM₁₀ and ozone.

Overall, these results indicate that the confounding effect of other pollutants can explain a portion of the observed association between particles and cardiovascular disease, potentially a major portion. Given the relationship among these pollutants, fully isolating this confounding effect will be extremely difficult. Perhaps ongoing analyses looking at large numbers of cities will shed further light on this controversy.

Consideration of Temperature

All studies listed in Table 1 included adjustments for temperature. In most cases, consideration of temperature was limited to including it as a covariate. Most studies sought to include the nonlinear nature of this association by using either a polynomial model or some form of piecewise model for temperature. Because of the clear evidence of increased cardiovascular morbidity during extreme weather, particularly cold weather, the potential interaction between PM₁₀ and temperature is of interest.

A small number of studies considered this interaction using a variety of approaches. In London, England, Atkinson et al. (6) found that the PM₁₀ effect was strongest in the lowest temperature tertile but did not provide numerical details of their analysis. In Hong Kong, Wong et al. (7) found that the effect of PM₁₀ disappeared during the cold season, but, in this tropical climate, the cold season is the more comfortable time of year. In Tucson, Arizona, Schwartz (4) found that the PM₁₀ effect was reduced during periods of warm temperature, but not significantly so. In Los Angeles, California, Linn et al. (11) found that the PM₁₀ effect was stronger in winter than in spring for CVD [0.96 (95% CI: 0.48, 1.43) vs -0.3 (95% CI: -1.0, 0.42)]. The effect for CVA was stronger in summer than in winter [1.2 (95% CI: -0.6, 3.2) vs -0.2 (95% CI: -1.2, 0.81)], but the statistical power was clearly limited with respect to CVA. None of those studies was conducted in a city with severe winters and only one was conducted in a temperate climate. Given the known relationship between cold temperatures and cardiovascular disease, the possibility of increased effects in winter warrants further investigation.

Effect of Particle Size

Summary

These studies were inconsistent with respect to the choice of time lags used in the analysis. Selecting the time lag with the maximum effect could result in an overestimation of the true effect size. This is evident in a comparison of studies that depended on post hoc rather than a priori selection of the time lag to be considered. In two of three age groups for combined cardiovascular disease and in three of four categories for specific diseases, the largest effect was associated with studies using post hoc selection of time lags. It would be preferable for researchers to base the selection of time lag or time lags on a priori hypotheses. To the extent possible, selection of time lag should be based on an understanding of the physiologic basis for the adverse cardiovascular effect of air pollutants.

Perhaps the greatest area of confusion and controversy related to the published studies involves adjustment for co-pollutants. Most researchers find that adjustment for co-pollutants tends to substantially reduce the effect of particles, but the importance of this reduction is controversial. Opinions range from the perspective that the PM₁₀ effect may be entirely the result of co-pollutants and that PM₁₀ is simply a surrogate for these pollutants (10,22) to the assertion at the other extreme that the PM₁₀ effect is independent of co-pollutants (8). It is essential that future studies consider co-pollutants in an effort to shed further light on this apparent inconsistency.

Other covariates may also influence the relationship between PM₁₀ and cardiovascular morbidity. Specifically, these studies suggest that either high or low temperature may modify the association of PM₁₀ with hospital admission for cardiovascular disease. This interaction should also be considered in future studies. Temperature may also interact with other covariates, particularly carbon monoxide. Failure to consider these interactions may result in inaccurate estimates of the PM₁₀ effect.

All persons represented by individual hospital admissions have serious underlying cardiovascular disease. It is likely that most of them would eventually be admitted to the hospital for acute disease. An association between air pollution and cardiovascular disease, to the extent that it is causal, means that the acute stress of air pollution either led to hospital admissions that would never have occurred otherwise or only influenced the timing of hospital admissions that would have occurred without a change in air quality. This is essentially the same issue that has been evaluated as a harvesting effect in the mortality studies. The extent to which the particle effect reflects a simple short-term shift in the timing of admissions has a major impact on any assessment of the public health impact of these pollutants. None of these morbidity studies explicitly considered the effect of harvesting. Future studies should do so.

Future research on the association between particulates and acute cardiovascular disease should focus on specific cardiovascular outcomes and should consider four key questions: a) To what extent do co-pollutants explain this association? b) Do temperature or other seasonal factors modify the particle effect and how might their interaction with other covariates influence the estimated particle effect? c) Do particle size and chemistry influence this relationship? d) To what extent does the observed effect reflect "harvesting" of pending admissions?

REFERENCES AND NOTES

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Last Updated: August 14, 2001