• socioeconomic status
• air pollution
• poverty

This issue of the journal includes three papers that touch on relations among socioeconomic status (SES), health, and air quality. Jerrett et al considered whether SES differentials in Hamilton, Ontario, modify the temporal relations between daily mortality and either coefficient of haze (COH) or SO₂.¹ Martins et al did a similar analysis with respect to PM₁₀ in Sao Paulo, Brazil.² The third paper, by Gouveia et al, also involved Sao Paulo but examined cross sectional relations between several pollutants and infant birth weight.³ As such, it involves SES factors only implicitly, by virtue of the trends seen by Martins et al² that link levels of PM₁₀ in Sao Paulo with residence in slums and other SES indicators. Inequitable distribution of environmental impacts within a city or region may raise issues of “environmental justice”,* but it may also be possible to get additional insights into the implied health relations by probing a little deeper into the nature and origins of such differential impacts.

Most time series studies are based on entire cities and spatially averaged air quality, in order to maximise statistical power and to preclude the necessity of assigning individual deaths to specific air quality monitors. Time series studies avoid SES confounding by design, as those factors do not vary on a daily basis. However, many air pollutants tend to vary in concert, especially those that are co-emitted by common sources, thus making it difficult to identify the most probable causal agent. Cross sectional studies may have less co-pollutant collinearity, but can suffer from SES confounding to the extent that SES may tend to decrease with residential proximity to major pollution sources. All of these issues are in play among these three studies.

Jerrett et al matched daily deaths in five zones of Hamilton, Ontario with COH and SO₂ data from each of the five available monitoring stations.¹ Total deaths were about the same in all five of the unequally sized zones. They then examined the relations between these mortality responses and each of 12 potential effect modifiers that were based on zone-wide averages (making this an “ecological” study). Although a random effects model showed that the heterogeneity among zonal risk estimates was not significant, the authors concluded that manufacturing employment and educational attainment were significant modifiers of the effect of COH on daily mortality. They proposed three possible rationales for this finding: additional pollutant exposures from the workplace, reduced measurement error because of less mobility, or surrogate effects of material deprivation in general. Interestingly, household income showed no tendency to modify the time series relations, and the mean distance to hospital appeared to rank third (after education), suggesting the importance of access to medical care in these acute situations. The risk estimates were not associated with the mean ambient COH level, even though COH was moderately correlated with both poverty and unemployment. The basic problem here is that it is not possible to identify which of the several different rationales might be worthy of public health scrutiny, based on only five observations. An obvious remedy is to use individual rather than aggregate data, including estimates of exposure.

Martins et al defined six subregions of Sao Paulo, each with a radius of 2 km but differing greatly in population.² One region had four air quality monitors; the others, only one. PM₁₀ was the only pollutant considered; mean values ranged from about 40 to 70 μg/m³ and appeared to be correlated with both slum housing and respiratory mortality response rate. Based on the data taken from the paper, an effect modification regression on both slum residency and the PM₁₀ level appeared to be slightly superior to one based on slum residency alone. This outcome may be relevant to the widespread occurrence in Sao Paulo of levels above the former US PM₁₀ annual standard. From that regression, it appears that the PM₁₀ exposure level is just as important as socioeconomic conditions, but we have no way of knowing what other pollutant exposures might also be playing a part. For example, NO₂ levels in Sao Paulo are about four times those in the US and another study of daily mortality in Sao Paulo showed large effects for CO and PM₁₀.⁴ Comparing this paper with a previous analysis of SES effects in Sao Paulo⁵ that was based on city-wide air quality suggests that local exposure (and thus individual) gradients could be very important.

Gouveia et al studied the relations between birth weights of Sao Paulo infants and first, second, and third trimester exposures to PM₁₀, CO, SO₂, NO₂, and O₃.³ A suite of potentially confounding variables was considered, but smoking, alcohol use, and poverty status were not among them. In terms of changes in mean birth weight, only first trimester exposures showed consistent negative effects, for all five pollutants. The decrements associated with mean levels of PM₁₀, CO, NO₂, were in the range 83–86 g; effects of SO₂ and O₃ were smaller and not significant. However, similar findings were not obtained with logistic regressions for the odds of low birth weight (LBW). For example, a birth weight decrement of 85 g applied to the entire population should have created a relative risk for LBW of about 1.5 (assuming a normal distribution), but the values reported were much smaller and SO₂ even showed significant beneficial effects in the first trimester. Furthermore, the actual fraction of LBW in Sao Paulo (5%) is less than then typically seen in the United States (7.7%), even though the mean US birth weight is 200 g higher.⁶ Furthermore, a study of air quality and birth weight in the north eastern US that controlled for smoking and alcohol found that CO showed the most consistent effects on LBW, with an OR of 1.3 for a 1 ppm increment in the third semester,⁷ and a similar study in northern Nevada also found a negative association between third trimester PM₁₀ and mean birth weight but not with the fraction of LBW.⁸ These discrepancies make it difficult to accept the Sao Paulo birth weight associations as causal.

Differential environmental impacts may result from at least two important pathways: differential exposures, or differential susceptibilities. Environmental justice refers primarily to exposures; note that the official definition* does not pertain to excessive vehicular or domestic emissions that may be an inevitable result of being poor in an urban setting. Additional exposure differentials may result from differences in housing quality, in terms of air exchange rates and the presence of indoor pollution sources. An important point to be made here is that, to the extent that outdoor air quality may be implicated, the culprits are much more likely to be local primary pollutants like CO or SO₂ than the more widespread secondary pollutants like PM_2.5 or O₃. Indeed, studies in Southern California show that wealthy suburban communities are more likely to experience consistently higher ozone levels than central city locations having higher densities of NO_x emissions.⁹ Finally, a residence that is transparent to outdoor air pollution will also be transparent to extreme weather effects. For example, studies of the 1995 major heat wave in Chicago found that lack of access to air conditioning was a major risk factor in heat related deaths.¹⁰

In addition to exacerbating exposures, poverty status may also involve increased susceptibility to environmental challenges by virtue of differences in underlying health status and access to medical care. For example, Gwynn and Thurston found that higher hospital admission-pollution risks were seen for Medicaid (poverty) patients than for those who were privately insured,¹¹ and Janssen et al reported that access to residential central air conditioning appeared to reduce the effect of PM₁₀ on daily mortality in 14 US cities.¹² However, Zanobetti and Schwartz reported that mortality-pollution effect modifications from SES were “modest” compared with those due to medical conditions,¹³ and Tolbert et al found that poverty status (as indicated by Medicaid insurance) increased the risk of children’s emergency room treatment for asthma although poverty did not significantly affect the role of air pollution in that regard.¹⁴ These issues are especially important for time series studies of the elderly population, which imply that the affected victims have succumbed on a particular day as a result of exposure to outdoor air quality no worse (and often better) than what has been experienced many times before. This paradox can only be rationalised in terms of the juxtaposition of a moderate environmental insult with an impaired ability to maintain homeostasis.¹⁵

Considering all of these factors, the real villain here is seen to be poverty in itself and the socioeconomic conditions that produce it. In many industrialised nations (including the United States) poverty leads to substandard medical care, substandard nutrition, substandard housing, and reliance on inefficient and excessively polluting vehicles and heating and cooking appliances. It has been estimated that as few as 10% of the vehicles on the road may produce most of the vehicular pollution; however, taking these vehicles off the road would impose intolerable costs on those least able to pay. To make matters even worse, in terms of disposable income, the poor will bear disproportionate shares of the economic burdens of any cost ineffective environmental regulations that unduly increase prices of housing, fuels, vehicles, or appliances. True environmental justice requires the costs imposed by environmental regulations to match their benefits for everyone, not just for society as a whole.