INAUGURAL ARTICLE by a Recently Elected Academy Member:National burden of disease in India from indoor air pollution

Air pollution has become a major concern in India in recent years both because it is now clear that large parts of the Indian urban population are exposed to some of the highest pollutant levels in the world (1, 2) and also because new studies around the world on the health effects of air pollution have increased confidence in estimates of the risks posed by air pollution exposures (3, 4). The situation in China and a number of other developing countries is similar. Overall premature mortality from outdoor urban air pollution has been calculated using exposure-response results from studies of urban outdoor pollution in developed countries to estimated air pollution levels based on the limited available measurements. Table ​Table11 presents the results of such studies for India. Because they are most frequently measured worldwide and have been the subject of intense epidemiological investigation in the last 15 years, particulates are used in most studies as the indicator pollutant, although other health-damaging pollutants (SOx, volatile organics, NOx, O3, etc.) are also usually present. Table 1 Estimate of annual premature mortality from air pollution in India (thousands of deaths) Annual concentrations reported at urban monitors in India for PM10, particles less than 10 microns in diameter,‡ range 90–600 μg/m3, with a population mean of about 200 μg/m3 (5). For comparison, at about 60 μg/m3, the most polluted urban area in the Unites States (6) in the early 1990s had annual concentrations substantially less than the cleanest Indian city reported, although, unlike the U.S. cities, many Indian cities are not yet instrumented. [The U.S. population mean is now less than 30 μg/m3 (2).] Even higher concentrations result, however, from the widespread practice of using unprocessed solid fuels (biomass and coal) for cooking and/or space heating in India and other developing countries, often in unventilated situations. Such concentrations result from high emissions factors from such fuels in simple small-scale combustion devices (14, 15). The frequent result is indoor particulate concentrations well above even the dirtiest of cities (1). Available data show a distribution of indoor PM10 24-h concentrations measured in Indian solid-fuel-using households ranging to well over 2000 μg/m3. The distribution of village means overlap with the higher end of Indian urban concentrations but extends considerably higher. During the cooking period itself, of course, much higher levels are reached indoors (see the supplemental data, which are published on the PNAS web site at www.pnas.org). In addition, high household emissions from solid-fuel use result in elevated “neighborhood” pollution in densely populated communities (16). In contrast to simple concentration, exposure is a function not only of the pollution level but also of the number of people involved and frequency and duration of their contact with the pollution—the number of person-hours of exposure (17). Few other activities involve as many person-hours as cooking does, because it is done in essentially every household every day in most of the world. The combination of high pollution levels in places with many person-hours is a prescription for large total population exposures. Indeed, indoor exposures to the combustion products of unprocessed solid fuels have been estimated to produce the majority of (nonsmoking) human exposures to particulates and probably to a range of other pollutants as well (1, 18). With its large, poor, urban and rural populations still using simple solid fuels, the Indian population bears a significant fraction of this exposure (10). It can be expected, of course, that the pattern of health effects would follow exposure patterns. The approach represented by Table ​Table11 has become commonly used in developed countries (see, for example, refs. 6 and 19–21) and, indeed, is suggested as a standard method for application more broadly (22–24) and has been applied globally (25). It has the distinct advantage of being derived from a large number of separate epidemiological studies, lending considerable confidence to the exposure-response relationships. When applied to the much higher indoor pollution levels in rural India, extremely high total ill-health is predicted, as can be seen, for example, in the estimates by Saksena and Dayal (12) in Table ​Table11. That such exposure-response relationships have been derived for outdoor air pollution in developed-country urban situations, however, raises a number of questions about their suitability for application in developing-country (LDC) populations, particularly those exposed to indoor air pollution in rural areas. The principal differences between developed-country urban and developing-country rural populations are as follows: 1. Differences in the pollutant mix attributable to different fuel sources mean that existing exposure-response estimates may not be applicable in developing countries; i.e., although particulates can be used as indicator of hazard in both cases, biomass fuels as commonly used in Indian households produce relatively more organic compounds (e.g., benzene, formaldehyde, 1,3-butadiene, polyaromatic hydrocarbons), and fossil fuels produce more sulfur oxides. Thus risk estimates derived for the latter fuel may not apply to the former. 2. In a similar fashion, the chemical and other characteristics of the particles produced by biomass combustion are not the same as those produced by fossil fuel use, although of course woodsmoke is found seasonally in the outdoor air of many developed-country cities. 3. Different populations have different exposure patterns; i.e., indoor concentrations tend to vary much more during the day (because of household cooking and heating schedules) than do outdoor urban levels. 4. Exposure levels are also different; i.e., the average exposure levels of concern in households using unvented biomass fuels are 10–50 times greater than the levels studied in most recent urban outdoor studies (1). As is common with toxicants, there may be a diminishing of the effect per unit increase in exposure (shallowing of the exposure-response curve's slope) at these high levels. 5. The patterns of disease, competing risk factors, and age distributions differ dramatically between urban developed-country populations, the world's richest, healthiest, and oldest populations, and people exposed to indoor air pollution in developing countries, who tend to be the poorest, most stressed, and youngest in the world. 6. Most developed-country studies are time-series studies that determine short-term changes in mortality and other endpoints in association with short-term changes in air pollution. Implications for long-term health are unclear (26). 7. The few long-term cohort studies may be confounded by even slight misclassification of smokers, because smoking is such a powerful risk factor for the same health endpoints. 8. Becuase it is not realistic to define zero pollution as the baseline value (the counterfactual level), it is unclear what level is appropriate for calculating attributable risk to air pollution. 9. These more fundamental concerns are in addition to severe constraints imposed by incomplete information on the distribution of air pollution levels experienced by the Indian population. There have been no studies of pollution levels in Indian households based on stratified random sampling designs, for example. (This is also a problem, although to a lesser extent, with outdoor pollution levels in Indian cities.) 10. Additional uncertainty is created because those relatively few particulate measurements done to date have been mostly with respect to total particulates, although most of the consistent exposure-response results have been with regard to smaller size fractions (PM10 or PM2.5; particles less than 10 μm or 2.5 μm in mean aerodynamic diameter, respectively). 11. To be most useful for policy making, such estimates should assess more than mortality but also derive lost life-years and time lost to associated diseases of different severities. Given these concerns, estimating ill health (mortality) by using this “top-down” approach is a rather crude and uncertain way of predicting the impact of air pollution for the exposures of interest. Given the apparent high total exposure to these populations, however, it has seemed well justified to apply the best available risk information, even if far from ideal.