Air Pollution, The Research – Part 6
2. Causality and Attribution
Our research reviewed two approaches to establish a causal relationship between fine particulate air pollution and health costs.
The first approach explained above, used exposure-response functions. These converted PM2.5 concentrations into relative risk (RR) ratios and population-attributable fractions (PAFs), from which we could attribute deaths and DALYs to air pollution.
However, attributing deaths and DALYs to air pollution is not necessarily the same as air pollution causing deaths and DALYs. Issues of confounding variables and reverse causality may be at play (as one example, people with lower incomes may both live in highly polluted areas and have worse health outcomes). This concern has been raised in recent literature.
On the other hand, this concern is not widely shared. A 2012 review finds “biological plausibility” of causal associations. Core to discussions of causality in epidemiology is the Bradford Hill criteria, a set of nine criteria to provide epidemiologic evidence of a causal relationship.
A more robust approach is to draw causal inferences from natural experiments using instrumental variables and difference-in-difference techniques. Coefficients can then be extrapolated to a generalized form. Studies that do this have found the following:
- A 10mg/m3 increase in PM2.5 increased infant mortality by 22% in sub-Saharan Africa.
- A 1 μg/m3 decrease in PM2.5 reduced deaths by 7.17 per 100,000, among medicare recipients in North-East America.
- A 10μg/m3 increase in PM2.5 increased daily deaths by 1.54% in US cities.
However, the limited number of natural experiments means any extrapolation is limited. Coefficients lack robustness, almost exclusively focusing on specific groups in particular countries, rather than giving an overall average across PM2.5 levels.
Thus, both approaches have advantages and disadvantages. The former approach suffers from concerns about confounders, whilst the latter suffers from a lack of generalizable evidence. The crux is whether to rely on causal inference from epidemiology. If so, then we can interpret association coefficients as estimates for effect sizes. If not, then only natural or quasi-experimental evidence can be used. Due to methodological uncertainty and the relative lack of studies using the potential outcomes framework, we pragmatically use the attribution approach. Thus, we use Vodonos et al’s paper as the foundation for our estimates on the health impact of PM2.5.
Notes
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Estimates vary wildly on the total burden of disease. These figures are from the GBD Study (Enter Risk: “Particulate matter pollution”, Cause: “All Causes”). A more thorough data review can be found at Our World In Data. Graphic source: Death rates from air pollution, 2019 (OWID)
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See Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019, page 1233. A risk factor increases the chance of developing a disease.
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The Cost of Air Pollution: Strengthening the Economic Case for Action: “Low- and middle-income countries account for 80 percent of the world’s population and for 93 percent of the deaths and nonfatal illness each year from air pollution.”
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Ambient (outdoor) air pollution Guidelines are 5 μg/m3 annual mean PM2.5 or 15 μg/m3 24-hour mean
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WHO Air Quality Guidelines : “PM2.5, fine particulate matter of 2.5 micrometers or less in diameter, is the most dangerous pollutant because it can penetrate the lung barrier and enter the blood system, causing cardiovascular and respiratory disease and cancers. It affects more people than other pollutants and has health impacts even at very low concentrations.”
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“Around 2.6 billion people cook using solid fuels (such as wood, crop wastes, charcoal, coal, and dung)… 3.8 million people die prematurely from illness attributable to household air pollution caused by the inefficient use of solid fuels and kerosene for cooking” Household air pollution and health
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Figure 2: Source categories responsible for the largest impact on mortality linked to outdoor air pollution in 2010. Exact source apportionment depends heavily on the city of choice
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Roser (2021) provides an accessible review of these mortality estimates in greater depth
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Air pollution. “The WHO estimates the total death toll to be lower than the sum of indoor and outdoor pollution deaths. This is because the deaths from risk factors are not summable. As the authors explain: “Some deaths may be attributed to more than one risk factor at the same time. For example, both smoking and ambient air pollution affect lung cancer. Some lung cancer deaths could have been averted by improving ambient air quality, or by reducing tobacco smoking.”4 Similarly the death of a particular person could have been averted by reducing indoor or outdoor air pollution” (Roser (2021))
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OECD Glossary of Statistical Terms – Dose response function Definition The exposure-response function captures relative risk (and therefore mortality) from exposure to a given level of pollution. Other names for this include dose-response and concentration-response functions.
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Austin Bradford Hill, “The Environment and Disease: Association or Causation?,” Proceedings of the Royal Society of Medicine, 58 (1965), 295-300. These criteria are commonly used within epidemiology to establish causality in absence of Randomised Control Trials
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A 2022 systematic review and meta-analysis suggests that “Increased long-term exposure to PM2.5 and NO2 is associated with depression”, whilst 2019 systematic review of 22 studies finds that “Long-term PM2.5, PM10, and NO2 exposure is not associated with depression.” [emphasis added]
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For example, higher PM2.5 exposure was negatively correlated with post-secondary attainment and earnings (see The Long-Run Economic Consequences of High-Stakes Examinations: Evidence from Transitory Variation in Pollution)
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