Pollution responsible for one-fifth of infant deaths in sub-Saharan Africa
*Study estimates 449,000 children died in 2015 because of excess dirty air
A new study estimates that 22 per cent of infant deaths in sub-Saharan Africa — a total of 449,000 — could be avoided by decreasing average levels of air pollution to the lowest levels observed in the region (a concentration of 2 micrograms per cubic metre).
The study published Thursday June 28, 2018 in the journal Nature used satellite-based measurements of air pollution. They combined these measurements with data from 65 household health surveys, which they used to determine mortality for almost one million births in 30 countries across sub-Saharan Africa between 2001 and 2015.
The authors’ study joins a growing body of work that explores international patterns of health outcomes through creative analyses of big data — a set of approaches pioneered by many on local geographical scales, but brought to the global-health stage by a project called the Global Burden of Disease Study (GBDS).
The authors also focused on infant mortality from all causes, whereas the GBDS emphasized mortality due to respiratory illness.
Neal et al., in the paper published in Nature, proposed, implemented and (importantly) scrutinized such an approach, exploring the impact of air quality on infant mortality in sub-Saharan Africa.
Big data can help to address many pervasive problems in the field of public health. For instance, large-scale data analyses are helping researchers to understand global patterns of disease, the range of factors that contribute to global health and the policies that provide the greatest potential for improvement.
In GBDS types of study, multiple sources of health, administrative and research data are pooled and subjected to mathematical modelling and complex statistical analysis. But this exciting branch of public-health research is still finding its place amid conventional epidemiological techniques that involve gathering data from direct observations in cases and controls, or in longitudinal studies.
GBDS data have previously been used to estimate links between local air quality and mortality on a global scale (for example, in the project’s 2016 report). But these analyses were dominated by data obtained from air-pollution monitoring stations, which are predominantly found in developed countries. In these areas, air pollution is typically lower than in sub-Saharan Africa.
The authors place their results in the context of the previous work, putting forward several reasons for the different values. These include differing assumptions about what level of improvement in air quality is attainable (improvement from a median of 25 to 2 µg m−3 in the present paper, compared with improvement to 5.8 µg m−3 in the earlier analyses) and different sets of mortality data.
Two estimates of the risk of infant death linked to air pollution. Air pollution can be measured as a concentration of breathable particulate matter (PM2.5) in micrograms per cubic metre (µg m−3). The GBDS estimated the relationship between increasing PM2.5 and relative risk of infant mortality due to respiratory infections globally. By contrast, Neal et al.3 used different data-analysis approaches to estimate the relative risk of all-cause infant mortality related to air pollution only in sub-Saharan Africa, where pollution rates are generally higher than in wealthier regions of the world. The general message is the same (a clear benefit from reducing levels of air pollution), but Neal et al. find a greater increase in mortality with increasing air pollution. The levels of uncertainty provide essential context for understanding the results.
Rather than being satisfied with the headline association alone, Neal and colleagues carefully review the uncertainty in their estimation. For instance, they detail how the results might be affected by analytical assumptions, such as a linear relationship between air pollution and mortality within the range of observed values, and potential biases associated with using satellite-based measurements as a proxy for air pollution at ground level.
They also consider potential confounders such as socio-economic status — it has previously been predicted that wealthier households would be less affected by air pollution than poorer households, but the authors show that this is not the case in their analysis. Such self-reflection is refreshing and essential, and places the results in an appropriate context for consideration by researchers and policy experts.
Neal et al. outline their data sources in their supplementary information, but future work can go further by filling in the details necessary to replicate and reproduce results from big-data studies. For example, detailed, peer-reviewed descriptions of data curation should be published, and the final data set should itself be deposited in citable repositories such as datadryad.org. By sharing citable analysis details and data, the value of studies such as Neal and colleagues’ could be even greater.
In summary, although big-data analyses cannot replace careful epidemiological studies, they can give broad insight into the potential benefits of public-health policies. In this case, Neal and colleagues’ work highlights the benefits of aspiring to reduce air pollution to the lowest levels observed in their data set, and provides assessments of the effects of more-modest changes in pollution levels. This type of analysis certainly has a place in the modern public-health toolbox.