Air pollution is a leading contributor to the global disease burden, which is a crucial concern
as the air quality across sub-Saharan Africa significantly and rapidly deteriorates with
accelerated urbanization, industrialization, and population growth. The synergistic association
between heat waves and air pollution is expected to exacerbate with the changing climate,
which poses a crucial threat to the health of vulnerable populations in low-income settings.
Studies which indicate associations between maternal and prenatal exposure to
environmental pollution and adverse health outcomes, highlight the need for further
investigation in African populations, as such vulnerable subpopulations are not consistently
investigated. Pregnant women who are exposed to heat stress coupled with air pollution are
more susceptible to adverse birth outcomes including; miscarriages, stillbirth, preterm birth,
low birth weight, and preeclampsia. Developing appropriate health sector responses and
adaptive interventions relies on identifying these vulnerable populations along with their level
of environmental risk. Socio-economic factors such as poverty, food and water insecurity, and
limited access to healthcare facilities perpetuate vulnerability among these communities.
Impacts of pollution exposure over periods of increased temperatures are difficult to measure
and require refined data science and analytical approaches. The current poor networks of
ground sensors for measuring air quality, piecemeal approaches to quantifying associations
with adverse health outcomes and dearth of translation from evidence to intervention warrants
a paradigm shift in approach. To address the lack of understanding of the environmental risk
impacts on the changing epidemiology in sub-Saharan Africa, the proposed research project
will aim to quantify the current and future impacts of air pollution on maternal and neonatal
health through innovative data science approaches such as machine learning, by accelerating
low-cost characterization of pollution exposure data while understanding its associations with
adverse outcomes related to pregnancy, childbirth and early life. Further to this we will develop
adaptive interventions that will help pregnant women and their children counter the risk
imposed by exposure to pollutants and build resilience against the high odds of adverse health
outcomes. The CHEAQI-MNCH project will provide an opportunity for emerging data scientists
and researchers in Africa to engage, collaborate and develop transferrable skills, while
contributing to a continental resource center for knowledge translation and dissemination
within the fields of climate and health.
