Our Research

The following projects include active grants with open positions. For more information, please contact us.

Integrating novel GIS and GPS data to assess the impact of built environments on changes in BMI, physical activity and cancer-related biomarkers (National Cancer Institute) – PIs: Benmarhnia and Jankowska

Just in Time Adaptive Interventions (JITAIs) employ temporal and spatial cues to prompt behavior change, but little is known about spatial predictors of behaviors at the minute level and beyond home neighborhoods. We investigate the relationship between minute level objective GIS measured walkability, greenspace, pollution and food environments and changes in BMI, physical activity (PA), and cancer related biomarkers in the context of 2 existing weight loss trials in women at risk for breast cancer. We focus on the development of advanced methods for dynamic environmental exposure estimation using GPS and accelerometer sensors, development of novel environmental measures through use of machine learning as applied to satellite imagery. Our results will ultimately inform JITAIs by developing and testing minute level spatial, temporal and behavioral interventions.


The role of ENDS use in changing rates of escalation and quitting of cigarette smoking in those under age 35 years in US population (National Cancer Institute) – PIs: Benmarhnia and Pierce

This project aims to disentangle the different trajectories that exist within US youth and young adults to better understand the role of non-combustible nicotine use in escalation and cessation of tobacco products. To address these questions, we leverage various causal inference methods including propensity score methods, and doubly robust estimation methods.


Characterizing air pollution episodes and evaluation of implemented policies (French Environment and Energy Management Agency, ADEME) – PI: Benmarhnia

Air pollution is one of the most documented environmental determinants of health. When emission levels are particularly high, pollution episodes or peaks appear. These days are defined based on concentrations exceeding a certain threshold for one or more air pollutants. In this project we first explore the dose-response relationships between different regulated pollutants and several health indicators in France and the links between extreme weather events. We then evaluate the effectiveness of measures to combat pollution peaks capitalizing on natural experiments and proposing novel quasi-experimental methods to evaluate these types of policies. Finally, we will examine the potential health benefits of sustainable urban modification interventions carried out by municipalities in France aimed at reducing emissions linked to vehicular traffic. We will start by carrying out a survey consisting of measures aimed at reducing air pollution linked to traffic in the medium and long term, building on existing work. We will then evaluate the health benefits associated with the implementation of the Paris low emission zone.


Health outcomes and health disparities associated with tropospheric ozone (CalEPA) - PI: Benmarhnia
Hundreds of research studies have confirmed that ozone harms people at levels currently found in the United States. Ground level Ozone (O3), in particular, was estimated to be responsible each year for more than 2,000 premature deaths, 3 million cases of acute respiratory symptoms and 1 million cases of school-loss days in the USA alone. Short term exposure to ozone is associated with an increase in hospital admissions related to different causes and for all-cause mortality. The essential ingredients for ozone come from nitrogen oxides (NOx) for example. If such ingredients are present under the right conditions including increased sunlight and hot temperatures, they react to form ground level ozone. The aim of this project is to comprehensively understand and quantify the different potential mechanisms linking heat and ozone on different health outcomes and inequalities in California to inform local policies.


Chronic disease and air pollution: disease trajectory and intervention (Health Canada) - PI: Hong Chen

Exposure to low levels of air pollution is clearly linked to survival, but important questions remain. The precise mechanism and pathways, whereby the accumulation of exposures elicits premature death, requires more precise elucidation. This information is crucial for supporting health guidance and preventive interventions, as well as for estimating the burden of air pollution. It has been hypothesized that repetitive exposures to air pollution may promote chronic disease states and/or enhance the progression of diseases by worsening severity and complication, which in turn could magnify health risks by exacerbating susceptibility for future events. The first objective of this project is to proceed from the question "does air pollution affect health?" to the question "how does it affect health?". We will comprehensively investigate the role of chronic exposure to air pollution (months to years) in affecting individuals’ trajectories over different health states, and how this unfolds along different physiological pathways. This information is crucial for health guidance and timely interventions, as well as for estimating the burden of exposure on various aspects of the disease trajectory, not only on death. The second objective of our project is to further evaluate the effectiveness of some widely-implemented or potential individual- and policy-level interventions in reducing air health effects. Air pollution has major public health and economic consequences, but considerable uncertainty exists concerning which actions can be taken to reduce its effects. To achieve the two objectives, we will draw on Big Data sources (i.e., CanCHEC and ONPHEC cohorts), and use various causal inference methods (e.g., causal mediation, g-formula, marginal structural modeling).


Using Machine learning techniques to characterize wildfire-specific PM2.5 and associated impacts on respiratory health in California (CalEPA) – PI: Benmarhnia

The Western United States (US) and other locations worldwide are experiencing more frequent and destructive wildfires in a changing climate. Fine particulate matter, PM2.5, is the main component of wildfire smoke adversely impacting respiratory health. PM2.5 can be inhaled into the deepest recesses of the lung and may enter the bloodstream and impair vital organs including the lungs. Furthermore, wildfires account for 29% of total PM2.5 emissions in the US, 50% in California (CA), and this proportion is expected to increase in the next years in the context of climate change. The aim of this project is to apply recent techniques based on machine learning algorithms to quantify the specific impacts of wildfire-specific PM2.5 on daily hospital admissions for respiratory diseases and analyze spatial variations in the estimated health burden to identify most vulnerable communities regarding the impact of wildfire smoke.