Research Areas

We develop methods to

1. better utilize increasingly spatially-resolved exposure data in health analysis;

2. explore which individual- and area-level factors render differential risks;

3. characterize the joint health effect of multiple (mixture) environmental exposures;

4. identify critical exposure windows, such as during pregnancy or lagged exposures; 

5. improve the accuracy and precision of common study designs for estimating health effects of short-term and long-term exposures?

• Time-to-event analysis of preterm birth accounting for gestational age uncertainties. Zhang et al. Annals of Applied Statistics 2025 

• Estimating heterogeneous exposure effects in the case-crossover design using BART. Englert et al. Journal of the American Statistical Association 2025 

• Modeling time-varying dispersion to improve estimation of the short-term health effect of environmental exposure in a time-series design. Zhang et al. Epidemiology 2025

• A scalar-on-quantile-function approach for estimating short-term health effects of environmental exposures. Zhang et al. Biometrics 2024

• Integrative statistical methods for exposure mixtures and health. Reich et al. Annals of Applied Statistics 2020

• R01 ES037289 New Statistical Methods for Estimating Health Effects of Environmental Exposures, NIEHS (2025-2030).

• R21 ES022795 Statistical Methods for Exposure Uncertainty in Air Pollution and Health Studies, NIEHS (2013-2015).

Our current investigations aim to

1. estimate short-term health effects of environmental exposures on hospital encounters for neurological conditions, substance use disorder, renal diseases and cardiovascular diseases; 

2. estimate health effects of environmental exposures on adverse pregnancy outcomes, such as reduced gestational length, miscarriages and gestational metabolic disease;

3. highlight climate-sensitive and disaster-related exposures, such as heat waves, wildfire and dust storm;

4. identify higher-risk subpopulations by age, social determinants of health, medication and chronic conditions;

5. incorporate quantitative research (e.g., patient interview and stakeholder focus group) to better inform data-driven epidemiologic analyses and triangulate findings for preventive strategies.

• Case-crossover assessment of the modifying effects of home medication use on acute kidney-related morbidity due to elevated ambient heat exposure in Atlanta, GA, from 2013 to 2019. McCann et al. BMJ Public Health 2025

• A time-to-event analysis of the association between ambient air pollution and risk of spontaneous abortion using vital records in the US state of Georgia (2005-2014). Hsiao et al. American Journal of Epidemiology 2026 

• Preterm and early-term delivery after heat waves in 50 US metropolitan areas. Darrow et al. JAMA Network Open 2024 

• Dust Storms and emergency department visits in 3 Southwestern states using NWS storm reports. Zheng et al.  JAMA Network Open 2025 

• Associations between short-term ambient temperature exposure and emergency department visits for amphetamine, cocaine, and opioid use in California from 2005 to 2019. Chang et al. Environment International 2023 

• Short-term associations between warm-season ambient temperature and emergency department visits for Alzheimer's disease and related dementia in five US states. Zhang et al. Environmental Research 2023

• P20 ES036110 Climate & Health Actionable Research and Translation Center (CHART) - Research Project, NIEHS (2023-2026)

• ROSES A.37 Neighborhood-scale Extreme Humid Heat Health Impacts, NASA (2022-2025)

• R01 ES028346 Extreme heat events and pregnancy duration: a national study, NIEHS (2018-2023).

• R21 ES032344 Dust storms and emergency department visits in four southwestern US states, NIEHS (2021-2023).

We develop:

1. scalable, spatial-temporal statistical models that integrate multiple data sources for air pollution estimation;

2. ensemble approaches to combine predictions from different models with rigorous uncertainty quantification;

3. distribution-based approaches to bias-correct climate model simulations for projection analyses;

4.  methods to address informative missingness and errors in data inputs.

• ensembleDownscaleR: R Package for Bayesian Ensemble Averaging of PM2. 5 Geostatistical Downscalers. Madden et al.. Remote Sensing 2025

• A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation. Murray et al. Environmental Research 2019

• Imputing satellite-derived aerosol optical depth using a multi-resolution spatial model and random forest for PM2. 5 prediction. Kianian et al. Remote Sensing 2021

• Projecting health impacts of future temperature: a comparison of quantile-mapping bias-correction methods. Qian et al. International Journal of Environmental Research and Public Health 2021

• Multivariate spatial prediction of air pollutant concentrations with INLA. Environmental Research Communications. Gong et al. 2021

• R01 ES027892 Data Integration Methods for Environmental Exposures with Applications to Air Pollution and Asthma Morbidity, NIEHS (2017-2022).

• MAIA (Multi-Angle Imager for Aerosols), NASA.

Our current projects aim to

1. develop time-series and spatial-temporal statistical models to account for under-ascertainment in respiratory infectious disease counts;

2. examine socio-demographic, health condition and environmental factors that can explain spatial heterogeneity in disease burden;

3. extend prior work with influenza to other respiratory infections, including respiratory syncytial virus and COVID-19; 

4. utilize bias-corrected community-level infectious activity as a risk factor for other health outcomes and as a modifier of other environmental exposures.

• A time-series approach for estimating emergency department visits attributable to seasonal influenza: results from 6 US cities, 2005-2006 to 2016-2017 seasons. Huang et al. American Journal of Epidemiology 2026

• County-level influenza-attributable emergency department visits and their spatial correlates in the United States: cross-sectional observational study. Huang et al. JMIR Public Health and Surveillance 2025

• Influenza Activity and Preterm Birth in the Atlanta Metropolitan Area: A Time-Series Analysis from 2010 to 2017. Zheng et al. Epidemiology 2025

• A Bayesian spatial–temporal varying coefficients model for estimating excess deaths associated with respiratory infections. Zhang et al. Journal of the Royal Statistical Society Series A: Statistics in Society 2025 

• Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Iuliano et al. The Lancet 2018

• R21 AI167418 Methods for Estimating Disease Burden of Seasonal Influenza, NIAID (2023-2025).

• R01 AI125842 Spatio-temporal data integration methods for infectious disease surveillance, NIAID (2017-2022)

• Natural history of type 2 diabetes in Indians: time to progression. Narayan et al. Diabetes Care 2024

• Spatial associations between measures of public transportation and diabetic foot ulcer outcomes in the state of Georgia: 2016–2019. Vanasse et al. BMJ Open Diabetes Research & Care 2024 

• Liquefied petroleum gas or biomass for cooking and effects on birth weight. Clasen et al. New England Journal of Medicine 2022 

• Design and rationale of the HAPIN study: a multicountry randomized controlled trial to assess the effect of liquefied petroleum gas stove and continuous fuel distribution. Clasen et al. Environmental Health Perspectives 2020

• UM1 HL134590 Household Air Pollution and Health: A Multi-Country LPG Intervention Trial, NHLBI (2016-2021).

• P01 HL154996 Precision Cardiovascular Diseases Phenotyping and Pathophysiological Pathways in the CARRS Cohort (Precision-CARRS), NHLBI (2022-2027)

• R21 MD017943 Neighborhood transportation vulnerability and geographic patterns of diabetes-related limb loss, NIMHD (2022-2024).