Dissertations and Theses

Date of Degree


Document Type


Degree Name

Doctor of Philosophy (Ph.D.)


Environmental, Occupational, and Geospatial Health Sciences


Glen Johnson

Committee Members

Glen Johnson

Brian Pavilonis

Ashish Joshi

Subject Categories

Environmental Public Health | Public Health


Asthma, COPD, Respiratory Exacerbation, Climate Variability, New York City, Spatio-Temporal


Background: Within the last century, the massive increase in emissions due to economic growth has made climate variability a major problem for many of the industrialized countries and an emerging problem for the rest of the world. With an increasing number of extreme weather events, such as heat waves, cold fronts, floods, heavy rain, and storms, individuals with Chronic Obstructive Pulmonary Disease (COPD) and asthma remain a vulnerable population, placing them at highest risk of respiratory exacerbation. This dissertation aims to contribute to our understanding of how climate variability, which can influence patterns of rainfall, temperature and other variables on a wide range of timescales, and sociodemographic factors impact respiratory disease exacerbation among New York City (NYC) residents.

Methods: Outcome variables of asthma and COPD exacerbation occurring in the inpatient and outpatient medical settings are from the New York State Department of Health Statewide Planning and Research Cooperative System (SPARCS). Secondary outcome variables of hospital length of stay (LOS) and total charges for the third study are also from SPARCS. Additional datasets include products for climate (temperature, precipitation, wind, relative humidity, PM 2.5) and sociodemographic potential predictor covariates (sex, age, race, income, poverty, educational attainment, and type of house heating fuel). The first study uses a space-time permutation model to determine spatial and temporal clusters of increased risk of respiratory exacerbation episodes across NYC neighborhoods. The second study uses Poisson regression using a series of three models (general linear model (GLM), general linear mixed model (GLMM) and a Bayesian spatio-temporal model) to further quantify the complexity of factors such as overall climate and socioeconomic status (SES). The third study applies Bayesian spatio-temporal modelling to predict the expected cases into the year 2039, total hospital charges and length of stay (inpatient setting only) based on Intergovernmental Panel on Climate Change (IPCC) predictive climatic factors and other predictive socio-demographic and SES factors.

Results: The first study determined temporal and spatial variation of respiratory exacerbation episodes across NYC neighborhoods throughout the study period, with disproportionate increases in ZCTAs demonstrating geographic and hypothesized socioeconomic disparity. Results from the second study indicate the associations between respiratory exacerbation and predictive climate and sociodemographic factors vary according to location and are both positive and negative (increase and decrease risk). The third study found respiratory exacerbation, hospital length of stay and total hospital charges had a combination of positive (increase risk, increase LOS and increase total hospital costs), negative (decrease risk, LOS, cost) and zero associations into the year 2039 with increased temperature and precipitation projections by the IPCC well as predictive sociodemographic and SES factors.

Conclusion: The studies in this dissertation provide additional evidence for the relationships between climate variability, sociodemographic factors and respiratory exacerbation in New York City. Spatio-temporal methods identify both time periods and spatial locations of increased risk and the role of predictive factors in certain neighborhoods. For example, the first and second studies determined neighborhoods in the south Bronx, upper Manhattan, eastern Brooklyn (as well as parts of north-western Brooklyn), north-central Queens (near LaGuardia airport) and southern Queens (near John F. Kennedy airport) all presented with elevated risk. These neighborhoods are in proximity to major highways and/or airports and are known to have the highest rates of populations that are either in poverty or black regardless of ethnicity. Race and poverty were consistent risk factors throughout the second and third studies, whereas increased temperatures were protective suggesting cooler temperatures may potentially increase risk instead. The third study determined long-term implications of IPCC projected climatic trends and predictive factors: factors determining SES (black, poverty status) would be predictive of respiratory exacerbation into 2039, and so would age for COPD outcomes; hospital LOS and total charges would be impacted by SES, age and IPCC projected precipitation increase, but for COPD outcomes only.

The findings from this research support on-going respiratory management goals, which aim to improve disease management through underlying clinical components including proper medication reconciliation, follow-up on medication adherence, education on diagnosis/prognosis and access to resources to better track environmental conditions (e.g., weather, pollutant levels). The results also support adaptation measures, which are aimed at factors that are difficult to change in themselves, such as living in disadvantaged neighborhoods that are more vulnerable to the impacts of climate variability. The findings can further translate into pragmatic and practical measures to alleviate the variation observed in risk factors of respiratory exacerbation, including the promotion of increased annual primary care physician visits, increased access to specialty care within the outpatient setting, population health informatics tools to easily connect patients to medical resources, and coalitions with local environmental justice groups to help carry out these measures. Limitations, including spatial, temporal, and methodological issues with the data sources are discussed, as are suggestions for future research.



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