Quantitative Methods for Stochastic High Frequency Spatio-Temporal and Non-Linear Analysis: Assessing Health Effects of Exposure to Extreme Ambient Temperature.
Liss, Alexander.
2015
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Abstract: Extreme
weather events, such as heat waves and cold spells, cause substantial excess mortality
and morbidity in the vulnerable elderly population, and cost billions of dollars. The
accurate and reliable assessment of adverse effects of extreme weather events on human
health is crucial for environmental scientists, economists, and public health officials
to ensure proper protection ... read moreof vulnerable populations and efficient allocation of scarce
resources. However, the methodology for the analysis of large national databases is yet
to be developed. The overarching objective of this dissertation is to examine the effect
of extreme weather on the elderly population of the Conterminous US (ConUS) with respect
to seasonality in temperature in different climatic regions by utilizing heterogeneous
high frequency and spatio-temporal resolution data. To achieve these goals the author:
1) incorporated dissimilar stochastic high frequency big data streams and distinct data
types into the integrated data base for use in analytical and decision support
frameworks; 2) created an automated climate regionalization system based on remote
sensing and machine learning to define climate regions for the Conterminous US; 3)
systematically surveyed the current state of the art and identified existing gaps in the
scientific knowledge; 4) assessed the dose-response relationship of exposure to
temperature extremes on human health in relatively homogeneous climate regions using
different statistical models, such as parametric and non-parametric, contemporaneous and
asynchronous, applied to the same data; 5) assessed seasonal peak timing and
synchronization delay of the exposure and the disease within the framework of
contemporaneous high frequency harmonic time series analysis and modification of the
effect by the regional climate; 6) modeled using hyperbolic functional form non-linear
properties of the effect of exposure to extreme temperature on human health. The
proposed climate regionalization method algorithmically forms eight climatically
homogeneous regions for Conterminous US from satellite Remote Sensing inputs. The
relative risk of hospitalizations due to extreme ambient temperature varied across
climatic regions. Difference in regional hospitalization rates suggests presence of an
adaptation effect to a prevailing climate. In various climatic regions the
hospitalizations peaked earlier than the peak of exposure. This suggests
disproportionally high impact of extreme weather events, such as cold spells or heat
waves when they occur early in the season. These findings provide an insight into the
use of high frequency disjoint data sets for the assessment of the magnitude, timing,
synchronization and non-linear properties of adverse health consequences due to exposure
to extreme weather events to the elderly in defined climatic regions. These findings
assist in the creation of decision support frameworks targeting preventions and
adaptation strategies such as improving infrastructure, providing energy assistance,
education and early warning notifications for the vulnerable population. This
dissertation offers a number of methodological innovations for the assessment of the
high frequency spatio-temporal and non-linear impacts of extreme weather events on human
health. These innovations help to ensure an improved protection of the elderly
population, aid policy makers in the development of efficient disaster prevention
strategies, and facilitate more efficient allocation of scarce
resources.
Thesis (Ph.D.)--Tufts University, 2015.
Submitted to the Dept. of Civil Engineering.
Advisor: Elena Naumova.
Committee: Magaly Koch, Gilbert Metcalf, and Kurt Pennell.
Keywords: Environmental health, Statistics, and Remote sensing.read less - ID:
- xk81jz04q
- Component ID:
- tufts:21477
- To Cite:
- TARC Citation Guide EndNote
