Economics Research: Publications since January 2023
%% Yashkin, Arseniy
@article{fds369683,
Author = {Arbeev, KG and Bagley, O and Yashkin, AP and Duan, H and Akushevich, I and Ukraintseva, SV and Yashin, AI},
Title = {Understanding Alzheimer's disease in the context of aging:
Findings from applications of stochastic process models to
the Health and Retirement Study.},
Journal = {Mechanisms of Ageing and Development},
Volume = {211},
Pages = {111791},
Year = {2023},
Month = {April},
url = {http://dx.doi.org/10.1016/j.mad.2023.111791},
Abstract = {There is growing literature on applications of
biodemographic models, including stochastic process models
(SPM), to studying regularities of age dynamics of
biological variables in relation to aging and disease
development. Alzheimer's disease (AD) is especially good
candidate for SPM applications because age is a major risk
factor for this heterogeneous complex trait. However, such
applications are largely lacking. This paper starts filling
this gap and applies SPM to data on onset of AD and
longitudinal trajectories of body mass index (BMI)
constructed from the Health and Retirement Study surveys and
Medicare-linked data. We found that APOE e4 carriers are
less robust to deviations of trajectories of BMI from the
optimal levels compared to non-carriers. We also observed
age-related decline in adaptive response (resilience)
related to deviations of BMI from optimal levels as well as
APOE- and age-dependence in other components related to
variability of BMI around the mean allostatic values and
accumulation of allostatic load. SPM applications thus allow
revealing novel connections between age, genetic factors and
longitudinal trajectories of risk factors in the context of
AD and aging creating new opportunities for understanding AD
development, forecasting trends in AD incidence and
prevalence in populations, and studying disparities in
those.},
Doi = {10.1016/j.mad.2023.111791},
Key = {fds369683}
}
@article{fds369841,
Author = {Akushevich, I and Yashkin, A and Kovtun, M and Kravchenko, J and Arbeev,
K and Yashin, AI},
Title = {Forecasting prevalence and mortality of Alzheimer's disease
using the partitioning models.},
Journal = {Exp Gerontol},
Volume = {174},
Pages = {112133},
Year = {2023},
Month = {April},
url = {http://dx.doi.org/10.1016/j.exger.2023.112133},
Abstract = {OBJECTIVES: Health forecasting is an important aspect of
ensuring that the health system can effectively respond to
the changing epidemiological environment. Common models for
forecasting Alzheimer's disease and related dementias
(AD/ADRD) are based on simplifying methodological
assumptions, applied to limited population subgroups, or do
not allow analysis of medical interventions. This study uses
5 %-Medicare data (1991-2017) to identify, partition, and
forecast age-adjusted prevalence and incidence-based
mortality of AD as well as their causal components. METHODS:
The core underlying methodology is the partitioning analysis
that calculates the relative impact each component has on
the overall trend as well as intertemporal changes in the
strength and direction of these impacts. B-spline functions
estimated for all parameters of partitioning models
represent the basis for projections of these parameters in
future. RESULTS: Prevalence of AD is predicted to be stable
between 2017 and 2028 primarily due to a decline in the
prevalence of pre-AD-diagnosis stroke. Mortality, on the
other hand, is predicted to increase. In all cases the
resulting patterns come from a trade-off of two
disadvantageous processes: increased incidence and
disimproved survival. Analysis of health interventions
demonstrates that the projected burden of AD differs
significantly and leads to alternative policy implications.
DISCUSSION: We developed a forecasting model of AD/ADRD
risks that involves rigorous mathematical models and
incorporation of the dynamics of important determinative
risk factors for AD/ADRD risk. The applications of such
models for analyses of interventions would allow for
predicting future burden of AD/ADRD conditional on a
specific treatment regime.},
Doi = {10.1016/j.exger.2023.112133},
Key = {fds369841}
}
@article{fds370816,
Author = {Yashkin, AP and Gorbunova, GA and Tupler, L and Yashin, AI and Doraiswamy, M and Akushevich, I},
Title = {Differences in Risk of Alzheimer's Disease Following
Later-Life Traumatic Brain Injury in Veteran and Civilian
Populations.},
Journal = {The Journal of Head Trauma Rehabilitation},
Year = {2023},
Month = {February},
url = {http://dx.doi.org/10.1097/htr.0000000000000865},
Abstract = {<h4>Objective</h4>To directly compare the effect of incident
age 68+ traumatic brain injury (TBI) on the risk of
diagnosis of clinical Alzheimer's disease (AD) in the
general population of older adults, and between male
veterans and nonveterans; to assess how this effect changes
with time since TBI.<h4>Setting and participants</h4>Community-dwelling
traditional Medicare beneficiaries 68 years or older from
the Health and Retirement Study (HRS).<h4>Design</h4>Fine-Gray
models combined with inverse-probability weighting were used
to identify associations between incident TBI, post-TBI
duration, and TBI treatment intensity, with a diagnosis of
clinical AD dementia. The study included 16 829 older adults
followed over the 1991-2015 period. For analyses of
veteran-specific risks, 4281 veteran males and 3093
nonveteran males were identified. Analysis of veteran
females was unfeasible due to the age structure of the
population. Information on occurrence(s) of TBI, and onset
of AD and risk-related comorbidities was constructed from
individual-level HRS-linked Medicare claim records while
demographic and socioeconomic risk factors were based on the
survey data.<h4>Results</h4>Later-life TBI was strongly
associated with increased clinical AD risk in the full
sample (pseudo-hazard ratio [HR]: 3.22; 95% confidence
interval [CI]: 2.57-4.05) and in veteran/nonveteran males
(HR: 5.31; CI: 3.42-7.94), especially those requiring
high-intensity/duration care (HR: 1.58; CI: 1.29-1.91).
Effect magnitude decreased with time following TBI (HR:
0.72: CI: 0.68-0.80).<h4>Conclusion</h4>Later-life TBI was
strongly associated with increased AD risk, especially in
those requiring high-intensity/duration care. Effect
magnitude decreased with time following TBI. Univariate
analysis showed no differences in AD risk between veterans
and nonveterans, while the protective effect associated with
veteran status in Fine-Gray models was largely due to
differences in demographics, socioeconomics, and morbidity.
Future longitudinal studies incorporating diagnostic
procedures and documentation quantifying lifetime TBI events
are necessary to uncover pathophysiological mediating and/or
moderating mechanisms between TBI and AD.},
Doi = {10.1097/htr.0000000000000865},
Key = {fds370816}
}
@article{fds370204,
Author = {Akushevich, I and Kravchenko, J and Yashkin, A and Doraiswamy, PM and Hill, CV and Alzheimer's Disease and Related Dementia Health
Disparities Collaborative Group},
Title = {Expanding the scope of health disparities research in
Alzheimer's disease and related dementias: Recommendations
from the "Leveraging Existing Data and Analytic Methods for
Health Disparities Research Related to Aging and Alzheimer's
Disease and Related Dementias" Workshop Series.},
Journal = {Alzheimer'S & Dementia (Amsterdam, Netherlands)},
Volume = {15},
Number = {1},
Pages = {e12415},
Year = {2023},
url = {http://dx.doi.org/10.1002/dad2.12415},
Abstract = {Topics discussed at the "Leveraging Existing Data and
Analytic Methods for Health Disparities Research Related to
Aging and Alzheimer's Disease and Related Dementias"
workshop, held by Duke University and the Alzheimer's
Association with support from the National Institute on
Aging, are summarized. Ways in which existing data
resources paired with innovative applications of both novel
and well-known methodologies can be used to identify the
effects of multi-level societal, community, and individual
determinants of race/ethnicity, sex, and geography-related
health disparities in Alzheimer's disease and related
dementia are proposed. Current literature on the
population analyses of these health disparities is
summarized with a focus on identifying existing gaps in
knowledge, and ways to mitigate these gaps using data/method
combinations are discussed at the workshop. Substantive
and methodological directions of future research capable of
advancing health disparities research related to aging are
formulated.},
Doi = {10.1002/dad2.12415},
Key = {fds370204}
}