Economics Research: Publications since January 2023

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@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}
}