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- Anjana, Ranjit Mohan, et al.
(author)
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Contrasting Associations Between Diabetes and Cardiovascular Mortality Rates in Low-, Middle-, and High-Income Countries: Cohort Study Data From 143,567 Individuals in 21 Countries in the PURE Study.
- 2020
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In: Diabetes care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 43:12, s. 3094-3101
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Journal article (peer-reviewed)abstract
- We aimed to compare cardiovascular (CV) events, all-cause mortality, and CV mortality rates among adults with and without diabetes in countries with differing levels of income.The Prospective Urban Rural Epidemiology (PURE) study enrolled 143,567 adults aged 35-70 years from 4 high-income countries (HIC), 12 middle-income countries (MIC), and 5 low-income countries (LIC). The mean follow-up was 9.0 ± 3.0 years.Among those with diabetes, CVD rates (LIC 10.3, MIC 9.2, HIC 8.3 per 1,000 person-years, P < 0.001), all-cause mortality (LIC 13.8, MIC 7.2, HIC 4.2 per 1,000 person-years, P < 0.001), and CV mortality (LIC 5.7, MIC 2.2, HIC 1.0 per 1,000 person-years, P < 0.001) were considerably higher in LIC compared with MIC and HIC. Within LIC, mortality was higher in those in the lowest tertile of wealth index (low 14.7%, middle 10.8%, and high 6.5%). In contrast to HIC and MIC, the increased CV mortality in those with diabetes in LIC remained unchanged even after adjustment for behavioral risk factors and treatments (hazard ratio [95% CI] 1.89 [1.58-2.27] to 1.78 [1.36-2.34]).CVD rates, all-cause mortality, and CV mortality were markedly higher among those with diabetes in LIC compared with MIC and HIC with mortality risk remaining unchanged even after adjustment for risk factors and treatments. There is an urgent need to improve access to care to those with diabetes in LIC to reduce the excess mortality rates, particularly among those in the poorer strata of society.
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| 2. |
- Li, Yibei, et al.
(author)
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A Duality-Based Approach to Inverse Kalman Filtering
- 2023
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In: 22nd IFAC World Congress Yokohama, Japan, July 9-14, 2023. - : Elsevier BV. ; , s. 10258-10263
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Conference paper (peer-reviewed)abstract
- In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.
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| 3. |
- Li, Yibei, et al.
(author)
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Inverse Kalman Filtering for Systems with Correlated Noises
- 2023
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In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3626-3631
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Conference paper (peer-reviewed)abstract
- This paper focuses on two inverse problems of the Kalman filter in which the process and measurement noises are correlated. The unknown covariance matrix in a stochastic system is reconstructed from observations of its posterior beliefs. For the standard inverse Kalman filtering problem, a novel duality-based formulation is proposed, where a well-defined inverse optimal control (IOC) problem is solved instead. Identifiability of the underlying model is proved, and a least squares estimator is designed that is statistically consistent. The time-invariant case using the steady-state Kalman gain is further studied. Since this inverse problem is ill-posed, a canonical class of covariance matrices is constructed, which can be uniquely identified from the dataset with asymptotic convergence. Finally, the performances of the proposed methods are illustrated by numerical examples.
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| 4. |
- Li, Yibei, et al.
(author)
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Inverse Kalman filtering problems for discrete-time systems
- 2024
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In: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 163
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Journal article (peer-reviewed)abstract
- In this paper, several inverse Kalman filtering problems are addressed, where unknown parameters and/or inputs in a filtering model are reconstructed from observations of the posterior estimates that can be noisy or incomplete. In particular, duality in inverse filtering and inverse optimal control is studied. It is shown that identifiability and solvability of the inverse Kalman filtering is closely related to that of an inverse linear quadratic regulator (LQR). Covariance matrices of model uncertainties are estimated by solving a well-posed inverse LQR problem. Identifiability of the considered inverse filtering models is established and least squares estimators are designed to be statistically consistent. In addition, algorithms are proposed to reconstruct the unknown sensor parameters as well as raw sensor measurements. Effectiveness and efficiency of the proposed methods are illustrated by numerical simulations.
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