Multistate Model Research Articles

Characterizing Risk Profiles and Transitions across Living and Care Settings in Sweden

Abstract Background Older adults are at increased risk of frequent healthcare transitions between health care (hospitals) and social care providers (formal care and institutions). Therefore, our objective was to quantify and characterize the potential risk profiles of multiple transitions between living and care settings for older adults. Methods Data were obtained from the National E-Infrastructure for Aging Research (NEAR), including the Swedish National Study on Aging and Care (SNAC) and the National Patient Register, to evaluate living and care transitions of over 7,000 individuals aged 60+, across homes, formal care, nursing homes, hospitals, and post-acute facilities. A multi-state model was used to represent moving likelihood. Results Over 15 years, the average length of stay was two years at home, one year in home care, one year in institutionalization, eight days in the hospital, and 17 days in post-acute facilities. Older age and being a woman increased the chance of receiving home care after hospital discharge (Hazard Ratio [HR] range 1.35-4.38, 1.06-1.08), whereas multimorbidity and slow walking speed were associated with an 11%-50% increased hazard of hospitalization and home care. Generally, cognitive impairment increased the hazard of institutionalization (HR range 1.99-2.15), and disability was associated with a higher hazard of nursing home placement (HR range 2.57-3.07). Conclusions These findings underscore that care transition patterns are associated with different sociodemographic, clinical, and functional characteristics. This highlights the complex and dynamic interplay between these characteristics and social and health care use. Key messages • The study emphasizes that hospitalization is a major driver of transitions between living and care settings for older adults. • Age, gender, multimorbidity, walking speed, cognitive impairment, and disability all play a role in shaping older adults’ transition patterns between different care settings.

Risk Profiles of Avoidable and Unavoidable Hospitalizations: a Swedish population-based study

Abstract Background Older adults frequently experience multiple hospitalizations, owing to their complex health and social needs. This study aims to comprehensively characterize the risk factors associated with avoidable and unavoidable hospitalization among older adults. Specific emphasis is placed on avoidable/unavoidable hospitalization originating from chronic or acute causes. Methods Data on 3,166 individuals aged ≥60 years were obtained from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), linked to the Swedish National Patient Register. Multistate models were used to investigate the risk profiles of participants experiencing their first and recurrent hospitalizations over 15 years. The Swedish National Board of Health and Welfare criteria were used to define avoidable/unavoidable hospitalizations. Results Women had a lower hazard of avoidable hospitalizations for chronic conditions as well as unavoidable hospitalizations (HR range 0.46-0.84). However, receiving informal care was associated with 88% increased hazard of avoidable hospitalizations due to chronic causes (95%CI 1.18-2.99), while receiving formal care was associated with 44% increasing hazard of avoidable hospitalization due to acute causes (95%CI1.04-1.98). Factors such as slow walking speed, multimorbidity, and polypharmacy were associated with a high risk of avoidable hospitalizations due to both acute (HR range1.27-3.03) and chronic (HR range1.63-2.54) causes. Conclusions Both clinical and functional factors have emerged as key predictors of avoidable hospitalization, underscoring the importance of comprehensive risk assessment and proactive healthcare management strategies to reduce the burden of hospital utilization in older adults. Key messages • Targeted interventions are essential to improve healthcare outcomes among older adults at risk of avoidable hospitalizations due to chronic and acute conditions. • The study identified predictors such as slow walking speed, multimorbidity, and polypharmacy, underscoring the need for comprehensive risk assessment to prevent avoidable hospitalization.

Intersecting Pathways: Understanding Dementia, Hearing Loss, and Health Disparities

Abstract Background Dementia is a major public health concern, affecting care dependency and disability in older ages. In Europe, around 14.1 million people live with dementia, a number that is expected to increase to 15.9 million by 2040. Hearing loss (HL), followed by education, has been proposed as the most important modifiable risk factor for dementia. This study aims to investigate how sex, educational attainment and HL intersect to influence dementia-free life expectancy (DemFLE) in older adults. Methods Using population register data covering all Finnish residents aged 50 years or older from 2010 to 2019, we apply discrete-time multistate Markov models. Transition probabilities between states, such as HL and dementia, are estimated with multinomial logit models stratified by sex and education. Multistate life tables are used to compute sex- and education-specific DemFLE. Results We identified 316,361 individuals with a diagnosis of dementia and 317,115 individuals with a diagnosis of HL, representing 1,449,873 and 2,738,970 person-years respectively. 14% of individuals with HL were eventually diagnosed with dementia, compared to only 8% of individuals without HL. We hypothesise that DemFLE will vary significantly by sex and education, with additional modulation by the presence of HL. Specifically, we expect that the presence of HL will further reduce DemFLE, and that the magnitude of this reduction will be relatively greater among those with lower educational attainment. Conclusions This study contributes to the emerging literature on the impact of hearing loss on dementia. While both dementia and hearing loss are irreversible health conditions, hearing loss can be managed by hearing aids. If we find evidence that hearing loss is associated with different levels of risk of dementia by educational attainment, it would suggest avenues for effective interventions and public health policies to ensure more equal ageing processes in the population. Key messages • Hearing loss may intersect with education and sex in the risk of dementia, providing evidence for the importance of addressing hearing health inequalities in dementia prevention efforts. • Understanding the interactions between hearing loss, education and sex can inform policies to promote healthy ageing and reduce the burden of dementia on individuals and health systems.

Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases

Structure-based virtual screening (SBVS) is a crucial computational approach in drug discovery, but its performance is sensitive to structural variations. Kinases, which are major drug targets, exemplify this challenge due to active site conformational changes caused by different inhibitor types. Most experimentally determined kinase structures have the DFGin state, potentially biasing SBVS towards type I inhibitors and limiting the discovery of diverse scaffolds. We introduce a multi-state modeling (MSM) protocol for AlphaFold2 (AF2) kinase structures using state-specific templates to address these challenges. Our comprehensive benchmarks evaluate predicted model qualities, binding pose prediction accuracy, and hit compound identification through ensemble SBVS. Results demonstrate that MSM models exhibit comparable or improved structural accuracy compared to standard AF2 models, enhancing pose prediction accuracy and effectively capturing kinase-ligand interactions. In virtual screening experiments, our MSM approach consistently outperforms standard AF2 and AF3 modeling, particularly in identifying diverse hit compounds. This study highlights the potential of MSM in broadening kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors, offering a promising solution to the structural bias problem in kinase-targeted drug discovery.

Uniformization and bounded Taylor series in Newton-Raphsonmethod improves computational performance for a multistate transition model estimation and inference.

Multistate transition models (MSTMs) are valuable tools depicting disease progression. However, due to the complexity of MSTMs, larger sample size and longer follow-up time in real-world data, the computation of statistical estimation and inference for MSTMs becomes challenging. A bounded Taylor series in Newton-Raphson procedure is proposed which leverages the uniformization technique to derive maximum likelihood estimates and corresponding covariance matrix. The proposed method, namely uniformization Taylor-bounded Newton-Raphson, is validated in three simulation studies, which demonstrate the accuracy in parameter estimation, the efficiency in computation time and robustness in terms of different situations. This method is also illustrated using a large electronic medical record data related to statin-induced side effects and discontinuation.

Impact of age at HBsAg seroclearance on hepatic outcomes and life expectancy in men with chronic HBV infection based on multi-state modeling of the natural history.

The effects of age at HBsAg seroclearance on clinical outcomes and survival in chronic hepatitis B (CHB) have not been adequately assessed. We evaluated the impact of age at HBsAg seroclearance on long-term outcomes, along with how coexisting factors modified risks and life expectancy in CHB patients. We used multi-state modeling approach to examine transitions through the CHB continuum in a longitudinal cohort study of male civil servants recruited in 1989-1992. Hepatic outcomes and deaths were identified by clinical evaluation and linkage with national health databases. Four sets of risk factors (CHB-related, metabolic, lifestyle, and genetic factors) were assessed. Of 2551 HBsAg carriers, with follow-up until 2021 or death, 695 achieved HBsAg seroclearance, 490 developed cirrhosis (88 decompensated), 252 developed hepatocellular carcinoma (HCC), and 652 died. The cumulative rates for HCC were 1.1% and 1.5% at 10years after HBsAg seroclearance, respectively, for patients achieving seroclearance at age 50 and 60; correspondingly, the rates for cirrhosis were 2.3% and 3.0%. Developing HBsAg seroclearance was associated with a reduced risk of cirrhosis (HR = 0.37, 95% CI 0.15-0.92) but not HCC. Patients experiencing HBsAg seroclearance lived longer years free of major liver diseases than HBsAg-persistent patients, and achieving seroclearance at age 50 (vs 60) led to a greater increase in the disease-free life expectancy. However, obesity and smoking were associated with adverse hepatic outcomes and loss of the disease-free life expectancy following HBsAg seroclearance. Our findings highlight the benefit of earlier HBsAg seroclearance for gains in disease-free life expectancy and the impact of obesity and smoking on loss of the life years free of major liver diseases following HBsAg seroclearance.

Bayesian blockwise inference for joint models of longitudinal and multistate data with application to longitudinal multimorbidity analysis.

Multistate models provide a useful framework for modelling complex event history data in clinical settings and have recently been extended to the joint modelling framework to appropriately handle endogenous longitudinal covariates, such as repeatedly measured biomarkers, which are informative about health status and disease progression. However, the practical application of such joint models faces considerable computational challenges. Motivated by a longitudinal multimorbidity analysis of large-scale UK health records, we introduce novel Bayesian inference approaches for these models that are capable of handling complex multistate processes and large datasets with straightforward implementation. These approaches decompose the original estimation task into smaller inference blocks, leveraging parallel computing and facilitating flexible model specification and comparison. Using extensive simulation studies, we show that the proposed approaches achieve satisfactory estimation accuracy, with notable gains in computational efficiency compared to the standard Bayesian estimation strategy. We illustrate our approaches by analysing the coevolution of routinely measured systolic blood pressure and the progression of three important chronic conditions, using a large dataset from the Clinical Practice Research Datalink Aurum database. Our analysis reveals distinct and previously lesser-known association structures between systolic blood pressure and different disease transitions.

Predicting the effect of nosocomial infection prevention on neonatal mortality and hospital stay in Ethiopia: a prospective longitudinal study

BackgroundNeonatal Nosocomial infections (NNIs) are a significant cause of morbidity and mortality for neonates in an intensive care unit. Neonatal causes of death in healthcare facilities are attributed to different factors. We aimed to investigate factors associated with NNIs, estimate the burden of NNIs, and assess how the prediction effects help to save medical mortality and length of hospital stay.MethodA prospective longitudinal study was conducted and data were collected from January 2022 to June 2022 from Jimma University Medical Center (JUMC). The data were gathered in a variety of ways, including an in-person interview with the patient’s caregiver, direct observations of neonatal patients, and a review of the study participants’ charts. This study includes patients aged 3 to 28 days who were admitted to the JUMC neonatal ward and stayed for at least 48 h. Multi-state model formulation and multivariate logistic regression were used for data analysis.ResultsA total of 545 neonates were included out of 688, and 30% (n = 164) of them acquired nosocomial infections (NIs); 98 (33%) of infected patients were born prematurely; and 71 (31.4%) were underweight at birth. NIs were higher in neonates with long hospital stay (AOR: 1.16, 95%CI: 1.13–1.20), use of urinary catheters (AOR: 3.09, 95%CI: 1.55–6.15), and undergoing surgical procedures (AOR: 2.42, 95%CI: 1.13–5.17). Patients who developed NIs had a higher risk of death (HR: 2, 95% CI: 1.31, 3.04). The burden of neonatal NIs was determined to have a risk of 0.3, a mortality rate of 9.6%, and an average duration of hospital stay of 14.6 days. Competing risk regression suggests that neonates with NIs have a significantly higher risk of death than those who are not infected (HR: 16.42, 95% CI: 8.70-30.98, p < 0.001). Assumed prevention that decreases the NIs rate in half would result in 101 lives and 1357 patient days saved from 10,000 neonatal inpatients.ConclusionUrinary catheterization and surgical procedure increased neonatal NIs. Longer hospital stay can increase the risk of NIs and can also result from the NIs. Our finding indicated that effective prevention of NIs could help reduce neonatal deaths and their hospital stays.

Multi-State Reliability Modeling and Assessment for Corrosion of Organic Coating-Substrate Structure

Abstract The organic coating-substrate structure suffers from corrosion reaction between the substrate material and water molecules during the storage stage. Digital twin is a promising tool for corrosion modeling and assessing the reliability of the organic coating-substrate structure. In this paper, a multi-state modelling method is proposed towards the digital twin and reliability modeling and analysis. Firstly, to analyze the physicochemical process involved in the corrosion of organic coating-substrate structures, a multi-physics simulation method is developed. Then, the degradation performance of the organic coating-substrate structure is discretized into several states, and a Markov model is utilized to model the degradation process of the structure to support the digital twine model construction. The transition intensities of the Markov model are estimated by using the multi-physics simulation data. In the proposed method, the multi-physics simulation method can incorporate the diffusion equation and the kinetic equation of the corrosion, allowing for the simulation of water molecule diffusion within the organic coating and the coupling simulation of the metal corrosion process. Subsequently, the reliability of the organic coating-substrate structure is analyzed under varying temperatures, humidity levels, and protective material parameters. The result shows that higher ambient temperatures and relative humidity levels contribute to an accelerated corrosion rate of the substrate, and the reliability decreases.

Association Between Variables and Transitions Among No Opioid Use, Opioid Use, and Subsequent Dropout Among Participants on Methadone Treatment: A Retrospective Study Utilizing a Multistate Model.

Although previous studies have reported the variables that influence opioid use or dropout among participants receiving methadone treatment, limited attention has been given to the variables related to transitions among no opioid use, opioid use, and dropout. This retrospective study utilized data collected from June 2010 to June 2022 at 11 methadone treatment clinics in Guangdong Province, China. Two transient states (no opioid use and opioid use) and 1 absorbing state (dropout) were defined based on monthly urine morphine test results and daily methadone intake records. We used a multistate model to explore the variables associated with transitions among no opioid use, opioid use, and dropout among participants. Among 3136 participants, with an average treatment duration of 497 days, 1646 (52.49%) underwent at least 1 period of opioid use, resulting in 3283 transitions from no opioid use to opioid use. The transitions between no opioid use and opioid use were significantly associated with variables such as age, gender, employment status, marital status, living situation, travel time to the clinic, human immunodeficiency virus and hepatitis C virus infection statuses, average methadone dosage, and attendance rates. The variables influencing participants' dropout varied depending on their opioid use behaviors. Additionally, the probability of a specified opioid use state remaining unchanged or transitioning to a different state at a defined time point would change over time. The opioid use behaviors of participants are dynamic. Methadone providers should offer targeted interventions based on participants' opioid use behaviors to effectively decrease rates of opioid use and improve retention.

Prospective association of the Mediterranean diet with the onset of cardiometabolic multimorbidity in a UK-based cohort: the EPIC-Norfolk study

BackgroundCardiometabolic multimorbidity (CMM), defined as the co-occurrence of two or more cardiometabolic diseases, including myocardial infarction (MI), stroke, and type 2 diabetes (T2D), is an increasing public health challenge. While poor diet is a known risk factor for a first cardiometabolic disease (FCMD), the relationship with subsequent occurrence of CMM is less studied. ObjectiveThis study aims to investigate the prospective association between baseline adherence to the Mediterranean diet and the onset of CMM across various follow-up durations. MethodsWe used data from the EPIC-Norfolk cohort study of 21,900 adults, aged 40-79 free of prevalent MI, stroke, and T2D at baseline (1993-1997). A median-based Mediterranean diet score (m-MDS) and a pyramid-based MDS (pyr-MDS) were used to measure baseline adherence to the Mediterranean diet. Multi-state modelling was employed to investigate associations with the FCMD and the subsequent CMM event. ResultsOver the entire follow-up period of 21.4 years (median), we observed 5028 FCMD and 734 CMM events. Multi-state analysis indicated that the association between baseline Mediterranean diet and the risk of CMM may be stronger in shorter follow-up durations. Particularly, baseline pyr-MDS was significantly associated with the risk of subsequent CMM transitioning from FCMD when follow-up durations were limited to 10 and 15 years, with HR (95% CI) being 0.67 (0.53, 0.84) and 0.80 (0.70, 0.92) per SD increase in pyr-MDS, respectively. Additionally, we observed that the risk of CMM transitioning from FCMD was modified by social class across shorter to longer follow-ups, where the impact of baseline Mediterranean diet was only significant in non-manual workers. ConclusionsBaseline adherence to the Mediterranean diet was potentially associated with a lower risk of CMM transitioning from FCMD, particularly during shorter follow-up periods.

Characterizing metabolomic signatures related to coffee and tea consumption and their association with incidence and dynamic progression of type 2 diabetes: A multi-state analysis.

Our study aimed to investigate the impact of tea and coffee consumption and related metabolomic signatures on dynamic transitions from diabetes-free status to incident type 2 diabetes (T2D), and subsequently to T2D-related complications and death. We included 438,970 participants in the UK Biobank who were free of diabetes and diabetes complications at baseline. Of these, 212,146 individuals had information on all metabolic biomarkers. We identified tea- and coffee-related metabolomic signatures using elastic net regression models. We examined associations of tea and coffee intake and related metabolomic signatures with the onset and progression of T2D using multi-state regression models. We observed that tea and coffee consumption and related metabolomic signatures were inversely associated with the risk of five T2D transitions. For example, HRs (95% CIs) per SD increase of the tea-related metabolomic signature were 0.87 (0.85, 0.89), 0.97 (0.95, 0.99), 0.91 (0.90, 0.92), 0.92 (0.91, 0.94), and 0.91 (0.90, 0.92) for transitions from diabetes-free state to incident T2D, from diabetes-free state to total death, from incident T2D to T2D complications, from incident T2D to death, and from T2D complications to death. These findings highlight the benefit of tea and coffee intake in reducing the risk of occurrence and progression of T2D.

Increased adult movements and decreased juvenile apparent survival of urban crows during COVID-19 lockdowns

The increasing abundance of animal species thriving in urban environments is a source of conflicts with managers and users of public spaces. Although opportunistic urban species often use resources originating from human food leftovers, the potential impact of a reduction in these resources on their demography is hard to quantify. The COVID-19 epidemic, which led many countries to set up lockdowns, gave us the opportunity to estimate the impact of a drastic reduction in such food resources and human activities on the demography of an urban bird population. Based on 7 years (2015–2021) of capture-mark-recapture of carrion crows (Corvus corone) in the city of Paris, France, we used multi-state models to examine the intra-annual (3-month time steps) apparent survival and movement patterns of crows during and outside COVID-19 lockdowns. We showed that the apparent survival of juvenile carrion crows decreased down during lockdown, while adult movements increased during this period, with more adult crows moving out of the urban district. Lockdown modified the demography of this urban crow population, suggesting that the reduction in food resources was sufficient to affect fitness and reduce carrying capacity.

Reliability Analysis for Degradation-Shock Processes with State-Varying Degradation Patterns Using Approximate Bayesian Computation (ABC) for Parameter Estimation

The degradation of products is an integral part of their life-cycle, often following predictable trajectories. However, sudden, unexpected events, termed ’shocks’, can substantially alter these degradation paths. Shocks can significantly influence the pace of degradation, leading to accelerated system failure. Moreover, they may initiate changes in degradation patterns, transitioning from linear to non-linear or random trajectories. To address this challenge, we present a novel multi-state reliability model for competing failure processes that account for degradation-shock dependencies by considering the state-varying degradation pattern. The degradation process is divided into s-states, with each state treated according to its pattern based on the time-transform Wiener process. The reliability function is derived based on soft failure caused by continuous degradation involving the s-states, the sudden increase in degradation caused by random shocks, and hard failure due to some shock processes. Additionally, we performed a sensitivity analysis to determine which parameters have the most significant impact on product reliability. Due to the complexity of the likelihood function, we adopted the ABC method to estimate the model parameters. A simulation study and a practical application with micro-electro-mechanical systems (MEMS) degradation results are used to demonstrate the efficiency and effectiveness of the proposed approach.

Association of Short-term Pain and Chronic Pain Intensity With Cardiometabolic Multimorbidity Progression: A Multistate Markov Model Analysis.

The impact of pain intensity on the progression trajectories of cardiometabolic multimorbidity (CMM) is not well understood. We attempted to dissect the relationship of short-term pain (STP) and chronic pain intensity with the temporal progression of CMM. We conducted a prospective cohort study based on the UK Biobank participants. Incident cases of cardiometabolic diseases (CMDs) were identified based on self-reported information and multiple health-related records in the UK Biobank. CMM was defined as the occurrence of at least 2 CMDs, including heart failure (HF), ischemic heart disease (IHD), stroke, and type 2 diabetes (T2D). The pain intensity was categorized into 5 levels based on pain duration and the number of sites involved, including chronic widespread pain (CWSP), chronic multilocation pain (CMLP), chronic single-location pain (CSLP), STP, and free-of-pain (FOP). Multistate models were used to assess the impact of pain intensity on the CMM trajectories from enrollment to initial cardiometabolic disease (ICMD), subsequently to CMM, and ultimately to death. A total of 429,145 participants were included. Over the course of a 12.8-year median follow-up, 13.1% (56,137/429,145) developed ICMD, 19.6% (10,979/56,137) further progressed to CMM, and a total of 5.3% (22,775/429,145) died. Compared with FOP, CMLP (hazard ratio [HR], 1.11; 95% confidence interval [CI], 1.06-1.17) and CWSP (HR, 1.26; 95% CI, 1.13-1.42) elevated the risk of transitioning from ICMD to CMM. STP (HR, 0.89; 95% CI, 0.82-0.96), CSLP (HR, 0.88; 95% CI, 0.82-0.95), and CMLP (HR, 0.87; 95% CI, 0.81-0.93) lowered the risk of transition from ICMD to mortality, and STP also reduced the risk of transition from enrollment to mortality (HR, 0.94; 95% CI, 0.89-0.98). The results of disease-specific transitions revealed that the influence of pain intensity varied across transitional stages. Specifically, CMLP and CWSP heightened the risk of conversion from T2D or IHD to CMM, whereas only CWSP substantially elevated the transition risk from HF to CMM. Our results highlighted reductions in chronic pain may mitigate both the onset and progression of CMM, potentially having an important impact on future revisions of cardiometabolic and pain-related guidelines.

External validation and calibration of risk equations for prediction of diabetic kidney diseases among patients with type 2 diabetes in Taiwan

BackgroundMost existing risk equations for predicting/stratifying individual diabetic kidney disease (DKD) risks were developed using relatively dated data from selective and homogeneous trial populations comprising predominately Caucasian type 2 diabetes (T2D) patients. We seek to adapt risk equations for prediction of DKD progression (microalbuminuria, macroalbuminuria, and renal failure) using empiric data from a real-world population with T2D in Taiwan.MethodsRisk equations from three well-known simulation models: UKPDS-OM2, RECODe, and CHIME models, were adapted. Discrimination and calibration were determined using the area under the receiver operating characteristic curve (AUROC), a calibration plot (slope and intercept), and the Greenwood-Nam-D’Agostino (GND) test. Recalibration was performed for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of risk equations to address risk variations among patients.ResultsThe RECODe equations for microalbuminuria and macroalbuminuria showed moderate discrimination (AUROC: 0.62 and 0.76) but underestimated the event risks (calibration slope > 1). The CHIME equation had the best discrimination for renal failure (AUROCs from CHIME, UKPDS-OM2 and RECODe: 0.77, 0.60 and 0.64, respectively). All three equations overestimated renal failure risk (calibration slope < 1). After rigorous updating, the calibration slope/intercept of the recalibrated RECODe for predicting microalbuminuria (0.87/0.0459) and macroalbuminuria (1.10/0.0004) risks as well as the recalibrated CHIME equation for predicting renal failure risk (0.95/-0.0014) were improved.ConclusionsRisk equations for prediction of DKD progression in real-world Taiwanese T2D patients were established, which can be incorporated into a multi-state simulation model to project and differentiate individual DKD risks for supporting timely interventions and health economic research.

Socioeconomic disparity in the natural history of cutaneous melanoma: evidence from two large prospective cohorts

BackgroundPrevious studies on the associations between socioeconomic status (SES) and cutaneous malignant melanoma (CMM) failed to distinguish the effects of different SES factors under an individual-data-based prospective study design.MethodsBased on...

Projecting the Likely Impact of COVID-19 Infections on the Prevalence of Dementia in the United States.

The association between COVID-19 infection and the onset of dementia among adults 65 years and older has the potential to increase the burden of dementia worldwide significantly. Our research, which focuses on understanding the likely increase in the burden of dementia due to COVID-19 infection in the USA, has crucial public policy implications. By providing these insights, we aim to empower policymakers, healthcare professionals, researchers, and public health officials to make informed decisions and plan for the future. Project the prevalence of dementia in the United States while accounting for the impact of COVID-19 infection on the onset of dementia. A dynamic multi-state population model was developed. The model was initialized with USA demographic data and estimates of age, gender, and race-specific transition rates from the Health and Retirement Study (HRS). The projected increase in the burden of dementia among Americans 65 years and older is a staggering 14.838 million by 2050. However, due to the COVID-19 pandemic, we anticipate an additional 265,000 to 677,000 older adults 65 years and older will be affected by dementia. This will escalate the burden of dementia to a potential 15.103 million to 15.515 million by 2050, a significant human toll that we must be prepared for. The projected dementia numbers underscore the urgent need for policy and intervention in social care services and healthcare needs planning. This includes providing robust support systems for caregivers and ensuring the healthcare staff is adequately trained to meet the healthcare needs of dementia patients and their families.

Florida sandhill crane survival along a wildland‐to‐urban land use gradient

AbstractThe Florida sandhill crane (Antigone canadensis pratensis) is a state‐threatened non‐migratory subspecies. Our understanding of adult crane survival in Florida, USA, is unclear, as it relies on decades‐old unpublished data of birds residing in natural areas. Since that time, the loss of natural habitat precipitated cranes using urbanized areas such as suburban lawns and roadside verges for foraging and loafing. Contemporary studies are needed to properly guide crane management and conservation efforts. We addressed this knowledge gap by estimating the annual survival rate for sandhill cranes in 12 central Florida counties. We used a live‐dead capture‐recapture multistate model, monitoring 118 adult cranes from June 2017 to May 2023; 76 were color‐banded and 42 were tagged with Global System for Mobile Communications (GSM) transmitters. Fifteen cranes died during the study, with vehicle strikes (n = 9) being the most prevalent identified source of mortality. Overall, the annual survival probability was estimated at 0.79 (95% credibility interval = 0.75–0.84). Using the subset of GSM‐tagged cranes, we observed no influence of urbanization on annual survival rate (βurbanization gradient = 0.007, 95% credibility interval = −0.008, 0.022). The adult survival rates we observed are lower than reported for other populations of sandhill cranes in North America that are considered stable or growing.

A systematic framework of resilience assessment based on multi-state transition modeling under two-phase recovery for hydrogen refueling stations

The safety of Hydrogen refueling station systems is becoming increasingly important with the growth of hydrogen energy. Existing assessment methods for hydrogen refueling stations primarily focus on reliability. They often neglect the recovery process after experiencing shocks. This paper establishes a comprehensive resilience assessment framework for Hydrogen refueling station. It considers a two-state recovery phase with a multi-state transition model. The entire system evolution process is divided into degradation and recovery phases. The transfer rate during the degradation phase is obtained using event and fault tree analysis. The recovery phase is further divided into emergency recovery and post-recovery, generating recovery rate for the entire process. Furthermore, four associated indices for resilience assessment are established: resistance, absorption, recovery, and robustness. A case study based on a typical Hydrogen refueling station system is implemented. The results show that the resilience of the Hydrogen refueling station can be improved by 12.5% with the support of the two-phase recovery process compared to the traditional recovery process. This demonstrates the framework's potential to enhance the reliable operation of Hydrogen refueling station in practice.