Multi-state models for the analysis of time-to-event data

Fracture risk assessment in the presence of competing risk of death

PURPOSE

To identify the optimal statistical approach for predicting the risk of fragility fractures in the presence of competing event of death.

METHODS

We used real-world data from the Dubbo Osteoporosis Epidemiology Study that has monitored 3035 elderly participants for bone health and mortality. Fragility fractures were ascertained radiologically. Mortality was confirmed by the State Registry. We considered four statistical models for predicting fracture risk: (i) conventional Cox's proportional hazard model, (ii) cause-specific model, (iii) Fine-Gray sub-distribution model, and (iv) multistate model. These models were fitted and validated in the development (60% of the original sample) and validation (40%) subsets, respectively. The model performance was assessed by discrimination and calibration analyses.

RESULTS

During a median follow-up of 11.3 years (IQR: 7.2, 16.2), 628 individuals (34.5%) in the development cohort fractured, and 630 (34.6%) died without a fracture. Neither the discrimination nor the 5-year prediction performance was significantly different among the models, though the conventional model tended to overestimate fracture risk (calibration-in-the-large index =  - 0.24; 95% CI: - 0.43, - 0.06). For 10-year risk prediction, the multistate model (calibration-in-the-large index =  - 0.05; 95% CI: - 0.20, 0.10) outperformed the cause-specific (- 0.23; - 0.30, - 0.08), Fine-Gray (- 0.31; - 0.46, - 0.16), and conventional model (- 0.54; - 0.70, - 0.39) which significantly overestimated fracture risk.

CONCLUSION

Adjustment for competing risk of death has minimum impact on the short-term prediction of fracture. However, the multistate model yields the most accurate prediction of long-term fracture risk and should be considered for predictive research in the elderly, who are also at high mortality risk. Fracture risk assessment might be compromised by the competing event of death. This study, using real-world data found a multistate model was superior to the current competing risk methods in fracture risk assessment. A multistate model is considered an optimal statistical method for predictive research in the elderly.

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

BACKGROUND

Neonatal 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.

METHOD

A 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.

RESULTS

A 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.

CONCLUSION

Urinary 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.

Inequalities in Transitions to Home Care: A Longitudinal Analysis of the Canadian Longitudinal Study on Aging

OBJECTIVES

To investigate inequalities in transitions to home care across a broad set of demographic and socioeconomic factors in Canadian middle-aged and older adults.

DESIGN

Longitudinal, retrospective cohort study.

SETTING AND PARTICIPANTS

A total of 51,338 community-dwelling adults aged 45+ years, using national data from the Canadian Longitudinal Study on Aging across 3 timepoints from 2011 to 2021.

METHODS

We analyzed transitions in home care use using multistate Markov models, with home care use and nonuse as transient states, and loss to follow-up as a terminal state. We calculated hazard ratios for transitions between states adjusting for factors related to home care need (ie, functional limitations, chronic conditions) within the following equity strata: income, education, immigration history, sex, gender, rurality, racial background, and tangible social support.

RESULTS

Across all timepoints, 5.4% of non-home care users transitioned to home care by the next timepoint and 33.2% of home care users continued to use home care at the next timepoint. Among non-home care users, identifying as a woman, female, white, completing higher levels of education, having higher income, and having less support available was associated with an increased likelihood of transitioning to home care use. Among home care users, higher income was also associated with a greater likelihood to discontinue using home care compared with lower income users. The association between income and home care use was stronger among female individuals.

CONCLUSIONS AND IMPLICATIONS

We found meaningful differences in home care transitions across several equity strata. Individuals with higher income have greater ability to access to private care, creating inequity in access to home care services. Gendered factors such as income and social support have important associations with home care use. Home care planning and policy must address the unique barriers and disadvantages diverse populations face to ensure equitable use of home care and promote healthy aging.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

The burden of diabetes-associated multiple long-term conditions on years of life spent and lost

Diabetes mellitus is a central driver of multiple long-term conditions (MLTCs), but population-based studies have not clearly characterized the burden across the life course. We estimated the age of onset, years of life spent and loss associated with diabetes-related MLTCs among 46 million English adults. We found that morbidity patterns extend beyond classic diabetes complications and accelerate the onset of severe MLTCs by 20 years earlier in life in women and 15 years earlier in men. By the age of 50 years, one-third of those with diabetes have at least three conditions, spend >20 years with them and die 11 years earlier than the general population. Each additional condition at the age of 50 years is associated with four fewer years of life. Hypertension, depression, cancer and coronary heart disease contribute heavily to MLTCs in older age and create the greatest community-level burden on years spent (813 to 3,908 years per 1,000 individuals) and lost (900 to 1,417 years per 1,000 individuals). However, in younger adulthood, depression, severe mental illness, learning disabilities, alcohol dependence and asthma have larger roles, and when they occur, all except alcohol dependence were associated with long periods of life spent (11-14 years) and all except asthma associated with many years of life lost (11-15 years). These findings provide a baseline for population monitoring and underscore the need to prioritize effective prevention and management approaches.

A pharmacometric multistate model for predicting long-term treatment outcomes of patients with pulmonary TB

BACKGROUND

Studying long-term treatment outcomes of TB is time-consuming and impractical. Early and reliable biomarkers reflecting treatment response and capable of predicting long-term outcomes are urgently needed.

OBJECTIVES

To develop a pharmacometric multistate model to evaluate the link between potential predictors and long-term outcomes.

METHODS

Data were obtained from two Phase II clinical trials (TMC207-C208 and TMC207-C209) with bedaquiline on top of a multidrug background regimen. Patients were typically followed throughout a 24 week investigational treatment period plus a 96 week follow-up period. A five-state multistate model (active TB, converted, recurrent TB, dropout, and death) was developed to describe observed transitions. Evaluated predictors included patient characteristics, baseline TB disease severity and on-treatment biomarkers.

RESULTS

A fast bacterial clearance in the first 2 weeks and low TB bacterial burden at baseline increased probability to achieve conversion, whereas patients with XDR-TB were less likely to reach conversion. Higher estimated mycobacterial load at the end of 24 week treatment increased the probability of recurrence. At 120 weeks, the model predicted 55% (95% prediction interval, 50%-60%), 6.5% (4.2%-9.0%) and 7.5% (5.2%-10%) of patients in converted, recurrent TB and death states, respectively. Simulations predicted a substantial increase of recurrence after 24 weeks in patients with slow bacterial clearance regardless of baseline bacterial burden.

CONCLUSIONS

The developed multistate model successfully described TB treatment outcomes. The multistate modelling framework enables prediction of several outcomes simultaneously, and allows mechanistically sound investigation of novel promising predictors. This may help support future biomarker evaluation, clinical trial design and analysis.

Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE)

This study aims to develop and apply multistate models to estimate, forecast, and manage hospital length of stay during the COVID-19 epidemic without using any external packages. Data from Bellvitge University Hospital in Barcelona, Spain, were analyzed, involving 2285 hospitalized COVID-19 patients with moderate to severe conditions. The implemented multistate model includes transition probabilities and risk rates calculated from transitions between defined states, such as admission, ICU transfer, discharge, and death. In addition to examining key factors like age and gender, diabetes, lymphocyte count, comorbidity burden, symptom duration, and different COVID-19 waves were analyzed. Based on the model, patients hospitalized stay an average of 11.90 days before discharge, 2.84 days before moving to the ICU, or 34.21 days before death. ICU patients remain for about 24.08 days, with subsequent stays of 124.30 days before discharge and 35.44 days before death. These results highlight hospital stays' varying durations and trajectories, providing critical insights into patient flow and healthcare resource utilization. Additionally, it can predict ICU peak loads for specific subgroups, aiding in preparedness. Future work will integrate the developed code into the hospital's Health Information System (HIS) following ISO 13606 EHR standards and implement recursive methods to enhance the model's efficiency and accuracy.

Clinical, oncological, and prognostic differences of patients with subsequent skeletal-related events in bone metastases

Aims

Advances in treatment have extended the life expectancy of patients with metastatic bone disease (MBD). Patients could experience more skeletal-related events (SREs) as a result of this progress. Those who have already experienced a SRE could encounter another local management for a subsequent SRE, which is not part of the treatment for the initial SRE. However, there is a noted gap in research on the rate and characteristics of subsequent SREs requiring further localized treatment, obligating clinicians to extrapolate from experiences with initial SREs when confronting subsequent ones. This study aimed to investigate the proportion of MBD patients developing subsequent SREs requiring local treatment, examine if there are prognostic differences at the initial treatment between those with single versus subsequent SREs, and determine if clinical, oncological, and prognostic features differ between initial and subsequent SRE treatments.

Methods

This retrospective study included 3,814 adult patients who received local treatment - surgery and/or radiotherapy - for bone metastasis between 1 January 2010 and 31 December 2019. All included patients had at least one SRE requiring local treatment. A subsequent SRE was defined as a second SRE requiring local treatment. Clinical, oncological, and prognostic features were compared between single SREs and subsequent SREs using Mann-Whitney U test, Fisher's exact test, and Kaplan-Meier curve.

Results

Of the 3,814 patients with SREs, 3,159 (83%) patients had a single SRE and 655 (17%) patients developed a subsequent SRE. Patients who developed subsequent SREs generally had characteristics that favoured longer survival, such as higher BMI, higher albumin levels, fewer comorbidities, or lower neutrophil count. Once the patient got to the point of subsequent SRE, their clinical and oncological characteristics and one-year survival (28%) were not as good as those with only a single SRE (35%; p < 0.001), indicating that clinicians' experiences when treating the initial SRE are not similar when treating a subsequent SRE.

Conclusion

This study found that 17% of patients required treatments for a second, subsequent SRE, and the current clinical guideline did not provide a specific approach to this clinical condition. We observed that referencing the initial treatment, patients in the subsequent SRE group had longer six-week, 90-day, and one-year median survival than patients in the single SRE group. Once patients develop a subsequent SRE, they have a worse one-year survival rate than those who receive treatment for a single SRE. Future research should identify prognostic factors and assess the applicability of existing survival prediction models for better management of subsequent SREs.

Occupational and Health-Related Risk Factors for Incident and Recurrent Back Disorders in Danish Fishers-A Register-Based Study

BACKGROUND

Fishers are at risk of back disorders due to their physically demanding work. The aim was to investigate risk factors for back disorders in fishers in Denmark.

METHODS

All male Danish registered fishers between 1994 and 2017 were included. ICD-10 codes classified back disorders (M40-M54* and DM99.1-4*). A multistate model on a cause-specific cox regression model was conducted.

RESULTS

Of 13,165 fishers included, 16% had a hospital contact with an incident back disorder, and 52% at least had 1 recurrent episode. Having worked in another occupation (HR 1.14; 95% CI: 1.02, 1.27) and another musculoskeletal disorder (HR 1.84; 95% CI: 1.69, 2.01) were significant risk factors for the incident back disorder. No risk factors were seen for recurrent episodes.

CONCLUSIONS

Risk factors for incident and recurrent back disorders were different; thus, episode-specific initiatives are needed to reduce back disorders among fishers.

Informing Structural Assumptions for Three State Oncology Cost-Effectiveness Models through Model Efficiency and Fit

The characteristics and relative strengths and weaknesses of partitioned survival models (PSMs) and state transition models (STMs) for three state oncology cost-effectiveness models have previously been studied. Despite clear and longstanding economic modeling guidelines, more than one structure is rarely presented, and the choice of structure appears correlated more with audience or precedent than disease, decision problem, or available data. One reason may be a lack of guidance and tools available to readily compare measures of internal validity such as the model fit and efficiency of different structures, or sensitivity of results to those choices. To address this gap, methods are presented to evaluate the fit and efficiency of three structures, with an accompanying R software package, psm3mkv. The methods are illustrated by analyzing interim and final analysis datasets of the KEYNOTE-826 randomized controlled trial. At both interim and final analyses, the STM Clock Reset structure provided the best and most efficient fit. Structural uncertainties had been reduced from interim to final analysis. Beyond measures of internal validity, guidelines highlight the importance of reflecting all available data, avoiding model selection purely on the basis of goodness of fit and strongly considering external validity. The method and software allow modelers to more easily evaluate and report model fit and efficiency, examine implicit assumptions, and reveal sensitivities to structural choices.

The PSY-SIM Model: Using Real-World Data to Inform Health Care Policy for Individuals With Chronic Psychotic Disorders

BACKGROUND AND HYPOTHESIS

Few microsimulation models have been developed for chronic psychotic disorders, severe and disabling mental disorders associated with poor medical and psychiatric outcomes, and high costs of care. The objective of this work was to develop a microsimulation model for individuals with chronic psychotic disorders and to use the model to examine the impact of a smoking cessation initiative on patient outcomes.

STUDY DESIGN

Using health records and survey data from Ontario, Canada, the PSY-SIM model was developed to simulate health and cost outcomes of individuals with chronic psychotic disorders. The model was then used to examine the impact of the Smoking Treatment for Ontario Patients (STOP) program from Ontario on the development of chronic conditions, life expectancy, quality of life, and lifetime health care costs.

STUDY RESULTS

Individuals with chronic psychotic disorders had a lifetime risk of 63% for congestive heart failure and roughly 50% for respiratory disease, cancer and diabetes, and a life expectancy of 76 years. The model suggests the STOP program can reduce morbidity and lead to survival and quality of life gains with modest increases in health care costs. At a long-term quit rate of 4.4%, the incremental cost-effectiveness ratio of the STOP program was $41,936/QALY compared with status quo.

CONCLUSIONS

Smoking cessation initiatives among individuals with chronic psychotic disorders can be cost-effective. These findings will be relevant for decision-makers and clinicians looking to improving health outcomes among this patient population.

Effect of early administration of tetracosactide on mortality and host response in critically ill patients requiring rescue surgery: a sensitivity analysis of the STOPSHOCK phase 3 randomized controlled trial

BACKGROUND

Undifferentiated shock is recognized as a criticality state that is transitional in immune-mediated topology for casual risk of lethal microcirculatory dysfunction. This was a sensitivity analysis of a drug (tetracosactide; TCS10) targeting melanocortin receptors (MCRs) in a phase 3 randomized controlled trial to improve cardiovascular surgical rescue outcome by reversing mortality and hemostatic disorders.

METHODS

Sensitivity analysis was based on a randomized, two-arm, multicenter, double-blind, controlled trial. The Naïve Bayes classifier was performed by density-based sensitivity index for principal strata as proportional hazard model of 30-day surgical risk mortality according to European System for Cardiac Operative Risk Evaluation inputs-outputs in 100 consecutive cases (from August to September 2013 from Emilia Romagna region, Italy). Patients included an agent-based TCS10 group (10 mg, single intravenous bolus before surgery; n = 56) and control group (n = 44) and the association with cytokines, lactate, and bleeding-blood transfusion episodes with the prior-risk log-odds for mortality rate in time-to-event was analyzed.

RESULTS

Thirty-day mortality was significantly improved in the TCS10 group vs. control group (0 vs. 8 deaths, P < 0.0001). Baseline levels of interleukin (IL)-6, IL-10, and lactate were associated with bleeding episodes, independent of TCS10 treatment [odds ratio (OR) = 1.90, 95% confidence interval (CI) 1.39-2.79; OR = 1.53, 95%CI 1.17-2.12; and OR = 2.92, 95%CI 1.40-6.66, respectively], while baseline level of Fms-like tyrosine kinase 3 ligand (Flt3L) was associated with lower bleeding rates in TCS10-treated patients (OR = 0.31, 95%CI 0.11-0.90, P = 0.03). For every 8 TCS10-treated patients, 1 bleeding case was avoided. Blood transfusion episodes were significantly reduced in the TCS10 group compared to the control group (OR = 0.32, 95%CI 0.14-0.73, P = 0.01). For every 4 TCS10-treated patients, 1 transfusion case was avoided.

CONCLUSIONS

Sensitivity index underlines the quality target product profile of TCS10 in the runway of emergency casualty care. To introduce the technology readiness level in real-life critically ill patients, further large-scale studies are required.

TRIAL REGISTRATION

European Union Drug Regulating Authorities Clinical Trials Database (EudraCT Number: 2007-006445-41 ).

Transition of mild cognitive impairment to Alzheimer's disease: Medications as modifiable risk factors

BACKGROUND

Mild cognitive impairment (MCI) is a pre-clinical stage of Alzheimer's disease (AD). Understanding the transition probabilities across the disease continuum of AD, ranging from MCI to AD to Mortality is crucial for the economic modeling of AD and effective planning of future interventions and healthcare resource allocation decisions. This study uses the Multi-state Markov model to quantify the transition probabilities along the disease progression and specifically investigates medications as modifiable risk factors of AD associated with accelerated or decelerated transition times from MCI to AD, MCI to mortality, and AD to mortality.

METHODS

Individuals with MCI were identified from the National Alzheimer's Coordinating Center between September 2005 and May 2021. A three-state Markov model was postulated to model the disease progression among three states: MCI, AD, and mortality with adjustment for demographics, genetic characteristics, comorbidities and medications. Transition probabilities, the total length of stay in each state, and the hazard ratios of the use of medications for diabetes, hypertension, and hypercholesterolemia (the known modifiable risk factors of AD) were evaluated for these transitions.

RESULTS

3,324 individuals with MCI were identified. The probability of developing AD after one year since the initial diagnosis of MCI is 14.9%. After approximately 6 years from the initial diagnosis of MCI, the probability of transitioning to AD increases to nearly 41.7% before experiencing a subsequent decline. The expected total lengths of stay were 5.38 (95% CI: 0.002-6.03) years at MCI state and 7.61 (95%CI: 0.002-8.88) years at AD state. Patients with active use of lipid-lowering agents were associated with significantly lower hazards of transitioning from MCI to AD (HR: 0.83, 95%CI:0.71-0.96), MCI to mortality (HR: 0.51, 95%CI:0.34-0.77), and AD to mortality (HR: 0.81, 95%CI:0.66-0.99).

CONCLUSIONS

Results suggest that lipid-lowering agents may confer a protective effect, delaying the onset of AD. Additionally, lipid-lowering agents indicate a favorable association with a longer survival time.

Disease progression in chronic heart failure is linear: Insights from multistate modelling

AIMS

Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF.

METHODS AND RESULTS

Consecutive patients (n = 4918) were enrolled (median age 75 [67-81] years, 61.3% men, 44% with HF and reduced ejection fraction). We generated a model by observing events during the first 2 years of follow-up. The model yielded surprisingly accurate predictions of how a population with HF will behave during subsequent years. As examples, the predicted transition probability from hospitalization to death was 0.11; the observed probabilities were 0.13, 0.14, and 0.16 at 3, 4, and 5 years, respectively. Similarly, the predicted transition intensity for rehospitalization was 0.35; the observed probabilities were 0.38, 0.34, and 0.35 at 3, 4, and 5 years, respectively. A multivariable model including covariates thought to influence outcome did not improve accuracy. Predicted average life expectancy was approximately 10 years for the unadjusted model and 13 years for the multivariable model, consistent with the observed mortality of 41% at 5 years.

CONCLUSIONS

A multistate Markov chain model for patients with chronic HF suggests that the proportion of patients transitioning each year from a given state to another remains constant. This finding suggests that the course of HF at a population level is more linear than is commonly supposed and predictable based on current patient status.

Impact of healthy lifestyle on the incidence and progression trajectory of mental disorders: A prospective study in the UK Biobank

BACKGROUND

Healthier lifestyle decreased the risk of mental disorders (MDs) such as depression and anxiety. However, research on the effects of a comprehensive healthy lifestyle on their progression is lacking.

METHODS

385,704 individuals without baseline MDs from the UK Biobank cohort were included. A composite healthy lifestyle score was computed by assessing alcohol intake, smoking status, television viewing time, physical activity, sleep duration, fruit and vegetable intake, oily fish intake, red meat intake, and processed meat intake. Follow-up utilized hospital and death register records. Multistate model was used to examine the role of healthy lifestyle on the progression of specific MDs, while a piecewise Cox regression model was utilized to assess the influence of healthy lifestyle across various phases of disease progression.

RESULTS

Higher lifestyle score reduced risks of transitions from baseline to anxiety and depression, as well as from anxiety and depression to comorbidity, with corresponding hazard ratios (HR) and 95 % confidence intervals (CI) of 0.94 (0.93, 0.95), 0.90 (0.89, 0.91), 0.94 (0.91, 0.98), and 0.95 (0.92, 0.98), respectively. Healthier lifestyle decreased the risk of transitioning from anxiety to comorbidity within 2 years post-diagnosis, with HR 0.93 (0.88, 0.98). Higher lifestyle scores at 2-4 years and 4-6 years post-depression onset were associated with reduced risk of comorbidity, with HR 0.93 (0.87, 0.99) and 0.92 (0.86, 0.99), respectively.

LIMITATION

The generalizability to other ethnic groups is limited.

CONCLUSION

This study observed a protective role of holistic healthy lifestyle in the trajectory of MDs and contributed to identifying critical progression windows.

Trajectories of Frailty and Clinical Outcomes in Older Adults With Atrial Fibrillation: Insights From the Shizuoka Kokuho Database

BACKGROUND

The increasing prevalence of frailty has gained considerable attention due to its profound influence on clinical outcomes. However, our understanding of the progression of frailty and long-term clinical outcomes in older individuals with atrial fibrillation remains scarce.

METHODS

Using data from 2012 to 2018 from a comprehensive claims database incorporating primary and hospital care records in Shizuoka, Japan, we selected patients aged ≥65 years with atrial fibrillation who initiated oral anticoagulant therapy. The trajectory of frailty was plotted using Sankey plots, illustrating the annual changes in their frailty according to the electronic frailty index during a 3-year follow-up after oral anticoagulant initiation, along with the incidence of clinical adverse outcomes. For deceased patients, we assessed their frailty status in the year preceding their death.

RESULTS

Of 6247 eligible patients (45.1% women; mean age, 79.3±8.0 years) at oral anticoagulant initiation, 7.7% were categorized as fit (electronic frailty index, 0-0.12), 30.1% as mildly frail (>0.12-0.24), 35.4% as moderately frail (>0.24-0.36), and 25.9% as severely frail (>0.36). Over the 3-year follow-up, 10.4% of initially fit patients transitioned to moderately frail or severely frail. Conversely, 12.5% of severely frail patients improved to fit or mildly frail. Death, stroke, and major bleeding occurred in 23.4%, 4.1%, and 2.2% of patients, respectively. Among the mortality cases, 74.8% (N=1183) and 3.5% (N=55) had experienced moderately or severely frail and either a stroke or major bleeding in the year preceding their death, respectively.

CONCLUSIONS

In a contemporary era of atrial fibrillation management, a minor fraction of older patients on oral anticoagulants died following a stroke or major bleeding. However, their frailty demonstrated a dynamic trajectory, and a substantial proportion of death was observed after transitioning to a moderately or severely frail state.

Incidence and predisposing factors of extra-articular manifestations in contemporary rheumatoid arthritis

OBJECTIVE

Rheumatoid arthritis [RA) is a chronic inflammatory disease, with potential for extra-articular manifestations (ExRA). The incidence and predisposing factors for ExRA and the mortality were evaluated in an early RA inception cohort.

METHODS

Patients (n = 1468; 69 % females, mean age (SD) 57.3(16.3) years) were consecutively included at the date of diagnosis, between 1 January 1996 and 31 December 2016, and assessed prospectively. In December 2016 development of ExRA was evaluated by a patient questionnaire and a review of medical records. Cumulative incidence and incidence rates were compared between 5-year periods and between patients included before and after 1 January 2001. Cox proportional hazard regression models were used to identify predictors for ExRA, and models with ExRA as time-dependent variables to estimate the mortality.

RESULTS

After a mean (SD) follow-up of 9.3(4.9) years, 238 cases (23.3 %) had ExRA and 151 (14.7 %) had ExRA without rheumatoid nodules. Most ExRA developed within 5 years from diagnosis. Rheumatoid nodules (10.5 %) and keratoconjunctivitis sicca (7.1 %) were the most frequent manifestations, followed by pulmonary fibrosis (6.1 %). The ExRA incidence among more recently diagnosed patients was similar as to the incidence among patients diagnosed before 2001. Seropositivity, smoking and early biological treatment were associated with development of ExRA. After 15 years 20 % had experienced ExRA. ExRA was associated with increased mortality, HR 3.029 (95 % CI 2.177-4.213).

CONCLUSIONS

Early development of ExRA is frequent, particularly rheumatoid nodules. Predisposing factors were age, RF positivity, smoking and early biological treatment. The patients with ExRA had a 3-fold increase in mortality.

Dynamic Risk Estimation of Adverse Events in Ambulatory LVAD Patients: A MOMENTUM 3 Analysis

BACKGROUND

Hemocompatibility-related adverse events affect patients after left ventricular assist device (LVAD) implantation but are hard to predict.

OBJECTIVES

Dynamic risk modeling with a multistate model can predict risk of gastrointestinal bleeding (GIB), stroke, or death in ambulatory patients.

METHODS

This was a secondary analysis of the MOMENTUM 3 (Multicenter Study of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy with HeartMate 3) trial. HeartMate 3 LVAD recipients who survived to hospital discharge and were followed for up to 2 years. A total of 145 variables were included in the multistate model with multivariate logistic regression. Model performance was assessed with the area under the curve in a holdout validation cohort. A risk stratification tool was created by dividing patients into categories of predicted risk using the final model variables and associated OR.

RESULTS

Among 2,056 LVAD patients, the median age was 59.4 years (20.4% women, 28.6% Black). At 2 years, the incidence of GIB, stroke, and death was 25.6%, 6.0%, and 12.3%, respectively. The multistate model included 39 total variables to predict risk of GIB (16 variables), stroke (10 variables), and death (19 variables). When ambulatory patients were classified according to their risk category, the 30-day observed event rate in the highest risk group for GIB, stroke, or death was 26.9%, 1.8%, and 4.8%, respectively. The multistate model predicted GIB, stroke, and death at any 30-day period with an area under the curve of 0.70, 0.69, and 0.86, respectively.

CONCLUSIONS

The multistate model informs 30-day risk in ambulatory LVAD recipients and allows recalculation of risk as new patient-specific data become available. The model allows for accurate risk stratification that predicts impending adverse events and may guide clinical decision making. (MOMENTUM 3 IDE Clinical Study Protocol; NCT02224755).

Modeling the HIV Cascade of Care Using Routinely Collected Clinical Data to Guide Programmatic Interventions and Policy Decisions

BACKGROUND

The HIV care cascade is a framework to examine effectiveness of HIV programs and progress toward global targets to end the epidemic but has been conceptualized as a unidirectional process that ignores cyclical care patterns. We present a dynamic cascade that accounts for patient "churn" and apply novel analytic techniques to readily available clinical data to robustly estimate program outcomes and efficiently assess progress toward global targets.

METHODS

Data were assessed for 35,649 people living with HIV and receiving care at 78 clinics in East Africa between 2014 and 2020. Patients were aged ≥15 years and had ≥1 viral load measurements. We used multi-state models to estimate the probability of being in 1 of 5 states of a dynamic HIV cascade: (1) in HIV care but not on antiretroviral therapy (ART), (2) on ART, (3) virally suppressed, (4) in a gap-in-care, and (5) deceased and compared these among subgroups. To assess progress toward global targets, we summed those probabilities across patients and generated population-level proportions of patients on ART and virally suppressed in mid-2020.

RESULTS

One year after enrollment, 2.8% of patients had not initiated ART, 86.7% were receiving ART, 57.4% were virally suppressed, 10.2% were disengaged from care, and 0.3% had died. At 5 years, the proportion on ART remained steady but viral suppression increased to 77.2%. Of those aged 15-25, >20% had disengaged from care and <60% were virally suppressed. In mid-2020, 90.1% of the cohort was on ART, 90.7% of whom had suppressed virus.

CONCLUSIONS

Novel analytic approaches can characterize patient movement through a dynamic HIV cascade and, importantly, by capitalizing on readily available data from clinical cohorts, offer an efficient approach to estimate population-level proportions of patients on ART and virally suppressed. Significant progress toward global targets was observed in our cohort but challenges remain among younger patients.

Recommendations on Methods for Assessing Multimorbidity Changes Over Time: Aligning the Method to the Purpose

BACKGROUND

The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity.

METHODS

We identify and discuss methods aligned to specific research objectives with the goals of (i) establishing a common language for assessing longitudinal changes in multimorbidity, (ii) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and (iii) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes.

RESULTS

We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with 4 general typologies for research objectives in the longitudinal study of multimorbidity: (i) examine individual change in multimorbidity, (ii) identify subgroups that follow similar trajectories of multimorbidity progression, (iii) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and (iv) examine the coprogression of multimorbidity with key health domains.

CONCLUSIONS

This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations.

Colorectal cancer recurrence and its impact on survival after curative surgery: An analysis based on multistate models

AIM

To investigate the usefulness of multistate models (MSM) for determining colorectal cancer (CRC) recurrence rate, to analyse the effect of different factors on tumour recurrence and death, and to assess the impact of recurrence for CRC prognosis.

METHODS

Observational follow-up study of incident CRC cases disease-free after curative resection in 2006-2013 (n = 994). Recurrence and mortality were analyzed with MSM, as well as covariate effects on transition probabilities.

RESULTS

Cumulative incidence of recurrence at 60 months was 13.7%. Five years after surgery, 70.3% of patients were alive and recurrence-free, and 8.4% were alive after recurrence. Recurrence has a negative impact on prognosis, with 5-year CRC-related mortality increasing from 3.8% for those who are recurrence-free 1-year after surgery to 33.6% for those with a recurrence. Advanced stage increases recurrence risk (HR = 1.53) and CRC-related mortality after recurrence (HR = 2.35). CRC-related death was associated with age in recurrence-free patients, and with comorbidity after recurrence. As expected, age≥75 years was a risk factor for non-CRC-related death with (HR = 7.76) or without recurrence (HR = 4.26), while its effect on recurrence risk was not demonstrated.

CONCLUSIONS

MSM allows detailed analysis of recurrence and mortality in CRC. Recurrence has a negative impact on prognosis. Advanced stage was a determining factor for recurrence and CRC-death after recurrence.

Multi-state Markov model for time to treatment changes for HIV/AIDS patients: a retrospective cohort national datasets, Ethiopia

BACKGROUND

Virological failure, drug resistance, toxicities, and other issues make it difficult for ART to maintain long-term sustainability. These issues would force a modification in the patient's treatment plan. The aim of this research was to determine whether first-line antiretroviral therapy is durable and to identify the factors that lead to patients on HAART changing their first highly active antiretroviral therapy regimen.

METHODS

A retrospective cohort study was conducted from October, 2019-March, 2020 across all regional states including Addis Ababa and Dire Dawa administrative cities. The target population is from all health facilities that have been providing ART service for at least the past 6 months as of October 2019. Multi-stage clustered sampling method was used to select study facilities and participants. Simple random selected ART medical records of patients ever enrolled in ART treatment services. We adopted a multi-state survival modelling (msm) approach assuming each treatment regimen as state. We estimate the transition probability of patients to move from one regimen to another for time to treatment change/switch. We estimated the transition probability, prediction probabilities and length of stay and factor associated with treatment modification of patients to move from one regimen to another.

RESULTS

Any of the six therapy combinations (14.4%) altered their treatment at least once during the follow-up period for a variety of reasons. Of the patients, 4,834 (13.26%) changed their treatments just once, while 371 (1.1%) changed it more than once. For 38.6% of the time, a treatment change was undertaken due to toxicity, another infection or comorbidity, or another factor, followed by New drugs were then made accessible and other factors 18.3% of the time, a drug was out of supply; 2.6% of those instances involved pregnancy; and 43.1% involved something else. Highly active anti-retroviral therapy (HAART) combinations TDF + 3TC + NVP, d4T + 3TC + NVP, and TDF + 3TC + EFV were high to treatment alterations in all reasons of treatment modifications, with 29.74%, 26.52%, and 19.52% treatment changes, respectively. Early treatment modification or regime change is one of the treatment combinations that include the d4T medication that creates major concern. The likelihood of staying and moving at the the start of s = 0 and 30-month transitions increased, but the likelihood of staying were declined. For this cohort dataset, the presence of opportunistic disease, low body weight, baseline CD4 count, and baseline TB positive were risk factors for therapy adjustment.

CONCLUSION

Given that the current study took into account a national dataset, it provides a solid basis for ART drug status and management. The patient had a higher likelihood of adjusting their treatment at some point during the follow-up period due to drug toxicity, comorbidity, drug not being available, and other factors, according to the prediction probability once more. Baseline TB positivity, low CD4 count, opportunistic disease, and low body weight were risk factors for therapy adjustment in this cohort dataset.

Multistate transition modelling of e-cigarette use and cigarette smoking among youth in the UK

INTRODUCTION

E-cigarette use remains a controversial topic, with questions over how people transition between e-cigarette use and cigarette smoking. This paper examined transitions into and out of nicotine product use in a representative sample of UK youth.

METHODS

We used Markov multistate transition probability models on data from 10 229 participants (10-25 years old) in the UK Household Longitudinal Study (2015-2021). We used four product use states ('never', 'non-current use', 'e-cigarette only' and 'smoking and dual use') and estimated likelihood of transitions according to sociodemographic characteristics.

RESULTS

Among participants who had never used nicotine products, most were still non-users a year later (92.9% probability; 95% CI 92.6%, 93.2%); a small proportion transitioned to using e-cigarettes only (4.0%; 95% CI 3.7%, 4.2%) and cigarettes (2.2%; 95% CI 2.0%, 2.4%). Those aged 14-17 years were the most likely to start using a nicotine product. E-cigarette use was less persistent overtime than cigarette smoking, with a 59.1% probability (95% CI 56.9%, 61.0%) of e-cigarette users still using after 1 year compared with 73.8% (95% CI 72.1%, 75.4%) for cigarette smoking. However, there was a 14% probability (95% CI 12.8%, 16.2%) that e-cigarette users went onto smoke cigarettes after 1 year, rising to 25% (95% CI 23%, 27%) after 3 years.

CONCLUSION

This study found that although overall nicotine product use was relatively rare, participants were more likely to experiment with e-cigarette use than cigarette smoking. This was mostly not persistent over time; however, approximately one in seven transitioned to cigarette smoking. Regulators should aim to deter all nicotine product use among children.

Lessons learned: avoiding bias via multi-state analysis of patients' trajectories in real-time

Objectives

Many studies have attempted to determine the disease severity and patterns of COVID-19. However, at the beginning of the pandemic, the complex patients' trajectories were only descriptively reported, and many analyses were worryingly prone to time-dependent-, selection-, and competing risk biases. Multi-state models avoid these biases by jointly analysing multiple clinical outcomes while taking into account their time dependency, including current cases, and modelling competing events. This paper uses a publicly available data set from the first wave in Israel as an example to demonstrate the benefits of analysing hospital data via multi-state methodology.

Methods

We compared the outcome of the data analysis using multi-state models with the outcome obtained when various forms of bias are ignored. Furthermore, we used Cox regression to model the transitions among the states in a multi-state model. This allowed for the comparison of the covariates' influence on transition rates between the two states. Lastly, we calculated expected lengths of stay and state probabilities based on the multi-state model and visualised it using stacked probability plots.

Results

Compared to standard methods, multi-state models avoid many biases in the analysis of real-time disease developments. The utility of multi-state models is further highlighted through the use of stacked probability plots, which visualise the results. In addition, by stratification of disease patterns by subgroups and visualisation of the distribution of possible outcomes, these models bring the data into an interpretable form.

Conclusion

To accurately guide the provision of medical resources, this paper recommends the real-time collection of hospital data and its analysis using multi-state models, as this method eliminates many potential biases. By applying multi-state models to real-time data, the gained knowledge allows rapid detection of altered disease courses when new variants arise, which is essential when informing medical and political decision-makers as well as the general population.

Childhood Transitions Between Weight Status Categories: Evidence from the UK Millennium Cohort Study

BACKGROUND

Assessing the cost-effectiveness of interventions targeting childhood excess weight requires estimates of the hazards of transitioning between weight status categories. Current estimates are based on studies characterized by insufficient sample sizes, a lack of national representativeness, and untested assumptions.

OBJECTIVES

We sought to (1) estimate transition probabilities and hazard ratios for transitioning between childhood weight status categories, (2) test the validity of the underlying assumption in the literature that transitions between childhood bodyweight categories are time-homogeneous, (3) account for complex sampling procedures when deriving nationally representative transition estimates, and (4) explore the impact of child, maternal, and sociodemographic characteristics.

METHODS

We applied a multistate transition modeling approach accounting for complex survey design to UK Millennium Cohort Study (MCS) data to predict transition probabilities and hazard ratios for weight status movements for children aged 3-17. Surveys were conducted at ages 3 (wave 2 in 2004), 5 (wave 3 in 2006), 7 (wave 4 in 2008), 11 (wave 5 in 2012), 14 (wave 6 in 2015), and 17 (wave 7 in 2018) years. We derived datasets that included repeated body mass index measurements across waves after excluding multiple births and children with missing or implausible bodyweight records. To account for the stratified cluster sample design of the MCS, we incorporated survey weights and jackknife replicates of survey weights. Using a validation dataset from the MCS, we tested the validity of our models. Finally, we estimated the relationships between state transitions and child, maternal, and sociodemographic factors.

RESULTS

The datasets for our primary analysis consisted of 10,399 children for waves 2-3, 10,729 for waves 3-4, 9685 for waves 4-5, 8593 for waves 5-6, and 7085 for waves 6-7. All datasets consisted of roughly equal splits of boys and girls. Under the assumption of time-heterogeneous transition rates (our base-case model), younger children (ages 3-5 and 5-7 years) had significantly higher annual transition probabilities of moving from healthy weight to overweight (0.033, 95% confidence interval [CI] 0.026-0.041, and 0.027, 95% CI 0.021-0.033, respectively) compared to older children (0.015, 95% CI 0.012-0.018, at ages 7-11; 0.018, 95% CI 0.013-0.023, at ages 11-14; and 0.018, 95% CI 0.013-0.025 at ages 14-17 years). However, the resolution of unhealthy weight was more strongly age-dependent than transitions from healthy weight to non-healthy weight states. Transition hazards differed by child, maternal, and sociodemographic factors.

CONCLUSIONS

Our models generated estimates of bodyweight status transitions in a representative UK childhood population. Compared to our scenario models (i.e., time-homogeneous transition rates), our base-case model fits the observed data best, indicating a non-time-homogeneous pattern in transitions between bodyweight categories during childhood. Transition hazards varied significantly by age and across subpopulations, suggesting that conducting subgroup-specific cost-effectiveness analyses of childhood weight management interventions will optimize decision-making.

Prognostic Implications of Early Prediction in Posttraumatic Epilepsy

Posttraumatic epilepsy (PTE) is a complication of traumatic brain injury that can increase morbidity, but predicting which patients may develop PTE remains a challenge. Much work has been done to identify a variety of risk factors and biomarkers, or a combination thereof, for patients at highest risk of PTE. However, several issues have hampered progress toward fully adapted PTE models. Such issues include the need for models that are well-validated, cost-effective, and account for competing outcomes like death. Additionally, while an accurate PTE prediction model can provide quantitative prognostic information, how such information is communicated to inform shared decision-making and treatment strategies requires consideration of an individual patient's clinical trajectory and unique values, especially given the current absence of direct anti-epileptogenic treatments. Future work exploring approaches integrating individualized communication of prediction model results are needed.

Adolescent Psychiatric Inpatient Episodes and Subsequent Labor Market Trajectories

PURPOSE

Adolescents with psychiatric disorders are known to be more often not in education, employment, or training (NEET) in young adulthood than their peers. However, since most of the available evidence is based on cross-sectional measurement of NEET, there is less evidence on the processes underlying these differences in labor market disadvantage. We assessed these processes by examining transitions between NEET and non-NEET states across young adulthood and the differences in these transitions by adolescent psychiatric inpatient treatment.

METHODS

We used longitudinal register data on all individuals born in Finland in 1980-1984 (N = 315,508) to identify psychiatric inpatient episodes between ages 10 and 19 and NEET between ages 20 and 34. We modeled the transitions between NEET and non-NEET states and the impact of psychiatric disorders on these transitions with multistate models.

RESULTS

Individuals who had psychiatric inpatient episodes in adolescence started their labor market careers as NEET twice as often as their peers. They were also more likely to transition into NEET states and less likely to transition out of NEET. In total, individuals with a history of psychiatric episodes spent from 1.8 to 6.9 more years as NEET between the ages 20 and 34 than their peers, depending on sex, baseline NEET, and diagnostic group.

DISCUSSION

Adolescents with severe psychiatric disorders are highly vulnerable in terms of labor market outcomes throughout their early adulthood. Supportive measures are required both at the start of employment trajectories and during later career stages.

Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its competitors had been demonstrated in earlier work and again in this paper. The main goal of this paper is to show a typical nature-inspired metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics.

Current and Future Cost Burden of Ischemic Stroke in Australia: Dynamic Model

BACKGROUND

Stroke remains one of the leading causes of morbidity and mortality in Australia. The objective of this study was to estimate the current and future cost burden of ischemic stroke (IS) in Australia.

METHOD

First, the annual chronic management cost per person following IS were derived for all people aged ≥30 years discharged from a public or private hospital in Victoria, Australia between July 2012 and June 2017 (with follow-up data until June 2018 [n = 34,471]). Then extrapolated the data from from Victoria to the whole Australian population aged between 30 years and 99 years to project the total healthcare costs following IS (combination of acute event and chronic management cost) over a 20-year period (2019-2038) using a dynamic multistate life table model. Data for the dynamic model were sourced from the Victorian Admitted Episodes Dataset (VAED) and supplemented with other published data.

RESULT

The estimated annual total chronic management cost following IS was 13,525 Australian dollars (AUD) per person (95% CI: AUD 13,380, AUD 13,670) for cohorts in the VAED between July 2012 and June 2017. The annual chronic management cost was estimated to decline following IS. The highest cost was incurred in the first year of follow-up post-IS (AUD 14,309 per person) and declined to AUD 9,776 in the sixth year of follow-up post-IS. The total healthcare cost for people aged 30-99 years was projected to be AUD 47.7 billion (95% UI: AUD 44.6 billion, AUD 51.0 billion) over the 20-year period (2019-2038) Australia-wide, of which 91.3% (AUD 43.6 billion) was attributed to chronic management costs and the remaining 8.7% (AUD 4.2 billion) were due to acute IS events.

CONCLUSION

IS has and will continue to have a considerable financial impact in the next 2 decades on the Australian healthcare system. Our estimated and projected cost burden following IS provides important information for decision making in relation to IS.

Relationship between an inconsistent degree of financial literacy and inconsistent decision-making in intertemporal choices

Intertemporal choice refers to the decision-making process involving trade-offs between rewards available at different points in time (such as choosing between smaller immediate rewards versus larger rewards later on). Empirical evidence often deviates from the exponential preferences predicted by the normative model. A hyperbolic discount function better mirrors individual behavior, explaining temporal inconsistency - whereby preferences vary over time by applying a higher discount in the present. Hyperbolic preferences are associated with addictive behaviors, such as smoking and alcohol consumption, as well as depression or attention deficit hyperactivity disorder. Established measures in the literature quantify the extent of deviation from exponential trend exhibited by hyperbolic preferences. In addition to behavioral and cognitive factors, it is essential to incorporate financial literacy into the examination of individual decision-making behaviors. The present study analyzes the relationship between the degree of decision-making inconsistency and the degree of financial literacy inconsistency across three dimensions: knowledge, behavior, and attitudes. It aims to illustrate while financial literacy is important, it is not sufficient to ensure rational choices. Rather, it reveals a strong correlation among its dimensions. The results of this research could be included when creating investor profiles required by MiFID, considering insights from behavioral finance studies in these profiles. What is more, understanding psychological biases that can influence financial decision-making empowers investors to make more informed decisions and avoid common pitfalls.

Prognostic value of HER2DX in early-stage HER2-positive breast cancer: a comprehensive analysis of 757 patients in the Sweden Cancerome Analysis Network-Breast dataset (SCAN-B)

BACKGROUND

The HER2DX risk-score has undergone rigorous validation in prior investigations involving patients with early-stage human epidermal growth factor receptor 2 (HER2)-positive (HER2+) breast cancer. In this study, we present the outcomes of the HER2DX risk-score within the most recent release of the Sweden Cancerome Analysis Network-Breast (SCAN-B) HER2+ cohort. This updated examination benefits from a larger patient sample, an extended follow-up duration, and detailed treatment information.

MATERIALS AND METHODS

Clinical and RNAseq data from the SCAN-B dataset were retrieved from Gene Expression Omnibus (GSE81538). Among the 6600 patients, 819 had HER2+ breast cancer, with 757 individuals with research-based HER2DX risk-scores and corresponding survival outcomes. The HER2DX risk-score was evaluated (i) as a continuous variable and (ii) using predefined cut-offs. The primary endpoint for this study was overall survival (OS). The Kaplan-Meier method and Cox models were used to estimate OS and a multistate model with four states was fitted to better characterize patients' follow-up.

RESULTS

The median follow-up time was 7.5 years (n = 757). The most common systemic therapy was chemotherapy with trastuzumab (82.0%) and most tumors were classified as T1-T2 (97.1%). The HER2DX risk-score as a continuous variable was significantly associated with OS after adjustment for clinical variables and treatment regimen [hazard ratios (HR) per 10-unit increment = 1.31, 95% confidence interval (CI) 1.13-1.51, P < 0.001] as well as within predefined risk groups (high versus low; HR = 2.57, 95% CI 1.36-4.85, P < 0.001). Patients classified as HER2DX high-risk also had higher risk of (i) breast cancer recurrence and (ii) death without previous recurrence. Within the subgroup of HER2+ T1N0 tumors (n = 297), those classified as high-risk demonstrated inferior OS compared to low-risk tumors (7-year OS 77.8% versus 96.8%, P < 0.001). The HER2DX mRNA ERBB2 score was associated with clinical HER2 status (area under the receiver operating characteristic curve = 0.91).

CONCLUSIONS

In patients with early-stage HER2+ breast cancer, HER2DX risk-score provides prognostic information beyond clinicopathological variables, including treatment regimen with or without trastuzumab.

A multi-state analysis of disease trajectories and mental health transitions in patients with type 2 diabetes: A population-based retrospective cohort study utilizing health administrative data

AIMS

To investigate the risk of major depression and dementia in patients with type 2 diabetes, including dementia resulting from depression, and their impact on diabetes-related complications and mortality.

METHODS

We conducted a population-based retrospective cohort study including 11,441 incident cases of diabetes in 2015-2017, with follow-up until 2022. A multi-state survival analysis was performed on a seven-state model with 15 transitions to capture disease progression and onset of mental disorders.

RESULTS

Eight-year probabilities of depression, dementia, diabetes-related complications, and death were 9.7% (95% CI 8.7-10.7), 0.9% (95% CI 0.5-1.3), 10.4% (95% CI 9.5-11.4), and 14.8% (95% CI 13.9-15.7), respectively. Depression increased the risk of dementia up to 3.7% (95% CI 2.0-5.4), and up to 10.3% (95% CI 0.3-20.4) if coupled with diabetes complications. Eight-year mortality was 37.5% (95% CI 33.1-42.0) after depression, 74.1% (95% CI 63.7-84.5) after depression plus complications, 76.4% (95% CI 68.8-83.9) after dementia, and 98.6% (95% CI 96.1-100.0) after dementia plus complications.

CONCLUSIONS

The interconnections observed across depression, dementia, complications, and mortality underscore the necessity for comprehensive and integrated approaches in managing diabetes. Early screening for depression, followed by timely and targeted interventions, may mitigate the risk of dementia and improve diabetes prognosis.

Competing risk multistate censored data modeling by propensity score matching method

The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results.

Role of residential greenspace in the trajectory of major neurological disorders: A longitudinal study in UK Biobank

BACKGROUND

Stroke and dementia are major neurological disorders that contribute significantly to disease burden and are interlinked in terms of risk. Nevertheless, there is currently no study investigating the influence of residential greenspace on the trajectory of these neurological disorders.

METHODS

This longitudinal study utilized data from the UK Biobank. Exposure to residential greenspace was measured by the percentage of total greenspace coverage within a 300-meter buffer zone surrounding the participants' residences. A multistate model was employed to illustrate the trajectory of major neurological disorders, and a piecewise Cox regression model was applied to explore the impact of residential greenspace on different time courses of disease transitions.

RESULTS

With 422,649 participants and a median follow-up period of 12.5 years, 8568 (2.0 %), 5648 (1.3 %), and 621 (0.1 %) individuals developed incident stroke, dementia, and comorbidity of both conditions, respectively. An increase in residential greenspace by one interquartile range was associated with reduced risks of transitions from baseline to stroke, dementia, and death, as well as from stroke to comorbidity. The corresponding hazard ratios (HRs) were 0.967 (95 % CI: 0.936, 0.998), 0.928 (0.892, 0.965), 0.925 (0.907, 0.942), and 0.799 (0.685, 0.933), respectively. Furthermore, the protective effect of residential greenspace on the transition from stroke or dementia to comorbidity was particularly pronounced within the first year and over 5 years after stroke and during the 2 to 3 years after dementia onset, with HRs of 0.692 (0.509, 0.941), 0.705 (0.542, 0.918), and 0.567 (0.339, 0.949), respectively.

CONCLUSION

This study observed a protective role of residential greenspace in the trajectory of major neurological disorders and contributed to identifying critical progression windows. These findings underscore the significance of environment-health interactions in the prevention of neurological disorders.

Hematological and biochemical markers influencing breast cancer risk and mortality: Prospective cohort study in the UK Biobank by multi-state models

BACKGROUND

Breast cancer is the most common cancer and the leading cause of cancer-related death among women. However, evidence concerning hematological and biochemical markers influencing the natural history of breast cancer from in situ breast cancer to mortality is limited.

METHODS

In the UK Biobank cohort, 260,079 women were enrolled during 2006-2010 and were followed up until 2019 to test the 59 hematological and biochemical markers associated with breast cancer risk and mortality. The strengths of these associations were evaluated using the multivariable Cox regression models. To understand the natural history of breast cancer, multi-state survival models were further applied to examine the effects of biomarkers on transitions between different states of breast cancer.

RESULTS

Eleven biomarkers were found to be significantly associated with the risk of invasive breast cancer, including mainly inflammatory-related biomarkers and endogenous hormones, while serum testosterone was also associated with the risk of in-situ breast cancer. Among them, C-reactive protein (CRP) was more likely to be associated with invasive breast cancer and its transition to death from breast cancer (HR for the highest quartile = 1.46, 95 % CI = 1.07-1.97), while testosterone and insulin-like growth factor-1 (IGF-1) were more likely to impact the early state of breast cancer development (Testosterone: HR for the highest quartile = 1.31, 95 % CI = 1.12-1.53; IGF-1: HR for the highest quartile = 1.17, 95 % CI = 1.00-1.38).

CONCLUSION

Serum CRP, testosterone, and IGF-1 have different impacts on the transitions of different breast cancer states, confirming the role of chronic inflammation and endogenous hormones in breast cancer progression. This study further highlights the need of closer surveillance for these biomarkers during the breast cancer development course.

Clinical Significance of Venous Thromboembolism in Patients with Advanced Cholangiocarcinoma

Background/Aims

Patients with active cancer frequently develop venous thromboembolism (VTE). However, there is little data about VTE in patients with advanced cholangiocarcinoma (CCA). Therefore, we investigated the clinical significance of VTE in patients with advanced CCA.

Methods

We analyzed the data of a total of 332 unresectable CCA patients diagnosed between 2010 and 2020 in this retrospective study. We investigated the incidence and risk factors for VTE, and its effect on survival in patients with advanced CCA.

Results

During a median follow-up of 11.6 months, 118 patients (35.5%) developed VTE. The cumulative incidence of VTE was 22.4% (95% confidence interval [CI], 0.18 to 0.27) at 3 months and 32.8% (95% CI, 0.27 to 0.38) at 12 months. Major vessel invasion was an independent risk factor for VTE (hazard ratio, 2.88; 95% CI, 1.92 to 4.31; p<0.001). Patients who developed VTE during follow-up had shorter overall survival than patients who did not (11.50 months vs 15.83 months, p=0.005). In multivariable analysis, VTE (hazard ratio, 1.58; 95% CI, 1.23 to 2.02; p<0.001) was associated with poor overall survival.

Conclusions

Major vessel invasion is related to the occurrence of VTE in advanced CCA. The development of VTE significantly decreases the overall survival and is an important unfavorable prognostic factor for survival.

Ascertainment and Statistical Issues for Randomized Trials of Cardiovascular Interventions for Cognitive Impairment and Dementia

There has been considerable progress in the prevention and treatment of cardiovascular disease, reducing the population burden of cardiovascular morbidity and mortality. Recently, some randomized trials, including the SPRINT (Systolic Blood Pressure Intervention Trial), have suggested that improvements in cardiovascular risk factors may also slow cognitive decline and reduce the eventual development of dementia. Unfortunately, the randomized trial template that has been used repeatedly to successfully demonstrate reductions in major adverse cardiac events faces several design and analytic obstacles when applied in the context of cognitive decline and dementia. Here, we review these obstacles, motivated by SPRINT and the context of selecting an appropriate cognitive end point for future preventive randomized trials. A few options are available, spanning neuropsychological test scores or composites reflecting specific domains of cognitive function, adjudicated cognitive impairment, or potentially physiological biomarkers. This choice entails considerations around statistical power, modes of ascertainment, the clinical relevance of treatment effects, a myriad of statistical issues (interval censoring, missing data, the competing risk of death, practice effects, etc), as well as ethical considerations around equipoise. Collectively, these considerations indicate that trials aiming to mitigate the cardiovascular contribution to cognitive decline and dementia will generally need to be large, inclusive of a wide age range of older adults, and with multiple years of follow-up.

Modifiable kidney disease risk factors among nondiabetic adults with obesity from the Multi-Ethnic Study of Atherosclerosis

OBJECTIVE

It is unknown whether weight change or physical fitness is associated with chronic kidney disease (CKD) risk among nondiabetic adults with obesity.

METHODS

This was a prospective, longitudinal cohort study of adults with obesity without baseline CKD or diabetes enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA). Linear mixed-effects and multistate models were adjusted for demographics, time-varying covariates including blood pressure, and comorbidities these were used to examine associations of weight change and slow walking pace (<2 miles/h) with (i) rate of annual estimated glomerular filtration rate (eGFR) decline and (ii) incident CKD, defined as eGFRCr-Cys  < 60 mL/min/1.73 m2 , and tested for interaction by baseline hypertension status.

RESULTS

Among 1208 included MESA participants (median BMI 33.0 kg/m2 [interquartile range 31.2-35.9]), 15% developed CKD. Slow walking pace was associated with eGFR decline (-0.27 mL/min/1.73 m2 /year; 95% CI: -0.42 to -0.12) and CKD risk (adjusted hazard ratio 1.48; 95% CI: 1.08 to 2.01). Weight gain was associated with CKD risk (adjusted hazard ratio 1.34; 95% CI: 1.02 to 1.78 per 5 kg weight gain from baseline). There was no significant interaction by baseline hypertension status.

CONCLUSIONS

Slow walking pace and weight gain were associated with CKD risk among adults with obesity who did not have diabetes at baseline.

Statistical methods applied for the assessment of the HIV cascade and continuum of care: a systematic scoping review

OBJECTIVES

This scoping review aims to identify and synthesise existing statistical methods used to assess the progress of HIV treatment programmes in terms of the HIV cascade and continuum of care among people living with HIV (PLHIV).

DESIGN

Systematic scoping review.

DATA SOURCES

Published articles were retrieved from PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL) Complete and Excerpta Medica dataBASE (EMBASE) databases between April and July 2022. We also strategically search using the Google Scholar search engine and reference lists of published articles.

ELIGIBILITY CRITERIA

This scoping review included original English articles that estimated and described the HIV cascade and continuum of care progress in PLHIV. The review considered quantitative articles that evaluated either HIV care cascade progress in terms of the Joint United Nations Programme on HIV and AIDS targets or the dynamics of engagement in HIV care.

DATA EXTRACTION AND SYNTHESIS

The first author and the librarian developed database search queries and screened the retrieved titles and abstracts. Two independent reviewers and the first author extracted data using a standardised data extraction tool. The data analysis was descriptive and the findings are presented in tables and visuals.

RESULTS

This review included 300 articles. Cross-sectional study design methods were the most commonly used to assess the HIV care cascade (n=279, 93%). In cross-sectional and longitudinal studies, the majority used proportions to describe individuals at each cascade stage (276/279 (99%) and 20/21 (95%), respectively). In longitudinal studies, the time spent in cascade stages, transition probabilities and cumulative incidence functions was estimated. The logistic regression model was common in both cross-sectional (101/279, 36%) and longitudinal studies (7/21, 33%). Of the 21 articles that used a longitudinal design, six articles used multistate models, which included non-parametric, parametric, continuous-time, time-homogeneous and discrete-time multistate Markov models.

CONCLUSIONS

Most literature on the HIV cascade and continuum of care arises from cross-sectional studies. The use of longitudinal study design methods in the HIV cascade is growing because such methods can provide additional information about transition dynamics along the cascade. Therefore, a methodological guide for applying different types of longitudinal design methods to the HIV continuum of care assessments is warranted.

A multistate modeling and simulation framework to learn dose-response of oncology drugs: Application to bintrafusp alfa in non-small cell lung cancer

The dose/exposure-efficacy analyses are often conducted separately for oncology end points like best overall response, progression-free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose-end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition-specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF-β and PD-L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non-small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose-specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2-months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies.

Risk factors for stroke-related functional disability and mortality at Felege Hiwot Referral Hospital, Ethiopia

BACKGROUND

Stroke is one of the top causes of functional disability around the world. The main objective was to identify stroke-related functional outcomes and risk factors. A good functional outcome is defined as the absence of problems secondary to the stroke event, a poor functional outcome as the presence of complications, and mortality as the existence of complications.

METHOD

A retrospective cohort analysis was used to observe factors in 298 eligible adult (18 or older) stroke patients who attend outpatient clinics every three months at Felege Hiwot Referral Hospital between September 2019 and August 2021 to predict outcomes.

RESULT

The likelihood of dying from a poor outcome was 9%, and the likelihood of recovering was 24%. The average time spent on good and poor outcomes for different levels of independent variables varies according to their risk. During the first three years of follow-up, the instantaneous risk with a 95% confidence interval of transitioning from good to poor outcome in the women, aged 60 or older, with hypertension, atrial fibrillation, and hemorrhage stroke versus men stroke patients, aged 18 to 59, without hypertension, atrial fibrillation, and ischemic stroke were 1.54 (1.10, 2.15), 1.73 (1.19, 2.52), 2.34 (1.55, 3.53), 2.74 (1.64, 4.56), and 1.52 (1.10, 2.19) respectively. The hazard ratio of transitioning from poor outcome to death for patients with diabetes mellitus and atrial fibrillation versus those without diabetes mellitus and atrial fibrillation was estimated to be 1.95 (1.10, 3.46) and 3.39 (1.67, 6.89), respectively.

CONCLUSION

Women over 60 with hypertension, atrial fibrillation, and hemorrhagic stroke were more likely to progress from a good to a poor outcome. Diabetes and atrial fibrillation were also risk factors for progressing from a poor outcome to death. The states and transitions, as well as a clinical control of the hazards for the transition through states, should improve the physician's decision-making process. Since gender and age are difficult to control, early intervention by patients and the hospital may be critical in influencing functional outcomes.

Ambient air pollution associated with incident asthma, subsequent cardiovascular disease and death: A trajectory analysis of a national cohort

No previous study has examined the impact of air pollution on the cardiovascular disease (CVD) trajectory, especially among asthmatic subjects. Based on the UK Biobank cohort, we retrieved 292,227 adults free of asthma and CVD aged 37-73 years at recruitment (2006-2010). Annual mean concentrations of particulate matter (PM10 and PM2.5) and nitrogen oxides (NO2 and NOx) were assessed at each individual's addresses. We used multi-state models to estimate the associations of air pollution with the trajectory from healthy to incident asthma, subsequent CVD, and death. During a median follow-up of 11.7 years, a total of 6338 (2.2%) participants developed asthma, among which, 638 (10.1%) subsequently proceeded to CVD. We observed significant impacts of various air pollutants on the CVD dynamic transitions, with a more substantial effect of particulate matter pollutants than gaseous air pollutants. For example, the hazard ratios (95% confidence intervals) for per interquartile range increase in PM2.5 and PM10 were 1.28 (1.13, 1.44) and 1.27 (1.13, 1.43) for transitions from incident asthma to subsequent CVD. In conclusion, long-term air pollution exposure could affect the CVD trajectory. Distinguishing the effect of air pollutants on CVD transition stages has great significance for CVD health management and clinical prevention, especially among asthma patients.

Evaluation Factors Affecting on Recurrence, Metastasis, and Survival of Breast Cancer in Iranian Women by Multi-State Model Approach

Background

We used the multistate model to investigate how prognostic factors of breast cancer are seen to affect the disease process.

Methods

This cohort study was conducted at Motamed Cancer Institute of Tehran, Iran on 2363 breast cancer patients admitted from 1978 to 2017, and they were followed up until 2018. We applied the multistate models, including four states: diagnosis, recurrence, metastasis, and final absorbing mortality state.

Results

Age over 50 years, positive lymph nodes and tumor size intensified the hazard of transition from diagnosis to metastasis (P=0.002, P<0.001 and P=0.001 respectively) and they also intensified the hazard of transition from diagnosis to mortality (P=0.010, P<0.001 and P<0.001 respectively). At the same time, the educational level decreased the hazard of mentioned transitions (P<0.001). Positive estrogen receptors reduced the hazard of transition from diagnosis to metastasis (P=0.007) and positive lymph nodes also intensified the hazard of transition from metastasis to mortality (P=0.040). Tumor size had an increasing role in the transitions from diagnosis to recurrence, recurrence to metastasis, and metastasis to mortality (P=0.014, P=0.018 and P=0.002 respectively).

Conclusion

Multistate model presented the detailed effects of prognostic factors on progression of breast cancer. Implementing early diagnosis strategies and providing informational programs, especially in younger ages and lower educational level patients may be helpful in reducing the hazard of transition to higher states of breast cancer and increasing the survival of Iranian women with breast cancer by controlling tumor size growth, lymph nodes involvements and estrogen receptor status.

Performance of Cox regression models for composite time-to-event endpoints with component-wise censoring in randomized trials

BACKGROUND

Composite time-to-event endpoints are beneficial for assessing related outcomes jointly in clinical trials, but components of the endpoint may have different censoring mechanisms. For example, in the PRagmatic EValuation of evENTs And Benefits of Lipid-lowering in oldEr adults (PREVENTABLE) trial, the composite outcome contains one endpoint that is right censored (all-cause mortality) and two endpoints that are interval censored (dementia and persistent disability). Although Cox regression is an established method for time-to-event outcomes, it is unclear how models perform under differing component-wise censoring schemes for large clinical trial data. The goal of this article is to conduct a simulation study to investigate the performance of Cox models under different scenarios for composite endpoints with component-wise censoring.

METHODS

We simulated data by varying the strength and direction of the association between treatment and outcome for the two component types, the proportion of events arising from the components of the outcome (right censored and interval censored), and the method for including the interval-censored component in the Cox model (upper value and midpoint of the interval). Under these scenarios, we compared the treatment effect estimate bias, confidence interval coverage, and power.

RESULTS

Based on the simulation study, Cox models generally have adequate power to achieve statistical significance for comparing treatments for composite outcomes with component-wise censoring. In our simulation study, we did not observe substantive bias for scenarios under the null hypothesis or when the treatment has a similar relative effect on each component outcome. Performance was similar regardless of if the upper value or midpoint of the interval-censored part of the composite outcome was used.

CONCLUSION

Cox regression is a suitable method for analysis of clinical trial data with composite time-to-event endpoints subject to different component-wise censoring mechanisms.

Association between ethnic background and COVID-19 morbidity, mortality and vaccination in England: a multistate cohort analysis using the UK Biobank

OBJECTIVES

Despite growing evidence suggesting increased COVID-19 mortality among people from ethnic minorities, little is known about milder forms of SARS-CoV-2 infection. We sought to explore the association between ethnic background and the probability of testing, testing positive, hospitalisation, COVID-19 mortality and vaccination uptake.

DESIGN

A multistate cohort analysis. Participants were followed between 8 April 2020 and 30 September 2021.

SETTING

The UK Biobank, which stores medical data on around half a million people who were recruited between 2006 and 2010.

PARTICIPANTS

405 541 subjects were eligible for analysis, limited to UK Biobank participants living in England. 23 891 (6%) of participants were non-white.

PRIMARY AND SECONDARY OUTCOME MEASURES

The associations between ethnic background and testing, testing positive, hospitalisation and COVID-19 mortality were studied using multistate survival analyses. The association with single and double-dose vaccination was also modelled. Multistate models adjusted for age, sex and socioeconomic deprivation were fitted to estimate adjusted HRs (aHR) for each of the multistate transitions.

RESULTS

18 172 (4.5%) individuals tested positive, 3285 (0.8%) tested negative and then positive, 1490 (6.9% of those tested positive) were hospitalised, and 129 (0.6%) tested positive at the moment of hospital admission (ie, direct hospitalisation). Finally, 662 (17.4%) died after admission. Compared with white participants, Asian participants had an increased risk of negative to positive transition (aHR 1.24 (95% CI 1.02 to 1.52)), testing positive (95% CI 1.44 (1.33 to 1.55)) and direct hospitalisation (1.61 (95% CI 1.28 to 2.03)). Black participants had an increased risk of hospitalisation following a positive test (1.71 (95% CI 1.29 to 2.27)) and direct hospitalisation (1.90 (95% CI 1.51 to 2.39)). Although not the case for Asians (aHR 1.00 (95% CI 0.98 to 1.02)), black participants had a reduced vaccination probability (0.63 (95% CI 0.62 to 0.65)). In contrast, Chinese participants had a reduced risk of testing negative (aHR 0.64 (95% CI 0.57 to 0.73)), of testing positive (0.40 (95% CI 0.28 to 0.57)) and of vaccination (0.78 (95% CI 0.74 to 0.83)).

CONCLUSIONS

We identified inequities in testing, vaccination and COVID-19 outcomes according to ethnicity in England. Compared with whites, Asian participants had increased risks of infection and admission, and black participants had almost double hospitalisation risk, and a 40% lower vaccine uptake.

RT education and COVID-19 pneumonia discharge quality

Background

There is a lack of data assessing the influence of respiratory therapist (RT) education on clinical outcomes. The primary objective of this study was to evaluate the impact of RTs holding advanced degrees or completing adult critical care competencies on discharge outcomes of patients with COVID-19 pneumonia.

Study Design and Methods

This retrospective, cross-sectional study included adults with confirmed COVID-19 admitted to the hospital for at least three days between March-May 2020. The academic degree held by each RT was considered advanced (baccalaureate or higher) or associate degree. Discharge outcomes were considered good, compromised, or poor when subjects' hospital discharge was directly to home, long-term care facility/rehabilitation center, or hospice/died, respectively. A time-to-event multi-state regression model was used to determine the impact of RT academic degree and adult critical care competencies on discharge outcomes using α=0.05.

Results

A total of 260 subjects (median age 59 y; 166 males) received clinical care from 132 RTs. RT median professional experience was six y (IQR 3-11), 70.8% had an advanced degree, and 70.8% completed adult critical care competencies. The time-to-event multi-state regression model showed that patients with >85% exposure to RTs with advanced degrees transitioned 3.72 times more frequently to good outcomes than RTs without advanced degrees (p=.001). Similarly, patients with >85% exposure to RTs with adult critical care competencies transitioned 5.10 times more frequently to good outcomes than RTs without adult critical care competencies (p<.001).

Conclusion

Patients with COVID-19 pneumonia who received greater than 85% of their care by RTs who earned advanced degrees or completed adult critical care competencies had improved discharge outcomes. This preliminary work suggests that advancing education for the respiratory therapist workforce may improve the discharge quality of patients with acute respiratory failure and should be further explored.

A Bayesian multivariate spatial approach for illness-death survival models

Illness-death models are a class of stochastic models inside the multi-state framework. In those models, individuals are allowed to move over time between different states related to illness and death. They are of special interest when working with non-terminal diseases, as they not only consider the competing risk of death but also allow us to study the progression from illness to death. The intensity of each transition can be modelled including both fixed and random effects of covariates. In particular, spatially structured random effects or their multivariate versions can be used to assess spatial differences between regions and among transitions. We propose a Bayesian methodological framework based on an illness-death model with a multivariate Leroux prior for the random effects. We apply this model to a cohort study regarding progression after an osteoporotic hip fracture in elderly patients. From this spatial illness-death model, we assess the geographical variation in risks, cumulative incidences and transition probabilities related to recurrent hip fracture and death. Bayesian inference is done via the integrated nested Laplace approximation.

Trends and ethnic differences in stroke recurrence and mortality in a biethnic population, 2000-2019: a novel application of an illness-death model

PURPOSE

To estimate temporal trends in post-stroke outcomes in Mexican Americans (MAs) and non-Hispanic whites (NHWs).

METHODS

We included first-ever ischemic strokes from a population-based study in South Texas (n = 5343, 2000-2019). We applied an illness-death model with three jointly specified Cox-type models to estimate ethnic differences and ethnic-specific temporal trends in recurrence (first stroke to recurrence), recurrence-free mortality (first stroke to death without recurrence), recurrence-affected mortality (first stroke to death with recurrence), and postrecurrence mortality (recurrence to death).

RESULTS

MAs had higher rates of postrecurrence mortality than NHWs in 2019 but lower rates in 2000. One-year risk of this outcome increased in MAs and decreased in NHWs, resulting in ethnic differences changing from -14.9% (95% CI -35.9%, -2.8%) in 2000 to 9.1% (1.7%, 18.9%) in 2018. For recurrence-free mortality, lower rates were observed in MAs until 2013. Ethnic differences in 1-year risk changed from -3.3% (95% CI -4.9%, -1.6%) in 2000 to -1.2% (-3.1%, 0.8%) in 2018. For stroke recurrence and recurrence-affected mortality, significant ethnic disparities persisted over the study period.

CONCLUSIONS

An ethnic disparity in postrecurrence mortality was newly identified, driven by the increasing trend in MAs but a decreasing trend in NHWs.

Estimating Transition Probabilities Across the Alzheimer's Disease Continuum Using a Nationally Representative Real-World Database in the United States

INTRODUCTION

Clinical Alzheimer's disease (AD) begins with mild cognitive impairment (MCI) and progresses to mild, moderate, or severe dementia, constituting a disease continuum that eventually leads to death. This study aimed to estimate the probabilities of transitions across those disease states.

METHODS

We developed a mixed-effects multi-state Markov model to estimate the transition probabilities, adjusted for 5 baseline covariates, using the Health and Retirement Study (HRS) database. HRS surveys older adults in the United States bi-annually. Alzheimer states were defined using the modified Telephone Interview of Cognitive Status (TICS-m).

RESULTS

A total of 11,292 AD patients were analyzed. Patients were 70.8 ± 9.0 years old, 54.9% female, and with 12.0 ± 3.3 years of education. Within 1 year from the initial state, the model estimated a higher probability of transition to the next AD state in earlier disease: 12.8% from MCI to mild AD and 5.0% from mild to moderate AD, but < 1% from moderate to severe AD. After 10 years, the probability of transition to the next state was markedly higher for all states, but still higher in earlier disease: 29.8% from MCI to mild AD, 23.5% from mild to moderate AD, and 5.7% from moderate to severe AD. Across all AD states, the probability of transition to death was < 5% after 1 year and > 15% after 10 years. Older age, fewer years of education, unemployment, and nursing home stay were associated with a higher risk of disease progression (p < 0.01).

CONCLUSIONS

This analysis shows that the risk of progression is greater in earlier AD states, increases over time, and is higher in patients who are older, with fewer years of education, unemployed, or in a nursing home at baseline. The estimated transition probabilities can provide guidance for future disease management and clinical trial design optimization, and can be used to refine existing cost-effectiveness frameworks.