Estimation Of Transition Probabilities Research Articles

Tracking and Transition Probability of Blood Pressure From Childhood to Midadulthood.

Despite its relevance for pediatric blood pressure (BP) screening, the long-term predictive utility and natural progression of pediatric BP classification remain understudied. To evaluate BP tracking from childhood to midadulthood using the American Academy of Pediatrics (AAP) thresholds and estimate transition probabilities among BP classifications over time considering multiple time points. The analyses were performed in 2023 using data gathered from September 1980 to August 2018 within the longitudinal Cardiovascular Risk in Young Finns Study. Participants had BP examined 9 times over 38 years, from childhood (aged 6-12 years) or adolescence (15-18 years) to young adulthood (21-27 years), late young adulthood (30-37 years), and midadulthood (39-56 years). BP classifications (normal, elevated, hypertension) were based on AAP guidelines for children and adolescents and the 2017 American College of Cardiology/American Heart Association guidelines for adults. Outcomes were BP classifications at follow-up visits. Tracking coefficients were calculated using generalized estimated equations. Transition probabilities among BP classifications were estimated using multistate Markov models. This study included 2918 participants (mean [SD] baseline age, 10.7 [5.0] years; 1553 female [53.2%]). Over 38 years, the tracking coefficient (odds ratio [OR]) for maintaining elevated BP/hypertension was 2.16 (95% CI, 1.95-2.39). Males had a higher probability than females of progressing to and maintaining hypertension and a lower probability of reverting to normal BP from childhood to midadulthood (transition probability: from normal BP to stage 2 hypertension, 0.20; 95% CI, 0.17-0.22 vs 0.08; 95% CI, 0.07-0.10; maintaining stage 2 BP, 0.32; 95% CI, 0.27-0.39 vs 0.14; 95% CI, 0.09-0.21; from stage 2 hypertension to normal BP, 0.23; 95% CI, 0.19-0.26 vs 0.58; 95% CI, 0.52-0.62. For both sexes, the probability of transitioning from adolescent hypertension to normal BP in midadulthood was lower (transition probability, ranging from 0.16; 95% CI, 0.14-0.19 to 0.44; 95% CI, 0.39-0.48) compared with childhood hypertension (transition probability, ranging from 0.23; 95% CI, 0.19-0.26 to 0.63; 95% CI, 0.61-0.66). The probability of maintaining normal BP sharply decreased in the first 5 to 10 years, stabilizing thereafter. Children with normal BP generally maintained this status into adolescence (male: transition probability, 0.64; 95% CI, 0.60-0.67; female: transition probability, 0.81; 95% CI, 0.79-0.84) but decreased by young adulthood (male: transition probability, 0.41; 95% CI, 0.39-0.44; female: transition probability, 0.69; 95% CI, 0.67-0.71). Results of this cohort study reveal an enduring association of childhood and adolescent BP (AAP thresholds) with later BP. Although childhood normal BP tends to be maintained into adolescence, the probability of reverting to and sustaining normal BP decreases notably from adolescence to young adulthood. The findings of this study underscore the importance of prevention to maintain normal BP starting in childhood, suggesting adolescence as a potential critical period. The results suggest the potential for less frequent screenings for children with initially normal BP.

Do discrepancies between subjective and objective health shift over time in later life? A markov transition model

Subjective health (SH) deteriorates less rapidly than objective health (OH) in older adults. However, scant evidence exists regarding if discrepancies between SH and OH shift in the same individuals over time. We explore whether such discrepancies change over time in a sample of older adults living in England, through a prospective, observational cohort study design. Using data from the English Longitudinal Study of Ageing, we followed a sample of 6803 older adults, aged 60+ years at baseline, over three waves of data collection (2002–2007), yielding two wave transitions. A ‘health asymmetry’ metric classified older adults into four categories at each wave, based on the level of agreement between their SH and OH scores (‘health pessimist’, ‘health optimist’, ‘good health realist’ and ‘poor health realist’). First-order Markov transition and generalised logit models yielded estimated transition probabilities and odds ratios for health asymmetry transitions over time. At baseline, 36.84% of the sample were ‘good health realists’, 33% were ‘poor health realists’, 14.54% were ‘health optimists’, and 15.62% were ‘health pessimists’. Good and poor health realists were likely to remain health realistic over time. Good health realists who did transition however, were likely to become health optimists. Subsequently, the proportion of health optimists in the sample increased over time. Health pessimists had a high probability of being lost to study attrition. In conclusion, health optimism (i.e. where SH is rated better than OH) becomes more prevalent over time, in later life. Future research should investigate if promoting positive SH appraisals among health pessimists and poor health realists can optimise health and survival outcomes.

What measures are needed to achieve a tobacco endgame target? A Singapore-based simulation study

BackgroundAn increasing number of countries are pursuing a tobacco ‘endgame’. We sought to determine the combination of measures it would take to achieve a tobacco endgame in the city-state of...

Estimation of AMOC transition probabilities using a machine learning based rare-event algorithm

Abstract The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the global climate, known to be a tipping element, as it could collapse under global warming. The main objective of this study is to compute the probability that the AMOC collapses within a specified time window, using a rare-event algorithm called Trajectory-Adaptive Multilevel Splitting (TAMS). However, the efficiency and accuracy of TAMS depend on the choice of the score function. Although the definition of the optimal score function, called “committor function” is known, it is impossible in general to compute it a priori. Here, we combine TAMS with a Next-Generation Reservoir Computing technique that estimates the committor function from the data generated by the rare-event algorithm. We test this technique in a stochastic box model of the AMOC for which two types of transition exist, the so-called F(ast)-transitions and S(low)-transitions. Results for the F-transtions compare favorably with those in the literature where a physically-informed score function was used. We show that coupling a rare-event algorithm with machine learning allows for a correct estimation of transition probabilities, transition times, and even transition paths for a wide range of model parameters. We then extend these results to the more difficult problem of S-transitions in the same model. In both cases of F-transitions and S-transitions, we also show how the Next-Generation Reservoir Computing technique can be interpreted to retrieve an analytical estimate of the committor function.

Estimation of Transition Probabilities from a Large Cohort (> 6000) of Australians Living with Multiple Sclerosis (MS) for Changing Disability Severity Classifications, MS Phenotype, and Disease-Modifying Therapy Classifications.

Multiple sclerosis (MS) is a chronic autoimmune/neurodegenerative disease associated with progressing disability affecting mostly women. We aim to estimate transition probabilities describing MS-related disability progression from no disability to severe disability. Transition probabilities are a vital input for health economics models. In MS, this is particularly relevant for pharmaceutical agency reimbursement decisions for disease-modifying therapies (DMTs). Data were obtained from Australian participants of the MSBase registry. We used a four-state continuous-time Markov model to describe how people with MS transition between disability milestones defined by the Expanded Disability Status Scale (scale 0-10): no disability (EDSS of 0.0), mild (EDSS of 1.0-3.5), moderate (EDSS of 4.0-6.0), and severe (EDSS of 6.5-9.5). Model covariates included sex, DMT usage, MS-phenotype, and disease duration, and analysis of covariate groups were also conducted. All data were recorded by the treating neurologist. A total of N = 6369 participants (mean age 42.5 years, 75.00% female) with 38,837 person-years of follow-up and 54,570 clinical reviews were identified for the study. Annual transition probabilities included: remaining in the no, mild, moderate, and severe states (54.24%, 82.02%, 69.86%, 77.83% respectively) and transitioning from no to mild (42.31%), mild to moderate (11.38%), and moderate to severe (9.41%). Secondary-progressive MS was associated with a 150.9% increase in the hazard of disability progression versus relapsing-remitting MS. People with MS have an approximately 45% probability of transitioning from the no disability state after one year, with people with progressive MS transitioning from this health state at a much higher rate. These transition probabilities will be applied in a publicly available health economics simulation model for Australia and similar populations, intended to support reimbursement of a plethora of existing and upcoming interventions including medications to reduce progression of MS.

One-size-fits-all versus risk-category-based screening interval strategies for cardiovascular disease prevention in Chinese adults: a prospective cohort study

In non-high-risk individuals, risk-category-based atherosclerotic cardiovascular disease (ASCVD) screening strategies may be more cost-effective than one-size-fits-all approaches. However, current decisions are constrained by a lack of research evidence. We aimed to explore appropriate risk-category-based screening interval strategies for non-high-risk individuals in ASCVD primary prevention in the Chinese population. We used data from 28,624 participants in the China Kadoorie Biobank (CKB) who had completed at least two field surveys. The risk assessment tools were the 10-year ASCVD risk prediction models developed based on the CKB cohort. We constructed multistate Markov models to model disease progression and estimate transition probabilities between different risk categories. The total person-years spent unidentified in the high-risk state over a 10-year period were calculated for each screening interval protocol. We also estimated the number of ASCVD events prevented, quality-adjusted life years (QALYs) gained, and costs saved when compared to the 3-yearly screening protocol. When compared to the uniform 3-yearly protocol, most risk-category-based screening interval protocols would identify more high-risk individuals timely, thus preventing more ASCVD events and gaining QALYs. A few of them would reduce total health-care costs. The protocol, which used 6-year, 3-year, and 2-year screening intervals for low-risk, intermediate-low-risk, and intermediate-high risk individuals, was optimal, and would reduce the person-years spent unidentified in the high-risk category by 17.9% (95% CI: 13.1%-21.9%), thus preventing an estimated 113 thousand (95% CI: 83-138) hard ASCVD events for Chinese adults aged 30-79 over a 10-year period. When using a lower cost of statin therapy, more screening protocols would gain QALYs while saving costs. For the primary prevention of ASCVD, risk-category-based screening protocols outperformed the one-size-fits-all approach in the Chinese population. This work was supported by National Natural Science Foundation of China (82192904, 82388102, 82192900) and grants (2023YFC2509400) from the National Key R&D Program of China. The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust (212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z), grants (2016YFC0900500) from the National Key R&D Program of China, National Natural Science Foundation of China (81390540, 91846303, 81941018), and Chinese Ministry of Science and Technology (2011BAI09B01).

Estimating error rates for single molecule protein sequencing experiments.

The practical application of new single molecule protein sequencing (SMPS) technologies requires accurate estimates of their associated sequencing error rates. Here, we describe the development and application of two distinct parameter estimation methods for analyzing SMPS reads produced by fluorosequencing. A Hidden Markov Model (HMM) based approach, extends whatprot, where we previously used HMMs for SMPS peptide-read matching. This extension offers a principled approach for estimating key parameters for fluorosequencing experiments, including missed amino acid cleavages, dye loss, and peptide detachment. Specifically, we adapted the Baum-Welch algorithm, a standard technique to estimate transition probabilities for an HMM using expectation maximization, but modified here to estimate a small number of parameter values directly rather than estimating every transition probability independently. We demonstrate a high degree of accuracy on simulated data, but on experimental datasets, we observed that the model needed to be augmented with an additional error type, N-terminal blocking. This, in combination with data pre-processing, results in reasonable parameterizations of experimental datasets that agree with controlled experimental perturbations. A second independent implementation using a hybrid of DIRECT and Powell's method to reduce the root mean squared error (RMSE) between simulations and the real dataset was also developed. We compare these methods on both simulated and real data, finding that our Baum-Welch based approach outperforms DIRECT and Powell's method by most, but not all, criteria. Although some discrepancies between the results exist, we also find that both approaches provide similar error rate estimates from experimental single molecule fluorosequencing datasets.

Bridging Continuous and Lattice-Based Models of Two-Dimensional Diffusion: A Systematic Approach for Estimating Transition Probabilities, Grid Size and Diffusivity

Lattice-based models have been broadly applied in mathematical and computational modeling of biological and biomedical systems for which spatial effects are important. These discrete models commonly include diffusion of mobile constituents as a key underlying mechanism. While the direct simulation of diffusion in continuous (off-lattice) domains is possible, it is computationally intensive, particularly when multiple coupled mechanisms are involved. This study presents a systematic approach for connecting continuous models of two-dimensional diffusion with internal obstacles to discrete, lattice-based (surrogate) models of diffusion. Results from continuous model simulations on a representative domain, and over many realizations, are used to develop accurate lattice-based surrogate models by exploiting internal symmetries. Probabilities determined for the lattice-based surrogate models are also connected to theoretical diffusivities for 2D random walks on a square lattice, necessitating the calibration of a spatial grid size. This approach can facilitate the inclusion of more accurate diffusive transport models of complex media within the general framework of lattice-based models that incorporate multiple coupled mechanisms.

Human learning of hierarchical graphs.

Humans are exposed to sequences of events in the environment, and the interevent transition probabilities in these sequences can be modeled as a graph or network. Many real-world networks are organized hierarchically and while much is known about how humans learn basic transition graph topology, whether and to what degree humans can learn hierarchical structures in such graphs remains unknown. We probe the mental estimates of transition probabilities via the surprisal effect phenomenon: humans react more slowly to less expected transitions. Using mean-field predictions and numerical simulations, we show that surprisal effects are stronger for finer-level than coarser-level hierarchical transitions, and that surprisal effects at coarser levels are difficult to detect for limited learning times or in small samples. Using a serial response experiment with human participants (n=100), we replicate our predictions by detecting a surprisal effect at the finer level of the hierarchy but not at the coarser level of the hierarchy. We then evaluate the presence of a trade-off in learning, whereby humans who learned the finer level of the hierarchy better also tended to learn the coarser level worse, and vice versa. This study elucidates the processes by which humans learn sequential events in hierarchical contexts. More broadly, our work charts a road map for future investigation of the neural underpinnings and behavioral manifestations of graph learning.

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

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. 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. 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. 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. 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.

Educational Status as a Mediator of Intergenerational Social Mobility in Europe: A Positional Analysis Approach

This paper investigates the transmission of educational attainment from parents to offspring as a mediator of intergenerational class mobility in Europe. The study covers the last two decades with data drawn from a cross-national large-scale sample survey, namely the European Social Survey (ESS), for the years 2002–2018. Interest has focused on the question of the persistence of inequality of educational opportunities by examining the attainment of nominal levels of education and the association between the educational attainment of the parent with the highest level of education and their descendants. The study also covers new trends in social mobility that consider education as a “positional good”, and a novel method of incorporating educational expansion into the transition probabilities is proposed, providing answers to whether the rising accessibility of educational qualifications attenuates the association between social origin and educational attainment. Therefore, the concept of positionality is taken into account in the estimation of intergenerational transition probabilities, and to complement the analysis, mobility measures are provided for both methods, nominal and positional. The proposed positional method is validated through a correlation analysis between the upward mobility scores (nominal and positional) with the Education Expansion Index (EEI) for the respective years. The upward mobility scores estimated via the positional method are more highly correlated with the EEI for all years, indicating a better alignment with the broader trends in educational participation and achievement.

Cost-effectiveness of low-dose colchicine in patients with chronic coronary disease in the netherlands.

Recent trials have shown that low-dose colchicine (0.5 mg once daily) reduces major cardiovascular events in patients with acute and chronic coronary syndromes. We aimed to estimate the cost-effectiveness of low-dose colchicine therapy in patients with chronic coronary disease when added to standard background therapy. This Markov cohort cost-effectiveness model used estimates of therapy effectiveness, transition probabilities, costs and quality of life obtained from the Low-dose Colchicine 2 (LoDoCo2) trial, as well as meta-analyses and public sources. In this trial, Low-dose colchicine was added to standard of care and compared to placebo. The main outcomes were cardiovascular events including myocardial infarction, stroke and coronary revascularisation, quality-adjusted life-year (QALY), the cost per QALY gained (incremental cost-effectiveness ratio), and net monetary benefit. In the model, low-dose colchicine therapy yielded 0.04 additional QALYs compared with standard of care at an incremental cost of €455 from a societal perspective and €729 from a healthcare perspective, resulting in a cost per QALY gained of €12,176/QALY from a societal perspective and €19,499/QALY from a healthcare perspective. Net monetary benefit was €1,414 from a societal perspective and €1,140 from a healthcare perspective. Low-dose colchicine has a 96% and 94% chance of being cost effective, from respectively a societal and healthcare perspective when using a willingness to pay of €50,000/QALY. Net monetary benefit would decrease below zero when annual low-dose colchicine costs would exceed an annual cost of €221 per patient. Adding low-dose colchicine to standard of care in patients with chronic coronary disease is cost-effective according to commonly accepted thresholds in Europe and Australia and compares favourably in cost-effectiveness to other drugs used in chronic coronary disease.

Learning Markovian dynamics with spectral maps.

The long-time behavior of many complex molecular systems can often be described by Markovian dynamics in a slow subspace spanned by a few reaction coordinates referred to as collective variables (CVs). However, determining CVs poses a fundamental challenge in chemical physics. Depending on intuition or trial and error to construct CVs can lead to non-Markovian dynamics with long memory effects, hindering analysis. To address this problem, we continue to develop a recently introduced deep-learning technique called spectral map [J. Rydzewski, J. Phys. Chem. Lett. 14, 5216-5220 (2023)]. Spectral map learns slow CVs by maximizing a spectral gap of a Markov transition matrix describing anisotropic diffusion. Here, to represent heterogeneous and multiscale free-energy landscapes with spectral map, we implement an adaptive algorithm to estimate transition probabilities. Through a Markov state model analysis, we validate that spectral map learns slow CVs related to the dominant relaxation timescales and discerns between long-lived metastable states.

Simplified Methods for Modelling Dependent Parameters in Health Economic Evaluations: A Tutorial.

In health economic evaluations, model parameters are often dependent on other model parameters. Although methods exist to simulate multivariate normal (MVN) distribution data and estimate transition probabilities in Markov models while considering competing risks, they are technically challenging for health economic modellers to implement. This tutorial introduces easily implementable applications for handling dependent parameters in modelling. Analytical proofs and proposed simplified methods for handling dependent parameters in typical health economic modelling scenarios are provided, and implementation of these methods are illustrated in seven examples along with the SAS and R code. Methods to quantify the covariance and correlation coefficients of correlated variables based on published summary statistics and generation of MVN distribution data are demonstrated using examples of physician visits data and cost component data. The use of univariate normal distribution data instead of MVN distribution data to capture population heterogeneity is illustrated based on the results from multiple regression models with linear predictors, and two examples are provided (linear fixed-effects model and Cox proportional hazards model). A conditional probability method is introduced to handle two or more state transitions in a single Markov model cycle and applied in examples of one- and two-way state transitions. This tutorial proposes an extension of routinely used methods along with several examples. These simplified methods may be easily applied by health economic modellers with varied statistical backgrounds.

Relationships of change in Clinical Dementia Rating (CDR) on patient outcomes and probability of progression: observational analysis

BackgroundUnderstanding the relationship among changes in Clinical Dementia Rating (CDR), patient outcomes, and probability of progression is crucial for evaluating the long-term benefits of disease-modifying treatments. We examined associations among changes in Alzheimer’s disease (AD) stages and outcomes that are important to patients and their care partners including activities of daily living (ADLs), geriatric depression, neuropsychiatric features, cognitive impairment, and the probabilities of being transitioned to a long-term care facility (i.e., institutionalization). We also estimated the total time spent at each stage and annual transition probabilities in AD.MethodsThe study included participants with unimpaired cognition, mild cognitive impairment (MCI) due to AD, and mild, moderate, and severe AD dementia in the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) database. The associations among change in AD stages and change in relevant outcomes were estimated using linear mixed models with random intercepts. The probability of transitioning to long-term care facilities was modeled using generalized estimating equations. The total length of time spent at AD stages and annual transition probabilities were estimated with multistate Markov models.ResultsThe estimated average time spent in each stage was 3.2 years in MCI due to AD and 2.2, 2.0, and 2.8 years for mild, moderate, and severe AD dementia, respectively. The annual probabilities of progressing from MCI to mild, moderate, and severe AD dementia were 20, 4, and 0.7%, respectively. The incremental change to the next stage of participants with unimpaired cognition, MCI, and mild, moderate, and severe AD dementia (to death) was 3.2, 20, 26.6, 31, and 25.3%, respectively. Changes in ADLs, neuropsychiatric features, and cognitive measures were greatest among participants who transitioned from MCI and mild AD dementia to more advanced stages. Participants with MCI and mild and moderate AD dementia had increasing odds of being transitioned to long-term care facilities over time during the follow-up period.ConclusionsThe findings demonstrated that participants with early stages AD (MCI or mild dementia) were associated with the largest changes in clinical scale scores. Early detection, diagnosis, and intervention by disease-modifying therapies are required for delaying AD progression. Additionally, estimates of transition probabilities can inform future studies and health economic modeling.

Estimating Transition Probabilities for Modeling Major Depression in Adolescents by Sex and Race or Ethnicity Combinations in the USA.

About one-fifth of US adolescents experienced major depressive symptoms, but few studies have examined longitudinal trends of adolescents developing depression or recovering by demographic factors. We estimated new transition probability inputs, and then used them in a simulation model to project the epidemiologic burden and trajectory of depression of diverse adolescents by sex and race or ethnicity combinations. Transition probabilities were first derived using parametric survival analysis of data from the National Longitudinal Study of Adolescent to Adult Health and then calibrated to cross-sectional data from the National Survey on Drug Use and Health. We developed a cohort state-transition model to simulate age-specific depression outcomes of US adolescents. A hypothetical adolescent cohort was modeled from 12-22years with annual transitions. Model outcomes included proportions of youth experiencing depression, recovery, or depression-free casesand were reported for a US adolescent population by sex, race or ethnicity, and sex and race or ethnicity combinations. At 22 years of age, approximately 16% of adolescents had depression, 12% were in recovery, and 72% had never developed depression. Depression prevalence peaked around 16-17years-old. Adolescents of multiracial or other race or ethnicity, White, American Indian or Alaska Native, and Hispanic, Latino, or Spanish descent were more likely to experience depression than other racial or ethnic groups. Depression trajectories generated by the model matched well with historical observational studies by sex and race or ethnicity, except for individuals from American Indian or Alaska Native and multiracial or other race or ethnicity backgrounds. This study validated new transition probabilities for future use in decision models evaluating adolescentdepression policies or interventions. Different sets of transition parameters by demographic factors (sex and race or ethnicity combinations) were generated to support future health equity research, including distributional cost-effectiveness analysis. Further data disaggregated with respect to race, ethnicity, religion, income, geography, gender identity, sexual orientation, and disability would be helpful to project accurate estimates for historicallyminoritized communities.

Persistence and disadoption of sustainable agricultural practices in the Mississippi Delta region

AbstractThe working lands programs in the United States are specifically tailored to support farmers in initiating or enhancing conservation practices. The expectation is that farmers receiving financial assistance will continue implementing these practices even after their initial contracts end, thereby increasing the programs’ cost‐effectiveness and promoting natural resources conservation. However, little is known about the likelihood of persistence and disadoption of conservation practices once the initial cost‐share contracts expire. Using the first‐order Markov chain framework on state‐level conservation practices acreage data from the Mississippi Delta region (2005–2022), this study provides quantitative estimates of changing (dis)adoption patterns of four conservation practices: cover crops (CCs), conservation tillage (CT), nutrient and irrigation management (NIM), and conservation crop rotation (CCR). The findings reveal a higher probability of persistence of NIM practices. In contrast, acres devoted to CCs and CT tend to transition into other alternative practices, indicating a greater likelihood of disadoption. Importantly, the likelihood of acres that initially adopt conservation practices with funding shifting to a condition of no conservation activity is minimal. The transition probability estimates facilitate the prediction of future adoption rates of conservation practices and aid funding agencies in prioritizing their conservation efforts.

Estimating Transition Probabilities Using Repeated Cross-sectional Data

Estimating Transition Probabilities Using Repeated Cross-sectional Data

Cost-effectiveness analysis of durvalumab plus tremelimumab as first-line therapy in patients with unresectable hepatocellular carcinoma

Background: The HIMALAYA trial found that durvalumab plus tremelimumab significantly prolonged progression-free survival and overall survival in patients with unresectable hepatocellular carcinoma (HCC) compared with sorafenib. Objective: This study aimed to investigate the cost-effectiveness of durvalumab plus tremelimumab compared with sorafenib in the first-line HCC setting. Design: A Markov model-based cost-effectiveness analysis. Methods: We created a Markov model to compare healthcare costs and clinical outcomes of HCC patients treated with durvalumab plus tremelimumab in the first-line setting compared with sorafenib. We estimated transition probabilities from randomized trials. Lifetime direct healthcare costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios were calculated for first-line durvalumab plus tremelimumab compared with sorafenib from a US payer’s perspective. Results: In the base case, first-line durvalumab plus tremelimumab was associated with an improvement of 0.29 QALYs compared with sorafenib. While both treatment strategies were associated with considerable lifetime expenditures, first-line durvalumab plus tremelimumab was less expensive than sorafenib ($188,405 vs $218,584). The incremental net monetary benefit for durvalumab plus tremelimumab versus sorafenib was $72,762 (valuing QALYs at $150,000 each). The results of durvalumab plus tremelimumab were better in terms of costs and health outcomes in patients with HBV-related HCC and high alpha-fetoprotein levels. Conclusion: First-line durvalumab plus tremelimumab was estimated to be dominant for the treatment of unresectable HCC compared with sorafenib from a US payer’s perspective.

ESTIMATION OF WORKING LIFE EXPECTANCY: APPLICATION OF A MULTISTATE MARKOV MODEL

Abstract Longer life expectancy in Western countries has led to concerns about sustainable workforces amid the rising costs of securing aging populations. Retirement age is no more a good indicator of the end of the working life, because many older workers move in-and-out of workforce. A more useful measure is working life expectancy (WLE), which is the expected number of remaining years from a given age that a person will work. This measure considers transitions across different employment states. The method adopted for estimation of WLE is along the same line to estimate health expectancy (HE) based on cross-sectional data without considering transitions in different states of work over time. A better approach is based on multi-state survival models considering transitions among states over time using longitudinal data. These models assume a Markov Process to estimate transition probabilities. WLE is the weighted sum of duration of stay in a state weighted by the transition probabilities. The method adopted in IDEAR consortium is a multistate survival model assuming a first-order Markov process in continuous time to estimate transition probabilities. Transition probabilities are estimated by the msm package. This model takes care of interval censoring where the exact dates of transitions from one state to another is not known. WLEs are estimated by using the package elect (van den Hout, 2019) incorporating age as a time dependent variable by using a Gompertz model instead of the exponential model. The method is demonstrated to estimate WLE using Swedish Longitudinal Occupational Survey of Health (SLOSH).