Population Model Research Articles

Chest computed tomography-based artificial intelligence-aided latent class analysis for diagnosis of severe pneumonia.

There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). This retrospective study included 413 patients hospitalized at Xinhua hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections (P <0.001) than patients with subphenotype-2 and had lower 30-day (P <0.001) and 90-day (P <0.001) mortality, and lower in-hospital (P=0.001) and 2-year (P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes (P<0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.

Predicting Intensive Care Admission in Children with Acute Asthma: A Meta-Analysis of Predictive Models

Background: Acute asthma is a common cause of pediatric emergency department visits and hospitalizations. Early identification of children at high risk of requiring intensive care unit (ICU) admission is crucial for optimal management and resource allocation. This meta-analysis aimed to evaluate the performance of predictive models for ICU admission in children presenting with acute asthma. Methods: A systematic search of PubMed, Embase, and Cochrane Library was conducted for studies published between 2013 and 2024 that developed or validated predictive models for ICU admission in children with acute asthma. Studies reporting sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were included. Methodological quality was assessed using the QUADAS-2 tool. Pooled estimates of diagnostic accuracy were calculated using a random-effects model. Results: Six studies (n = 2,850 children) met the inclusion criteria. The predictive models included clinical features (respiratory rate, oxygen saturation, accessory muscle use), lung function measures (peak expiratory flow rate), and blood gas analysis. Pooled sensitivity ranged from 0.71 (95% CI 0.59-0.82) to 0.78 (95% CI 0.72-0.83), specificity from 0.79 (95% CI 0.75-0.83) to 0.86 (95% CI 0.78-0.91), and AUROC from 0.79 (95% CI 0.72-0.86) to 0.88 (95% CI 0.84-0.92). Conclusion: Several predictive models demonstrate moderate to high accuracy in identifying children with acute asthma at risk of ICU admission. However, heterogeneity in model performance highlights the need for further research to validate existing models in diverse populations and develop more robust tools to guide clinical decision-making.

REsolved ALMA and SMA Observations of Nearby Stars. REASONS: A population of 74 resolved planetesimal belts at millimetre wavelengths

Planetesimal belts are ubiquitous around nearby stars, and their spatial properties hold crucial information for planetesimal and planet formation models. We present resolved dust observations of 74 planetary systems as part of the REsolved ALMA and SMA Observations of Nearby Stars (REASONS) survey and archival reanalysis. We uniformly modelled interferometric visibilities for the entire sample to obtain the basic spatial properties of each belt, and combined these with constraints from multi-wavelength photometry. We report key findings from a first exploration of this legacy dataset: (1) Belt dust masses are depleted over time in a radially dependent way, with dust being depleted faster in smaller belts, as predicted by collisional evolution. (2) Most belts are broad discs rather than narrow rings, with much broader fractional widths than rings in protoplanetary discs. We link broad belts to either unresolved substructure or broad planetesimal discs produced if protoplanetary rings migrate. (3) The vertical aspect ratios ($h=H/R$) of 24 belts indicate orbital inclinations of sim 1-20$^ circ $, implying relative particle velocities of sim 0.1-4 km/s, and no clear evolution of heights with system age. This could be explained by early stirring within the belt by large bodies (with sizes of at least sim 140 km to the size of the Moon), by inheritance of inclinations from the protoplanetary disc stage, or by a diversity in evolutionary pathways and gravitational stirring mechanisms. We release the REASONS legacy multidimensional sample of millimetre-resolved belts to the community as a valuable tool for follow-up multi-wavelength observations and population modelling studies.

A modeling approach to forecast local demographic trends in metapopulations.

Predicting animal population trajectories into the future has become a central exercise in both applied and fundamental ecology. Because demographic models classically assume population closure, they tend to provide inaccurate predictions when applied locally to interconnected subpopulations that are part of a larger metapopulation. Ideally, one should explicitly model dispersal among subpopulations, but in practice this is prevented by the difficulty of estimating dispersal rates in the wild. To forecast the local demography of connected subpopulations, we developed a new demographic model (hereafter, the two-scale model) that disentangles two processes occurring at different spatial scales. First, at the larger scale, a closed population model describes changes in metapopulation size over time. Second, total metapopulation size is redistributed among subpopulations, using time-varying proportionality parameters. This two-step approach ensures that the long-term growth of every subpopulation is constrained by the overall metapopulation growth rate. It implicitly accounts for the interconnectedness among subpopulations and avoids unrealistic trajectories. Using realistic simulations, we compared the performance of this new model with that of a classical closed population model at predicting subpopulations' trajectories over 30 years. While the classical model predicted future subpopulation sizes with an average bias of 30% and produced predictive errors sometimes >500%, the two-scale model showed very little bias (<3%) and never produced predictive errors >20%. We also applied both models to a real dataset on European shags (Gulosus aristotelis) breeding along the Atlantic coast of France. Again, the classical model predicted highly unrealistic growths, as large as a 200-fold increase over 30 years for some subpopulations. The two-scale model predicted very sensible growths, never larger than a threefold increase over the 30-year time horizon, which is more in accordance with this species' life history. This two-scale model provides an effective solution to forecast the local demography of connected subpopulations in the absence of data on dispersal rates. In this context, it is a better alternative than closed population models and a more parsimonious option than full-dispersal models. Because the only data required are simple counts, this model could be useful to many large-scale wildlife monitoring programs.

Shared decision making in primary malignant bone tumour surgery around the knee in children and young adults: protocol for a prospective study

BackgroundChildren and young adults needing surgery for a primary malignant bone tumour around the knee face a difficult, life-changing decision. A previous study showed that this population wants to be involved more in the decision-making process and that more involvement leads to less decisional stress and regret. Therefore, a well-designed and standardized decision-making process based on the principles of shared decision-making needs to be designed, implemented, and evaluated.MethodsWe developed a shared decision-making (SDM) model for this patient population, including an online decision aid (DA). This model has been implemented in the standard care of patients with a primary malignant bone tumour around the knee. Following implementation, we will analyse its effect on the decision-making process and the impact on patient experiences using questionnaires and interviews. Moreover, potential areas for improvement will be identified.DiscussionGiven the importance of involving patients and parents in surgical decision-making, particularly in life-changing surgery such as malignant bone tumour surgery, and given the lack of SDM models applicable for this purpose, we want to share our model with the international community, including our study protocol for evaluating and optimising the model. This study will generate valuable knowledge to facilitate the optimisation of current patient care for local treatment. The sharing of our implementation and study protocol can serve as an example for other centres interested in implementing SDM methods in an era characterized by more empowered patients and parents who desire autonomy and reliable and realistic information.

Pop-cosmos: Scaleable Inference of Galaxy Properties and Redshifts with a Data-driven Population Model

We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pretrained population model (pop-cosmos) that was calibrated using purely photometric data. This model specifies a prior distribution over 16 stellar population synthesis (SPS) parameters using a score-based diffusion model, and includes a data model with detailed treatment of nebular emission. We use a GPU-accelerated affine-invariant ensemble sampler to achieve fast posterior sampling under this model for 292,300 individual galaxies in the COSMOS2020 catalog, leveraging a neural network emulator (Speculator) to speed up the SPS calculations. We apply both the pop-cosmos population model and a baseline prior inspired by Prospector-α, and compare these results to published COSMOS2020 redshift estimates from the widely used EAZY and LePhare codes. For the ∼12,000 galaxies with spectroscopic redshifts, we find that pop-cosmos yields redshift estimates that have minimal bias (∼10−4), high accuracy (σ MAD = 7 × 10−3), and a low outlier rate (1.6%). We show that the pop-cosmos population model generalizes well to galaxies fainter than its r < 25 mag training set. The sample we have analyzed is ≳3× larger than has previously been possible via posterior sampling with a full SPS model, with average throughput of 15 GPU-sec per galaxy under the pop-cosmos prior, and 0.6 GPU-sec per galaxy under the Prospector prior. This paves the way for principled modeling of the huge catalogs expected from upcoming Stage IV galaxy surveys.

MedVantage: A Primary Care Model for Populations With High Social and Medical Needs

MedVantage: A Primary Care Model for Populations With High Social and Medical Needs

Modeling of Long-term Afterglow Counterparts to Gravitational Wave Events: The Full View of GRB 170817A

The arrival of gravitational wave astronomy and a growing number of time-domain-focused observatories are set to lead to an increasing number of detections of short gamma-ray bursts (GRBs) launched with a moderate inclination to Earth. Being nearby events, these are also prime candidates for very long-term follow-up campaigns and very long-baseline interferometry, which has implications for multi-messenger modeling, data analysis, and statistical inference methods applied to these sources. Here, we present a comprehensive modeling update that directly incorporates into afterglowpy astrometric observations of the GRB position, Poissonian statistics for faint sources, and modeling of a trans-relativistic population of electrons. We use the revolutionary event GW170817 to demonstrate the impact of these extensions both for the best-fit physics parameters and model selection methods that assess the statistical significance of additional late-time emission components. By including in our analysis the latest Chandra X-ray observations of GRB 170817A, we find only weak evidence (≲2σ) for a new emission component at late times, which makes for a slightly more natural fit to the centroid evolution and prediction for the external medium density.

The simple analytics of the environmental Kuznets curve: a reformulation

PurposeThis paper aims to review the relationship between per capita income and aggregate emission in an economy populated by rational agents. According to the environmental Kuznets curve (EKC) hypothesis, pollution–income relationship is an inverted U-shaped curve. This paper aims to derive that relationship in an endogenous way and extends the relevant literature in an important way.Design/methodology/approachThis paper formulated a general equilibrium model of homogenous population with identical tastes and preferences. Production side is modelled with firms operating in a monopolistically competitive environment. The approach is modelling the economy in an analytical way so that closed form solutions can be achieved. Model simulations have also been performed to get a clear view of results.FindingsThis study shows that increasing returns to scale in abatement technology ceases to be a sufficient condition for the generation of the EKC hypothesis. The general equilibrium structure of the model allows studying the endogenous evolution of income, emissions and prices of the abatement goods in a unified setting.Originality/valueThe paper is novel and original in nature. The results are new in the literature. These results extend and generalise the previous research work in this area in an important way. The sufficient condition that is obtained in this study limits the applicability of EKC in an otherwise identical economy as used in previous literature. Therefore, this paper adds value to the ongoing research related to EKC.

BCI for Exoskeletons and Prostheses from Rehabilitation to Human Enhancement

Brain-computer interface (BCI) technology has revolutionized the development of motor rehabilitation and body enhancement. However, a significant gap exists between the limited use of BCI for exoskeletons and prosthetics in healthy individuals and its wide application in motor-impaired patients. This literature review aims to bridge that gap and highlight the potential of BCI technology for future applications in body enhancement for healthy individuals. We review the literature across neuroscience, biomedical engineering, and robotics, focusing on BCI applications in prosthetics and exoskeletons for both motor-impaired and healthy individuals. We summarize the methods for adapting and transferring prosthetic structures designed for individuals with disabilities to healthy individuals and identify the most suitable model for the healthy population. Our findings highlight the transformative potential of BCI technology to significantly enhance human capabilities, inspire innovation in assistive devices, and improve productivity and quality of life.

Development and validation of the post-CAR prognostic index for large B-cell lymphoma patients after CAR-T progression in third or later line treatment

Chimeric antigen receptor (CAR) T-cell therapy fails to achieve durable responses in over 60% of relapsed/refractory (R/R) large B-cell lymphoma (LBCL) patients in the third or later line setting. After CAR-T failure, survival outcomes are heterogeneous and a prognostic model in this patient population is lacking. A training cohort of 216 patients with progressive disease (PD) after CAR-T from 12 Spanish centers was used to develop the Post-CAR Prognostic Index (PC-PI); primary endpoint was overall survival (OS) from CAR-T progression. Validation was performed in an external cohort from three different European centers (n = 204). The prognostic score incorporated five variables, assessed at time of PD to CAR-T: ECOG (> 0), hemoglobin (< 10 g/dL), LDH (≥ 2xULN), number of extranodal sites (> 1) and time from CAR-T to PD (< 4 months). Patients were classified in four risk groups with distinct OS (p-value < 0.05 in all comparisons). In the validation cohort, median OS in the low (31%), intermediate-low (26%), intermediate-high (17%) and high risk (26%) were 15.7, 7.1, 1.8 and 1.0 months, respectively (p < 0.05 in all comparisons). Results were consistent following adjustment for subsequent treatment. In the external cohort, the PC-PI showed a C-statistic of 0.79 (95%CI 0.76–0.82), outperforming IPI and R-IPI. In conclusion, the PC-PI score is a novel tool for OS prediction and could facilitate risk-adapted management of LBCL patients relapsing after CAR T-cells. Additionally, these results will help stratification and interpretation of trials and real-world data incorporating CART-exposed patients.

The evolution and causes of the change in the demographic model of Russia. Message 1. General assessment of historical, medical, social and demographic events

The demographic situation in different periods was determined by the general patterns of childbearing, epidemics and the state of society. In Russia, even against the background of great upheavals, population growth has been observed for several centuries with a high birth rate, which exceeded the total mortality rate. Epidemics, wars and political events of the twentieth century, as well as the change of priorities in the 1990s, left their mark and led to a decrease in the population. This analytical review is devoted to the evolutionary changes in the Russian population model. The main historical, medical, social and demographic events that dynamically affect the demographic situation in our country are considered. Special attention is paid to the factors influencing the decline in the population in the country, which must be taken into account when planning and implementing state and territorial programs to stimulate the birth rate, taking into account the main political and economic changes.

Cardiovascular risk prediction using metabolomic biomarkers and polygenic risk scores: a cohort study and modelling analyses

Abstract Background/Introduction Metabolomic biomarker scores and polygenic risk scores (PRS) have shown promise for improving cardiovascular disease (CVD) prediction, but have not yet been evaluated in the context of current prediction models (SCORE2) and ESC recommendations for 10-year prediction of fatal and non-fatal CVD. Purpose To assess whether metabolomics biomarkers and PRSs can improve 10-year CVD risk prediction when added to SCORE2, and whether improvements are meaningful at ESC 2021 recommended risk thresholds for treatment consideration. Methods Metabolomics biomarker scores were constructed and compared to PRS and SCORE2 in 170,000 UK Biobank participants (5,096 incident CVD cases) aged 40–69 without previous CVD, diabetes, or lipid-lowering treatment. Improvement in risk discrimination when added to SCORE2 was assessed using Harrel’s C-index. Improvement in risk stratification following ESC guideline risk thresholds was assessed using categorical net reclassification. Population modelling was subsequently applied to estimate the impact on CVD prevention if applied at scale. Results Risk discrimination provided by SCORE2 (C-index: 0.718) was similarly improved by addition of metabolomic biomarker scores (ΔC-index: 0.011 [0.009–0.014]) and PRSs (ΔC-index 0.009; [0.007–0.012]). Addition of both metabolomic biomarker scores and PRSs to SCORE2 yielded the largest improvement risk discrimination, with ΔC-index 0.019 (0.016–0.022). Concomitant improvements in risk stratification were observed in categorical net reclassification index, with net case reclassification of 13.04% (11.67–14.41%). Modelling metabolic biomarker scores and PRSs for targeted risk-reclassification increased the number of CVD events prevented per 100,000 screened from 201 to 370 (ΔCVDprevented: 170 [158–182]) while essentially maintaining the number of statins prescribed per CVD event prevented. Conclusions Combining metabolomic biomarker scores and PRSs with SCORE2 moderately enhances prediction of first-onset CVD, and could have substantial population health benefit if applied at scale.

Clinicians' Perspectives and an Ethical Analysis of Safer Supply Opioid Prescribing.

In British Columbia, Canada, many physicians providing care to individuals with high-risk opioid use disorder adopted safer supply (SS) opioid prescribing in the spring of 2020 with the goal of facilitating public health measures for COVID-19. This prescribing practice continued after measures were lifted. This study aimed to explore prescribers' perspectives following several years of local experience in prescribing SS opioids, primarily in the form of hydromorphone tablets, and to apply ethical concepts to explore current challenges and ongoing sources of provider distress. Addiction medicine SS prescribers participated in individual or small group semi-structured interviews. Each interview was transcribed and analysed for recurrent themes. Themes were then integrated into a narrative ethics discussion. Eleven addiction medicine physicians practicing in various settings within Vancouver participated in this study. Six themes were identified: clinical assessment, clinician distress, gaps in care, models of safer supply, research, and special populations. Ethical dilemmas in prescribing SS are identified and explored through a discussion of biomedical ethics principles and the physician role.

Transferability of vaccination interventions from a community perspective: One size does not fit all

Abstract Background Removing health system barriers to childhood vaccination is an urgent matter in the migrant community in Greece, the Turkish and Moroccan communities in the Netherlands, the Ukrainian minority in Poland, and the marginalized Roma community in Slovakia. The objective of this study was to select and adapt potentially transferable evidence-based interventions for HPV and MMR vaccine uptake in partnership with the underserved communities. Methods Using a participatory approach, we assessed transferability of six pre-selected interventions with the Population, Intervention, Environment and Transfer (PIET-T) models: All countries investigated two interventions with trained community members to support vaccination as health promoters. Greece and the Netherlands included a school-based vaccination program. The Netherlands added two interventions addressing providers’ HPV vaccine communication, and Slovakia a multilevel intervention. Results We identified barriers and facilitators for successful intervention transfer in each of the target contexts. The transferability analysis showed that an intervention with health promoters is potentially transferable with adaptations in all countries. Principles of trust in the educator, tailored and trustworthy information, help in navigating the health system for vaccination and easy access at no cost are transferable key elements in all contexts. Adaptations refer to the intervention elements that need to be tailored to specific contextual needs. School-based vaccination was nowhere transferable. Health provider training needs to be culturally sensitive and adapted to the Dutch system. The multilevel intervention in Slovakia is not fully transferable. Conclusions Participatory transferability analysis was essential for decision-making and intervention adaptation. All countries independently selected a health promoter intervention as having potential to remove the identified health system barriers in the respective context.

Heat-related risk in outdoor workers in the context of climate change: a systematic review

Abstract Background Climate change is one of the paramount challenges in the current global health setting. From an occupational health perspective, outdoor workers are amongst the most vulnerable populations to climate change and heat-related risk. A systematic review was performed, aiming to assess heat-related health outcomes in outdoor workers. Methods The research query was structured using the Population, Intervention, Comparison and Outcome (PICO) model, and including synonyms and Medical Subject Headings (MeSH) terms for the following keywords: “outdoor workers”, “climate change”, and “occupational risk”. The systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements, screening three databases (PubMed, Web of Science, and Scopus). Results After duplicate removal and title and abstract screening, the final screening by full-text resulted in 10 included studies. The included studies were performed mostly on construction (included in 5 studies, 50.0%) and agriculture (in 5 studies, 50.0%) workers. Eight studies (80.0%) had a majority of male workers, while one (10.0%) had a majority of female workers, and one study (10.0%) did not report on the gender ratio. Four of the included studies (40.0%) reported a high prevalence of heat-related symptoms, ranging from 64.0% to 90.3% of workers. Three studies (30.0%) showcased a prevalence of heat-related symptoms in less than 50% of workers (ranging from 11.6% to 47.5%). Three studies (30.0%) evaluated odds ratio, highlighting an increased odds ratio of heat-related symptoms in outdoor workers. Conclusions This systematic review highlights heat-related stress in outdoor workers as an important occupational risk. Mitigation and prevention strategies should be developed and implemented at a company, national, and supra-national level, to ensure the safety and wellbeing of outdoor workers worldwide. Key messages • A systematic review was performed to assess risks related to climate change in outdoor workers. Heat-related symptoms were reported with a high prevalence, ranging from 64.0% to 90.3% of workers. • Mitigation and prevention strategies should be developed and implemented at a company, national, and supra-national level, to ensure the safety and wellbeing of outdoor workers worldwide.

Within-chain parallelization-Giving Stan Jet Fuel for population modeling in pharmacometrics.

Stan is a powerful probabilistic programming language designed mainly for Bayesian data analysis. Torsten is a collection of Stan functions that handles the events (e.g., dosing events) and solves the ODE systems that are frequently present in pharmacometric models. To perform a Bayesian data analysis, most models in pharmacometrics require Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution. However, MCMC is computationally expensive and can be time-consuming, enough so that people will often forgo Bayesian methods for a more traditional approach. This paper shows how to speed up the sampling process in Stan by within-chain parallelization through both multi-threading using Stan's reduce_sum() function and multi-processing using Torsten's group ODE solver. Both methods show substantial reductions in the time necessary to sufficiently sample from the posterior distribution compared with a basic approach with no within-chain parallelization.

1:2 Strong Resonance and Hybrid Control of Discrete Conformable Fractional Order Bacteria Population Model

1:2 Strong Resonance and Hybrid Control of Discrete Conformable Fractional Order Bacteria Population Model

Dynamics of a Discrete Population Model

In order to explore the influences of the strong Allee effect on population evolution in two-patch environments, this paper constructs a population model with discrete diffusion and analyzes its dynamics. Using the center manifold theorem, we discuss the codimension-one bifurcation, such as flip bifurcation, of the model. The qualitative structures and stability of the model at the degenerate fixed point are obtained from the approximation of flow. Based on the theoretical analysis results, we investigate the influences of the strong Allee effect and diffusion on population evolution. The qualitative structures of degenerate fixed point and numerical simulation show that the strong Allee effect will make the population tend to a stable size when the two initial population patch sizes are equal and exceed a threshold. But the stable size of population does not exceed the carrying capacity, depending on the constant Allee effect and carrying capacity, which differ from the conclusion of logistic model that population size tends to the carrying capacity. However, if the two initial population patches are equal in size and below this threshold, or if the sizes of the initial population patches are unequal, the population size will decrease. The fixed point becomes degenerate with respect to the strong Allee effect causing the population tend to a stable size below carrying capacity, which exacerbates the complexity of population evolution.

A Clinical and Economic Comparison of Cell-Based Versus Recombinant Influenza Vaccines in Adults 18–64 Years in the United States

Background: This analysis compares the cost-effectiveness of a cell-based influenza vaccine to a recombinant influenza vaccine, and each to no vaccination. The analysis is based on United States (US) commercial and societal perspectives. Methods: A Susceptible–Exposed–Infectious–Recovered (SEIR) transmission model of the total US population followed with a cost-effectiveness model for 18–64-year-olds was used to estimate the clinical and economic impact of vaccination over one influenza season (2018–2019). Deterministic and probabilistic sensitivity analyses were conducted. Results: Both enhanced vaccines prevented a substantial number of influenza cases and influenza-related deaths compared to no vaccination. The cell-based vaccine was associated with higher quality-adjusted life years (QALYs) gained compared to the recombinant vaccine or no vaccination. The cell-based vaccine had a 36% lower vaccination cost, amounting to $2.8 billion in cost savings, compared to the recombinant vaccine. The incremental cost-effectiveness ratios (ICERs) for the cell-based vaccine, compared to the recombinant vaccine or no vaccination, were dominant from all payer perspectives, regardless of risk groups. Conclusions: Overall, the cell-based vaccine was cost-saving compared to the recombinant vaccine for subjects aged 18–64 years in the US, achieving comparable health outcomes with a significant reduction in associated costs.