Population-based Model Research Articles

Population-Based Model of the Fraction of Incidental COVID-19 Hospitalizations during the Omicron BA.1 Wave in the United States

1. Background: Some reports have suggested that as many as one-half of all hospital inpatients identified as COVID-19-positive during the Omicron BA.1 variant-driven wave were incidental cases admitted primarily for reasons other than their viral infections. To date, however, there are no prospective longitudinal studies of a representative panel of hospitals based on pre-established criteria for determining whether a patient was, in fact, admitted as a result of the disease. 2. Materials and Methods: To fill this gap, we developed a formula to estimate the fraction of incidental COVID-19 hospitalizations that relies on measurable, population-based parameters. We applied our approach to a longitudinal panel of 164 counties throughout the United States, covering a 4-week interval ending in the first week of January 2022. 3. Results: Within this panel, we estimated that COVID-19 incidence was rising exponentially at a rate of 9.34% per day (95% CI, 8.93–9.87). Assuming that only one-quarter of all Omicron BA.1 infections had been reported by public authorities, we further estimated the aggregate prevalence of active SARS-CoV-2 infection during the first week of January to be 3.45%. During the same week, among 250 high-COVID-volume hospitals within our 164-county panel, an estimated one in four inpatients was COVID-positive. Based upon these estimates, we computed that 10.6% of such COVID-19-positive hospitalized patients were incidental infections. Across individual counties, the median fraction of incidental COVID-19 hospitalizations was 9.5%, with an interquartile range of 6.7 to 12.7%. 4. Conclusion: Incidental COVID-19 infections appear to have been a nontrivial fraction of all COVID-19-positive hospitalized patients during the Omicron BA.1 wave. In the aggregate, however, the burden of patients admitted for complications of their viral infections was far greater.

Prediction of ICU Patients' Deterioration Using Machine Learning Techniques.

Assessing vital sign measurements within hospital settings presents a valuable opportunity for data analysis and knowledge extraction. By generating adaptable, personalized prediction models of patient vital signs, these models can yield clinically relevant insights not achievable through population-based models. This study aims to compare several statistical forecasting models to determine their real-life applicability. The primary objectives of this paper are to evaluate whether the following measurements: blood pressure, oxygen saturation, temperature and heart rate can predict deterioration in Intensive Care Unit (ICU) patients. Additionally, we aim to identify which of these measurements contributes most significantly to our prediction. Lastly, we seek to determine the most accurate data mining technique for real-life data applications. This retrospective chart review study utilized data from patients admitted to the ICU at a tertiary hospital between January and December, 2019. Data mining techniques for prediction included logistic regression, support vector machine classifier, k-nearest neighbors (KNN), gradient boosting classifier, and Naive Bayes classifier. A comprehensive comparison of these techniques was performed, focusing on accuracy, precision, recall, and F-measure. To achieve the research objectives, the SelectKBest class was applied to extract the most contributory features for prediction. Blood pressure ranked first with a score of 9.98, followed by respiratory rate, temperature, and heart rate. Analysis of 653 patient records indicated that 129 patients expired, while 542 patients were discharged either to their homes or other facilities. Among the five training models, two demonstrated the highest accuracy in predicting patient deterioration or survival at 88.83% and 84.72%, respectively. The gradient boosting classifier accurately predicted 115 out of 129 expired patients, while the KNN correctly predicted 109 out of 129 expired patients. Machine learning has the potential to enhance clinical deterioration prediction compared to traditional methods. This allows healthcare professionals to implement preventative measures and improve patients' quality of life, ultimately increasing average life expectancy. Although our research focused exclusively on ICU patients, data mining techniques can be applied in various contexts both within and outside the hospital setting.

The optimal strategy of incompatible insect technique (IIT) using Wolbachia and the application to malaria control

For decades, techniques to control vector population with low environmental impact have been widely explored in both field and theoretical studies. The incompatible insect technique (IIT) using Wolbachia, based on cytoplasmic incompatibility, is a technique that Wolbachia-infected male mosquitoes are incapable of producing viable offspring after mating with wild-type female mosquitoes. While the IIT method experimentally ensured its effectiveness in several field works, the failure of female mosquito population control by replacement owing to the accidental contamination of Wolbachia-infected female mosquitoes has been a concern and an obstacle in implementing the IIT method in nature. In this study, we develop a population-based IIT mathematical model using cytoplasmic incompatibility and evaluate the effectiveness of the IIT method in scenarios where contamination is present or absent. In addition, by extending the model to assess the disease infection status of the human population with malaria, we evaluate the optimal release strategy and cost for successful disease control. Our study proves that IIT could be a promising method to control mosquito-borne diseases without perfect suppression of vector mosquito population regardless of contamination.

Estimating long-term outcomes in classic Hodgkin lymphoma: a United States population-based oncology simulation model based on overall survival from the ECHELON-1 trial

The six-year ECHELON-1 update showed a survival advantage for frontline (1 L) A + AVD (brentuximab vedotin, doxorubicin, vinblastine, dacarbazine) vs ABVD (doxorubicin, bleomycin, vinblastine, dacarbazine) for stage III/IV classic Hodgkin lymphoma (cHL). As clinical trials have limited ability to track patients for extended periods, we developed an oncology simulation model using ECHELON-1 data to estimate population-based cHL outcomes in the US over 10 years (through 2031). The model included a scenario without (64.5% ABVD, 35.5% PET-adapted ABVD utilization) and scenarios with 1 L A + AVD (27%–80%k utilization). At 27%–80% A + AVD utilization, the model estimated 13.6%–31.7% fewer deaths, 2.4%–6.3% more patients ≥5 years progression free, 9.4%–24.4% fewer stem cell transplants (SCTs), and 7.8%–22.5% fewer second cancers over 10 years. These results suggest that the improved outcomes observed in the ECHELON-1 update with A + AVD vs ABVD may translate to more patients alive and fewer with primary relapsed/refractory cHL, SCTs, and second cancers.

Probabilistic evaluation of plan quality for time-dependent anatomical deformations in head and neck cancer patients

PurposeIn addition to patient set-up uncertainties, anatomical deformations, e.g., weight loss, lead to time-dependent differences between the planned and delivered dose in a radiotherapy course that currently cannot easily be predicted. The aim of this study was to create time-varying prediction models to describe both the average and residual anatomical deformations. MethodsWeekly population-based principal component analysis models were generated from on-treatment cone-beam CT scans (CBCTs) of 30 head and neck cancer patients, with additional data of 35 patients used as a validation cohort. We simulated treatment courses accounting for a) anatomical deformations, b) set-up uncertainties and c) a combination of both. The dosimetric effects of the simulated deformations were compared to a direct dose accumulation based on deformable registration of the CBCT data. ResultsSet-up uncertainties were seen to have a larger effect on the organ at risk (OAR) doses than anatomical deformations for all OARs except the larynx and the primary CTV. Distributions from simulation results were in good agreement with those of the accumulated dose. ConclusionsWe present a novel method of modelling time-varying organ deformations in head and neck cancer. The effect on the OAR doses from these deformations are smaller than the effect of set-up uncertainties for most OARs. These models can, for instance, be used to predict which patients could benefit from adaptive radiotherapy, prior to commencing treatment.

Development and External Validation of a Novel Nomogram Predicting Cancer-specific Mortality–free Survival in Surgically Treated Papillary Renal Cell Carcinoma Patients

BackgroundAccurate prediction of cancer control outcomes in renal cell carcinoma (RCC) patients is important for counselling, follow-up planning, and selection of appropriate adjuvant trial designs. ObjectiveTo develop and externally validate a novel contemporary population-based model for predicting cancer-specific mortality–free survival (CSM-FS) in surgically treated papillary RCC (papRCC) patients and to compare it with established risk categories (Leibovich 2018). Design, setting, and participantsWithin the Surveillance, Epidemiology, and End Results database (2004–2019), we identified surgically treated papRCC patients (n = 3978). The population was randomly divided into development (50%, n = 1989) and external validation (50%, n = 1989) cohorts. Of the external validation cohort, 97% (n = 1930) of patients were included in a head-to-head comparison of the Leibovich 2018 risk categories addressing nonmetastatic patients. Outcome measurements and statistical analysisUnivariable Cox regression models tested the statistical significance in the prediction of CSM-FS. The most parsimonious model with the best validation metrics was selected as the multivariable nomogram. Accuracy, calibration, and decision curve analyses (DCAs) tested the Cox regression–based nomogram, as well as the Leibovich 2018 risk categories in the external validation cohort. Results and limitationsAge at diagnosis, grade, T stage, N stage, and M stage qualified for inclusion in the novel nomogram. In external validation, the accuracy of the novel nomogram was 0.83 at 5 yr and 0.80 at 10 yr. In nonmetastatic patients, 5- and 10-yr accuracy of the novel nomogram was 0.77 and 0.76, respectively. Conversely, 5- and 10-yr accuracy of the Leibovich 2018 risk categories was 0.70 and 0.66, respectively. The novel nomogram exhibited smaller departures from ideal predictions in calibration plots and higher net benefit in DCAs, when it was compared with the Leibovich 2018 risk categories. Limitations include the retrospective nature of the study, absence of a central pathological review, and inclusion of only North American patients. ConclusionsThe novel nomogram may represent a valuable clinical aid, when papRCC CSM-FS predictions are required. Patient summaryWe developed an accurate tool to predict death due to papillary kidney cancer in a North American population.

Thirdhand tobacco smoke exposure increases the genetic background-dependent risk of pan-tumor development in Collaborative Cross mice

Increasing evidence has shown that thirdhand smoke (THS) exposure is likely to induce adverse health effects. An important knowledge gap remains in our understanding of THS exposure related to cancer risk in the human population. Population-based animal models are useful and powerful in investigating the interplay between host genetics and THS exposure on cancer risk. Here, we used the Collaborative Cross (CC) mouse population-based model system, which recapitulates the genetic and phenotypic diversity observed in the human population, to assess cancer risk after a short period of exposure, between 4 and 9 weeks of age. Eight CC strains (CC001, CC019, CC026, CC036, CC037, CC041, CC042 and CC051) were included in our study. We quantified pan-tumor incidence, tumor burden per mouse, organ tumor spectrum and tumor-free survival until 18 months of age. At the population level, we observed a significantly increased pan-tumor incidence and tumor burden per mouse in THS-treated mice as compared to the control (p = 3.04E-06). Lung and liver tissues exhibited the largest risk of undergoing tumorigenesis after THS exposure. Tumor-free survival was significantly reduced in THS-treated mice compared to control (p = 0.044). At the individual strain level, we observed a large variation in tumor incidence across the 8 CC strains. CC036 and CC041 exhibited a significant increase in pan-tumor incidence (p = 0.0084 and p = 0.000066, respectively) after THS exposure compared to control. We conclude that early-life THS exposure increases tumor development in CC mice and that host genetic background plays an important role in individual susceptibility to THS-induced tumorigenesis. Genetic background is an important factor that should be taken into account when determining human cancer risk of THS exposure.

Estimating long-term progression-free and overall survival in patients with peripheral T-cell lymphoma: A US population-based oncology simulation model based on 5-year results from the ECHELON-2 trial.

BACKGROUND: The ECHELON-2 5-year update showed continued clinically meaningful improvements in progression-free survival (PFS) and overall survival with frontline (1L) A+CHP (brentuximab vedotin in combination with cyclophosphamide, doxorubicin, prednisone) vs CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone) in CD30-expressing peripheral T-cell lymphomas (PTCLs). OBJECTIVE: To estimate PTCL annual prevalence in the United States in 2031 without and with A+CHP using data from the ECHELON-2 5-year update. METHODS: Population-level outcomes were estimated using a dynamic oncology simulation model. Utilization of 1L CHOP (65% utilization) and CHOP plus etoposide (35% utilization) were varied over time and compared with scenarios incorporating 1L A+CHP (20%-50% utilization; base case: 40% utilization) per expert clinicians' opinion. Additional inputs included PTCL incidence and PFS for consolidation and post-1L therapies from published sources. PFS (51.4% [95% CI = 42.8%-59.4%] vs 43.0% [35.8%-50.0%]) and overall survival (hazard ratio = 0.72 [0.53-0.99]) for A+CHP and CHOP came from ECHELON-2. RESULTS: In 2031, an estimated 2,082 patients will be diagnosed with PTCL. Approximately 1,412 additional patients will be alive and progression free, and 106 fewer patients will require second-line therapy with 40% A+CHP utilization vs no A+CHP utilization. Varying 1L A+CHP utilization from 20%-50% vs no 1L A+CHP utilization added 732 to 1,752 patients alive and progression free. CONCLUSIONS: In this oncology simulation model, the improvements in survival outcomes seen with A+CHP vs CHOP in the ECHELON-2 5-year results translated into more estimated patients with PTCL progression free and alive for at least 5 years following 1L A+CHP vs CHOP and a decreased need for post-1L therapy. DISCLOSURES: This study was funded by Seagen Inc. Dr Liu and Dr Yu are employees and shareholders of Seagen Inc. Mr Bloudek is and Dr Mordi was an employee of Curta Health, which received funding from Seagen Inc. for the conduct of this study. Dr Burke received consulting fees from Genentech/Roche, AbbVie, Seattle Genetics, Bayer, AstraZeneca, Adaptive Biotechnologies, Verastem, MorphoSys, Kura, Epizyme, BeiGene, Kymera, Novartis, Bristol Myers Squibb, TG Therapeutics, Lilly, and Nurix; and received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events in speakers bureaus for BeiGene and Seagen Inc. Dr Phillips received consulting fees from AstraZeneca, MorphoSys, Epizyme, Roche/Genentech, Epizyme Eli Lilly, AbbVie, BeiGene, Pharmacyclics, Bristol Myers Squibb, Xencor, Seagen Inc., TG Therapeutics, Bayer, Incyte, and Gilead; and received payment for honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Epizyme and Seagen Inc.

Population-based model selection for an accurate estimation of time-integrated activity using non-linear mixed-effects modelling

PurposePersonalized treatment planning in Molecular Radiotherapy (MRT) with accurately determining the absorbed dose is highly desirable. The absorbed dose is calculated based on the Time-Integrated Activity (TIA) and the dose conversion factor. A crucial unresolved issue in MRT dosimetry is which fit function to use for the TIA calculation. A data-driven population-based fitting function selection could help solve this problem. Therefore, this project aims to develop and evaluate a method for accurately determining TIAs in MRT, which performs a Population-Based Model Selection within the framework of the Non-Linear Mixed-Effects (NLME-PBMS) model. MethodsBiokinetic data of a radioligand for the Prostate-Specific Membrane Antigen (PSMA) for cancer treatment were used. Eleven fit functions were derived from various parameterisations of mono-, bi-, and tri-exponential functions. The functions' fixed and random effects parameters were fitted (in the NLME framework) to the biokinetic data of all patients. The goodness of fit was assumed acceptable based on the visual inspection of the fitted curves and the coefficients of variation of the fitted fixed effects. The Akaike weight, the probability that the model is the best among the whole set of considered models, was used to select the fit function most supported by the data from the set of functions with acceptable goodness of fit. NLME-PBMS Model Averaging (MA) was performed with all functions having acceptable goodness of fit. The Root-Mean-Square Error (RMSE) of the calculated TIAs from individual-based model selection (IBMS), a shared-parameter population-based model selection (SP-PBMS) reported in the literature, and the functions from NLME-PBMS method to the TIAs from MA were calculated and analysed. The NLME-PBMS (MA) model was used as the reference as this model considers all relevant functions with corresponding Akaike weights. ResultsThe function f3a=A1e-λ1+λphyst+A2e-λphyst was selected as the function most supported by the data with an Akaike weight of (54 ± 11) %. Visual inspection of the fitted graphs and the RMSE values show that the NLME model selection method has a relatively better or equivalent performance than the IBMS or SP-PBMS methods. The RMSEs of the IBMS, SP-PBMS, and NLME-PBMS (f3a) methods are 7.4%, 8.8%, and 2.4%, respectively. ConclusionA procedure including fitting function selection in a population-based method was developed to determine the best fit function for calculating TIAs in MRT for a given radiopharmaceutical, organ and set of biokinetic data. The technique combines standard practice approaches in pharmacokinetics, i.e. an Akaike-weight-based model selection and the NLME model framework.

Ethical considerations surrounding health-related big data research.

As health-related big data research (HRBDR) has drastically increased over the last years due to the rapid development of big data analytics, a range of important ethical issues are raised. In this study, a systematic literature review was conducted. Several and interesting results emerged from this review. The term ″big data″ has not yet been clearly defined. The already existing ethical principles and concepts need to be revisited in the new HRBDR context. Traditional research ethics notions like privacy and informed consent are to be reconsidered. HRBDR creates new ethical issues such those related to trust / trustworthiness and public values such as reciprocity, transparency, inclusivity and common good. The implementation of dynamic consent rather than broad consent is currently highlighted as the more satisfying solution. Ethical review committees in their current form are ill-suited to provide exclusive ethical oversight on HRBDR projects. Expanding Ethical Review Committees' purview and members' expertise, as well as creating novel oversight bodies by promoting a co-governance system including public and all the stakeholders involved are strongly recommended. The mechanism of ″social licence″, that is, informal permissions granted to researchers by society, can serve as a guideline. High-stakes decisions are often made under uncertainty. Machine learning algorithms are highly complex and in some cases opaque, and may yield biased decisions or discrimination. Improved interdisciplinary dialogue along with considering aspects like auditing, benchmarking, confidence / trust and explainability /interpretability may address concerns about HRBDR ethics. Finally and most importantly, research ethics shifts towards a population-based model of ethics.

P831 Clinical factors associated with severity in patients with Inflammatory Bowel Disease in Brazil (On Behalf of GEDIIB)

Abstract Background Brazil has shown an increase in Inflammatory Bowel Disease (IBD) cases. GEDIIB (Brazilian Organization of Crohn’s Disease and Colitis) established a data platform to create a national registry of IBD patients. The study aimed to characterize the profile of IBD patients and identify clinical factors associated with IBD severity. Methods A cohort study was conducted between Jul/20 and Aug/22. Data obtained from medical records and/or directly from patients were registered via REDCap. Local institutional review boards approved the study protocol. We designed a population-based risk model aimed at stratifying severe disease based on one or more outcome variables: previous hospitalization, surgery, and biologics. Univariate and bivariate analyses and Poisson modeling were used. Results A total of 1,179 patients were included: 600 (51%) with ulcerative colitis (UC), 568 (48%) with Crohn’s Disease (CD), and 11 (0.9%) with indeterminate colitis. The mean age was 34.4±14.7y, 59% female, 73% Caucasian, and 85.3% non-smoker. Regarding the initial symptoms, 42% presented diarrhea, 38% abdominal pain, and 20% weight loss. The age of IBD symptom onset ranged from 1-87 years (32.3±14.4). According to the Montreal classification of CD, A1: 5%, A2: 63%, A3: 32%; L1: 29.7%, L2: 14.3%, L3: 41.2%, B1: 32%, B2: 26.7%, B3: 11.3%; perianal 15.5%. In UC, 46.3% presented pancolitis and 30% left-sided colitis. Only 3.9% were malnourished, 30.9% were overweight, and 18% were obese. The main extraintestinal manifestations were rheumatologic (21%). Regarding medical treatment, 68.1% of the patients received biologics (45% Infliximab, 29% Adalimumab, 9.7% Vedolizumab, and 8.8% Ustekinumab), 67% salicylates, 47.6% immunosuppressors, and 0.8% Tofacitinib. Of those submitted to surgery (34.1%, n=439), 54% were elective versus 46% urgent; most procedures (80%) were open/laparotomy, while 20% were laparoscopic. Of those, 8.5% were colectomy. The presence of CD, pancolitis, the absence of (isolated) proctitis, younger age (<20 years), rheumatologic manifestations, and no history of smoking were found to be independent risk factors. Conclusion This is the first epidemiological study using the national patient registry organized by GEDIIB. The profile of patients with severe disease is consistent with the data in the literature, characterized by younger age, greater extent of disease, and extraintestinal manifestations. Further epidemiological studies should be encouraged to guide national policies aimed at the early diagnosis and treatment of IBD.

Strengthening the Center for Medicare and Medicaid Innovation's Approach to Constructing Alternative Payment Models.

The Center for Medicare and Medicaid Innovation (CMMI) seeks to develop evidence-based alternative payment models (APM) to improve health care quality and reduce costs, but its performance in achieving these goals has been mixed. In October 2021, CMMI released its Innovation Strategy Refresh to highlight challenges faced by payment models and suggest new strategic approaches for the upcoming decade. While a welcome recast of organizational goals, the Refresh leaves space for how CMMI will address persistent issues. These include how CMMI can best engage physicians and patients in APMs, minimize conflicting incentives among APMs, reduce selection bias in model participation, and, ultimately, transition away from the fee-for-service framework that underlies much of Medicare reimbursement. This article provides guidance to CMMI's vision by examining challenges within CMMI's strategy for model building and offering solutions to mitigate these issues. These strategies include engaging beneficiaries in APM incentives, expanding operational flexibility to improve clinical behaviors (e.g., waivers), rectifying issues with conflicting model incentives, building voluntary short-term and mandatory long-term incentives to mitigate selection bias, and transitioning to an overriding population-based model to constrain net costs. Policy Points The Center for Medicare and Medicaid Innovation (CMMI) seeks to develop evidence-based alternative payment models (APM) to improve care quality and reduce health care cost, but its performance in achieving these goals has been mixed. In October 2021, CMMI released a "strategic refresh" of its goals but left space for how persistent issues to model development would be addressed. We propose strategies to engage physicians and patients in APMs, minimize conflicting incentives among APMs, reduce selection bias in model participation, and, ultimately, transition away from the fee-for-service framework that underlies much of Medicare reimbursement.

A population-based model identifying optimal candidate for primary tumor resection in distant metastatic laryngeal carcinoma.

To developed a model to screen distant metastatic laryngeal carcinoma (DMLC) patients who would benefit from the primary tumor resection. The propensity score matching (PSM) was utilized to avoid disproportionate distributions of the confounding factors. We hypothesized that patients who underwent surgery would benefit from surgery by having a longer median cancer-specific survival (CSS) than patients without surgery. Multivariable Cox model was used to explore the independent factors of CSS and overall survival (OS) among PSM population. We used these factors to construct a nomogram to identify surgery benefit patients. The predictive performance and clinical practicability of the nomogram were determined by area under the curve (AUC), calibration curve, and decision curve. The CSS and OS for patients who received primary tumor resection were significantly longer than those without resection (median CSS: 19months vs. 10months, P = 0.009; median OS: 21months vs. 10months, P = 0.001). The nomogram displayed a good degree of discrimination and calibration. The mean AUC of the nomogram was 0.70 (95% confidence interval [CI] 0.66-0.76) through threefold cross-validation. A predictive model was established and might be used to screen the optimal candidates for primary tumor surgery in DMLC patients.

A population-based model for rationing COVID-19 vaccine

BACKGROUND As COVID-19 vaccines develop, methods for identifying vulnerability within groups to prioritized vaccination remain un-established. This paper presents a novel approach based on population-based analysis of viral pneumonia vulnerability, as an example. METHODS The analysis employed an anonymous, 16-year, population dataset (n = 768,460) consisting of International Classification of Diseases (ICD-9) diagnoses, demographics, and dates identifying those with viral pneumonia and permitting linkage of these individuals to all their associated diagnoses for calculation of odds ratios and proportions of disorders before and after the index viral pneumonia diagnosis. RESULTS Females and males had results of differing magnitude. For those with viral pneumonia, the mean number of diagnoses was greater in both the subsample and whole sample, with associated diagnoses arising about 4 years on average before the viral pneumonia index diagnosis. Within the subsample, compared to those without, the temporal analysis revealed distinct over-representation for those with viral pneumonia at visit one and over the first fifty visits. Further, those with viral pneumonia had diagnoses not represented in the group without viral pneumonia. CONCLUSIONS The population-based analysis of temporal hyper-morbidity may be a viable and economical approach to identifying viral pneumonia vulnerability. The approach presented in this paper may provide an economical means of identifying vulnerability to COVID-19 in regions where comparable data are available for analysis. Rational approaches may optimize vaccination and help to limit the spread of the disease and to some extent alleviate the health service burden.

Spatiotemporal distribution of veterinary and human drugs and its predictability in Japanese catchments

Little is known about the predictability of mass flows of veterinary drugs in Asian catchments, where effluent from livestock farms is a major source. We therefore conducted this study to understand the applicability and limitations of a population-based emission model, which assumed usage of veterinary and human drugs to be evenly distributed over the national livestock or human population throughout the year, and sources to be effluent discharges at livestock farms, households, and sewage treatment plants in Japanese catchments. We monitored five veterinary drugs (lincomycin, sulfamonomethoxine, tiamulin, tylosin, and tilmicosin), two human and livestock drugs (sulfamethoxazole and trimethoprim), two human drugs (carbamazepine and clarithromycin), and a metabolite (sulfapyridine) of a human drug once a month over 2 years in eight Japanese rivers which have active livestock farming in their catchments. Mass flows of carbamazepine and sulfapyridine were stable, while those of veterinary drugs fluctuated widely, especially sulfamonomethoxine and tilmicosin, whose 25 %–100 % ranges averaged 1.5 and 1.2 log units, respectively, attributable mainly to their usage patterns. The model accurately predicted mean mass flows of carbamazepine in the rivers with errors of <±0.3 log unit. Although it slightly to moderately overestimated those of the other four human-related compounds, the incorporation of an empirical correction factor, determined to minimize mean absolute error (MAE) among the rivers, substantially lowered their MAEs to <0.23 log units. However, the MAEs of the five veterinary drugs were as high as 0.42 (sulfamonomethoxine) to 0.60 (tiamulin) log units even with the coefficient, likely due mainly to the spatial distribution of their usage per capita. So as not to overlook spatiotemporal elevation of risks of veterinary drugs, a stochastic method should be applied in their management. This is the first study to assess the use of spatiotemporal homogeneity in usage per capita of veterinary drugs in Asian catchments.

Effects of geometric individualisation of a human spine model on load sharing: neuro-musculoskeletal simulation reveals significant differences in ligament and muscle contribution

In spine research, two possibilities to generate models exist: generic (population-based) models representing the average human and subject-specific representations of individuals. Despite the increasing interest in subject specificity, individualisation of spine models remains challenging. Neuro-musculoskeletal (NMS) models enable the analysis and prediction of dynamic motions by incorporating active muscles attaching to bones that are connected using articulating joints under the assumption of rigid body dynamics. In this study, we used forward-dynamic simulations to compare a generic NMS multibody model of the thoracolumbar spine including fully articulated vertebrae, detailed musculature, passive ligaments and linear intervertebral disc (IVD) models with an individualised model to assess the contribution of individual biological structures. Individualisation was achieved by integrating skeletal geometry from computed tomography and custom-selected muscle and ligament paths. Both models underwent a gravitational settling process and a forward flexion-to-extension movement. The model-specific load distribution in an equilibrated upright position and local stiffness in the L4/5 functional spinal unit (FSU) is compared. Load sharing between occurring internal forces generated by individual biological structures and their contribution to the FSU stiffness was computed. The main finding of our simulations is an apparent shift in load sharing with individualisation from an equally distributed element contribution of IVD, ligaments and muscles in the generic spine model to a predominant muscle contribution in the individualised model depending on the analysed spine level.

Risk Adjustment And Promoting Health Equity In Population-Based Payment: Concepts And Evidence.

The objective of risk adjustment is not to predict spending accurately but to support the social goals of a payment system, which include equity. Setting population-based payments at accurate predictions risks entrenching spending levels that are insufficient to mitigate the impact of social determinants on health care use and effectiveness. Instead, to advance equity, payments must be set above current levels of spending for historically disadvantaged groups. In analyses intended to guide such reallocations, we found that current risk adjustment for the community-dwelling Medicare population overpredicts annual spending for Black and Hispanic beneficiaries by $376-$1,264. The risk-adjusted spending for these populations is lower than spending for White beneficiaries despite the former populations' worse risk-adjusted health and functional status. Thus, continued movement from fee-for-service to population-based payment models that omit race and ethnicity from risk adjustment (as current models do) should result in sizable resource reallocations and incentives that support efforts to address racial and ethnic disparities in care. We found smaller overpredictions for less-educated beneficiaries and communities with higher proportions of residents who are Black, Hispanic, or less educated, suggesting that additional payment adjustments that depart from predictive accuracy are needed to support health equity. These findings also suggest that adding social risk factors as predictors to spending models used for risk adjustment may be counterproductive or accomplish little.

Statistical Model for Excitation and Hypersynchronization in the Small Neural Populations

The mathematical modeling of epileptic seizures appearing in small neural populations can follow a few alternative ways: modeling of individual cells and their interaction vs. modeling groups and clusters on neurons. The purpose of this work is invention of a novel continuous (population-based) model for the appearance of the hyper-synchronized firing cells of the epileptiform type. In the same time, we use here the master equations based on the transition probabilities among different states of the cell excitation and hyper-synchronization. We developed an ODE model combining the dynamical equations for different sub-populations (unexcited, excited, and, as our novelty, hypersynchronized). Our model may serve as a simple but powerful tool to analyze the appearance and development of epileptiform dynamics in artificial neural networks. It can cover different cases of microepilepsy, and also may open the gate for studying drug-resistant epilepsy regime. Our dynamical set can be extended with the control inputs mimicking the external perturbations of the neural clusters with the electrical or optogenetic signals. In this case, the set of control algorithms can be applied to detect and suppress the epileptiform dynamics. Thus, the dynamic processes of epilepsy in small neural populations do not demand necessary the development of detailed models for individual neurons. Even the ‘averaged’ dynamical set for the unexcited, excited and hypersynchronized sub-populations can serve as an efficient tool for investigation and numerical simulations of microscopic seizures.

A penalty-based algorithm proposal for engineering optimization problems.

This paper presents a population-based evolutionary computation model for solving continuous constrained nonlinear optimization problems. The primary goal is achieving better solutions in a specific problem type, regardless of metaphors and similarities. The proposed algorithm assumes that candidate solutions interact with each other to have better fitness values. The interaction between candidate solutions is limited with the closest neighbors by considering the Euclidean distance. Furthermore, Tabu Search Algorithm and Elitism selection approach inspire the memory usage of the proposed algorithm. Besides, this algorithm is structured on the principle of the multiplicative penalty approach that considers satisfaction rates, the total deviations of constraints, and the objective function value to handle continuous constrained problems very well. The performance of the algorithm is evaluated with real-world engineering design optimization benchmark problems that belong to the most used cases by evolutionary optimization researchers. Experimental results show that the proposed algorithm produces satisfactory results compared to the other algorithms published in the literature. The primary purpose of this study is to provide an algorithm that reaches the best-known solution values rather than duplicating existing algorithms through a new metaphor. We constructed the proposed algorithm with the best combination of features to achieve better solutions. Different from similar algorithms, constrained engineering problems are handled in this study. Thus, it aims to prove that the proposed algorithm gives better results than similar algorithms and other algorithms developed in the literature.

Cuffless Blood Pressure Estimation During Moderate- and Heavy-Intensity Exercise Using Wearable ECG and PPG.

To develop and evaluate an accurate method for cuffless blood pressure (BP) estimation during moderate- and heavy-intensity exercise. Twelve participants performed three cycling exercises: a ramp-incremental exercise to exhaustion, and moderate and heavy pseudorandom binary sequence exercises on an electronically braked cycle ergometer over the course of 21 minutes. Subject-specific and population-based nonlinear autoregressive models with exogenous inputs (NARX) were compared with feedforward artificial neural network (ANN) models and pulse arrival time (PAT) models. Population-based NARX models, (applying leave-one-subject-out cross-validation), performed better than the other models and showed good capability for estimating large changes in mean arterial pressure (MAP). The models were unable to track consistent decreases in BP during prolonged exercise caused by reduction in peripheral vascular resistance, since this information is apparently not encoded in the employed proxy physiological signals (electrocardiography and forehead PPG) used for BP estimation. Nevertheless, the population-based NARX model had an error standard deviation of 11.0 mmHg during the entire exercise window, which improved to 9.0 mmHg when the model was periodically calibrated every 7 minutes. Population-based NARX models can estimate BP during moderate- and heavy-intensity exercise but need periodic calibration to account for the change in vascular resistance during exertion. MAP can be continuously tracked during exercise using only wearable sensors, making monitoring exercise physiology more convenient and accessible.