Population-based Modeling Research Articles

Quantifying Distributions of Parameters for Cardiac Action Potential Models Using the Hamiltonian Monte Carlo Method.

Cardiac action potential (AP) models are typically given with a single set of parameter values; however, this approach does not consider variability and uncertainty across individuals and experimental conditions. As an alternative to single-value parameter fitting, we sought to use a Bayesian approach, the Hamiltonian Monte Carlo (HMC) algorithm, to find distributions of physiological parameter values for cardiac AP models across a range of cycle lengths (CLs) and dynamics. To assess HMC's accuracy for cardiac data, we applied it to synthetic APs from the Mitchell-Shaeffer (MS) and Fenton-Karma (FK) models with added noise over a range of physiological CLs, some of which included alternans. To show the applicability of HMC to experimental data, we calculated parameter distributions for both models using micro-electrode recordings of zebrafish APs from a range of CLs. For synthetic APs generated from three CLs using the MS (FK) models, HMC produced unimodal quasi-symmetric distributions for all five (13) parameters. APs generated by setting all parameters in the MS (FK) model to the modes of their corresponding marginal distributions yielded errors in voltage traces below 5.0% (0.6%). We also obtained distributions for the MS (FK) model parameters using zebrafish data to construct the first minimal model of the zebrafish AP, with voltage trace errors below 4.8% (3.4%). We have shown that HMC can identify not only a single set of parameter values but also viable distributions for cardiac AP model parameters using synthetic and experimental data. Because HMC generates samples from the parameter distributions based on input data, it can produce families of parameterizations that can be used in population-based modeling approaches without the need for rejecting a large number of randomly generated candidate parameterizations. HMC also has the potential to provide quantitative measures of spatial/individual variability and uncertainty.

Economic impact of avoidable cancer deaths caused by diagnostic delay during the COVID-19 pandemic: A national population-based modelling study in England, UK

IntroductionDelays in cancer diagnosis arose from the commencement of non-pharmaceutical interventions (NPI) introduced in the UK in March 2020 in response to the COVID-19 pandemic. Our earlier work predicted this will lead to approximately 3620 avoidable deaths for four major tumour types (breast, bowel, lung, and oesophageal cancer) in the next 5 years. Here, using national population-based modelling, we estimate the health and economic losses resulting from these avoidable cancer deaths. We also compare these with the impact of an equivalent number of COVID-19 deaths to understand the welfare consequences of the different health conditions. MethodsWe estimate health losses using quality-adjusted life years (QALYs) and lost economic productivity using the human capital (HC) approach. The analysis uses linked English National Health Service (NHS) cancer registration and hospital administrative datasets for patients aged 15–84 years, diagnosed with breast, colorectal, and oesophageal cancer between 1st Jan to 31st Dec 2010, with follow-up data until 31st Dec 2014, and diagnosed with lung cancer between 1st Jan to 31st Dec 31 2012, with follow-up data until 31st Dec 2015. Productivity losses are based on the estimation of excess additional deaths due to cancer at 1, 3 and 5 years for the four cancer types, which were derived from a previous analysis using this dataset. A total of 500 random samples drawn from the total number of COVID-19 deaths reported by the Office for National Statistics, stratified by gender, were used to estimate productivity losses for an equivalent number of deaths (n = 3620) due to SARS-CoV-2 infection. ResultsWe collected data for 32,583 patients with breast cancer, 24,975 with colorectal cancer, 6744 with oesophageal cancer, and 29,305 with lung cancer. We estimate that across the four site-specific cancers combined in England alone, additional excess cancer deaths would amount to a loss of 32,700 QALYs (95% CI 31,300-34,100) and productivity losses of £103.8million GBP (73.2–132.2) in the next five years. For breast cancer, we estimate a loss of 4100 QALYS (3900–4400) and productivity losses of £23.2 m (18.2–28.6); for colorectal cancer, 15,000 QALYS (14,100–16,000) lost and productivity losses of £35.7 m (22.4–48.7); for lung cancer 10,900 QALYS (9,900–11,700) lost and productivity losses of £38.3 m (14.0–59.9) for lung cancer; and for oesophageal cancer, 2700 QALYS (2300–3,100) lost and productivity losses of £6.6 m (–6 to –17.6). In comparison, the equivalent number of COVID-19 deaths caused approximately 21,450 QALYs lost, as well as productivity losses amounting to £76.4 m (73.5–79.2). ConclusionPremature cancer deaths resulting from diagnostic delays during the first wave of the COVID-19 pandemic in the UK will result in significant economic losses. On a per-capita basis, this impact is, in fact, greater than that of deaths directly attributable to COVID-19. These results emphasise the importance of robust evaluation of the trade-offs of the wider health, welfare and economic effects of NPI to support both resource allocation and the prioritisation of time-critical health services directly impacted in a pandemic, such as cancer care.

Impact of tobacco control policies implementation on future lung cancer incidence in Europe: An international, population-based modeling study.

SummaryBackgroundDespite recent trends in declining smoking rates, tobacco smoking remains the most preventable cause of cancer in Europe. We aimed to estimate numbers and proportions of future lung cancer cases that could be potentially prevented over a 20-year period if countries in Europe were to achieve a comprehensive implementation of tobacco control policies.MethodsHistorical data from population-based cancer incidence (or mortality) was used to predict sex-specific lung cancer incidence for 30 European countries up to 2037. Hypothetical country-specific smoking prevalence that would be expected if countries would have achieved the highest-level implementation of tobacco control policies (defined by the maximum total score of the Tobacco Control Scale, TCS) was estimated by combining national prevalence data on current smoking and data on the status of implementation of key tobacco control policies. Resulting numbers and proportions of potentially preventable lung cancer cases were estimated taking into account latency periods between changes in smoking prevalence and excess cancer risks.FindingsIn Europe, an estimated 1·65 million lung cancer cases (21·2%, 19·8% in men and 23·2% in women) could be prevented over a 20-year period with the highest-level implementation of tobacco control policies. Large variation was seen in European regions and countries reflecting the current level of tobacco control, with the largest potential for prevention in Western Europe (24·5%), Southern Europe (23·1%) and Eastern Europe (22·5%), and the lowest but still substantial potential for further prevention in Northern Europe (12·5%). In women, among whom lung cancer incidence is expected to increase, we estimated somewhat larger proportions of preventable lung cancer cases ranging from 9·9 to 33·9% as compared to men (8·6–28·5%). In the final year of study period (2037), these proportions even exceed 50% in women for some countries.InterpretationImproved and expanded implementation of evidence-based tobacco control policies at the most comprehensive level could reduce future lung cancer incidence considerably across Europe.FundingThe study was funded by the 10.13039/501100005972German Cancer Aid (“Deutsche Krebshilfe”), grant number 70112097.

Global demand for cancer surgery and an estimate of the optimal surgical and anaesthesia workforce between 2018 and 2040: a population-based modelling study.

The growing demand for cancer surgery has placed a global strain on health systems. In-depth analyses of the global demand for cancer surgery and optimal workforce requirements are needed to plan service provision. We estimated the global demand for cancer surgery and the requirements for an optimal surgical and anaesthesia workforce, using benchmarks based on clinical guidelines. Using models of benchmark surgical use based on clinical guidelines, we estimated the proportion of cancer cases with an indication for surgery across 183 countries, stratified by income group. These proportions were multiplied by age-adjusted national estimates of new cancer cases using GLOBOCAN 2018 data and then aggregated to obtain the estimated number of surgical procedures required globally. The numbers of cancer surgical procedures in 44 high-income countries were divided by the actual number of surgeons and anaesthetists in the respective countries to calculate cancer procedures per surgeon and anaesthetist ratios. Using the median (IQR) of these ratios as benchmarks, we developed a three-tiered optimal surgical and anaesthesia workforce matrix, and the predictions were extrapolated up to 2040. Our model estimates that the number of cancer cases globally with an indication for surgery will increase by 5 million procedures (52%) between 2018 (9 065 000) and 2040 (13 821 000). The greatest relative increase in surgical demand will occur in 34 low-income countries, where we also observed the largest gaps in workforce requirements. To match the median benchmark for high-income countries, the surgical workforce in these countries would need to increase by almost four times and the anaesthesia workforce by nearly 5·5 times. The greatest increase in optimal workforce requirements from 2018 to 2040 will occur in low-income countries (from 28 000 surgeons to 58 000 surgeons; 107% increase), followed by lower-middle-income countries (from 166 000 surgeons to 277 000 surgeons; 67% increase). The global demand for cancer surgery and the optimal workforce are predicted to increase over the next two decades and disproportionately affect low-income countries. These estimates provide an appropriate framework for planning the provision of surgical services for cancer worldwide. University of New South Wales Scientia Scholarship and UK Research and Innovation Global Challenges Research Fund.

Impacts of implementing Healthy Building guidelines for daily PM2.5 limit on premature deaths and economic losses in urban China: A population-based modeling study

Given a large fraction of people’s exposure to urban PM2.5 occur indoors, reducing indoor PM2.5 levels may offer a more feasible and immediate way to save substantial lives and economic losses attributable to PM2.5 exposure. We aimed to estimate the premature mortality and economic loss reductions associated with achieving the newly established Chinese indoor air guideline and a few hypothetical indoor PM2.5 guideline values. We used outdoor PM2.5 concentrations from 1497 monitoring sites in 339 Chinese cities in 2015, coupled with a steady-state mass balance model, to estimate indoor concentrations of outdoor-infiltrated PM2.5. Using province-specific time-activity patterns for urban residents, we estimated outdoor and indoor exposures to PM2.5 of outdoor origin. We then proceeded to use localized census-based concentration-response models and the value of statistical life estimates to calculate premature deaths and economic losses attributable to PM2.5 exposure across urban China. Finally, we estimated potentially avoidable mortality and corresponding economic losses by meeting the current 24-hour based guideline and various hypothetical indoor limits for PM2.5. In 2015 in urban areas of mainland China, the city-specific annual mean outdoor and indoor PM2.5 concentrations ranged 9–108 μg/m3 and 5–56 μg/m3, respectively. Indoor exposures contributed 62%–91% daily and 68%–83% annually to the total time-weighted exposures. The potential reductions in total deaths and economic losses for the scenario in which daily indoor concentrations met the current guideline of 75 μg/m3, 37.5 μg/m3, and 25 μg/m3 were 16.9 (95% CI: 0.7–62.1) thousand, 87.7 (95% CI: 9.7–197.7) thousand, and 165.5 (95% CI: 30.8–304.0) thousand, respectively. The corresponding reductions in economic losses were 5.7 (95% CI: 0.2–34.8) billion, 29.4 (95% CI: 2.4–109.6) billion, and 55.2 (95% CI: 7.7–168.0) billion US Dollars, respectively. Deaths and economic losses would be reduced exponentially within the range of 0–75 μg/m3 for hypothetical indoor PM2.5 limits. The findings demonstrate the effectiveness of reducing indoor concentrations of outdoor-originated PM2.5 in saving substantial lives and economic losses in China. The analysis provides quantitative evidence to support the implementation of an indoor air quality guideline or standard for PM2.5.

Vaccinating Adolescents and Children Significantly Reduces Covid-19 Morbidity and Mortality Across All Ages: A Population-Based Modeling Study Using the UK as an Example

Background: Debate persists around the risk-benefit balance of vaccinating adolescents and children against Covid-19. Central to this debate is quantifying the contribution of adolescents and children to the transmission of SARS-CoV-2, and the potential impact of vaccinating these age groups.Methods: We developed a novel SEIR mathematical disease transmission model that quantifies the impact of different vaccination strategies on population-level SARS-CoV-2 infections and clinical outcomes. The model employs both age- and time-dependent social mixing patterns to capture the impact of changes in restrictions. The model was used to assess the impact of vaccinating adolescents and children on the natural history of the Covid-19 pandemic across all age groups, using the UK as an example.Results: The base case model demonstrates significant increases in Covid-19 disease burden in the UK following relaxation of restrictions from 19 July 2021, if vaccines are limited to those ≥18 years and vulnerable adolescents (≥12 years). This vaccination strategy would result in approximately 60% of new infections being in those Conclusions: This study demonstrates that vaccinating adolescents and children has the potential to play a vital role in reducing SARS-CoV-2 infections and subsequent Covid-19 morbidity and mortality across all ages. Our results have major global public health implications and provide valuable information to inform a potential pandemic exit strategy.Funding: Funding for this study was provided by Moderna, Inc.Declaration of Interest: TS, ARM and PM are employees of Health Economics and Outcomes Research Ltd., Cardiff, UK. Health Economics and Outcomes Research Ltd received fees from Moderna in relation to this study. MM is an employee of Moderna, Inc. CAT and POB are employees of Moderna, Inc. and hold Moderna stocks/stock options. ME and WDS have no conflicts of interest to declare.

Delayed access to lung cancer screening and treatment during the COVID-19 pandemic: Are we headed for a lung cancer pandemic?

Delayed access to lung cancer screening and treatment during the COVID-19 pandemic: Are we headed for a lung cancer pandemic?

Modeling motor-evoked potentials from neural field simulations of transcranial magnetic stimulation

ObjectiveTo develop a population-based biophysical model of motor-evoked potentials (MEPs) following transcranial magnetic stimulation (TMS). MethodsWe combined an existing MEP model with population-based cortical modeling. Layer 2/3 excitatory and inhibitory neural populations, modeled with neural-field theory, are stimulated with TMS and feed layer 5 corticospinal neurons, which also couple directly but weakly to the TMS pulse. The layer 5 output controls mean motoneuron responses, which generate a series of single motor-unit action potentials that are summed to estimate a MEP. ResultsA MEP waveform was generated comparable to those observed experimentally. The model captured TMS phenomena including a sigmoidal input–output curve, common paired pulse effects (short interval intracortical inhibition, intracortical facilitation, long interval intracortical inhibition) including responses to pharmacological interventions, and a cortical silent period. Changes in MEP amplitude following theta burst paradigms were observed including variability in outcome direction. ConclusionsThe model reproduces effects seen in common TMS paradigms. SignificanceThe model allows population-based modeling of changes in cortical dynamics due to TMS protocols to be assessed in terms of changes in MEPs, thus allowing a clear comparison between population-based modeling predictions and typical experimental outcome measures.

A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)

BackgroundPremature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable health policies and effective delivery of public health services. This study protocol proposes the development and validation of the Premature Mortality Risk Prediction Tool (PreMPoRT) that will predict the incidence of premature mortality using large population-based community health surveys and multivariable modeling approaches.MethodsPreMPoRT will be developed and validated using various training, validation, and test data sets generated from the six cycles of the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Database from 2000 to 2017. Population-level risk factor information on demographic characteristics, health behaviors, area level measures, and other health-related factors will be used to develop PreMPoRT and to predict the incidence of premature mortality, defined as death prior to age 75, over a 5-year period. Sex-specific Weibull accelerated failure time models will be developed using a Canadian provincial derivation cohort consisting of approximately 500,000 individuals, with approximately equal proportion of males and females, and about 12,000 events of premature mortality. External validation will be performed using separate linked files (CCHS cycles 2007–2008, 2009–2010, and 2011–2012) from the development cohort (CCHS cycles 2000–2001, 2003–2004, and 2005–2006) to check the robustness of the prediction model. Measures of overall predictive performance (e.g., Nagelkerke’s R2), calibration (e.g., calibration plots), and discrimination (e.g., Harrell’s concordance statistic) will be assessed, including calibration within defined subgroups of importance to knowledge users and policymakers.DiscussionUsing routinely collected risk factor information, we anticipate that PreMPoRT will produce population-based estimates of premature mortality and will be used to inform population strategies for prevention.

Impact of population aging on the burden of vaccine-preventable diseases among older adults in the United States

ABSTRACT Despite vaccination recommendations, the burden of vaccine-preventable diseases remains high in older adults in the United States (US), contributing to substantial morbidity, mortality, and health care resource use and costs. To adequately plan for health care resource needs and to help inform vaccination policies, burden of disease projections that account for population aging over the coming decades are needed. As a first step, this exploratory study projects the burden of influenza, pertussis, herpes zoster, and pneumococcal disease in adults aged 50 y and older in the US, using a population-based modeling framework with separate decision trees for each vaccine-preventable disease. The model uses projected population estimates from the US Census Bureau to account for changes in the US population over time and then calculates expected numbers of cases and associated costs for each disease, keeping current estimates of age-specific disease incidence, vaccine coverage, and efficacy constant over time. This approach was used to focus the exploratory analysis on the burden of disease that may be expected due to population changes alone, assuming that all else remains unchanged. Due to population growth and the shifting age distribution over the next 30 y, the annual societal economic burden for the four vaccine-preventable diseases is projected to increase from approximately $35 billion to $49 billion, resulting in cumulative costs of approximately $1.3 trillion, as well as more than 1 million disease-related deaths. Given such notable burden, further efforts to increase vaccination coverage and effectiveness in older adults are needed.

Neuronal Population Models Reveal Specific Linear Conductance Controllers Sufficient to Rescue Preclinical Disease Phenotypes

Objective: During the preclinical phase of drug development, potential drug candidates are often screened for their ability to alleviate certain in vitro electrophysiological features among neurons. This ability is assessed by measuring treatment outcomes using the population mean, both across different cells and different animals. The go/no-go decision for progressing a drug to a clinical trial is then based on ‘average effects’, yet these measures may not be sufficient to mitigate clinical end point risk. Population-based modeling is widely used to represent the intrinsic variability of electrophysiological features among healthy, disease and drug treated neuronal phenotypes. We pursued a method for optimizing therapeutic target design by identifying a single coherent set of ion channel targets for recovery of the healthy (Wild type) cellular phenotype simultaneously across multiple measures. Specifically, we aimed to determine the set of target modulations that best recover a heterogeneous Huntington’s disease (HD) population of model neurons into a multivariate region of phenotypic measurements corresponding to the healthy excitability profile of a heterogenous Wild type (WT) population of model neurons. Methods: Our approach combines mechanistic simulations with populations modeling of striatal neurons using evolutionary algorithms for population optimization to design ‘virtual drugs’. We introduce efficacy metrics to score population of model outcomes and use these to rank our virtual candidates. Results: We found that virtual drugs identified using heuristic approaches performed better than single target modulators and those derived from standard classification methods. We compare a real drug to the virtual candidates and demonstrate a novel in silico triaging method.

Changing patterns of mortality during the COVID-19 pandemic: Population-based modelling to understand palliative care implications

Background: COVID-19 has directly and indirectly caused high mortality worldwide. Aim: To explore patterns of mortality during the COVID-19 pandemic and implications for palliative care, service planning and research. Design: Descriptive analysis and population-based modelling of routine data. Participants and setting: All deaths registered in England and Wales between 7 March and 15 May 2020. We described the following mortality categories by age, gender and place of death: (1) baseline deaths (deaths that would typically occur in a given period); (2) COVID-19 deaths and (3) additional deaths not directly attributed to COVID-19. We estimated the proportion of people who died from COVID-19 who might have been in their last year of life in the absence of the pandemic using simple modelling with explicit assumptions. Results: During the first 10 weeks of the pandemic, there were 101,614 baseline deaths, 41,105 COVID-19 deaths and 14,520 additional deaths. Deaths in care homes increased by 220%, while home and hospital deaths increased by 77% and 90%, respectively. Hospice deaths fell by 20%. Additional deaths were among older people (86% aged ⩾ 75 years), and most occurred in care homes (56%) and at home (43%). We estimate that 22% (13%–31%) of COVID-19 deaths occurred among people who might have been in their last year of life in the absence of the pandemic. Conclusion: The COVID-19 pandemic has led to a surge in palliative care needs. Health and social care systems must ensure availability of palliative care to support people with severe COVID-19, particularly in care homes.

The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study.

BackgroundSince a national lockdown was introduced across the UK in March, 2020, in response to the COVID-19 pandemic, cancer screening has been suspended, routine diagnostic work deferred, and only urgent symptomatic cases prioritised for diagnostic intervention. In this study, we estimated the impact of delays in diagnosis on cancer survival outcomes in four major tumour types.MethodsIn this national population-based modelling study, we used linked English National Health Service (NHS) cancer registration and hospital administrative datasets for patients aged 15–84 years, diagnosed with breast, colorectal, and oesophageal cancer between Jan 1, 2010, and Dec 31, 2010, with follow-up data until Dec 31, 2014, and diagnosed with lung cancer between Jan 1, 2012, and Dec 31, 2012, with follow-up data until Dec 31, 2015. We use a routes-to-diagnosis framework to estimate the impact of diagnostic delays over a 12-month period from the commencement of physical distancing measures, on March 16, 2020, up to 1, 3, and 5 years after diagnosis. To model the subsequent impact of diagnostic delays on survival, we reallocated patients who were on screening and routine referral pathways to urgent and emergency pathways that are associated with more advanced stage of disease at diagnosis. We considered three reallocation scenarios representing the best to worst case scenarios and reflect actual changes in the diagnostic pathway being seen in the NHS, as of March 16, 2020, and estimated the impact on net survival at 1, 3, and 5 years after diagnosis to calculate the additional deaths that can be attributed to cancer, and the total years of life lost (YLLs) compared with pre-pandemic data.FindingsWe collected data for 32 583 patients with breast cancer, 24 975 with colorectal cancer, 6744 with oesophageal cancer, and 29 305 with lung cancer. Across the three different scenarios, compared with pre-pandemic figures, we estimate a 7·9–9·6% increase in the number of deaths due to breast cancer up to year 5 after diagnosis, corresponding to between 281 (95% CI 266–295) and 344 (329–358) additional deaths. For colorectal cancer, we estimate 1445 (1392–1591) to 1563 (1534–1592) additional deaths, a 15·3–16·6% increase; for lung cancer, 1235 (1220–1254) to 1372 (1343–1401) additional deaths, a 4·8–5·3% increase; and for oesophageal cancer, 330 (324–335) to 342 (336–348) additional deaths, 5·8–6·0% increase up to 5 years after diagnosis. For these four tumour types, these data correspond with 3291–3621 additional deaths across the scenarios within 5 years. The total additional YLLs across these cancers is estimated to be 59 204–63 229 years.InterpretationSubstantial increases in the number of avoidable cancer deaths in England are to be expected as a result of diagnostic delays due to the COVID-19 pandemic in the UK. Urgent policy interventions are necessary, particularly the need to manage the backlog within routine diagnostic services to mitigate the expected impact of the COVID-19 pandemic on patients with cancer.FundingUK Research and Innovation Economic and Social Research Council.

A novel population-based occupancy modeling approach for district-scale simulations compared to standard-based methods

District-scale building energy models can be a powerful tool for the integration of renewable energy sources and efficiency measures in urban areas. One key limitation of these models, however, has been their rather simplified treatment of building occupants. Since it is their activities which create the needs for energy in an area, an improved analysis of the effects of occupants on demand at the district scale is needed.This paper presents a novel population-based approach (PopAp) inspired by agent-based transportation models, in which a population of occupants was defined based on class and employee registers and each was given an individual daily schedule. This approach was then used to assess the effect of occupant presence modeling on district-scale energy demand simulations by comparing the data-centric PopAp method to standard-based deterministic and stochastic approaches.The maximum number of occupants in the area was found to be 33% higher for the deterministic model compared to the data-centric PopAp results, a deviation that was especially pronounced in education buildings. The results for space heating, space cooling and electricity demand for lighting and appliances show that while the mean deviation between models on a yearly basis is within 10% for all demands, on an hourly scale the deviation for space cooling and electricity exceeded 15%. Given the importance of the hourly scale for peak demand prediction for technology sizing, more detailed occupant modeling approaches should be considered when planning energy systems.

Phase II study of alisertib as a single agent in recurrent or progressive atypical teratoid rhabdoid tumors.

10542 Background: We conducted a Phase II study of alisertib, small-molecule inhibitor of Aurora A kinase, as single-agent treatment in patients < 22 y with recurrent or progressive atypical teratoid rhabdoid tumors (ATRT) (NCT02114229). Methods: Patients received alisertib once daily [80 mg/m2 (enteric-coated tablets) or 60 mg/m2 (liquid)] on Days 1–7 of a 21-day cycle for 2 y or until progressive disease (PD). Therapy was considered promising if ≥10 patients were without PD by MR imaging at 12 wk. Molecular groups were determined using Infinium Methylation EPIC BeadChips and the Heidelberg classifier. Alisertib plasma concentrations were measured in cycle 1, on Days 1 (single dose) and 7 (steady state) and analyzed using population-based modeling. Results: Data from 30 patients representing all 3 molecular groups [SHH (10/26), MYC (10/26), TYR (6/26), unknown (4/26)] was analyzed. One patient remains on therapy. The study did not meet the efficacy end point as only 8/29 patients were without PD after 12 wk, including 1 with partial response. Progression-free survival (PFS) was 31%±8.2% at 6 months and 15.8%±6.5% at 1 y. One- year overall survival (OS) was 42.1%±9.2%. One patient remained on treatment for > 12 months, and another for > 18 months. The median treatment duration was 44 days (range, 2–653 days). There was no difference in OS (p = 0.096) or PFS (p = 0.98) by molecular groups. Neutropenia was the most common adverse effect (77%). After single-dose alisertib, we observed higher mean maximum concentration (Cmax) 10.1±3.0 µM and faster time to Cmax (Tmax = 1.2±0.7 h) in the 22 patients who received liquid formulation than those who received tablets (Cmax = 5.7±2.4 µM, Tmax = 3.4±1.4 h). Drug exposure did not differ between the formulations (AUC0-∞ = 58.6±25 h·µM). Average apparent oral clearance was 2.32 L/h per m2, which was about half that reported in adults. Serial CSF samples were collected in 2 patients; the CSF/plasma AUC ratios were 1.2%-2.7%. Conclusions: Although the study did not meet the efficacy end point, alisertib was well tolerated as a single agent in children with recurrent ATRT. A third of the patients demonstrated disease stabilization for > 6 months. Treatment response beyond 1 y was seen in 2 patients Clinical trial information: NCT02114229.

Predicting overall survival (OS) and overall response (OR) following durvalumab treatment in patients with multiple cancer types using a hybrid modeling strategy.

e15161 Background: This study aimed to predict OS/OR and identify key predictors in patients with diverse cancer types treated with durvalumab, a PD-L1 targeting monoclonal antibody, using a hybrid modeling strategy that combines population pharmacodynamic (PD) modeling and machine learning (ML) algorithms. Methods: Individual longitudinal tumor size measurements and OS/OR data were available for 855 patients who received durvalumab therapy (10 mg/kg Q2W or 20 mg/kg Q4W; NCT01693562). Nine cancer types included non-small cell lung cancer (NSCLC), bladder cancer (BC), microsatellite instability-high (MSI-H) cancer, hepatocellular carcinoma (HCC), squamous cell carcinoma of the head and neck (SCCHN), gastroesophageal cancer (GEC), ovarian cancer (OC), pancreatic adenocarcinoma (PDAC) and triple-negative breast cancer (TNBC). A tumor kinetic model was developed to characterize diverse temporal profiles using a population-based modeling approach. Individual estimated tumor kinetic model parameters and patient demographic/physiological factors were used as inputs for predicting OS/OR using several ML approaches. Results: The final tumor kinetic model with liver metastasis (LM), neutrophil/lymphocyte ratio (NLR), tumor size at baseline (TBSL) and cancer types as covariates characterized the temporal tumor size data well. HCC and MSI-H cancer have the slowest tumor growth rate constant (kg), while GEC, SCCHN and TNBC have the fastest kg. BC, NSCLC and OC have the highest tumor killing rate constant. The most important predictors of OS identified by ML approach were tumor kinetic parameters (kg, fraction of drug-sensitive cells, time-delay in immune response), along with baseline disease factors, including hemoglobin (HGBBL), albumin (ALB), and NLR. Decision tree-based algorithms showed the best performance in predicting OR with accuracy above 90%. In addition to tumor kinetic parameters, PD-L1 expression on tumor cells (TC) and ALB were the most important predictors of OR. Conclusions: A combined population PD/ML approach showed good predictions of OS/OR in patients with different cancer types treated with durvalumab. LM, NLR,TBSL and cancer types were found to be important factors for tumor kinetics. In addition to tumor kinetic parameters, HGBBL, ALB, and NLR were found to be important predictors of OS, and TC and ALB were found to be important predictors of OR. These findings could provide a guidance on patient selection in future clinical trials.

A Modeling Exercise to Identify Predictors of Student Readiness for Advanced Pharmacy Practice Experiences

Objective. To model the relationship of common pharmacy education assessment data including student demographics, pre-pharmacy performance, core didactic performance, and external testing measures to identify predictors of student readiness for advanced pharmacy practice experiences (APPEs). Methods. The associations between 23 predictive covariates from 226 graduating students from 2015-2018 (5786 observations) and APPE readiness as measured by midpoint core APPE scores were modeled. Multiple linear and Poisson regression models with backward selection were used. A selection criterion of p >.10 was used for covariate elimination from the model. Three models were evaluated: average of all midpoint core APPE rotation scores; average of midpoint acute care pharmacy practice and ambulatory care APPE rotation scores; and number of midpoint core clerkship failing scores. Results. The average age of the population at admission was 25.4±4.5 years, 47% were female, and 75.2% had prior degrees. Across the three prediction models, knowledge-retention covariates were the strongest predictors. Total score on the Pharmacy Curriculum Outcomes Assessment was a modest yet consistent predictor across the models. All other significant predictors were unique to the various models. Conclusion. This four-year, population-based modeling study of the relationship of common pharmacy education assessment data to APPE midpoint scores shows a modest correlation with knowledge-based measures. There is a need for greater innovation in this area of research.

Cancer care for refugees: time to invest in people and systems

Cancer care for refugees: time to invest in people and systems

The economic burden of cancer care for Syrian refugees: a population-based modelling study

Cancer represents a substantial health burden for refugees and host countries. However, no reliable data on the costs of cancer care for refugees are available, which limits the planning of official development assistance in humanitarian settings. We aimed to model the direct costs of cancer care among Syrian refugee populations residing in Jordan, Lebanon, and Turkey. In this population-based modelling study, direct cost per capita and per incident case for cancer care were estimated using generalised linear models, informed by a representative dataset of cancer costs drawn from 27 EU countries. A range of regression specifications were tested, in which cancer costs were modelled using different independent variables: gross domestic product (GDP) per capita, crude or age-standardised incidence, crude or age-standardised mortality, and total host country population size. Models were compared using the Akaike information criterion. Total cancer care costs for Syrian refugees in Jordan, Lebanon, and Turkey were calculated by multiplying the estimated direct cancer care costs (per capita) by the total number of Syrian refugees, or by multiplying the estimated direct cancer costs (per incident case [crude or age-standardised]) by the number of incident cancer cases in Syrian refugee populations. All costs are expressed in 2017 euros (€). Total cancer care costs for all 4·74 million Syrian refugees in Jordan, Lebanon, and Turkey in 2017 were estimated to be €140·23 million using the cost per capita approach, €79·02 million using the age-standardised incidence approach, and €33·68 million using the crude incidence approach. Under the lowest estimation, and with GDP and total country population as model predictors, the financial burden of cancer care was highest for Turkey (€25·18 million), followed by Lebanon (€6·40 million), and then Jordan (€2·09 million). Cancer among the Syrian refugee population represents a substantial financial burden for host countries and humanitarian agencies, such as the UN Refugee Agency. New ways to provide financial assistance need to be found and must be coupled with clear, prioritised pathways and models of care for refugees with cancer. UK Research and Innovation Global Challenges Research Fund: Research for Health in Conflict-Middle East and North Africa region (R4HC-MENA).

Trusted Computation Using ABM and PBM Decision Models for ITS

Conventional models in the intelligent transportation system (ITS) are confronted by large computational overheads and how they react during real-time scenarios. To appropriately manage the communication process in real-time, a trust-based mechanism can provide an efficient approach to acclimatize its deeds based on indecision sensory information. However, the computational models are not fully demoralized by the businesses owing to the lack of automated integration. In this study, we perform agent-based modeling (ABM) and population-based modeling (PBM) in the ITS mechanism during data transmission and record exchange for real-time communication. In addition, a trust evaluation process is performed to legitimize each device with the integration of ABM and PBM models. The simulation results show that the proposed mechanism is 89% more efficient than baseline methods in various networking scenarios, such as message alteration, distributed denial of service attacks, and information falsification threats.