Fernando Alarid-Escudero, Ph.D., is an Assistant Professor of Health Policy at Stanford University School of Medicine. He obtained his Ph.D. in Health Decision Sciences from the University of Minnesota School of Public Health. He was an Assistant Professor at the Center for Research and Teaching in Economics (CIDE) Región Centro, Aguascalientes, Mexico, from 2018 to 2022, prior to coming to Stanford. His research focuses on developing statistical and decision-analytic models to identify optimal prevention, control, and treatment policies to address a wide range of public health problems and develops novel methods to quantify the value of future research. Dr. Alarid-Escudero is a member of three cancers (colorectal [CRC], bladder, and gastric) of the Cancer Intervention and Surveillance Modeling Network (CISNET) consortium, a group of investigators sponsored by the National Cancer Institute in the U.S. that uses simulation modeling to evaluate the impact of cancer control interventions (e.g., prevention, screening, and treatment) on population trends in incidence and mortality. Dr. Alarid-Escudero co-founded the Stanford-CIDE Coronavirus Simulation Modeling (SC-COSMO) workgroup (https://www.sc-cosmo.org). He also co-founded the Decision Analysis in R for Technologies in Health (DARTH) workgroup (http://darthworkgroup.com) and the Collaborative Network on Value of Information (ConVOI; https://www.convoi-group.org), international and multi-institutional collaborative efforts where we develop transparent and open-source solutions to implement decision analysis and quantify the value of potential future investigation for health policy analysis. He received a BSc in Biomedical Engineering from the Metropolitan Autonomous University in Iztapalapa (UAM-I), and a Master’s in Economics from CIDE, both in Mexico.
Assistant Professor, Health Policy
Honors & Awards
Doctoral Dean’s Scholarship, University of Minnesota School of Public Health (2012)
Fulbright-Garcia Robles Doctoral Fellowship, Fulbright (2012-2014)
SMDM Lee B. Lusted Student Prize in Quantitative Methods and Theoretical Developments, Society for Medical Decision Making (SMDM) (2014)
SMDM Lee B. Lusted Student Prize in Quantitative Methods and Theoretical Developments, Society for Medical Decision Making (SMDM) (2015)
Best Presentation at School of Public Health’s Research Day, University of Minnesota School of Public Health (2016)
SMDM Lee B. Lusted Student Prize for Outstanding Student Research (Europe), Society for Medical Decision Making (SMDM) (2016)
University of Minnesota Doctoral Dissertation Fellowship, University of Minnesota Graduate School (2016)
Delta Omega Member, Honorary Society in Public Health, Pi Chapter (2017)
Best short course “Hands-on Model Calibration in R” at the 2018 SMDM North American Meeting, Society for Medical Decision Making (SMDM) (2019)
Mexican National System of Researchers (SNI) Level I, Mexico’s National Council of Science and Technology (CONACyT) (2019-2021)
COVID-19 Decision Modeling Initiative (CDMI) Leader, Society for Medical Decision Making (SMDM) (2020)
Mexican National System of Researchers (SNI) Level II, Mexico’s National Council of Science and Technology (CONACyT) (2022-2025)
Boards, Advisory Committees, Professional Organizations
Editorial Board Member, Medical Decision Making (MDM) journals (Medical Decision Making and MDM Policy & Practice) (2021 - Present)
Member, Society for Medical Decision Making (SMDM) (2013 - Present)
Trustee, Society for Medical Decision Making (SMDM) (2018 - 2021)
Ph.D., University of Minnesota School of Public Health, Decision Sciences (2017)
M. Econ., Center for Research and Teaching in Economics (CIDE), Mexico, Economics (2009)
B.Sc., Metropolitan Autonomous University (UAM) - Iztapalapa, Mexico, Biomedical Engineering (2006)
Doctoral Dissertation Advisor (AC)
Effects of Mitigation and Control Policies in Realistic Epidemic Models Accounting for Household Transmission Dynamics.
Medical decision making : an international journal of the Society for Medical Decision Making
Compartmental infectious disease (ID) models are often used to evaluate nonpharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations in which multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling.We developed a multicompartment susceptible-exposed-infectious-recovered-susceptible-vaccinated (MC-SEIRSV) modeling framework, allowing nonexponentially distributed duration in exposed and infectious compartments, that tracks within-household and community transmission. We simulated epidemics that varied by community and household transmission rates, waning immunity rate, household size (3 or 5 members), and numbers of exposed and infectious compartments (1-3 each). We calibrated otherwise identical models without household structure to the early phase of each parameter combination's epidemic curve. We compared each model pair in terms of epidemic forecasts and predicted NPI and vaccine impacts on the timing and magnitude of the epidemic peak and its total size. Meta-analytic regressions characterized the relationship between household structure inclusion and the size and direction of biases.Otherwise similar models with and without household structure produced equivalent early epidemic curves. However, forecasts from models without household structure were biased. Without intervention, they were upward biased on peak size and total epidemic size, with biases also depending on the number of exposed and infectious compartments. Model-estimated NPI effects of a 60% reduction in community contacts on peak time and size were systematically overestimated without household structure. Biases were smaller with a 20% reduction NPI. Because vaccination affected both community and household transmission, their biases were smaller.ID models without household structure can produce biased outcomes in settings in which within-household and community transmission differ.Infectious disease models rarely separate household transmission from community transmission. The pace of household transmission may differ from community transmission, depends on household size, and can accelerate epidemic growth.Many infectious disease models assume exponential duration distributions for infected states. However, the duration of most infections is not exponentially distributed, and distributional choice alters modeled epidemic dynamics and intervention effectiveness.We propose a mathematical framework for household and community transmission that allows for nonexponential duration times and a suite of interventions and quantified the effect of accounting for household transmission by varying household size and duration distributions of infected states on modeled epidemic dynamics.Failure to include household structure induces biases in the modeled overall course of an epidemic and the effects of interventions delivered differentially in community settings. Epidemic dynamics are faster and more intense in populations with larger household sizes and for diseases with nonexponentially distributed infectious durations. Modelers should consider explicitly incorporating household structure to quantify the effects of non-pharmaceutical interventions (e.g., shelter-in-place).
View details for DOI 10.1177/0272989X231205565
View details for PubMedID 37953597
Approaches to developing de novo cancer population models to examine questions about cancer and race in bladder, gastric, and endometrial cancer and multiple myeloma: the Cancer Intervention and Surveillance Modeling Network incubator program.
Journal of the National Cancer Institute. Monographs
2023; 2023 (62): 219-230
We are developing 10 de novo population-level mathematical models in 4 malignancies (multiple myeloma and bladder, gastric, and uterine cancers). Each of these sites has documented disparities in outcome that are believed to be downstream effects of systemic racism.Ten models are being independently developed as part of the Cancer Intervention and Surveillance Modeling Network incubator program. These models simulate trends in cancer incidence, early diagnosis, treatment, and mortality for the general population and are stratified by racial subgroup. Model inputs are based on large population datasets, clinical trials, and observational studies. Some core parameters are shared, and other parameters are model specific. All models are microsimulation models that use self-reported race to stratify model inputs. They can simulate the distribution of relevant risk factors (eg, smoking, obesity) and insurance status (for multiple myeloma and uterine cancer) in US birth cohorts and population.The models aim to refine approaches in prevention, detection, and management of 4 cancers given uncertainties and constraints. They will help explore whether the observed racial disparities are explainable by inequities, assess the effects of existing and potential cancer prevention and control policies on health equity and disparities, and identify policies that balance efficiency and fairness in decreasing cancer mortality.
View details for DOI 10.1093/jncimonographs/lgad021
View details for PubMedID 37947329
- NordICC Trial Results in Line With Expected Colorectal Cancer Mortality Reduction After Colonoscopy: A Modeling Study GASTROENTEROLOGY 2023; 165 (4): 1077-1079.e2
Cost-effectiveness of trastuzumab deruxtecan in HER2 low metastatic breast cancer
LIPPINCOTT WILLIAMS & WILKINS. 2023
View details for Web of Science ID 001053772000312
Breastfeeding is associated with the intelligence of school-age children in Mexico.
Maternal & child nutrition
Breastfeeding has been consistently associated with higher intelligence since childhood. However, this relation could be confounded due to maternal selection bias. We estimated the association between predominant breastfeeding and intelligence in school-age children considering potential selection bias and we simulated the intelligence gap reduction between low versus higher socioeconomic status children by increasing breastfeeding. We analysed predominant breastfeeding practices (breastmilk and water-based liquids) of children 0-3 years included in the Mexican Family Life Survey (MxFLS-1). Intelligence was estimated as the z-score of the abbreviated Raven score, measured at 6-12 years in the MxFLS-2 or MxFLS-3. We predicted breastfeeding duration among children with censored data with a Poisson model. We used the Heckman selection model to assess the association between breastfeeding and intelligence, correcting for selection bias and stratified by socioeconomic status. Results show after controlling for selection bias, a 1-month increase in predominant breastfeeding duration was associated with a 0.02SD increase in the Raven z-score (p<0.05). The children who were predominantly breastfed for 4-6 months versus <1 month had 0.16SD higher Raven z-score (p<0.05). No associations were found using multiple linear regression models. Among low socioeconomic status children, increasing predominantly breastfeeding duration to 6 months would increase their mean Raven z-score from -0.14 to -0.07SD and reduce by 12.5% the intelligence gap with high socioeconomic status children. In conclusion, predominant breastfeeding duration was significantly associated with childhood intelligence after controlling for maternal selection bias. Increased breastfeeding duration may reduce poverty-driven intelligence inequities.
View details for DOI 10.1111/mcn.13534
View details for PubMedID 37218453
Cost effectiveness of non-drug interventions that reduce nursing home admissions for people living with dementia.
Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Six million Americans live with Alzheimer's disease and Alzheimer's disease and related dementias (AD/ADRD), a major health-care cost driver. We evaluated the cost effectiveness of non-pharmacologic interventions that reduce nursing home admissions for people living with AD/ADRD.METHODS: We used a person-level microsimulation to model the hazard ratios (HR) on nursing home admission for four evidence-based interventions compared to usual care: Maximizing Independence at Home (MIND), NYU Caregiver (NYU); Alzheimer's and Dementia Care (ADC); and Adult Day Service Plus (ADS Plus). We evaluated societal costs, quality-adjusted life years and incremental cost-effectiveness ratios.RESULTS: All four interventions cost less and are more effective (i.e., cost savings) than usual care from a societal perspective. Results did not materially change in 1-way, 2-way, structural, and probabilistic sensitivity analyses.CONCLUSION: Dementia-care interventions that reduce nursing home admissions save societal costs compared to usual care. Policies should incentivize providers and health systems to implement non-pharmacologic interventions.
View details for DOI 10.1002/alz.12964
View details for PubMedID 37021724
Emulator-based Bayesian calibration of the CISNET colorectal cancer models.
medRxiv : the preprint server for health sciences
To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANN) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.The optimal ANN for SimCRC had four hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had one hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 hours for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, like the CISNET CRC models.
View details for DOI 10.1101/2023.02.27.23286525
View details for PubMedID 36909607
View details for PubMedCentralID PMC10002763
A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.
Medical decision making : an international journal of the Society for Medical Decision Making
In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transition probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time dependent). This tutorial illustrates adding 2 types of time dependence using a previously published cost-effectiveness analysis of multiple strategies as an example. The first is simulation-time dependence, which allows for the transition probabilities to vary as a function of time as measured since the start of the simulation (e.g., varying probability of death as the cohort ages). The second is state-residence time dependence, allowing for history by tracking the time spent in any particular health state using tunnel states. We use these time-dependent cSTMs to conduct cost-effectiveness and probabilistic sensitivity analyses. We also obtain various epidemiological outcomes of interest from the outputs generated from the cSTM, such as survival probability and disease prevalence, often used for model calibration and validation. We present the mathematical notation first, followed by the R code to execute the calculations. The full R code is provided in a public code repository for broader implementation.
View details for DOI 10.1177/0272989X221121747
View details for PubMedID 36112849
Dynamics of Respiratory Infectious Diseases in Incarcerated and Free-Living Populations: A Simulation Modeling Study.
Medical decision making : an international journal of the Society for Medical Decision Making
Historically, correctional facilities have had large outbreaks of respiratory infectious diseases like COVID-19. Hence, importation and exportation of such diseases from correctional facilities raises substantial concern.We developed a stochastic simulation model of transmission of respiratory infectious diseases within and between correctional facilities and the community. We investigated the infection dynamics, key governing factors, and relative importance of different infection routes (e.g., incarcerations and releases versus correctional staff). We also developed machine-learning meta-models of the simulation model, which allowed us to examine how our findings depended on different disease, correctional facility, and community characteristics.We find a magnification-reflection dynamic: a small outbreak in the community can cause a larger outbreak in the correction facility, which can then cause a second, larger outbreak in the community. This dynamic is strongest when community size is relatively small as compared with the size of the correctional population, the initial community R-effective is near 1, and initial prevalence of immunity in the correctional population is low. The timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting. Because the release rates from prisons are low, our model suggests correctional staff may be a more important infection entry route into prisons than incarcerations and releases; in jails, where incarceration and release rates are much higher, our model suggests the opposite.We find that across many combinations of respiratory pathogens, correctional settings, and communities, there can be substantial magnification-reflection dynamics, which are governed by several key factors. Our goal was to derive theoretical insights relevant to many contexts; our findings should be interpreted accordingly.We find a magnification-reflection dynamic: a small outbreak in a community can cause a larger outbreak in a correctional facility, which can then cause a second, larger outbreak in the community.For public health decision makers considering contexts most susceptible to this dynamic, we find that the dynamic is strongest when the community size is relatively small, initial community R-effective is near 1, and the initial prevalence of immunity in the correctional population is low; the timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting.We find that correctional staff may be a more important infection entry route into prisons than incarcerations and releases; however, for jails, the relative importance of the entry routes may be reversed.For modelers, we combine simulation modeling, machine-learning meta-modeling, and interpretable machine learning to examine how our findings depend on different disease, correctional facility, and community characteristics; we find they are generally robust.
View details for DOI 10.1177/0272989X221115364
View details for PubMedID 35904128
An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example
MEDICAL DECISION MAKING
Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, in which transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, and conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs.
View details for DOI 10.1177/0272989X221103163
View details for Web of Science ID 000821110200001
View details for PubMedID 35770931
Methods for Communicating the Impact of Parameter Uncertainty in a Multiple-Strategies Cost-Effectiveness Comparison
MEDICAL DECISION MAKING
Analyzing and communicating uncertainty is essential in medical decision making. To judge whether risks are acceptable, policy makers require information on the expected outcomes but also on the uncertainty and potential losses related to the chosen strategy. We aimed to compare methods used to represent the impact of uncertainty in decision problems involving many strategies, enhance existing methods, and provide an open-source and easy-to-use tool.We conducted a systematic literature search to identify methods used to represent the impact of uncertainty in cost-effectiveness analyses comparing multiple strategies. We applied the identified methods to probabilistic sensitivity analysis outputs of 3 published decision-analytic models comparing multiple strategies. Subsequently, we compared the following characteristics: type of information conveyed, use of a fixed or flexible willingness-to-pay threshold, output interpretability, and the graphical discriminatory ability. We further proposed adjustments and integration of methods to overcome identified limitations of existing methods.The literature search resulted in the selection of 9 methods. The 3 methods with the most favorable characteristics to compare many strategies were 1) the cost-effectiveness acceptability curve (CEAC) and cost-effectiveness acceptability frontier (CEAF), 2) the expected loss curve (ELC), and 3) the incremental benefit curve (IBC). The information required to assess confidence in a decision often includes the average loss and the probability of cost-effectiveness associated with each strategy. Therefore, we proposed the integration of information presented in an ELC and CEAC into a single heat map.This article presents an overview of methods presenting uncertainty in multiple-strategy cost-effectiveness analyses, with their strengths and shortcomings. We proposed a heat map as an alternative method that integrates all relevant information required for health policy and medical decision making.To assess confidence in a chosen course of action, decision makers require information on both the probability and the consequences of making a wrong decision.This article contains an overview of methods for presenting uncertainty in multiple-strategy cost-effectiveness analyses.We propose a heat map that combines the probability of cost-effectiveness from the cost-effectiveness acceptability curve (CEAC) with the consequences of a wrong decision from the expected loss curve.Collapsing of the CEAC can be reduced by relaxing the CEAC, as proposed in this article.Code in Microsoft Excel and R is provided to easily analyze data using the methods discussed in this article.
View details for DOI 10.1177/0272989X221100112
View details for Web of Science ID 000799927800001
View details for PubMedID 35587181
Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models
FRONTIERS IN PHYSIOLOGY
2022; 13: 780917
Background: We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making. Methods: We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework. Results: The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of -0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $653 and $685, respectively, at a willingness-to-pay (WTP) threshold of $66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters' posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $809 at the same WTP threshold. Conclusion: Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty.
View details for DOI 10.3389/fphys.2022.780917
View details for Web of Science ID 000803912100001
View details for PubMedID 35615677
View details for PubMedCentralID PMC9124835
CDX2 Biomarker Testing and Adjuvant Therapy for Stage II Colon Cancer: An Exploratory Cost-Effectiveness Analysis
VALUE IN HEALTH
2022; 25 (3): 409-418
Adjuvant chemotherapy is not recommended for patients with average-risk stage II (T3N0) colon cancer. Nevertheless, a subgroup of these patients who are CDX2-negative might benefit from adjuvant chemotherapy. We evaluated the cost-effectiveness of testing for the absence of CDX2 expression followed by adjuvant chemotherapy (fluorouracil combined with oxaliplatin [FOLFOX]) for patients with stage II colon cancer.We developed a decision model to simulate a hypothetical cohort of 65-year-old patients with average-risk stage II colon cancer with 7.2% of these patients being CDX2-negative under 2 different interventions: (1) test for the absence of CDX2 expression followed by adjuvant chemotherapy for CDX2-negative patients and (2) no CDX2 testing and no adjuvant chemotherapy for any patient. We derived disease progression parameters, adjuvant chemotherapy effectiveness and utilities from published analyses, and cancer care costs from the Surveillance, Epidemiology, and End Results (SEER)-Medicare data. Sensitivity analyses were conducted.Testing for CDX2 followed by FOLFOX for CDX2-negative patients had an incremental cost-effectiveness ratio of $5500/quality-adjusted life-years (QALYs) compared with no CDX2 testing and no FOLFOX (6.874 vs 6.838 discounted QALYs and $89 991 vs $89 797 discounted US dollar lifetime costs). In sensitivity analyses, considering a cost-effectiveness threshold of $100 000/QALY, testing for CDX2 followed by FOLFOX on CDX2-negative patients remains cost-effective for hazard ratios of <0.975 of the effectiveness of FOLFOX in CDX2-negative patients in reducing the rate of developing a metastatic recurrence.Testing tumors of patients with stage II colon cancer for CDX2 and administration of adjuvant treatment to the subgroup found CDX2-negative is a cost-effective and high-value management strategy across a broad range of plausible assumptions.
View details for DOI 10.1016/j.jval.2021.07.019
View details for Web of Science ID 000793515100010
View details for PubMedID 35227453
View details for PubMedCentralID PMC8894795
Effectiveness of COVID-19 vaccines among incarcerated people in California state prisons: retrospective cohort study.
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Prisons and jails are high-risk settings for COVID-19. Vaccines may substantially reduce these risks, but evidence is needed on COVID-19 vaccine effectiveness for incarcerated people, who are confined in large, risky congregate settings.We conducted a retrospective cohort study to estimate effectiveness of mRNA vaccines, BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna), against confirmed SARS-CoV-2 infections among incarcerated people in California prisons from December 22, 2020 through March 1, 2021. The California Department of Corrections and Rehabilitation provided daily data for all prison residents including demographic, clinical, and carceral characteristics, as well as COVID-19 testing, vaccination, and outcomes. We estimated vaccine effectiveness using multivariable Cox models with time-varying covariates, adjusted for resident characteristics and infection rates across prisons.Among 60,707 cohort members, 49% received at least one BNT162b2 or mRNA-1273 dose during the study period. Estimated vaccine effectiveness was 74% (95% confidence interval [CI], 64-82%) from day 14 after first dose until receipt of second dose and 97% (95% CI, 88-99%) from day 14 after second dose. Effectiveness was similar among the subset of residents who were medically vulnerable: 74% [95% CI, 62-82%] and 92% [95% CI, 74-98%] from 14 days after first and second doses, respectively.Consistent with results from randomized trials and observational studies in other populations, mRNA vaccines were highly effective in preventing SARS-CoV-2 infections among incarcerated people. Prioritizing incarcerated people for vaccination, redoubling efforts to boost vaccination, and continuing other ongoing mitigation practices are essential in preventing COVID-19 in this disproportionately affected population.
View details for DOI 10.1093/cid/ciab1032
View details for PubMedID 35083482
Retention in Care, Mortality, Loss-to-Follow-Up, and Viral Suppression among Antiretroviral Treatment-Naive and Experienced Persons Participating in a Nationally Representative HIV Pre-Treatment Drug Resistance Survey in Mexico
2021; 10 (12)
We describe associations of pretreatment drug resistance (PDR) with clinical outcomes such as remaining in care, loss to follow-up (LTFU), viral suppression, and death in Mexico, in real-life clinical settings. We analyzed clinical outcomes after a two-year follow up period in participants of a large 2017-2018 nationally representative PDR survey cross-referenced with information of the national ministry of health HIV database. Participants were stratified according to prior ART exposure and presence of efavirenz/nevirapine PDR. Using a Fine-Gray model, we evaluated virological suppression among resistant patients, in a context of competing risk with lost to follow-up and death. A total of 1823 participants were followed-up by a median of 1.88 years (Interquartile Range (IQR): 1.59-2.02): 20 (1%) were classified as experienced + resistant; 165 (9%) naïve + resistant; 211 (11%) experienced + non-resistant; and 1427 (78%) as naïve + non-resistant. Being ART-experienced was associated with a lower probability of remaining in care (adjusted Hazard Ratio(aHR) = 0.68, 0.53-0.86, for the non-resistant group and aHR = 0.37, 0.17-0.84, for the resistant group, compared to the naïve + non-resistant group). Heterosexual cisgender women compared to men who have sex with men [MSM], had a lower viral suppression (aHR = 0.84, 0.70-1.01, p = 0.06) ART-experienced persons with NNRTI-PDR showed the worst clinical outcomes. This group was enriched with women and persons with lower education and unemployed, which suggests higher levels of social vulnerability.
View details for DOI 10.3390/pathogens10121569
View details for Web of Science ID 000736892400001
View details for PubMedID 34959524
View details for PubMedCentralID PMC8706073
Effectiveness of COVID-19 Vaccines among Incarcerated People in California State Prisons: A Retrospective Cohort Study.
medRxiv : the preprint server for health sciences
Background: Prisons and jails are high-risk settings for COVID-19 transmission, morbidity, and mortality. COVID-19 vaccines may substantially reduce these risks, but evidence is needed of their effectiveness for incarcerated people, who are confined in large, risky congregate settings.Methods: We conducted a retrospective cohort study to estimate effectiveness of mRNA vaccines, BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna), against confirmed SARS-CoV-2 infections among incarcerated people in California prisons from December 22, 2020 through March 1, 2021. The California Department of Corrections and Rehabilitation provided daily data for all prison residents including demographic, clinical, and carceral characteristics, as well as COVID-19 testing, vaccination status, and outcomes. We estimated vaccine effectiveness using multivariable Cox models with time-varying covariates that adjusted for resident characteristics and infection rates across prisons.Findings: Among 60,707 residents in the cohort, 49% received at least one BNT162b2 or mRNA-1273 dose during the study period. Estimated vaccine effectiveness was 74% (95% confidence interval [CI], 64-82%) from day 14 after first dose until receipt of second dose and 97% (95% CI, 88-99%) from day 14 after second dose. Effectiveness was similar among the subset of residents who were medically vulnerable (74% [95% CI, 62-82%] and 92% [95% CI, 74-98%] from 14 days after first and second doses, respectively), as well as among the subset of residents who received the mRNA-1273 vaccine (71% [95% CI, 58-80%] and 96% [95% CI, 67-99%]).Conclusions: Consistent with results from randomized trials and observational studies in other populations, mRNA vaccines were highly effective in preventing SARS-CoV-2 infections among incarcerated people. Prioritizing incarcerated people for vaccination, redoubling efforts to boost vaccination and continuing other ongoing mitigation practices are essential in preventing COVID-19 in this disproportionately affected population.Funding: Horowitz Family Foundation, National Institute on Drug Abuse, Centers for Disease Control and Prevention, National Science Foundation, Open Society Foundation, Advanced Micro Devices.
View details for DOI 10.1101/2021.08.16.21262149
View details for PubMedID 34426814
Age-specific rates of onset of cannabis use in Mexico
2021; 122: 107038
Over the previous two decades, the lifetime prevalence of cannabis use has risen among Mexico's population.Estimate the sex- and age-specific rates of onset of cannabis use over time.Five nationally representative cross-sectional surveys, the Mexican National Surveys of Addictions (1998, 2002, 2008, 2012) and the Mexican National Survey on Drugs, Alcohol, and Tobacco Consumption (2016).Mexico.Pooled sample of 141,342 respondents aged between 12 and 65 years of which 43.6%(n = 61,658) are male and 56.4% (n = 79,684) are female.We estimated the age-specific rates of onset of cannabis as the conditional rate of consuming cannabis for the first time at a specific age.Time-to-event flexible-parametric models with spline specifications of the hazard function. Stratified analysis by sex and control for temporal trends by year of data collection or decennial birth cohort.Age-specific rates of onset of cannabis use per 1,000 individuals increased over time for females and males. Peak rates of onset of cannabis use per 1,000 ranged from 0.935 (95%CI = [0.772, 1.148]) in 1998, to 5.391 (95%CI = [4.924, 5.971]) in 2016 for females; and from 7.513 (95%CI = [6.732, 10.063]) in 1998, to 26.107 (95%CI = [25.918,30.654]) in 2016 for males. Across decennial birth-cohorts, peak rates of onset of cannabis use per 1,000 individuals for females ranged from 0.234 (95%CI = [0.078, 0.768]) for those born in the 1930s, to 14.611 (95%CI = [13.243, 16.102]) for those born in the 1990s; and for males, from 4.086 (95%CI = [4.022, 7.857]) for those born in the 1930s, to 38.693 (95%CI = [24.847, 48.670]) for those born in the 1990s.Rates of onset of cannabis increased over the previous two decades for both females and males but remained higher for males. Across recent cohorts, the rates of onset have increased at a faster rate among females than males.
View details for DOI 10.1016/j.addbeh.2021.107038
View details for Web of Science ID 000680031000020
View details for PubMedID 34325204
View details for PubMedCentralID PMC8645182
Prioritizing Research Informing Antibiotic Prophylaxis Guidelines for Knee Arthroplasty Patients
JDR CLINICAL & TRANSLATIONAL RESEARCH
2022; 7 (3): 298-306
Guidelines for routine antibiotic prophylaxis (AP) before dental procedures to prevent periprosthetic joint infection (PJI) have been hampered by the lack of prospective clinical trials.To apply value-of-information (VOI) analysis to quantify the value of conducting further clinical research to reduce decision uncertainty regarding the cost-effectiveness of AP strategies for dental patients undergoing total knee arthroplasty (TKA).An updated decision model and probabilistic sensitivity analysis (PSA) evaluated the cost-effectiveness of AP and decision uncertainty for 3 AP strategies: no AP, 2-y AP, and lifetime AP. VOI analyses estimated the value and cost of conducting a randomized controlled trial (RCT) or observational study. We used a linear regression meta-modeling approach to calculate the population expected value of partial perfect information and a Gaussian approximation to calculate population expected value of sample information, and we subtracted the cost for research to obtain the expected net benefit of sampling (ENBS). We determined the optimal trial sample sizes that maximized ENBS.Using a willingness-to-pay threshold of $100,000 per quality-adjusted life-year, the PSA found that a no-AP strategy had the highest expected net benefit, with a 60% probability of being cost-effective, and 2-y AP had a 37% probability. The optimal sample size for an RCT to determine AP efficacy and dental-related PJI risk would require approximately 421 patients per arm with an estimated cost of $14.7 million. The optimal sample size for an observational study to inform quality-of-life parameters would require 2,211 patients with an estimated cost of $1.2 million. The 2 trial designs had an ENBS of approximately $25 to $26 million.Given the uncertainties associated with AP guidelines for dental patients after TKA, we conclude there is value in conducting further research to inform the risk of PJI, effectiveness of AP, and quality-of-life values.The results of this value-of-information analysis demonstrate that there is substantial uncertainty around clinical, health status, and economic parameters that may influence the antibiotic prophylaxis guidance for dental patients with total knee arthroplasty. The analysis supports the contention that conducting additional clinical research to reduce decision uncertainty is worth pursuing and will inform the antibiotic prophylaxis debate for clinicians and dental patients with prosthetic joints.
View details for DOI 10.1177/23800844211020272
View details for Web of Science ID 000667309900001
View details for PubMedID 34137291
BayCANN: Streamlining Bayesian Calibration With Artificial Neural Network Metamodeling
FRONTIERS IN PHYSIOLOGY
2021; 12: 662314
Purpose: Bayesian calibration is generally superior to standard direct-search algorithms in that it estimates the full joint posterior distribution of the calibrated parameters. However, there are many barriers to using Bayesian calibration in health decision sciences stemming from the need to program complex models in probabilistic programming languages and the associated computational burden of applying Bayesian calibration. In this paper, we propose to use artificial neural networks (ANN) as one practical solution to these challenges. Methods: Bayesian Calibration using Artificial Neural Networks (BayCANN) involves (1) training an ANN metamodel on a sample of model inputs and outputs, and (2) then calibrating the trained ANN metamodel instead of the full model in a probabilistic programming language to obtain the posterior joint distribution of the calibrated parameters. We illustrate BayCANN using a colorectal cancer natural history model. We conduct a confirmatory simulation analysis by first obtaining parameter estimates from the literature and then using them to generate adenoma prevalence and cancer incidence targets. We compare the performance of BayCANN in recovering these "true" parameter values against performing a Bayesian calibration directly on the simulation model using an incremental mixture importance sampling (IMIS) algorithm. Results: We were able to apply BayCANN using only a dataset of the model inputs and outputs and minor modification of BayCANN's code. In this example, BayCANN was slightly more accurate in recovering the true posterior parameter estimates compared to IMIS. Obtaining the dataset of samples, and running BayCANN took 15 min compared to the IMIS which took 80 min. In applications involving computationally more expensive simulations (e.g., microsimulations), BayCANN may offer higher relative speed gains. Conclusions: BayCANN only uses a dataset of model inputs and outputs to obtain the calibrated joint parameter distributions. Thus, it can be adapted to models of various levels of complexity with minor or no change to its structure. In addition, BayCANN's efficiency can be especially useful in computationally expensive models. To facilitate BayCANN's wider adoption, we provide BayCANN's open-source implementation in R and Stan.
View details for DOI 10.3389/fphys.2021.662314
View details for Web of Science ID 000658770100001
View details for PubMedID 34113262
View details for PubMedCentralID PMC8185956
Cost-effectiveness of prevention and early detection of gastric cancer in Western countries
BEST PRACTICE & RESEARCH CLINICAL GASTROENTEROLOGY
2021; 50-51: 101735
Gastric cancer (GC) is a significant global health problem, with Helicobacter pylori infection estimated to be responsible for 89% of non-cardiac GC cases, or 78% of all GC cases. The International Agency for Research on Cancer has called for Helicobacter pylori test-and-treat strategies in countries with high rates of GC. However, for countries with low rates of GC, such as most Western countries, the balance between benefits, harms and costs of screening is less clear-cut. GC is a disease with a well-characterized precancerous process, providing the basis for primary and secondary prevention efforts. However, rigorous data assessing the impact of such interventions in Western countries are lacking. In the absence of clinical trials, modelling offers a unique approach to evaluate the potential impact of various screening and surveillance interventions. In this paper, we provide an overview of modelling studies evaluating the cost-effectiveness of GC screening and surveillance in Western countries.
View details for DOI 10.1016/j.bpg.2021.101735
View details for Web of Science ID 000648871800013
View details for PubMedID 33975689
POLICY COMPARISON OF NON-PHARMACEUTICAL INTERVENTIONS AND RE-OPENING IN MEXICO CITY, MEXICO: USING A NEAR-TERM VALIDATED MODEL TO CONTROL COVID-19 EPIDEMIC PEAKS AND REBOUNDS
SAGE PUBLICATIONS INC. 2021: E203-E204
View details for Web of Science ID 000648637500164
A NEW METHOD FOR ESTIMATING THE CASE DETECTION FRACTION OF AN EMERGING EPIDEMIC AND AN APPLICATION TO COVID-19
SAGE PUBLICATIONS INC. 2021: E53-E55
View details for Web of Science ID 000648637500053
METHODS FOR CONSTRUCTING SUB-NATIONAL CONTACT MATRICES FOR TRANSMISSION MODELS OF RESPIRATORY VIRUSES LIKE SARS-COV-2 (COVID-19)
SAGE PUBLICATIONS INC. 2021: E62-E64
View details for Web of Science ID 000648637500059
POLICY ANALYSIS OF NON-PHARMACEUTICAL INTERVENTIONS AND RE-OPENING IN THE STATE OF HIDALGO, MEXICO
SAGE PUBLICATIONS INC. 2021: E200-E202
View details for Web of Science ID 000648637500163
ACCOUNTING FOR HOUSEHOLD TRANSMISSION DYNAMICS IN REALISTIC EPIDEMIC MODELS
SAGE PUBLICATIONS INC. 2021: E234-E236
View details for Web of Science ID 000648637500186
Comparing the Cost-Effectiveness of Innovative Colorectal Cancer Screening Tests
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE
2021; 113 (2): 154-161
Colorectal cancer (CRC) screening with colonoscopy and the fecal immunochemical test (FIT) is underused. Innovative tests could increase screening acceptance. This study determined which of the available alternatives is most promising from a cost-effectiveness perspective.The previously validated Microsimulation Screening Analysis-Colon model was used to evaluate the cost-effectiveness of screening with capsule endoscopy every 5 or 10 years, computed tomographic colonography every 5 years, the multi-target stool DNA test every 1 or 3 years, and the methylated SEPT9 DNA plasma assay (mSEPT9) every 1 or 2 years. We also compared these strategies with annual FIT screening and colonoscopy screening every 10 years. Quality-adjusted life-years gained (QALYG), number of colonoscopies, and incremental cost-effectiveness ratios were projected. We assumed a willingness-to-pay threshold of $100 000 per QALYG.Among the alternative tests, computed tomographic colonography every 5 years, annual mSEPT9, and annual multi-target stool DNA screening had incremental cost-effectiveness ratios of $1092, $63 253, and $214 974 per QALYG, respectively. Other screening strategies were more costly and less effective than (a combination of) these 3. Under the assumption of perfect adherence, annual mSEPT9 screening resulted in more QALYG, CRC cases averted, and CRC deaths averted than annual FIT screening but led to a high rate of colonoscopy referral (51% after 3 years, 69% after 5 years). The alternative tests were not cost-effective compared with FIT and colonoscopy.This study suggests that for individuals not willing to participate in FIT or colonoscopy screening, mSEPT9 is the test of choice if the high colonoscopy referral rate is acceptable to them.
View details for DOI 10.1093/jnci/djaa103
View details for Web of Science ID 000637287500008
View details for PubMedID 32761199
View details for PubMedCentralID PMC7850547
Covid-19 in the California State Prison System: An Observational Study of Decarceration, Ongoing Risks, and Risk Factors.
medRxiv : the preprint server for health sciences
Correctional institutions nationwide are seeking to mitigate Covid-19-related risks.To quantify changes to California's prison population since the pandemic began and identify risk factors for Covid-19 infection.We described residents' demographic characteristics, health status, Covid-19 risk scores, room occupancy, and labor participation. We used Cox proportional hazard models to estimate the association between rates of Covid-19 infection and room occupancy and out-of-room labor, respectively.California state prisons (March 1-October 10, 2020).Residents of California state prisons.Changes in the incarcerated population's size, composition, housing, and activities. For the risk factor analysis, the exposure variables were room type (cells vs dormitories) and labor participation (any room occupant participating in the prior 2 weeks) and the outcome variable was incident Covid-19 case rates.The incarcerated population decreased 19.1% (119,401 to 96,623) during the study period.On October 10, 2020, 11.5% of residents were aged ≥60, 18.3% had high Covid-19 risk scores, 31.0% participated in out-of-room labor, and 14.8% lived in rooms with ≥10 occupants. Nearly 40% of residents with high Covid-19 risk scores lived in dormitories. In 9 prisons with major outbreaks (6,928 rooms; 21,750 residents), dormitory residents had higher infection rates than cell residents (adjusted hazard ratio [AHR], 2.51 95%CI, 2.25-2.80) and residents of rooms with labor participation had higher rates than residents of other rooms (AHR, 1.56; 95%CI, 1.39-1.74).Inability to measure density of residents' living conditions or contact networks among residents and staff.Despite reductions in room occupancy and mixing, California prisons still house many medically vulnerable residents in risky settings. Reducing risks further requires a combination of strategies, including rehousing, decarceration, and vaccination.Horowitz Family Foundation; National Institute on Drug Abuse; National Science Foundation Graduate Research Fellowship; Open Society Foundations.
View details for DOI 10.1101/2021.03.04.21252942
View details for PubMedID 33758868
View details for PubMedCentralID PMC7987024
Dependence of COVID-19 Policies on End-of-Year Holiday Contacts in Mexico City Metropolitan Area: A Modeling Study.
MDM policy & practice
2021; 6 (2): 23814683211049249
Background. Mexico City Metropolitan Area (MCMA) has the largest number of COVID-19 (coronavirus disease 2019) cases in Mexico and is at risk of exceeding its hospital capacity in early 2021. Methods. We used the Stanford-CIDE Coronavirus Simulation Model (SC-COSMO), a dynamic transmission model of COVID-19, to evaluate the effect of policies considering increased contacts during the end-of-year holidays, intensification of physical distancing, and school reopening on projected confirmed cases and deaths, hospital demand, and hospital capacity exceedance. Model parameters were derived from primary data, literature, and calibrated. Results. Following high levels of holiday contacts even with no in-person schooling, MCMA will have 0.9 million (95% prediction interval 0.3-1.6) additional COVID-19 cases between December 7, 2020, and March 7, 2021, and hospitalizations will peak at 26,000 (8,300-54,500) on January 25, 2021, with a 97% chance of exceeding COVID-19-specific capacity (9,667 beds). If MCMA were to control holiday contacts, the city could reopen in-person schools, provided they increase physical distancing with 0.5 million (0.2-0.9) additional cases and hospitalizations peaking at 12,000 (3,700-27,000) on January 19, 2021 (60% chance of exceedance). Conclusion. MCMA must increase COVID-19 hospital capacity under all scenarios considered. MCMA's ability to reopen schools in early 2021 depends on sustaining physical distancing and on controlling contacts during the end-of-year holiday.
View details for DOI 10.1177/23814683211049249
View details for PubMedID 34660906
View details for PubMedCentralID PMC8512280
Outbreaks of COVID-19 variants in US prisons: a mathematical modelling analysis of vaccination and reopening policies.
The Lancet. Public health
Residents of prisons have experienced disproportionate COVID-19-related health harms. To control outbreaks, many prisons in the USA restricted in-person activities, which are now resuming even as viral variants proliferate. This study aims to use mathematical modelling to assess the risks and harms of COVID-19 outbreaks in prisons under a range of policies, including resumption of activities.We obtained daily resident-level data for all California state prisons from Jan 1, 2020, to May 15, 2021, describing prison layouts, housing status, sociodemographic and health characteristics, participation in activities, and COVID-19 testing, infection, and vaccination status. We developed a transmission-dynamic stochastic microsimulation parameterised by the California data and published literature. After an initial infection is introduced to a prison, the model evaluates the effect of various policy scenarios on infections and hospitalisations over 200 days. Scenarios vary by vaccine coverage, baseline immunity (0%, 25%, or 50%), resumption of activities, and use of non-pharmaceutical interventions (NPIs) that reduce transmission by 75%. We simulated five prison types that differ by residential layout and demographics, and estimated outcomes with and without repeated infection introductions over the 200 days.If a viral variant is introduced into a prison that has resumed pre-2020 contact levels, has moderate vaccine coverage (ranging from 36% to 76% among residents, dependent on age, with 40% coverage for staff), and has no baseline immunity, 23-74% of residents are expected to be infected over 200 days. High vaccination coverage (90%) coupled with NPIs reduces cumulative infections to 2-54%. Even in prisons with low room occupancies (ie, no more than two occupants) and low levels of cumulative infections (ie, <10%), hospitalisation risks are substantial when these prisons house medically vulnerable populations. Risks of large outbreaks (>20% of residents infected) are substantially higher if infections are repeatedly introduced.Balancing benefits of resuming activities against risks of outbreaks presents challenging trade-offs. After achieving high vaccine coverage, prisons with mostly one-to-two-person cells that have higher baseline immunity from previous outbreaks can resume in-person activities with low risk of a widespread new outbreak, provided they maintain widespread NPIs, continue testing, and take measures to protect the medically vulnerable.Horowitz Family Foundation, National Institute on Drug Abuse, Centers for Disease Control and Prevention, National Science Foundation, Open Society Foundation, Advanced Micro Devices.
View details for DOI 10.1016/S2468-2667(21)00162-6
View details for PubMedID 34364404
COVID-19 in the California State Prison System: an Observational Study of Decarceration, Ongoing Risks, and Risk Factors.
Journal of general internal medicine
Correctional institutions nationwide are seeking to mitigate COVID-19-related risks.To quantify changes to California's prison population since the pandemic began and identify risk factors for COVID-19 infection.For California state prisons (March 1-October 10, 2020), we described residents' demographic characteristics, health status, COVID-19 risk scores, room occupancy, and labor participation. We used Cox proportional hazard models to estimate the association between rates of COVID-19 infection and room occupancy and out-of-room labor, respectively.Residents of California state prisons.Changes in the incarcerated population's size, composition, housing, and activities. For the risk factor analysis, the exposure variables were room type (cells vs. dormitories) and labor participation (any room occupant participating in the prior 2 weeks) and the outcome variable was incident COVID-19 case rates.The incarcerated population decreased 19.1% (119,401 to 96,623) during the study period. On October 10, 2020, 11.5% of residents were aged ≥60, 18.3% had high COVID-19 risk scores, 31.0% participated in out-of-room labor, and 14.8% lived in rooms with ≥10 occupants. Nearly 40% of residents with high COVID-19 risk scores lived in dormitories. In 9 prisons with major outbreaks (6,928 rooms; 21,750 residents), dormitory residents had higher infection rates than cell residents (adjusted hazard ratio [AHR], 2.51 95% CI, 2.25-2.80) and residents of rooms with labor participation had higher rates than residents of other rooms (AHR, 1.56; 95% CI, 1.39-1.74).Despite reductions in room occupancy and mixing, California prisons still house many medically vulnerable residents in risky settings. Reducing risks further requires a combination of strategies, including rehousing, decarceration, and vaccination.
View details for DOI 10.1007/s11606-021-07022-x
View details for PubMedID 34291377
- Covid-19 Vaccine Acceptance in California State Prisons. The New England journal of medicine 2021
Estimating Population-Based Recurrence Rates of Colorectal Cancer over Time in the United States
CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION
2020; 29 (12): 2710-2718
Population-based metastatic recurrence rates for patients diagnosed with nonmetastatic colorectal cancer cannot be estimated directly from population-based cancer registries because recurrence information is not reported. We derived population-based colorectal cancer recurrence rates using disease-specific survival data based on our understanding of the colorectal cancer recurrence-death process.We used a statistical continuous-time multistate survival model to derive population-based annual colorectal cancer recurrence rates from 6 months to 10 years after colorectal cancer diagnosis using relative survival data from the Surveillance, Epidemiology, and End Results Program. The model was based on the assumption that, after 6 months of diagnosis, all colorectal cancer-related deaths occur only in patients who experience a metastatic recurrence first, and that the annual colorectal cancer-specific death rate among patients with recurrence was the same as in those diagnosed with de novo metastatic disease. We allowed recurrence rates to vary by post-diagnosis time, age, stage, and location for two diagnostic time periods.In patients diagnosed in 1975-1984, annual recurrence rates 6 months to 5 years after diagnosis ranged from 0.054 to 0.060 in stage II colon cancer, 0.094 to 0.105 in stage II rectal cancer, and 0.146 to 0.177 in stage III colorectal cancer, depending on age. We found a statistically significant decrease in colorectal cancer recurrence among patients diagnosed in 1994-2003 compared with those diagnosed in 1975-1984 for 6 months to 5 years after diagnosis (hazard ratios between 0.43 and 0.70).We derived population-based annual recurrence rates for up to 10 years after diagnosis using relative survival data.Our estimates can be used in decision-analytic models to facilitate analyses of colorectal cancer interventions that are more generalizable.
View details for DOI 10.1158/1055-9965.EPI-20-0490
View details for Web of Science ID 000601401800036
View details for PubMedID 32998946
View details for PubMedCentralID PMC7747688
Cost-effectiveness analysis of a multidisciplinary health-care model for patients with type-2 diabetes implemented in the public sector in Mexico: A quasi-experimental, retrospective evaluation
DIABETES RESEARCH AND CLINICAL PRACTICE
2020; 167: 108336
In 2007, the Ministry of Health (MoH) in Mexico implemented a multidisciplinary health-care model (MHC) for patients with type-2 diabetes (T2D), which has proven more effective in controlling this condition than the conventional health-care model (CHC).We compared the cost-effectiveness of the MHC vs. the CHC for patients with T2D using a quasi-experimental, retrospective design. Epidemiologic and cost data were obtained from a randomly selected sample of health-care units, using medical records as well as patient- and facility-level data. We modelled the cost-effectiveness of the MHC at one, 10 and 20 years using a simulation model.The average cumulative costs per patient at 20 years were US$4,225 for the MHC and US$4,399 for the CHC. With a willingness to pay one gross domestic product (GDP) per capita per quality-adjusted life year (QALY) (US$8,910), the incremental net benefits per patient were US$1,450 and US$3,737 at 10 and 20 years, respectively. The MHC was cost-effective from the third year onward; however, increasing coverage to 500 patients per year rendered it cost-effective at year one.The MHC is cost-effective at 10 and 20 years. Cost-effectiveness can be achieved in the short term by increasing MHC coverage.
View details for DOI 10.1016/j.diabres.2020.108336
View details for Web of Science ID 000580069500006
View details for PubMedID 32755762
View details for PubMedCentralID PMC8010712
Estimating the Natural History of Cervical Carcinogenesis Using Simulation Models: A CISNET Comparative Analysis
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE
2020; 112 (9): 955-963
The natural history of human papillomavirus (HPV)-induced cervical cancer (CC) is not directly observable, yet the age of HPV acquisition and duration of preclinical disease (dwell time) influences the effectiveness of alternative preventive policies. We performed a Cancer Intervention and Surveillance Modeling Network (CISNET) comparative modeling analysis to characterize the age of acquisition of cancer-causing HPV infections and implied dwell times for distinct phases of cervical carcinogenesis.Using four CISNET-cervical models with varying underlying structures but fit to common US epidemiological data, we estimated the age of acquisition of causal HPV infections and dwell times associated with three phases of cancer development: HPV, high-grade precancer, and cancer sojourn time. We stratified these estimates by HPV genotype under both natural history and CC screening scenarios, because screening prevents cancer development that affects the mix of detected cancers.The median time from HPV acquisition to cancer detection ranged from 17.5 to 26.0 years across the four models. Three models projected that 50% of unscreened women acquired their causal HPV infection between ages 19 and 23 years, whereas one model projected these infections occurred later (age 34 years). In the context of imperfect compliance with US screening guidelines, the median age of causal infection was 4.4-15.9 years later compared with model projections in the absence of screening.These validated CISNET-CC models, which reflect some uncertainty in the development of CC, elucidate important drivers of HPV vaccination and CC screening policies and emphasize the value of comparative modeling when evaluating public health policies.
View details for DOI 10.1093/jnci/djz227
View details for Web of Science ID 000593054300014
View details for PubMedID 31821501
View details for PubMedCentralID PMC7492768
- A Summary of the 2020 Gastric Cancer Summit at Stanford University. Gastroenterology 2020
Discussing Cervical Cancer Screening Options: Outcomes to Guide Conversations Between Patients and Providers.
MDM policy & practice
2020; 5 (2): 2381468320952409
Purpose. In 2018, the US Preventive Services Task Force (USPSTF) endorsed three strategies for cervical cancer screening in women ages 30 to 65: cytology every 3 years, testing for high-risk types of human papillomavirus (hrHPV) every 5 years, and cytology plus hrHPV testing (co-testing) every 5 years. It further recommended that women discuss with health care providers which testing strategy is best for them. To inform such discussions, we used decision analysis to estimate outcomes of screening strategies recommended for women at age 30. Methods. We constructed a Markov decision model using estimates of the natural history of HPV and cervical neoplasia. We evaluated the three USPSTF-endorsed strategies, hrHPV testing every 3 years and no screening. Outcomes included colposcopies with biopsy, false-positive testing (a colposcopy in which no cervical intraepithelial neoplasia grade 2 or worse was found), treatments, cancers, and cancer mortality expressed per 10,000 women over a shorter-than-lifetime horizon (15-year). Results. All strategies resulted in substantially lower cancer and cancer death rates compared with no screening. Strategies with the lowest likelihood of cancer and cancer death generally had higher likelihood of colposcopy and false-positive testing. Conclusions. The screening strategies we evaluated involved tradeoffs in terms of benefits and harms. Because individual women may place different weights on these projected outcomes, the optimal choice for each woman may best be discerned through shared decision making.
View details for DOI 10.1177/2381468320952409
View details for PubMedID 32885045
A Multidimensional Array Representation of State-Transition Model Dynamics
MEDICAL DECISION MAKING
2020; 40 (2): 242-248
Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g., disease incidence and lifetime risk). In this article, we propose an alternative approach to compute and store cSTMs outcomes that capture both state occupancy and transition dynamics. This approach produces a multidimensional array from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages of the multidimensional array over the traditional cohort trace and provide potential applications of the proposed approach with an example coded in R to facilitate the implementation of our method.
View details for DOI 10.1177/0272989X19893973
View details for Web of Science ID 000509850200001
View details for PubMedID 31989862
View details for PubMedCentralID PMC7065927
CALCULATING THE EXPECTED VALUE OF SAMPLE INFORMATION IN PRACTICE: CONSIDERATIONS FROM THREE CASE STUDIES
SAGE PUBLICATIONS INC. 2020: E337–E338
View details for Web of Science ID 000509275600285
How do Covid-19 policy options depend on end-of-year holiday contacts in Mexico City Metropolitan Area? A Modeling Study.
medRxiv : the preprint server for health sciences
With more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the largest number of Covid-19 cases in Mexico and is at risk of exceeding its hospital capacity in late December 2020.We used SC-COSMO, a dynamic compartmental Covid-19 model, to evaluate scenarios considering combinations of increased contacts during the holiday season, intensification of social distancing, and school reopening. Model parameters were derived from primary data from MCMA, published literature, and calibrated to time-series of incident confirmed cases, deaths, and hospital occupancy. Outcomes included projected confirmed cases and deaths, hospital demand, and magnitude of hospital capacity exceedance.Following high levels of holiday contacts even with no in-person schooling, we predict that MCMA will have 1·0 million (95% prediction interval 0·5 - 1·7) additional Covid-19 cases between December 7, 2020 and March 7, 2021 and that hospitalizations will peak at 35,000 (14,700 - 67,500) on January 27, 2021, with a >99% chance of exceeding Covid-19-specific capacity (9,667 beds). If holiday contacts can be controlled, MCMA can reopen in-person schools provided social distancing is increased with 0·5 million (0·2 - 1·0) additional cases and hospitalizations peaking at 14,900 (5,600 - 32,000) on January 23, 2021 (77% chance of exceedance).MCMA must substantially increase Covid-19 hospital capacity under all scenarios considered. MCMA's ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled.Society for Medical Decision Making, Gordon and Betty Moore Foundation, and Wadhwani Institute for Artificial Intelligence Foundation.Evidence before this study: As of mid-December 2020, Mexico has the twelfth highest incidence of confirmed cases of Covid-19 worldwide and its epidemic is currently growing. Mexico's case fatality ratio (CFR) - 9·1% - is the second highest in the world. With more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the highest number and incidence rate of Covid-19 confirmed cases in Mexico and a CFR of 8·1%. MCMA is nearing its current hospital capacity even as it faces the prospect of increased social contacts during the 2020 end-of-year holidays. There is limited Mexico-specific evidence available on epidemic, such as parameters governing time-dependent mortality, hospitalization and transmission. Literature searches required supplementation through primary data analysis and model calibration to support the first realistic model-based Covid-19 policy evaluation for Mexico, which makes this analysis relevant and timely.Added value of this study: Study strengths include the use of detailed primary data provided by MCMA; the Bayesian model calibration to enable evaluation of projections and their uncertainty; and consideration of both epidemic and health system outcomes. The model projects that failure to limit social contacts during the end-of-year holidays will substantially accelerate MCMA's epidemic (1·0 million (95% prediction interval 0·5 - 1·7) additional cases by early March 2021). Hospitalization demand could reach 35,000 (14,700 - 67,500), with a >99% chance of exceeding current capacity (9,667 beds). Controlling social contacts during the holidays could enable MCMA to reopen in-person schooling without greatly exacerbating the epidemic provided social distancing in both schools and the community were maintained. Under all scenarios and policies, current hospital capacity appears insufficient, highlighting the need for rapid capacity expansion.Implications of all the available evidence: MCMA officials should prioritize rapid hospital capacity expansion. MCMA's ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled.
View details for DOI 10.1101/2020.12.21.20248597
View details for PubMedID 33398301
View details for PubMedCentralID PMC7781344
The household secondary attack rate of SARS-CoV-2: A rapid review.
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Although much of the public health effort to combat COVID-19 has focused on disease control strategies in public settings, transmission of SARS-CoV-2 within households remains an important problem. The nature and determinants of household transmission are poorly understood.To address this gap, we gathered and analyzed data from 22 published and pre-published studies from 10 countries (20,291 household contacts) that were available through September 2, 2020. Our goal was to combine estimates of the SARS-CoV-2 household secondary attack rate (SAR) and explore variation in estimates of the household SAR.The overall pooled random-effects estimate of the household SAR was 17.1% (95% CI: 13.7-21.2%). In study-level, random-effects meta-regressions stratified by testing frequency (1 test, 2 tests, >2 tests), SAR estimates were 9.2% (95% CI: 6.7-12.3%), 17.5% (95% CI: 13.9-21.8%), and 21.3% (95% CI: 13.8-31.3%), respectively. Household SAR tended to be higher among older adult contacts and among contacts of symptomatic cases.These findings suggest that SAR reported using a single follow-up test may be underestimated and that testing household contacts of COVID-19 cases on multiple occasions may increase the yield for identifying secondary cases.
View details for DOI 10.1093/cid/ciaa1558
View details for PubMedID 33045075
Computing the Expected Value of Sample Information Efficiently: Practical Guidance and Recommendations for Four Model-Based Methods.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
2020; 23 (6): 734–42
Value of information (VOI) analyses can help policy makers make informed decisions about whether to conduct and how to design future studies. Historically a computationally expensive method to compute the expected value of sample information (EVSI) restricted the use of VOI to simple decision models and study designs. Recently, 4 EVSI approximation methods have made such analyses more feasible and accessible. Members of the Collaborative Network for Value of Information (ConVOI) compared the inputs, the analyst's expertise and skills, and the software required for the 4 recently developed EVSI approximation methods. Our report provides practical guidance and recommendations to help inform the choice between the 4 efficient EVSI estimation methods. More specifically, this report provides: (1) a step-by-step guide to the methods' use, (2) the expertise and skills required to implement the methods, and (3) method recommendations based on the features of decision-analytic problems.
View details for DOI 10.1016/j.jval.2020.02.010
View details for PubMedID 32540231
Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies.
Medical decision making : an international journal of the Society for Medical Decision Making
Background. Investing efficiently in future research to improve policy decisions is an important goal. Expected value of sample information (EVSI) can be used to select the specific design and sample size of a proposed study by assessing the benefit of a range of different studies. Estimating EVSI with the standard nested Monte Carlo algorithm has a notoriously high computational burden, especially when using a complex decision model or when optimizing over study sample sizes and designs. Recently, several more efficient EVSI approximation methods have been developed. However, these approximation methods have not been compared, and therefore their comparative performance across different examples has not been explored. Methods. We compared 4 EVSI methods using 3 previously published health economic models. The examples were chosen to represent a range of real-world contexts, including situations with multiple study outcomes, missing data, and data from an observational rather than a randomized study. The computational speed and accuracy of each method were compared. Results. In each example, the approximation methods took minutes or hours to achieve reasonably accurate EVSI estimates, whereas the traditional Monte Carlo method took weeks. Specific methods are particularly suited to problems where we wish to compare multiple proposed sample sizes, when the proposed sample size is large, or when the health economic model is computationally expensive. Conclusions. As all the evaluated methods gave estimates similar to those given by traditional Monte Carlo, we suggest that EVSI can now be efficiently computed with confidence in realistic examples. No systematically superior EVSI computation method exists as the properties of the different methods depend on the underlying health economic model, data generation process, and user expertise.
View details for DOI 10.1177/0272989X20912402
View details for PubMedID 32297840
PRACTICAL CONSIDERATIONS FOR THE EFFICIENT COMPUTATION OF THE EXPECTED VALUE OF SAMPLE INFORMATION TO PRIORITIZE RESEARCH IN HEALTH CARE
SAGE PUBLICATIONS INC. 2020: E63–E64
View details for Web of Science ID 000509275600063
Potential Bias Associated with Modeling the Effectiveness of Healthcare Interventions in Reducing Mortality Using an Overall Hazard Ratio
2020; 38 (3): 285-296
Clinical trials often report intervention efficacy in terms of the reduction in all-cause mortality between the treatment and control arms (i.e., an overall hazard ratio [oHR]) instead of the reduction in disease-specific mortality (i.e., a disease-specific hazard ratio [dsHR]). Using oHR to reduce all-cause mortality beyond the time horizon of the trial may introduce bias if the relative proportion of other-cause mortality increases with age. We sought to quantify this oHR extrapolation bias and propose a new approach to overcome this bias.We simulated a hypothetical cohort of patients with a generic disease that increased background mortality by a constant additive disease-specific rate. We quantified the bias in terms of the percentage change in life expectancy gains with the intervention under an oHR compared with a dsHR approach as a function of the cohort start age, the disease-specific mortality rate, dsHR, and the duration of the intervention's effect. We then quantified the bias in a cost-effectiveness analysis (CEA) of implantable cardioverter-defibrillators based on efficacy estimates from a clinical trial.For a cohort of 50-year-old patients with a disease-specific mortality of 0.05, a dsHR of 0.5, a calculated oHR of 0.55, and a lifetime duration of effect, the bias was 28%. We varied these key parameters over wide ranges and the resulting bias ranged between 3 and 140%. In the CEA, the use of oHR as the intervention's effectiveness overestimated quality-adjusted life expectancy by 9% and costs by 3%, biasing the incremental cost-effectiveness ratio by - 6%.The use of an oHR approach to model the intervention's effectiveness beyond the time horizon of the trial overestimates its benefits. In CEAs, this bias could decrease the cost of a QALY, overestimating interventions' cost effectiveness.
View details for DOI 10.1007/s40273-019-00859-5
View details for Web of Science ID 000498763000001
View details for PubMedID 31755032
View details for PubMedCentralID PMC7024667
Midwife-led care and obstetrician-led care for low-risk pregnancies: A cost comparison
BIRTH-ISSUES IN PERINATAL CARE
2020; 47 (1): 57-66
Low-risk pregnant women cared for by midwives have similar birth outcomes to women cared for by physicians, although experiencing fewer medical procedures. However, limited research has assessed cost implications in the United States. Using national data, we assessed costs and resource use of midwife-led care vs obstetrician-led care for low-risk pregnancies using a decision-analytic approach.We developed a decision-analytic model of costs (health plan payments to clinicians) and use of medical procedures during childbirth (epidural analgesia, labor induction, cesarean birth, episiotomy) and outcomes of care (birth at preterm gestation) that may differ with midwife-led vs obstetrician-led care. Model parameters for obstetric procedures were generated using Listening to Mothers III data, a national survey of women who gave birth in US hospitals in 2011-2012 and other published estimates. Cost estimates came from published or publicly available information on health insurance claims payments.The costs of childbirth for low-risk women with midwife-led care were, on average, $2262 less than births to low-risk women cared for by obstetricians. These cost differences derive from lower rates of preterm birth and episiotomy among women with midwife-led care, compared with obstetrician-led care. Across the population of US women with low-risk births each year (approximately 2.6 million), the model predicted substantially fewer preterm births (167 259 vs 219 427 for midwife-led vs obstetrician-led care) and fewer episiotomies (170 504 vs 415 686, for midwife-led vs obstetrician-led care).A shift from obstetrician-led care to midwife-led care for low-risk pregnancies could be cost saving.
View details for DOI 10.1111/birt.12464
View details for Web of Science ID 000493742100001
View details for PubMedID 31680337
A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling
2019; 37 (11): 1329-1339
The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world.
View details for DOI 10.1007/s40273-019-00837-x
View details for Web of Science ID 000496297600005
View details for PubMedID 31549359
View details for PubMedCentralID PMC6871515
A Cost-effectiveness Analysis of Systemic Therapy for Metastatic Hormone-sensitive Prostate Cancer
EUROPEAN UROLOGY ONCOLOGY
2019; 2 (6): 649-655
Following the recent publication of results from randomized trials that have demonstrated a survival benefit for the addition of docetaxel or abiraterone acetate to androgen deprivation therapy (ADT) in the treatment of metastatic prostate cancer, it is important to assess whether the benefits of treatment with these agents outweigh their costs.To perform a cost-effectiveness analysis of immediate docetaxel or abiraterone acetate treatment in addition to ADT in men with metastatic hormone-sensitive prostate cancer (PC).We developed a state-transition model to simulate the natural progression of metastatic PC. Model parameters were derived from the published literature and through calibration to observed epidemiological data. Following diagnosis, a hypothetical cohort of men with metastatic hormone-sensitive PC could be treated with docetaxel+ADT, abiraterone+ADT, or ADT alone. Once disease progresses to castration-resistant PC, treatment with one of the approved therapies in this setting was initiated.The outcomes measured were quality-adjusted life years (QALYs) and costs from a US private payer, health sector perspective.Compared to treatment with ADT alone, docetaxel and abiraterone resulted in a discounted quality-adjusted survival gain of 3.6 and 22.0mo, respectively. Using standard cost-effectiveness metrics, treatment with docetaxel and ADT provides high value for money (ie, is cost effective) with an incremental cost-effectiveness ratio (ICER) of $34723, compared to an ICER of $295212 for abiraterone. The monthly cost of abiraterone would have to be less than $3114 for it to be cost effective.Docetaxel+ADT is likely the most cost-effective treatment option for men with metastatic hormone-sensitive PC. Although potentially more effective than docetaxel, the costs of abiraterone would have to be considerably lower to match the cost effectiveness of docetaxel+ADT.This study evaluated the balance of costs and benefits for treatment of metastatic hormone-sensitive prostate cancer with docetaxel plus androgen deprivation therapy (ADT), abiraterone plus ADT, or ADT alone. We found that treatment with docetaxel plus ADT likely represents the most cost-effective option in this setting.
View details for DOI 10.1016/j.euo.2019.01.004
View details for Web of Science ID 000496493400005
View details for PubMedID 31411985
A Value of Information Analysis of Research on the 21-Gene Assay for Breast Cancer Management
VALUE IN HEALTH
2019; 22 (10): 1102-1110
The 21-gene assay Oncotype DX (21-GA) shows promise as a guide in deciding when to initiate adjuvant chemotherapy in women with hormone receptor-positive early-stage breast cancer. Nevertheless, its routine use remains controversial, owing to insufficient evidence of its clinical utility and cost-effectiveness. Accordingly, we aim to quantify the value of conducting further research to reduce decision uncertainty in the use of the 21-GA.Using value of information methods, we first generated probability distributions of survival and costs for decision making with and without the 21-GA alongside traditional risk prediction. These served as the input to a comparison of 3 alternative study designs: a retrospective observational study to update risk classification from the 21-GA, a prospective observational study to estimate prevalence of chemotherapy use, and a randomized controlled trial (RCT) of the 21-GA predictive value.We found that current evidence strongly supports the use of the 21-GA in intermediate- and high-risk women. Further research should focus on low-risk women, among whom the cost-effectiveness findings remained equivocal. For this population, we identified a high value of reducing uncertainty in the 21-GA use for all proposed research studies. The RCT had the greatest potential to efficiently reduce the likelihood of choosing a suboptimal strategy, providing a value between $162 million and $1.1 billion at willingness-to-pay thresholds of $150 000 to $200 000/quality-adjusted life years.Future research to inform 21-GA decision making is of high value. The RCT of the 21-GA predictive value has the greatest potential to efficiently reduce decision uncertainty around 21-GA use in women with low-risk early-stage breast cancer.
View details for DOI 10.1016/j.jval.2019.05.004
View details for Web of Science ID 000487811000003
View details for PubMedID 31563252
View details for PubMedCentralID PMC7343670
Estimated Quality of Life and Economic Outcomes Associated With 12 Cervical Cancer Screening Strategies: A Cost-effectiveness Analysis
JAMA INTERNAL MEDICINE
2019; 179 (7): 867-878
Many cervical cancer screening strategies are now recommended in the United States, but the benefits, harms, and costs of each option are unclear.To estimate the cost-effectiveness of 12 cervical cancer screening strategies.The cross-sectional portion of this study enrolled a convenience sample of 451 English-speaking or Spanish-speaking women aged 21 to 65 years from September 22, 2014, to June 16, 2016, identified at women's health clinics in San Francisco. In this group, utilities (preferences) were measured for 23 cervical cancer screening-associated health states and were applied to a decision model of type-specific high-risk human papillomavirus (hrHPV)-induced cervical carcinogenesis. Test accuracy estimates were abstracted from systematic reviews. The evaluated strategies were cytologic testing every 3 years for women aged 21 to 65 years with either repeat cytologic testing in 1 year or immediate hrHPV triage for atypical squamous cells of undetermined significance (ASC-US), cytologic testing every 3 years for women age 21 to 29 years followed by cytologic testing plus hrHPV testing (cotesting), or primary hrHPV testing alone for women aged 30 to 65 years. Screening frequency, abnormal test result management, and the age to switch from cytologic testing to hrHPV testing (25 or 30 years) were varied. Analyses were conducted from both the societal and health care sector perspectives.Utilities for 23 cervical cancer screening-associated health states (cross-sectional study) and quality-adjusted life-years (QALYs) and total costs for each strategy.Utilities were measured in a sociodemographically diverse group of 451 women (mean [SD] age, 38.2 [10.7] years; 258 nonwhite [57.2%]). Cytologic testing every 3 years with repeat cytologic testing for ASC-US yielded the most lifetime QALYs and conferred more QALYs at higher costs ($2166 per QALY) than the lowest-cost strategy (cytologic testing every 3 years with hrHPV triage of ASC-US). All cytologic testing plus hrHPV testing (cotesting) and primary hrHPV testing strategies provided fewer QALYs at higher costs. Adding indirect costs did not change the conclusions. In sensitivity analyses, hrHPV testing every 5 years with genotyping triage beginning at age 30 years was the lowest-cost strategy when hrHPV test sensitivity was markedly higher than cytologic test sensitivity or when hrHPV test cost was equated to the lowest reported cytologic test cost ($14).Cytologic testing every 3 years for women aged 21 to 29 years with either continued cytologic testing every 3 years or switching to a low-cost hrHPV test every 5 years confers a reasonable balance of benefits, harms, and costs. Comparative modeling is needed to confirm the association of these novel utilities with cost-effectiveness.
View details for DOI 10.1001/jamainternmed.2019.0299
View details for Web of Science ID 000477893300004
View details for PubMedID 31081851
View details for PubMedCentralID PMC6515585
The Curve of Optimal Sample Size (COSS): A Graphical Representation of the Optimal Sample Size from a Value of Information Analysis
2019; 37 (7): 871-877
Value of information (VOI) analysis quantifies the opportunity cost associated with decision uncertainty, and thus informs the value of collecting further information to avoid this cost. VOI can inform study design, optimal sample size selection, and research prioritization. Recent methodological advances have reduced the computational burden of conducting VOI analysis and have made it easier to evaluate the expected value of sample information, the expected net benefit of sampling, and the optimal sample size of a study design ([Formula: see text]). The volume of VOI analyses being published is increasing, and there is now a need for VOI studies to conduct sensitivity analyses on VOI-specific parameters. In this practical application, we introduce the curve of optimal sample size (COSS), which is a graphical representation of [Formula: see text] over a range of willingness-to-pay thresholds and VOI parameters (example data and R code are provided). In a single figure, the COSS presents summary data for decision makers to determine the sample size that optimizes research funding given their operating characteristics. The COSS also presents variation in the optimal sample size given variability or uncertainty in VOI parameters. The COSS represents an efficient and additional approach for summarizing results from a VOI analysis.
View details for DOI 10.1007/s40273-019-00770-z
View details for Web of Science ID 000470791900001
View details for PubMedID 30761461
View details for PubMedCentralID PMC6556417
Cost-effectiveness Analysis of Active Surveillance Strategies for Men with Low-risk Prostate Cancer
ELSEVIER SCIENCE BV. 2019: 910-917
Active surveillance (AS) has become the recommended management strategy for men with low-risk prostate cancer. However, there is considerable uncertainty about the optimal follow-up schedule in terms of the tests to perform and their frequency.To assess the costs and benefits of different AS follow-up strategies compared to watchful waiting (WW) or immediate treatment.A state-transition Markov model was developed to simulate the natural history (ie, no testing or intervention) of prostate cancer for a hypothetical cohort of 50-yr-old men newly diagnosed with low-risk prostate cancer. Following diagnosis, men were hypothetically managed with immediate treatment, watchful waiting, or one of several AS strategies. AS follow-up was performed either with transrectal ultrasound-guided biopsy or magnetic resonance imaging (MRI) which was scheduled annually, biennially, every 3yrs, according to the PRIAS protocol (yrs 1, 4, 7, and 10, and then every 5yr) or every 5yr. Diagnosis of higher-grade or -stage disease while on AS resulted in curative treatment.We measured discounted quality-adjusted life years (QALYs), discounted lifetime medical costs (2017 US$), and incremental cost-effectiveness ratios (ICERs).Compared to WW, MRI-based surveillance performed every 5yr improved quality-adjusted survival by 4.47 quality-adjusted months and represented high-value health care at the Medicare reimbursement rate using standard cost-effectiveness metrics. Biopsy-based strategies were less effective and less costly than the corresponding MRI-based strategies for each testing interval. MRI-based surveillance at more frequent intervals had ICERs greater than $800000 per QALY and would not be considered cost-effective according to standard metrics. Our results were sensitive to the diagnostic accuracy and costs of both biopsy modes in detecting clinically significant cancer.Incorporation of MRI into surveillance protocols at Medicare reimbursement rates and decreasing the intensity of repeat testing may be cost-effective options for men opting for conservative management of low-risk prostate cancer.Our study modeled outcomes for men with low-risk prostate cancer undergoing watchful waiting, immediate treatment, or active surveillance with different follow-up schedules. We found that conservative management of low-risk disease optimizes health outcomes and costs. Furthermore, we showed that decreasing the intensity of active surveillance follow-up and incorporating magnetic resonance imaging (MRI) into surveillance protocols can be cost-effective, depending on the MRI costs.
View details for DOI 10.1016/j.eururo.2018.10.055
View details for Web of Science ID 000467916500021
View details for PubMedID 30425010
COMPARING THE COST-EFFECTIVENESS OF NEW COLORECTAL CANCER SCREENING TESTS
W B SAUNDERS CO-ELSEVIER INC. 2019: S21
View details for Web of Science ID 000467106000055
"Time Traveling Is Just Too Dangerous" but Some Methods Are Worth Revisiting: The Advantages of Expected Loss Curves Over Cost-Effectiveness Acceptability Curves and Frontier
VALUE IN HEALTH
2019; 22 (5): 611-618
Cost-effectiveness acceptability curves (CEACs) and the cost-effectiveness acceptability frontier (CEAF) are the recommended graphical representations of uncertainty in a cost-effectiveness analysis (CEA). Nevertheless, many limitations of CEACs and the CEAF have been recognized by others. Expected loss curves (ELCs) overcome these limitations by displaying the expected foregone benefits of choosing one strategy over others, the optimal strategy in expectation, and the value of potential future research all in a single figure.To revisit ELCs, illustrate their benefits using a case study, and promote their adoption by providing open-source code.We used a probabilistic sensitivity analysis of a CEA comparing 6 cerebrospinal fluid biomarker test-and-treat strategies in patients with mild cognitive impairment. We showed how to calculate ELCs for a set of decision alternatives. We used the probabilistic sensitivity analysis of the case study to illustrate the limitations of currently recommended methods for communicating uncertainty and then demonstrated how ELCs can address these issues.ELCs combine the probability that each strategy is not cost-effective on the basis of current information and the expected foregone benefits resulting from choosing that strategy (ie, how much is lost if we recommended a strategy with a higher expected loss). ELCs display how the optimal strategy switches across willingness-to-pay thresholds and enables comparison between different strategies in terms of the expected loss.ELCs provide a more comprehensive representation of uncertainty and overcome current limitations of CEACs and the CEAF. Communication of uncertainty in CEA would benefit from greater adoption of ELCs as a complementary method to CEACs, the CEAF, and the expected value of perfect information.
View details for DOI 10.1016/j.jval.2019.02.008
View details for Web of Science ID 000468149900018
View details for PubMedID 31104743
View details for PubMedCentralID PMC6530578
Incorporating Biomarkers into the Primary Prostate Biopsy Setting: A Cost-Effectiveness Analysis
JOURNAL OF UROLOGY
2018; 200 (6): 1215-1220
We performed a cost-effectiveness analysis using the PHI (Prostate Health Index), 4Kscore®, SelectMDx™ and the EPI (ExoDx™ Prostate [IntelliScore]) in men with elevated prostate specific antigen to determine the need for biopsy.We developed a decision analytical model in men with elevated prostate specific antigen (3 ng/ml or greater) in which 1 biomarker test was used to determine which hypothetical individuals required biopsy. In the current standard of care strategy all individuals underwent biopsy. Model parameters were derived from a comprehensive review of the literature. Costs were calculated from a health sector perspective and converted into 2017 United States dollars.The cost and QALYs (quality adjusted life-years) of the current standard of care, which was transrectal ultrasound guided biopsy, was $3,863 and 18.085, respectively. Applying any of the 3 biomarkers improved quality adjusted survival compared to the current standard of care. The cost of SelectMDx, the PHI and the EPI was lower than performing prostate biopsy in all patients. However, the PHI was more costly and less effective than the SelectMDx strategy. The EPI provided the highest QALY with an incremental cost-effectiveness ratio of $58,404 per QALY. The use of biomarkers could reduce the number of unnecessary biopsies by 24% to 34% compared to the current standard of care.Applying biomarkers in men with elevated prostate specific antigen to determine the need for biopsy improved quality adjusted survival by decreasing the number of biopsies performed and the treatment of indolent disease. Using SelectMDx or the EPI following elevated prostate specific antigen but before proceeding to biopsy is a cost-effective strategy in this setting.
View details for DOI 10.1016/j.juro.2018.06.016
View details for Web of Science ID 000449558500076
View details for PubMedID 29906434
Nonidentifiability in Model Calibration and Implications for Medical Decision Making
MEDICAL DECISION MAKING
2018; 38 (7): 810-821
Calibration is the process of estimating parameters of a mathematical model by matching model outputs to calibration targets. In the presence of nonidentifiability, multiple parameter sets solve the calibration problem, which may have important implications for decision making. We evaluate the implications of nonidentifiability on the optimal strategy and provide methods to check for nonidentifiability.We illustrate nonidentifiability by calibrating a 3-state Markov model of cancer relative survival (RS). We performed 2 different calibration exercises: 1) only including RS as a calibration target and 2) adding the ratio between the 2 nondeath states over time as an additional target. We used the Nelder-Mead (NM) algorithm to identify parameter sets that best matched the calibration targets. We used collinearity and likelihood profile analyses to check for nonidentifiability. We then estimated the benefit of a hypothetical treatment in terms of life expectancy gains using different, but equally good-fitting, parameter sets. We also applied collinearity analysis to a realistic model of the natural history of colorectal cancer.When only RS is used as the calibration target, 2 different parameter sets yield similar maximum likelihood values. The high collinearity index and the bimodal likelihood profile on both parameters demonstrated the presence of nonidentifiability. These different, equally good-fitting parameter sets produce different estimates of the treatment effectiveness (0.67 v. 0.31 years), which could influence the optimal decision. By incorporating the additional target, the model becomes identifiable with a collinearity index of 3.5 and a unimodal likelihood profile.In the presence of nonidentifiability, equally likely parameter estimates might yield different conclusions. Checking for the existence of nonidentifiability and its implications should be incorporated into standard model calibration procedures.
View details for DOI 10.1177/0272989X18792283
View details for Web of Science ID 000445463300005
View details for PubMedID 30248276
View details for PubMedCentralID PMC6156799
Revisiting assumptions about age-based mixing representations in mathematical models of sexually transmitted infections
2018; 36 (37): 5572-5579
Sexual mixing between heterogeneous population subgroups is an integral component of mathematical models of sexually transmitted infections (STIs). This study compares the fit of different mixing representations to survey data and the impact of different mixing assumptions on the predicted benefits of hypothetical human papillomavirus (HPV) vaccine strategies.We compared novel empirical (data-driven) age mixing structures with the more commonly-used assortative-proportionate (A-P) mixing structure. The A-P mixing structure assumes that a proportion of sexual contacts - known as the assortativity constant, typically estimated from survey data or calibrated - occur exclusively within one's own age group and the remainder mixes proportionately among all age groups. The empirical age mixing structure was estimated from the National Survey on Sexual Attitudes and Lifestyles 3 (Natsal-3) using regression methods, and the assortativity constant was estimated from Natsal-3 as well. Using a simplified HPV transmission model under each mixing assumption, we calibrated the model to British HPV16 prevalence data, then estimated the reduction in steady-state prevalence and the number of infections averted due to expanding HPV vaccination from 12- through 26-year-old females alone to 12-year-old males or 27- to 39-year-old females.Empirical mixing provided a better fit to the Natsal-3 data than the best-fitting A-P structure. Using the model with empirical mixing as a reference, the model using the A-P structure often under- or over-estimated the benefits of vaccination, in one case overestimating by 2-fold the number of infections prevented due to extended female catch-up in a high vaccine uptake setting.An empirical mixing structure more accurately represents sexual mixing survey data, and using the less accurate, yet commonly-used A-P structure has a notable effect on estimates of HPV vaccination benefits. This underscores the need for mixing structures that are less dependent on unverified assumptions and are directly informed by sexual behavior data.
View details for DOI 10.1016/j.vaccine.2018.07.058
View details for Web of Science ID 000445980200016
View details for PubMedID 30093290
View details for PubMedCentralID PMC6367925
- Force of infection of Helicobacter pylori in Mexico: evidence from a national survey using a hierarchical Bayesian model (vol 146, pg 961, 2018) EPIDEMIOLOGY AND INFECTION 2018; 146 (8): 1070
Force of infection of Helicobacter pylori in Mexico: evidence from a national survey using a hierarchical Bayesian model?
EPIDEMIOLOGY AND INFECTION
2018; 146 (8): 961-969
Helicobacter pylori (H. pylori) is present in the stomach of half of the world's population. The force of infection describes the rate at which susceptibles acquire infection. In this article, we estimated the age-specific force of infection of H. pylori in Mexico. Data came from a national H. pylori seroepidemiology survey collected in Mexico in 1987-88. We modelled the number of individuals with H. pylori at a given age as a binomial random variable. We assumed that the cumulative risk of infection by a given age follows a modified exponential catalytic model, allowing some fraction of the population to remain uninfected. The cumulative risk of infection was modelled for each state in Mexico and were shrunk towards the overall national cumulative risk curve using Bayesian hierarchical models. The proportion of the population that can be infected (i.e. susceptible population) is 85.9% (95% credible interval (CR) 84.3%-87.5%). The constant rate of infection per year of age among the susceptible population is 0.092 (95% CR 0.084-0.100). The estimated force of infection was highest at birth 0.079 (95% CR 0.071-0.087) decreasing to zero as age increases. This Bayesian hierarchical model allows stable estimation of state-specific force of infection by pooling information between the states, resulting in more realistic estimates.
View details for DOI 10.1017/S0950268818000857
View details for Web of Science ID 000440755700005
View details for PubMedID 29656725
View details for PubMedCentralID PMC5997526
ACTIVE SURVEILLANCE FOLLOW-UP STRATEGIES: A COST-EFFECTIVENESS ANALYSIS
ELSEVIER SCIENCE INC. 2018: E209
View details for Web of Science ID 000429166600452
Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial
MEDICAL DECISION MAKING
2018; 38 (3): 400-422
Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.
View details for DOI 10.1177/0272989X18754513
View details for Web of Science ID 000429896800010
View details for PubMedID 29587047
View details for PubMedCentralID PMC6349385
A Gaussian Approximation Approach for Value of Information Analysis
MEDICAL DECISION MAKING
2018; 38 (2): 174-188
Most decisions are associated with uncertainty. Value of information (VOI) analysis quantifies the opportunity loss associated with choosing a suboptimal intervention based on current imperfect information. VOI can inform the value of collecting additional information, resource allocation, research prioritization, and future research designs. However, in practice, VOI remains underused due to many conceptual and computational challenges associated with its application. Expected value of sample information (EVSI) is rooted in Bayesian statistical decision theory and measures the value of information from a finite sample. The past few years have witnessed a dramatic growth in computationally efficient methods to calculate EVSI, including metamodeling. However, little research has been done to simplify the experimental data collection step inherent to all EVSI computations, especially for correlated model parameters. This article proposes a general Gaussian approximation (GA) of the traditional Bayesian updating approach based on the original work by Raiffa and Schlaifer to compute EVSI. The proposed approach uses a single probabilistic sensitivity analysis (PSA) data set and involves 2 steps: 1) a linear metamodel step to compute the EVSI on the preposterior distributions and 2) a GA step to compute the preposterior distribution of the parameters of interest. The proposed approach is efficient and can be applied for a wide range of data collection designs involving multiple non-Gaussian parameters and unbalanced study designs. Our approach is particularly useful when the parameters of an economic evaluation are correlated or interact.
View details for DOI 10.1177/0272989X17715627
View details for Web of Science ID 000422787900004
View details for PubMedID 28735563
View details for PubMedCentralID PMC8608426
Prioritizing Future Research on Allopurinol and Febuxostat for the Management of Gout: Value of Information Analysis
2017; 35 (10): 1073-1085
The aim of this study was to quantify the value of conducting additional research and reducing uncertainty regarding the cost effectiveness of allopurinol and febuxostat for the management of gout.We used a previously developed Markov model that evaluated the cost effectiveness of nine urate-lowering strategies: no treatment, allopurinol-only fixed dose (300 mg), allopurinol-only dose escalation (up to 800 mg), febuxostat-only fixed dose (80 mg), febuxostat-only dose escalation (up to 120 mg), allopurinol-febuxostat sequential therapy fixed dose, allopurinol-febuxostat sequential therapy dose escalation, febuxostat-allopurinol sequential therapy fixed dose, and febuxostat-allopurinol sequential therapy dose escalation. Each strategy was evaluated over the lifetime of a hypothetical gout patient. We calculated population expected value of perfect information (EVPI). We used a linear regression meta-modeling approach to calculate population expected value of partial perfect information (EVPPI), and a Gaussian approximation to calculate the population expected value of sample information for parameters (EVSI) and the expected net benefit of sampling (ENBS) for four potential study designs: (1) an allopurinol efficacy trial; (2) a febuxostat efficacy trial; (3) a prospective observational study evaluating health utilities; and (4) a comprehensive study evaluating the efficacy of allopurinol and febuxostat and health utilities. A 5-year decision time horizon was used in the base-case analysis.EVPI varied by a decision maker's willingness-to-pay (WTP) per quality-adjusted life-year (QALY) and was $US900 million for WTP of $US60,000 per QALY. Population EVPPI was highest across all WTP values for study design #4. For study design #4 and a WTP of $US60,000 per QALY, the optimal sample size was 735 patients per study arm.Future studies are needed to evaluate the effectiveness of allopurinol and febuxostat dose escalation.
View details for DOI 10.1007/s40273-017-0526-0
View details for Web of Science ID 000411333800008
View details for PubMedID 28631197
An Overview of R in Health Decision Sciences
MEDICAL DECISION MAKING
2017; 37 (7): 735-746
As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R's popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.
View details for DOI 10.1177/0272989X16686559
View details for Web of Science ID 000408792900001
View details for PubMedID 28061043
- A KINKED HEALTH INSURANCE MARKET: Employer-Sponsored Insurance under the Cadillac Tax AMERICAN JOURNAL OF HEALTH ECONOMICS 2017; 3 (4): 455-476
Trade-offs Between Efficacy and Cardiac Toxicity of Adjuvant Chemotherapy in Early-Stage Breast Cancer Patients: Do Competing Risks Matter?
2017; 23 (4): 401-409
Evidence about treatment efficacy and long-term toxicities for adjuvant chemotherapy in patients with early-stage breast cancer is often presented in different formats and studies. This leads to challenges for patients and their physicians to adequately weigh the trade-offs between effectiveness and long-term cardiac toxicity when making decisions about adjuvant chemotherapy. We used a decision-analytic framework to quantify these trade-offs by combining the available evidence into a single, comparable metric. We developed a Markov model to simulate a hypothetical cohort of newly diagnosed breast cancer patients under three scenarios: no treatment, anthracycline (AC)-based adjuvant chemotherapy (more effective but also more cardiotoxic), and non-AC-based adjuvant chemotherapy. We derived the model parameters from medical literature (e.g., clinical trials). Our primary outcome is 10-year mortality, and other metrics such as cause of death; life years (LYs) and quality-adjusted LYs over 10 years were evaluated in sensitivity analysis. For 55-year-old women with a 10-year risk of metastatic recurrence <12.5% no chemotherapy resulted in the preferred strategy. In general, non-AC-based adjuvant chemotherapy resulted in lower 10-year mortality than AC-based chemotherapy. Patients with low risk of metastatic recurrence are better off without adjuvant chemotherapy regardless of the outcome considered (i.e., the risks of cardiac toxicity from chemotherapy outweighed the benefits). Trade-offs between effectiveness and induced cardiac toxicity impact health outcomes. The choice of adjuvant treatment must consider the patient's risk of distant recurrence and the quality of life associated with different health outcomes.
View details for DOI 10.1111/tbj.12757
View details for Web of Science ID 000405317600004
View details for PubMedID 28117517
Modeling the Cost-Effectiveness of Doula Care Associated with Reductions in Preterm Birth and Cesarean Delivery
BIRTH-ISSUES IN PERINATAL CARE
2016; 43 (1): 20-27
One in nine US infants is born before 37 weeks' gestation, incurring medical costs 10 times higher than full-term infants. One in three infants is born by cesarean; cesarean births cost twice as much as vaginal births. We compared rates of preterm and cesarean birth among Medicaid recipients with prenatal access to doula care (nonmedical maternal support) with similar women regionally. We used data on this association to mathematically model the potential cost-effectiveness of Medicaid coverage of doula services.Data came from two sources: all Medicaid-funded, singleton births at hospitals in the West North Central and East North Central US (n = 65,147) in the 2012 Nationwide Inpatient Sample, and all Medicaid-funded singleton births (n = 1,935) supported by a community-based doula organization in the Upper Midwest from 2010 to 2014. We analyzed routinely collected, de-identified administrative data. Multivariable regression analysis was used to estimate associations between doula care and outcomes. A probabilistic decision-analytic model was used for cost-effectiveness estimates.Women who received doula support had lower preterm and cesarean birth rates than Medicaid beneficiaries regionally (4.7 vs 6.3%, and 20.4 vs 34.2%). After adjustment for covariates, women with doula care had 22 percent lower odds of preterm birth (AOR 0.77 [95% CI 0.61-0.96]). Cost-effectiveness analyses indicate potential savings associated with doula support reimbursed at an average of $986 (ranging from $929 to $1,047 across states).Based on associations between doula care and preterm and cesarean birth, coverage reimbursement for doula services would likely be cost saving or cost-effective for state Medicaid programs.
View details for DOI 10.1111/birt.12218
View details for Web of Science ID 000370450400003
View details for PubMedID 26762249
View details for PubMedCentralID PMC5544530
Cost-benefit analysis: HIV/AIDS prevention in migrants in Central America.
SALUD PUBLICA DE MEXICO
2013; 55: S23-S30
To quantify the costs and benefits of three HIV prevention interventions in migrants in Central America: voluntary counseling and testing, treatment of sexually transmitted infections, and condom distribution.The methods were: a) identification and quantification of costs; b) quantification of benefits, defined as the potential savings in antiretroviral treatment of HIV cases prevented; and c) estimation of the cost-benefit ratio.The model estimated that 9, 21 and 8 cases of HIV were prevented by voluntary counseling and testing, treatment for sexually transmitted infections and condom distribution per 10 000 migrants, respectively. In Panama, condom distribution and treatment for sexually transmitted infections had a return of US$131/USD and US$69.8/USD. Returns in El Salvador were US$2.0/USD and US$42.3/USD in voluntary counseling and testing and condom distribution, respectively.The potential savings on prevention have a large variation between countries. Nevertheless, the cost-benefit estimates suggest that the HIV prevention programs in Central America can potentially result in monetary savings in the long run.
View details for Web of Science ID 000321231400005
View details for PubMedID 23918053
View details for PubMedCentralID PMC3914404
EEG signal register for Brain Computer Interface (BCI) based on steady state visual evoked potential (SSVEP)
SPRINGER-VERLAG BERLIN. 2008: 87-90
View details for Web of Science ID 000261093200021