Fernando Alarid-Escudero | Stanford University School of Medicine (original) (raw)

Papers by Fernando Alarid-Escudero

ABSTRACTBackgroundPrisons and jails are high-risk settings for COVID-19 transmission, morbidity, ... more ABSTRACTBackgroundPrisons 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.MethodsWe 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.FindingsAmong 60,707 r...

Medical Decision Making, 2021

The Lancet Public Health, 2021

Background Residents of prisons have experienced disproportionate COVID-19-related health harms. ... more Background 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. Methods 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. Findings 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. Interpretation 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.

Journal of General Internal Medicine, 2021

BACKGROUND: Correctional institutions nationwide are seeking to mitigate COVID-19-related risks. ... more BACKGROUND: Correctional institutions nationwide are seeking to mitigate COVID-19-related risks. OBJECTIVE: To quantify changes to California's prison population since the pandemic began and identify risk factors for COVID-19 infection. DESIGN: 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. PARTICIPANTS: Residents of California state prisons. MAIN MEASURES: 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. KEY RESULTS: 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). CONCLUSION: 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.

Clinical Infectious Diseases, 2020

Background Although much of the public health effort to combat coronavirus disease 2019 (COVID-19... more Background Although much of the public health effort to combat coronavirus disease 2019 (COVID-19) has focused on disease control strategies in public settings, transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within households remains an important problem. The nature and determinants of household transmission are poorly understood. Methods To address this gap, we gathered and analyzed data from 22 published and prepublished studies from 10 countries (20 291 household contacts) that were available through 2 September 2020. Our goal was to combine estimates of the SARS-CoV-2 household secondary attack rate (SAR) and to explore variation in estimates of the household SAR. Results The overall pooled random-effects estimate of the household SAR was 17.1% (95% confidence interval [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% (...

New England Journal of Medicine, 2021

PharmacoEconomics, 2019

Background Clinical trials often report intervention efficacy in terms of the reduction in all-ca... more Background 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. Methods 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. Results 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%. Conclusions 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.

Vaccine, 2018

Background-Sexual mixing between heterogeneous population subgroups is an integral component of m... more Background-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. Methods-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. Results-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

Value in Health, 2019

The 21-gene assay Oncotype DX (21-GA) shows promise as a guide in deciding when to initiate adjuv... more 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. Methods: 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. Results: 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

Best Practice & Research Clinical Gastroenterology

Gastric cancer (GC) is a significant global health problem, with Helicobacter pylori infection es... more 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.

![Research paper thumbnail of feralaes/dshr: First release of `dshr](https://a.academia-assets.com/images/blank-paper.jpg)

<code>dshr</code> is an R package that includes the functions to compute a disease-sp... more <code>dshr</code> is an R package that includes the functions to compute a disease-specific hazard ratios (dsHR) from a published overall hazard ratio (oHR) of a healthcare intervention under different assumptions. This first released version of <code>dshr</code> includes the methods described in the following publication: Alarid-Escudero F, Kuntz KM "Potential bias associated with modeling the effectiveness of healthcare interventions in reducing mortality using an overall hazard ratio." PharmacoEconomics 2019 http://dx.doi.org/10.1007/s40273-019-00859-5 (Forthcoming)

University of Minnesota Ph.D. dissertation. August 2017. Major: Health Services Research, Policy ... more University of Minnesota Ph.D. dissertation. August 2017. Major: Health Services Research, Policy and Administration. Advisors: Karen Kuntz, Eva Enns. 1 computer file (PDF); ix, 128 pages.

Vitae, Apr 1, 2012

How to cite Complete issue More information about this article Journal's homepage in redalyc.org ... more How to cite Complete issue More information about this article Journal's homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative VITAE, REVISTA DE LA FACULTAD DE QUÍMICA FARMACÉUTICA

Medical Decision Making

Decision models can combine information from different sources to simulate the long-term conseque... more 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.

Frontiers in Physiology

Purpose: Bayesian calibration is generally superior to standard direct-search algorithms in that ... more 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 ana...