Pacientes que rehúsan el tratamiento antirretroviral en el medio penitenciario Patients who refuse antiretroviral treatment in prison

Introducción: En los estudios existentes sobre pacientes VIH+ la población a estudio ha sido tomada de manera homogénea, sin diferenciar aquella que cumple los requerimientos terapéuticos de la que no lo hace. Quizás por la dificultad en el acceso al grupo de pacientes que rehúsan el tratamiento antirretroviral. El medio penitenciario nos permite acceder a esta población, hasta hoy no estudiada. El objetivo de este estudio es describir el estado clínico y psicosocial de los reclusos seropositivos que rehúsan el TARV, comparándolo con el de aquellos que sí están en TARV o no se les indica tomarlo. Métodos: Estudio transversal con 585 reclusos VIH positivos ingresados en tres prisiones andaluzas entre mayo-julio de 2004. Como variable de agrupación se empleó rehusar el TARV, tomarlo o no hacerlo por no estar indicado. Como independientes se incluyeron sociodemográficas, psicosociales, clínicas y relacionadas con el medio penitenciario. Resultados: El 16,8% de los reclusos rehusaban el TARV, mientras el 56,3% estaban en tratamiento y al 26,8% no le estaba indicado. Entre los reclusos que rehusaban el TARV aparece una mayor prevalencia de coinfección por VHC, mayor consumo intrapenitenciario de opiáceos y tratamiento con metadona, más juicios pendientes y más entradas en prisión. Conclusiones: Estos resultados ponen de relieve la existencia de un grupo poblacional, accesible gracias al medio penitenciario, con características propias que no sigue las indicaciones terapéuticas y que representa un riesgo no sólo para su salud, sino para la de la comunidad.Introduction: Current studies of HIV+ patients in the prison population have been carried out without considering differences that might exist between patients who accept retroviral treatment and those who do not. One possible reason for this may be the difficulty in gaining access to patients who refuse antiretroviral treatment. However, the prison environment makes it possible to locate and study this type of patient, who up till now has not been the subject of study. The aim of this article is to describe the clinical and psychosocial state of HIV+ inmates who refuse ARVT and compare this data with patients receiving treatment and others for whom treatment has not been indicated. Methods: Cross-sectional study using 585 HIV+ inmates in three prisons in Andalusia from May to June 2004. Refusal, acceptance and non-indication of ARVT treatment was the grouping variable used. The independent variables were socio-demographic, psychosocial, clinical and other variables relating to the prison environment. Results: 16.8% of patients refused ARVT, while 56.3% were receiving treatment and another 26.8 were not indicated for any medication. Amongst the patients that refused ARVT there was a greater prevalence of HIV co-infection, higher inprison consumption of opiates and methadone treatment, more cases pending and higher rates of recidivism. Conclusions: these results highlight the existence of a group with unique characteristics that is accessible thanks to the special conditions within the prison environment. It is a group that chooses not to follow therapeutic indications and which represents a risk factor not only for its own health, but also for the community at large

Metamodel-based robust simulation-optimization:An overview

Optimization of simulated systems is the goal of many techniques, but most of them assume known environments. Recently, “robust” methodologies accounting for uncertain environments have been developed. Robust optimization tackles problems affected by uncertainty, providing solutions that are in some sense insensitive to perturbations in the model parameters. Several alternative methods have been proposed for achieving robustness in simulation-based optimization problems, adopting different experimental designs and/or metamodeling techniques. This chapter reviews the current state of the art on robust optimization approaches based on simulated systems. First, we summarize robust Mathematical Programming. Then we discuss Taguchi’s approach introduced in the 1970s. Finally, we consider methods to tackle robustness using metamodels, and Kriging in particular. The proposed methodology uses Taguchi’s view of the uncertain world, but replaces his statistical techniques by Kriging. We illustrate the resulting methodology through basic inventory models

Robust optimization in simulation: Taguchi and Krige combined

Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a "robust" methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by design and analysis of simulation experiments based on Kriging (Gaussian process model); moreover, we use bootstrapping to quantify the variability in the estimated Kriging metamodels. In addition, we combine Kriging with nonlinear programming, and we estimate the Pareto frontier. We illustrate the resulting methodology through economic order quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that differ from the classic EOQ. We also compare our results with results we previously obtained using response surface methodology instead of Kriging

Risk of COVID-19 after natural infection or vaccinationResearch in context

Background: While vaccines have established utility against COVID-19, phase 3 efficacy studies have generally not comprehensively evaluated protection provided by previous infection or hybrid immunity (previous infection plus vaccination). Individual patient data from US government-supported harmonized vaccine trials provide an unprecedented sample population to address this issue. We characterized the protective efficacy of previous SARS-CoV-2 infection and hybrid immunity against COVID-19 early in the pandemic over three-to six-month follow-up and compared with vaccine-associated protection. Methods: In this post-hoc cross-protocol analysis of the Moderna, AstraZeneca, Janssen, and Novavax COVID-19 vaccine clinical trials, we allocated participants into four groups based on previous-infection status at enrolment and treatment: no previous infection/placebo; previous infection/placebo; no previous infection/vaccine; and previous infection/vaccine. The main outcome was RT-PCR-confirmed COVID-19 >7–15 days (per original protocols) after final study injection. We calculated crude and adjusted efficacy measures. Findings: Previous infection/placebo participants had a 92% decreased risk of future COVID-19 compared to no previous infection/placebo participants (overall hazard ratio [HR] ratio: 0.08; 95% CI: 0.05–0.13). Among single-dose Janssen participants, hybrid immunity conferred greater protection than vaccine alone (HR: 0.03; 95% CI: 0.01–0.10). Too few infections were observed to draw statistical inferences comparing hybrid immunity to vaccine alone for other trials. Vaccination, previous infection, and hybrid immunity all provided near-complete protection against severe disease. Interpretation: Previous infection, any hybrid immunity, and two-dose vaccination all provided substantial protection against symptomatic and severe COVID-19 through the early Delta period. Thus, as a surrogate for natural infection, vaccination remains the safest approach to protection. Funding: National Institutes of Health

Risk of COVID-19 after natural infection or vaccinationResearch in context

Summary: Background: While vaccines have established utility against COVID-19, phase 3 efficacy studies have generally not comprehensively evaluated protection provided by previous infection or hybrid immunity (previous infection plus vaccination). Individual patient data from US government-supported harmonized vaccine trials provide an unprecedented sample population to address this issue. We characterized the protective efficacy of previous SARS-CoV-2 infection and hybrid immunity against COVID-19 early in the pandemic over three-to six-month follow-up and compared with vaccine-associated protection. Methods: In this post-hoc cross-protocol analysis of the Moderna, AstraZeneca, Janssen, and Novavax COVID-19 vaccine clinical trials, we allocated participants into four groups based on previous-infection status at enrolment and treatment: no previous infection/placebo; previous infection/placebo; no previous infection/vaccine; and previous infection/vaccine. The main outcome was RT-PCR-confirmed COVID-19 >7–15 days (per original protocols) after final study injection. We calculated crude and adjusted efficacy measures. Findings: Previous infection/placebo participants had a 92% decreased risk of future COVID-19 compared to no previous infection/placebo participants (overall hazard ratio [HR] ratio: 0.08; 95% CI: 0.05–0.13). Among single-dose Janssen participants, hybrid immunity conferred greater protection than vaccine alone (HR: 0.03; 95% CI: 0.01–0.10). Too few infections were observed to draw statistical inferences comparing hybrid immunity to vaccine alone for other trials. Vaccination, previous infection, and hybrid immunity all provided near-complete protection against severe disease. Interpretation: Previous infection, any hybrid immunity, and two-dose vaccination all provided substantial protection against symptomatic and severe COVID-19 through the early Delta period. Thus, as a surrogate for natural infection, vaccination remains the safest approach to protection. Funding: National Institutes of Health