Experience of primary care for people with HIV: a mixed-method analysis

Background Advances in treatment have transformed HIV into a long-term condition (LTC), presenting fresh challenges for health services, HIV specialists and general practitioners (GPs). Aim To explore the experience of people living with HIV (PLHIV) regarding using their GPs. Design and setting A mixed-method analysis using data from two sources: a nationally-representative survey of PLHIV and a qualitative study with London-based PLHIV. Methods Univariate logistic regression for quantitative data and Framework analysis for qualitative data. Results The survey had 4,422 participants; the qualitative study included 52 participants. In both studies, GP registration and HIV status disclosure were high. Similar to general population trends, recent GP use was associated with poor self-rated health status, co-morbidities, older age and lower socioeconomic status. Two-thirds reported a good experience with GPs; a lower proportion felt comfortable asking HIV-related questions. Actual or perceived HIV stigma were consistently associated with poor satisfaction. In the interviews, participants with additional LTCs valued sensitive and consistent support from GPs. Some anticipated, and sometimes experienced, problems relating to HIV status, GPs’ limited experience and time to manage their complex needs. Sometimes they took their own initiatives to facilitate coordination and communication. For PLHIV, a ‘good’ GP offered continuity and took time to know and accept them without judgement. Conclusion We suggest clarification of roles and provision of relevant support to build confidence in GPs and primary care staff to care for PLHIV. As PLHIV population ages, there is a strong need to develop trusting patient/GP relationships and HIV-friendly GP practices

Models for Minimax Stochastic Linear Optimization Problems with Risk Aversion

We propose a semidefinite optimization (SDP) model for the class of minimax two-stage stochastic linear optimization problems with risk aversion. The distribution of second-stage random variables belongs to a set of multivariate distributions with known first and second moments. For the minimax stochastic problem with random objective, we provide a tight SDP formulation. The problem with random right-hand side is NP-hard in general. In a special case, the problem can be solved in polynomial time. Explicit constructions of the worst-case distributions are provided. Applications in a production-transportation problem and a single facility minimax distance problem are provided to demonstrate our approach. In our experiments, the performance of minimax solutions is close to that of data-driven solutions under the multivariate normal distribution and better under extremal distributions. The minimax solutions thus guarantee to hedge against these worst possible distributions and provide a natural distribution to stress test stochastic optimization problems under distributional ambiguity.Singapore-MIT Alliance for Research and TechnologyNational University of Singapore. Dept. of Mathematic

An ALM model for pension funds using integrated chance constraints

We discuss integrated chance constraints in their role of short-term risk constraints in a strategic ALM model for Dutch pension funds. The problem is set up as a multistage recourse model, with special attention for modeling short-term risk prompted by the development of new guidelines by the regulating authority for Dutch pension funds. The paper concludes with a numerical illustration of the importance of such short-term risk constraints

On the Power of Robust Solutions in Two-Stage Stochastic and Adaptive Optimization Problems

We consider a two-stage mixed integer stochastic optimization problem and show that a static robust solution is a good approximation to the fully adaptable two-stage solution for the stochastic problem under fairly general assumptions on the uncertainty set and the probability distribution. In particular, we show that if the right-hand side of the constraints is uncertain and belongs to a symmetric uncertainty set (such as hypercube, ellipsoid or norm ball) and the probability measure is also symmetric, then the cost of the optimal fixed solution to the corresponding robust problem is at most twice the optimal expected cost of the two-stage stochastic problem. Furthermore, we show that the bound is tight for symmetric uncertainty sets and can be arbitrarily large if the uncertainty set is not symmetric. We refer to the ratio of the optimal cost of the robust problem and the optimal cost of the two-stage stochastic problem as the stochasticity gap. We also extend the bound on the stochasticity gap for another class of uncertainty sets referred to as positive. If both the objective coefficients and right-hand side are uncertain, we show that the stochasticity gap can be arbitrarily large even if the uncertainty set and the probability measure are both symmetric. However, we prove that the adaptability gap (ratio of optimal cost of the robust problem and the optimal cost of a two-stage fully adaptable problem) is at most four even if both the objective coefficients and the right-hand side of the constraints are uncertain and belong to a symmetric uncertainty set. The bound holds for the class of positive uncertainty sets as well. Moreover, if the uncertainty set is a hypercube (special case of a symmetric set), the adaptability gap is one under an even more general model of uncertainty where the constraint coefficients are also uncertain.National Science Foundation (U.S.) (NSF Grant DMI-0556106)National Science Foundation (U.S.) (NSF Grant EFRI-0735905

Where do we diagnose HIV infection? Monitoring new diagnoses made in nontraditional settings in England, Wales and Northern Ireland.

OBJECTIVES: The objectives of the study were to describe 10-year trends in HIV diagnosis setting and to explore predictors of being diagnosed outside a sexual health clinic (SHC). METHODS: Analyses of national HIV surveillance data were restricted to adults (aged ≥ 15 years) diagnosed in 2005-2014 in England, Wales and Northern Ireland. Logistic regression identified factors associated with diagnosis outside an SHC (2011-2014). RESULTS: Between 2005 and 2014, 63 599 adults were newly diagnosed with HIV infection; 83% had a diagnosis setting reported. Most people were diagnosed in SHCs (69%) followed by: medical admissions/accident and emergency (A&E; 8.6%), general practice (6.4%), antenatal services (5.5%), out-patient services (3.6%), infectious disease units (2.7%) and other settings (4.0%). The proportion of people diagnosed outside SHCs increased from 2005 to 2014, overall (from 27% to 32%, respectively) and among men who have sex with men (MSM) (from 14% to 21%) and black African men (from 25% to 37%) and women (from 39% to 52%) (all trend P < 0.001). Median CD4 increased across all settings, but was highest in SHCs (384 cells/μL) and lowest in medical admissions/A&E (94 cells/μL). Predictors of being diagnosed outside SHCs included: acquiring HIV through heterosexual contact [adjusted odds ratio (aOR) 1.99; 95% confidence interval (CI) 1.81-2.18] or injecting drug use (aOR: 3.28; 95% CI: 2.56-4.19; reference: MSM), being diagnosed late (< 350 cells/μL) (aOR: 2.55; 95% CI: 2.36-2.74; reference: diagnosed promptly) and being of older age at diagnosis (35-49 years: aOR: 1.60; 95% CI: 1.39-1.83; ≥ 50 years: aOR: 2.48; 95% CI: 2.13-2.88; reference: 15-24 years). CONCLUSIONS: The proportion of HIV diagnoses made outside SHCs has increased over the past decade in line with evolving HIV testing guidelines. However, the rate of late diagnosis remains high, indicating that further expansion of testing is necessary, as many people may have had missed opportunities for earlier diagnosis

A Geometric Characterization of the Power of Finite Adaptability in Multistage Stochastic and Adaptive Optimization

In this paper, we show a significant role that geometric properties of uncertainty sets, such as symmetry, play in determining the power of robust and finitely adaptable solutions in multistage stochastic and adaptive optimization problems. We consider a fairly general class of multistage mixed integer stochastic and adaptive optimization problems and propose a good approximate solution policy with performance guarantees that depend on the geometric properties of the uncertainty sets. In particular, we show that a class of finitely adaptable solutions is a good approximation for both the multistage stochastic and the adaptive optimization problem. A finitely adaptable solution generalizes the notion of a static robust solution and specifies a small set of solutions for each stage; the solution policy implements the best solution from the given set, depending on the realization of the uncertain parameters in past stages. Therefore, it is a tractable approximation to a fully adaptable solution for the multistage problems. To the best of our knowledge, these are the first approximation results for the multistage problem in such generality. Moreover, the results and the proof techniques are quite general and also extend to include important constraints such as integrality and linear conic constraints.National Science Foundation (U.S.) (Grant EFRI-0735905

Engineering Design with Digital Thread

Digital Thread offers the opportunity to use information generated across the product lifecycle to design the next generation of products. In this paper, we introduce a mathematical methodology that establishes the data-driven design and decision problem associated with Digital Thread. Our objectives are twofold: 1) Provide a mathematical definition of Digital Thread in the context of conceptual and preliminary design and establish a methodology for how information along the Digital Thread enters into the design problem as well how design decisions affect the Digital Thread. 2) Develop a data-driven design method that incorporates data from different sources from across the product life cycle. We illustrate aspects of our methodology through an example design of a structural fiber-steered composite component.United States. Air Force. Office of Scientific Research (Grant FA9550-16-1-0108)SUTD-MIT International Design Centre (IDC