The Incremental Cooperative Design of Preventive Healthcare Networks

This document is the Accepted Manuscript version of the following article: Soheil Davari, 'The incremental cooperative design of preventive healthcare networks', Annals of Operations Research, first published online 27 June 2017. Under embargo. Embargo end date: 27 June 2018. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-017-2569-1.In the Preventive Healthcare Network Design Problem (PHNDP), one seeks to locate facilities in a way that the uptake of services is maximised given certain constraints such as congestion considerations. We introduce the incremental and cooperative version of the problem, IC-PHNDP for short, in which facilities are added incrementally to the network (one at a time), contributing to the service levels. We first develop a general non-linear model of this problem and then present a method to make it linear. As the problem is of a combinatorial nature, an efficient Variable Neighbourhood Search (VNS) algorithm is proposed to solve it. In order to gain insight into the problem, the computational studies were performed with randomly generated instances of different settings. Results clearly show that VNS performs well in solving IC-PHNDP with errors not more than 1.54%.Peer reviewe

Institutional capacity for health systems research in East and Central African schools of public health: experiences with a capacity assessment tool

BACKGROUND: Despite significant investments in health systems research (HSR) capacity development, there is a dearth of information regarding how to assess HSR capacity. An alliance of schools of public health (SPHs) in East and Central Africa developed a tool for the self-assessment of HSR capacity with the aim of producing institutional capacity development plans. METHODS: Between June and November 2011, seven SPHs across the Democratic Republic of Congo, Ethiopia, Kenya, Rwanda, Tanzania, and Uganda implemented this co-created tool. The objectives of the institutional assessments were to assess existing capacities for HSR and to develop capacity development plans to address prioritized gaps. A mixed-method approach was employed consisting of document analysis, self-assessment questionnaires, in-depth interviews, and institutional dialogues aimed at capturing individual perceptions of institutional leadership, collective HSR skills, knowledge translation, and faculty incentives to engage in HSR. Implementation strategies for the capacity assessment varied across the SPHs. This paper reports findings from semi-structured interviews with focal persons from each SPH, to reflect on the process used at each SPH to execute the institutional assessments as well as the perceived strengths and weaknesses of the assessment process. Results The assessment tool was robust enough to be utilized in its entirety across all seven SPHs resulting in a thorough HSR capacity assessment and a capacity development plan for each SPH. Successful implementation of the capacity assessment exercises depended on four factors: (i) support from senior leadership and collaborators, (ii) a common understanding of HSR, (iii) adequate human and financial resources for the exercise, and (iv) availability of data. Methods of extracting information from the results of the assessments, however, were tailored to the unique objectives of each SPH. Conclusions This institutional HSR capacity assessment tool and the process for its utilization may be valuable for any SPH. The self-assessments, as well as interviews with external stakeholders, provided diverse sources of input and galvanized interest around HSR at multiple levels.DFI

Regrets Associated with Providing Healthcare: Qualitative Study of Experiences of Hospital-Based Physicians and Nurses

Regret is an unavoidable corollary of clinical practice. Physicians and nurses perform countless clinical decisions and actions, in a context characterised by time pressure, information overload, complexity and uncertainty

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa