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Health system resilience reflects the ability to continue service delivery in the face of extraordinary shocks. We examined the case of the United Nations Relief and Works Agency (UNRWA) and its delivery of services to Palestine refugees in Syria during the ongoing crisis to identify factors enabling system resilience. The study is a retrospective qualitative case study utilizing diverse methods. We conducted 35 semi-structured interviews with UNRWA clinical and administrative professionals engaged in health service delivery over the period of the Syria conflict. Through a group model building session with a sub-group of eight of these participants, we then elicited a causal loop diagram of health system functioning over the course of the war, identifying pathways of threat and mitigating resilience strategies. We triangulated analysis with data from UNRWA annual reports and routine health management information. The UNRWA health system generally sustained service provision despite individual, community and system challenges that arose during the conflict. We distinguish absorptive, adaptive and transformative capacities of the system facilitating this resilience. Absorptive capacities enabled immediate crisis response, drawing on available human and organizational resources. Adaptive capacities sustained service delivery through revised logistical arrangements, enhanced collaborative mechanisms and organizational flexibility. Transformative capacity was evidenced by the creation of new services in response to changing community needs. Analysis suggests factors such as staff commitment, organizational flexibility and availability of collaboration mechanisms were important assets in maintaining service continuity and quality. This evidence regarding alternative strategies adopted to sustain service delivery in Syria is of clear relevance to other actors seeking organizational resilience in crisis contexts.

Resistance and tolerance are two alternative strategies hosts can adopt to survive infections. Both strategies may be genetically controlled. To date, the relative contribution of resistance and tolerance to infection outcome is poorly understood. Here, we use a bioluminescent Listeria monocytogenes (Lm) infection challenge model to study the genetic determination and dynamic contributions of host resistance and tolerance to listeriosis in four genetically diverse mouse strains. Using conventional statistical analyses, we detect significant genetic variation in both resistance and tolerance, but cannot capture the time-dependent relative importance of either host strategy. We overcome these limitations through the development of novel statistical tools to analyse individual infection trajectories portraying simultaneous changes in infection severity and health. Based on these tools, early expression of resistance followed by expression of tolerance emerge as important hallmarks for surviving Lm infections. Our trajectory analysis further reveals that survivors and non-survivors follow distinct infection paths (which are also genetically determined) and provides new survival thresholds as objective endpoints in infection experiments. Future studies may use trajectories as novel traits for mapping and identifying genes that control infection dynamics and outcome. A Matlab script for user-friendly trajectory analysis is provided.

Background: A vaccination programme targeted against human papillomavirus (HPV) types16 and 18 was introduced in the UK in 2008, with the aim of decreasing incidence of cervical disease. Vaccine roll out to 12-13 year old girls with a catch-up programme for girls aged up to 17 years and 364 days was accompanied by a very comprehensive public health information (PHI) campaign which described the role of HPV in the development of cervical cancer. Methods: A brief questionnaire, designed to assess acquisition of knowledge of HPV infection and its association to cervical cancer, was administered to two different cohorts of male and female 1 st year medical students (school leavers: 83% in age range 17-20) at a UK university. The study was timed so that the first survey in 2008 immediately followed a summer's intensive PHI campaign and very shortly after vaccine roll-out (150 students). The second survey was exactly one year later over which time there was a sustained PHI campaign (213 students).

BackgroundA host can adopt two response strategies to infection: resistance (reduce pathogen load) and tolerance (minimize impact of infection on performance). Both strategies may be under genetic control and could thus be targeted for genetic improvement. Although there is evidence that supports a genetic basis for resistance to porcine reproductive and respiratory syndrome (PRRS), it is not known whether pigs also differ genetically in tolerance. We determined to what extent pigs that have been shown to vary genetically in resistance to PRRS also exhibit genetic variation in tolerance. Multi-trait linear mixed models and random regression sire models were fitted to PRRS Host Genetics Consortium data from 1320 weaned pigs (offspring of 54 sires) that were experimentally infected with a virulent strain of PRRS virus to obtain genetic parameter estimates for resistance and tolerance. Resistance was defined as the inverse of within-host viral load (VL) from 0 to 21 (VL21) or 0 to 42 (VL42) days post-infection and tolerance as the slope of the reaction-norm of average daily gain (ADG21, ADG42) on VL21 or VL42.ResultsMulti-trait analysis of ADG associated with either low or high VL was not indicative of genetic variation in tolerance. Similarly, random regression models for ADG21 and ADG42 with a tolerance slope fitted for each sire did not result in a better fit to the data than a model without genetic variation in tolerance. However, the distribution of data around average VL suggested possible confounding between level and slope estimates of the regression lines. Augmenting the data with simulated growth rates of non-infected half-sibs (ADG0) helped resolve this statistical confounding and indicated that genetic variation in tolerance to PRRS may exist if genetic correlations between ADG0 and ADG21 or ADG42 are low to moderate.ConclusionsEvidence for genetic variation in tolerance of pigs to PRRS was weak when based on data from infected piglets only. However, simulations indicated that genetic variance in tolerance may exist and could be detected if comparable data on uninfected relatives were available. In conclusion, of the two defense strategies, genetics of tolerance is more difficult to elucidate than genetics of resistance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-017-0312-7) contains supplementary material, which is available to authorized users.

BackgroundHigh resistance (the ability of the host to reduce pathogen load) and tolerance (the ability to maintain high performance at a given pathogen load) are two desirable host traits for producing animals that are resilient to infections. For Porcine Reproductive and Respiratory Syndrome (PRRS), one of the most devastating swine diseases worldwide, studies have identified substantial genetic variation in resistance of pigs, but evidence for genetic variation in tolerance has so far been inconclusive. Resistance and tolerance are usually considered as static traits. In this study, we used longitudinal viremia measurements of PRRS virus infected pigs to define discrete stages of infection based on viremia profile characteristics. These were used to investigate host genetic effects on viral load (VL) and growth at different stages of infection, to quantify genetic variation in tolerance at these stages and throughout the entire 42-day observation period, and to assess whether the single nucleotide polymorphism (SNP) WUR10000125 (WUR) with known large effects on resistance confers significant differences in tolerance.ResultsGenetic correlations between resistance and growth changed considerably over time. Individuals that expressed high genetic resistance early in infection tended to grow slower during that time-period, but were more likely to experience lower VL and recovery in growth by the later stage. The WUR genotype was most strongly associated with VL at early- to mid-stages of infection, and with growth at mid- to late-stages of infection. Both, single-stage and repeated measurements random regression models identified significant genetic variation in tolerance. The WUR SNP was significantly associated only with the overall tolerance slope fitted through all stages of infection, with the genetically more resistant AB pigs for the WUR SNP being also more tolerant to PRRS.ConclusionsThe results suggest that genetic selection for improved tolerance of pigs to PRRS is possible in principle, but may be feasible only with genomic selection, requiring intense recording schemes that involve repeated measurements to reliably estimate genetic effects. In the absence of such records, consideration of the WUR genotype in current selection schemes appears to be a promising strategy to improve simultaneously resistance and tolerance of growing pigs to PRRS.Electronic supplementary materialThe online version of this article (10.1186/s12711-018-0420-z) contains supplementary material, which is available to authorized users.

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We read with interest the Lancet Editorial on artificial intelligence (AI) in health care (Dec 23, 2017(Dec 23, , p 2739). 1 Deep learning as a form of AI risks being overhyped. Deep neural networks contain multiple layers of nodes connected by adjustable weights. Learning occurs by adjusting these weights until the desired inputto-output function is achieved. 2 With many millions of weights, huge amounts of data are required for learning, a process facilitated by recent increases in computational power. However, the learning algorithm, known as the error back-propagation algorithm, was invented in the 1980s and has been used to train neural networks ever since.Two decades ago, our neural network system scored sleep and diagnosed sleep disorders. 3 Our machine learning algorithm, 4,5 which now provides early warning of deterioration in many hospitals, was commercialised a decade ago. 6 A key change occurred in the early 2000s. Since then, error backpropagation learns features directly from the input data, rather than relying on expert-selected features (eg, microaneurysms for a neural network assessing diabetic retinopathy). The first layers become implicit feature detectors.The success of deep learning has been shown mainly in problems with inputs of image (or image-like) data, as shown in medical image analysis, 7,8 speech recognition, and board game playing. Deep learning also lacks explanatory power; deep neural networks cannot explain how a diagnosis is reached and the features enabling discrimination are not easily identifiable.Clinicians should be aware of the capabilities as well as current limitations of AI. Properly integrated AI will improve patient outcomes and health-care efficiency. Augmented intelligence at the point of care is likely to precede AI without human involvement. LT and PW are supported by the Biomedical Research Centre, Oxford. Both authors have received funding from the National Institute for Health Research. The authors have developed an electronic observations application for which Drayson Health has purchased a sole licence. Drayson Health has a research agreement with the University of Oxford and has paid LT personal fees for consultancy as a member of its Strategic Advisory Board. Drayson Health might pay PW consultancy fees in the future.