Multilingualism and strategic planning for HIV/AIDS-related health care and communication [version 1; peer review: 2 approved]

Background: Many lower and middle income countries (LMICs) have high levels of linguistic diversity, meaning that health information and care is not available in the languages spoken by the majority of the population. This research investigates the extent to which language needs are taken into account in planning for HIV/AIDS-related health communication in development contexts. / Methods: We analysed all HIV/AIDS-related policy documents and reports available via the websites of the Department for International Development UK, The Global Fund, and the Ministries of Health and National AIDS commissions of Burkina Faso, Ghana and Senegal. We used quantitative and qualitative analysis to assess the level of prominence given to language issues, ascertain the level at which mentions occur (donor/funder/national government or commission), and identify the concrete plans for interlingual communication cited in the documents. / Results: Of the 314 documents analysed, 35 mention language or translation, but the majority of the mentions are made in passing or in the context of providing background socio-cultural information, the implications of which are not explored. At donor level (DFID), no mentions of language issues were found. Only eight of the documents (2.5%) outline concrete actions for addressing multilingualism in HIV/AIDS-related health communication. These are limited to staff training for sign language, and the production of multilingual resources for large-scale sensitization campaigns. / Conclusions: The visibility of language needs in formal planning and reporting in the context of HIV/AIDS-related health care is extremely low. Whilst this low visibility should not be equated to a complete absence of translation or interpreting activity on the ground, it is likely to result in insufficient resources being dedicated to addressing language barriers. Further research is needed to fully understand the ramifications of the low prominence given to questions of language, not least in relation to its impact on gender equality

Analysis of Familial Hemophagocytic Lymphohistiocytosis type 4 (FHL-4) mutant proteins reveals that S-acylation is required for the function of syntaxin 11 in natural killer cells

Natural killer (NK) cell secretory lysosome exocytosis and cytotoxicity are impaired in familial hemophagocytic lymphohistiocytosis type 4 (FHL-4), a disorder caused by mutations in the gene encoding the SNARE protein syntaxin 11. We show that syntaxin 11 binds to SNAP23 in NK cells and that this interaction is reduced by FHL-4 truncation and frameshift mutation proteins that delete all or part of the SNARE domain of syntaxin 11. In contrast the FHL-4 mutant proteins bound to the Sec-1/Munc18-like (SM) protein Munc18-2. We demonstrate that the C-terminal cysteine rich region of syntaxin 11, which is deleted in the FHL-4 mutants, is S-acylated. This posttranslational modification is required for the membrane association of syntaxin 11 and for its polarization to the immunological synapse in NK cells conjugated to target cells. Moreover, we show that Munc18-2 is recruited by syntaxin 11 to intracellular membranes in resting NK cells and to the immunological synapse in activated NK cells. This recruitment of Munc18-2 is abolished by deletion of the C-terminal cysteine rich region of syntaxin 11. These results suggest a pivotal role for S-acylation in the function of syntaxin 11 in NK cells

Evaluating the performance of malaria genetics for inferring changes in transmission intensity using transmission modelling

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilise these methodologies for malaria we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterised using estimated relationships between complexity of infection and age from 5 regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterise the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance

Ivermectin as a novel complementary malaria control tool to reduce incidence and prevalence: a modelling study.

BACKGROUND: Ivermectin is a potential new vector control tool to reduce malaria transmission. Mosquitoes feeding on a bloodmeal containing ivermectin have a reduced lifespan, meaning they are less likely to live long enough to complete sporogony and become infectious. We aimed to estimate the effect of ivermectin on malaria transmission in various scenarios of use. METHODS: We validated an existing population-level mathematical model of the effect of ivermectin mass drug administration (MDA) on the mosquito population and malaria transmission against two datasets: clinical data from a cluster- randomised trial done in Burkina Faso in 2015 wherein ivermectin was given to individuals taller than 90 cm and entomological data from a study of mosquito outcomes after ivermectin MDA for onchocerciasis or lymphatic filariasis in Burkina Faso, Senegal, and Liberia between 2008 and 2013. We extended the existing model to include a range of complementary malaria interventions (seasonal malaria chemoprevention and MDA with dihydroartemisinin-piperaquine) and to incorporate new data on higher doses of ivermectin with a longer mosquitocidal effect. We consider two ivermectin regimens: a single dose of 400 μg/kg (1 × 400 μg/kg) and three consecutive daily doses of 300 μg/kg per day (3 × 300 μg/kg). We simulated the effect of these two doses in a range of usage scenarios in different transmission settings (highly seasonal, seasonal, and perennial). We report percentage reductions in clinical incidence and slide prevalence. FINDINGS: We estimate that MDA with ivermectin will reduce prevalence and incidence and is most effective in areas with highly seasonal transmission. In a highly seasonal moderate transmission setting, three rounds of ivermectin only MDA at 3 × 300 μg/kg (rounds spaced 1 month apart) and 70% coverage is predicted to reduce clinical incidence by 71% and prevalence by 34%. We predict that adding ivermectin MDA to seasonal malaria chemoprevention in this setting would reduce clinical incidence by an additional 77% in children younger than 5 years compared with seasonal malaria chemoprevention alone; adding ivermectin MDA to MDA with dihydroartemisinin-piperaquine in this setting would reduce incidence by an additional 75% and prevalence by an additional 64% (all ages) compared with MDA with dihydroartemisinin-piperaquine alone. INTERPRETATION: Our modelling predictions suggest that ivermectin could be a valuable addition to the malaria control toolbox, both in areas with persistently high transmission where existing interventions are insufficient and in areas approaching elimination to prevent resurgence. FUNDING: Imperial College Junior Research Fellowship

Correlations between psychometric schizotypy, scan path length, fixations on the eyes and face recognition.

Psychometric schizotypy in the general population correlates negatively with face recognition accuracy, potentially due to deficits in inhibition, social withdrawal, or eye-movement abnormalities. We report an eye-tracking face recognition study in which participants were required to match one of two faces (target and distractor) to a cue face presented immediately before. All faces could be presented with or without paraphernalia (e.g., hats, glasses, facial hair). Results showed that paraphernalia distracted participants, and that the most distracting condition was when the cue and the distractor face had paraphernalia but the target face did not, while there was no correlation between distractibility and participants' scores on the Schizotypal Personality Questionnaire (SPQ). Schizotypy was negatively correlated with proportion of time fixating on the eyes and positively correlated with not fixating on a feature. It was negatively correlated with scan path length and this variable correlated with face recognition accuracy. These results are interpreted as schizotypal traits being associated with a restricted scan path leading to face recognition deficits

Neurogenic inflammation after traumatic brain injury and its potentiation of classical inflammation

Background: The neuroinflammatory response following traumatic brain injury (TBI) is known to be a key secondary injury factor that can drive ongoing neuronal injury. Despite this, treatments that have targeted aspects of the inflammatory pathway have not shown significant efficacy in clinical trials. Main body: We suggest that this may be because classical inflammation only represents part of the story, with activation of neurogenic inflammation potentially one of the key initiating inflammatory events following TBI. Indeed, evidence suggests that the transient receptor potential cation channels (TRP channels), TRPV1 and TRPA1, are polymodal receptors that are activated by a variety of stimuli associated with TBI, including mechanical shear stress, leading to the release of neuropeptides such as substance P (SP). SP augments many aspects of the classical inflammatory response via activation of microglia and astrocytes, degranulation of mast cells, and promoting leukocyte migration. Furthermore, SP may initiate the earliest changes seen in blood-brain barrier (BBB) permeability, namely the increased transcellular transport of plasma proteins via activation of caveolae. This is in line with reports that alterations in transcellular transport are seen first following TBI, prior to decreases in expression of tight-junction proteins such as claudin-5 and occludin. Indeed, the receptor for SP, the tachykinin NK1 receptor, is found in caveolae and its activation following TBI may allow influx of albumin and other plasma proteins which directly augment the inflammatory response by activating astrocytes and microglia. Conclusions: As such, the neurogenic inflammatory response can exacerbate classical inflammation via a positive feedback loop, with classical inflammatory mediators such as bradykinin and prostaglandins then further stimulating TRP receptors. Accordingly, complete inhibition of neuroinflammation following TBI may require the inhibition of both classical and neurogenic inflammatory pathways.Frances Corrigan, Kimberley A. Mander, Anna V. Leonard and Robert Vin

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England.

Background: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient’s “bed pathway” - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. Methods: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. Results: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: “Ward, CC, Ward”, “Ward, CC”, “CC” and “CC, Ward”. Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. Conclusions: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. Trial registration: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.</p

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages