SARS-CoV-2 antibodies protect against reinfection for at least 6 months in a multicentre seroepidemiological workplace cohort.

Identifying the potential for SARS-CoV-2 reinfection is crucial for understanding possible long-term epidemic dynamics. We analysed longitudinal PCR and serological testing data from a prospective cohort of 4,411 United States employees in 4 states between April 2020 and February 2021. We conducted a multivariable logistic regression investigating the association between baseline serological status and subsequent PCR test result in order to calculate an odds ratio for reinfection. We estimated an odds ratio for reinfection ranging from 0.14 (95% CI: 0.019 to 0.63) to 0.28 (95% CI: 0.05 to 1.1), implying that the presence of SARS-CoV-2 antibodies at baseline is associated with around 72% to 86% reduced odds of a subsequent PCR positive test based on our point estimates. This suggests that primary infection with SARS-CoV-2 provides protection against reinfection in the majority of individuals, at least over a 6-month time period. We also highlight 2 major sources of bias and uncertainty to be considered when estimating the relative risk of reinfection, confounders and the choice of baseline time point, and show how to account for both in reinfection analysis.

Correction: Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.

[This corrects the article DOI: 10.1371/journal.pmed.1003793.].

Building resilience to mosquito-borne diseases in the Caribbean.

Small island developing states in the Caribbean are among the most vulnerable countries on the planet to climate variability and climate change. In the last 3 decades, the Caribbean region has undergone frequent and intense heat waves, storms, floods, and droughts. This has had a detrimental impact on population health and well-being, including an increase in infectious disease outbreaks. Recent advances in climate science have enhanced our ability to anticipate hydrometeorological hazards and associated public health challenges. Here, we discuss progress towards bridging the gap between climate science and public health decision-making in the Caribbean to build health system resilience to extreme climatic events. We focus on the development of climate services to help manage mosquito-transmitted disease epidemics. There are numerous areas of ongoing biological research aimed at better understanding the direct and indirect impacts of climate change on the transmission of mosquito-borne diseases. Here, we emphasise additional factors that affect our ability to operationalise this biological understanding. We highlight a lack of financial resources, technical expertise, data sharing, and formalised partnerships between climate and health communities as major limiting factors to developing sustainable climate services for health. Recommendations include investing in integrated climate, health and mosquito surveillance systems, building regional and local human resource capacities, and designing national and regional cross-sectoral policies and national action plans. This will contribute towards achieving the Sustainable Development Goals (SDGs) and maximising regional development partnerships and co-benefits for improved health and well-being in the Caribbean.

The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises.

TRANSLATIONS: For the Chinese, French, German, and Spanish translations of the abstract see Supplementary Materials section.

C-Reactive Protein Testing to Guide Antibiotic Prescribing for COPD Exacerbations.

BACKGROUND: Point-of-care testing of C-reactive protein (CRP) may be a way to reduce unnecessary use of antibiotics without harming patients who have acute exacerbations of chronic obstructive pulmonary disease (COPD). METHODS: We performed a multicenter, open-label, randomized, controlled trial involving patients with a diagnosis of COPD in their primary care clinical record who consulted a clinician at 1 of 86 general medical practices in England and Wales for an acute exacerbation of COPD. The patients were assigned to receive usual care guided by CRP point-of-care testing (CRP-guided group) or usual care alone (usual-care group). The primary outcomes were patient-reported use of antibiotics for acute exacerbations of COPD within 4 weeks after randomization (to show superiority) and COPD-related health status at 2 weeks after randomization, as measured by the Clinical COPD Questionnaire, a 10-item scale with scores ranging from 0 (very good COPD health status) to 6 (extremely poor COPD health status) (to show noninferiority). RESULTS: A total of 653 patients underwent randomization. Fewer patients in the CRP-guided group reported antibiotic use than in the usual-care group (57.0% vs. 77.4%; adjusted odds ratio, 0.31; 95% confidence interval [CI], 0.20 to 0.47). The adjusted mean difference in the total score on the Clinical COPD Questionnaire at 2 weeks was -0.19 points (two-sided 90% CI, -0.33 to -0.05) in favor of the CRP-guided group. The antibiotic prescribing decisions made by clinicians at the initial consultation were ascertained for all but 1 patient, and antibiotic prescriptions issued over the first 4 weeks of follow-up were ascertained for 96.9% of the patients. A lower percentage of patients in the CRP-guided group than in the usual-care group received an antibiotic prescription at the initial consultation (47.7% vs. 69.7%, for a difference of 22.0 percentage points; adjusted odds ratio, 0.31; 95% CI, 0.21 to 0.45) and during the first 4 weeks of follow-up (59.1% vs. 79.7%, for a difference of 20.6 percentage points; adjusted odds ratio, 0.30; 95% CI, 0.20 to 0.46). Two patients in the usual-care group died within 4 weeks after randomization from causes considered by the investigators to be unrelated to trial participation. CONCLUSIONS: CRP-guided prescribing of antibiotics for exacerbations of COPD in primary care clinics resulted in a lower percentage of patients who reported antibiotic use and who received antibiotic prescriptions from clinicians, with no evidence of harm. (Funded by the National Institute for Health Research Health Technology Assessment Program; PACE Current Controlled Trials number, ISRCTN24346473.).

Practical guidance for planning resources required to support publicly-funded adaptive clinical trials.

Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely.

Development, environmental degradation, and disease spread in the Brazilian Amazon.

The Amazon is Brazil's greatest natural resource and invaluable to the rest of the world as a buffer against climate change. The recent election of Brazil's president brought disputes over development plans for the region back into the spotlight. Historically, the development model for the Amazon has focused on exploitation of natural resources, resulting in environmental degradation, particularly deforestation. Although considerable attention has focused on the long-term global cost of "losing the Amazon," too little attention has focused on the emergence and reemergence of vector-borne diseases that directly impact the local population, with spillover effects to other neighboring areas. We discuss the impact of Amazon development models on human health, with a focus on vector-borne disease risk. We outline policy actions that could mitigate these negative impacts while creating opportunities for environmentally sensitive economic activities.

The local burden of disease during the first wave of the COVID-19 epidemic in England: estimation using different data sources from changing surveillance practices.

BACKGROUND: The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need ("pillar 1") before expanding to community-wide symptomatics ("pillar 2"). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths. METHODS: We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020-30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA. RESULTS: A model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%. CONCLUSIONS: Limitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.

Tracking the impacts of climate change on human health via indicators: lessons from the Lancet Countdown.

BACKGROUND: In the past decades, climate change has been impacting human lives and health via extreme weather and climate events and alterations in labour capacity, food security, and the prevalence and geographical distribution of infectious diseases across the globe. Climate change and health indicators (CCHIs) are workable tools designed to capture the complex set of interdependent interactions through which climate change is affecting human health. Since 2015, a novel sub-set of CCHIs, focusing on climate change impacts, exposures, and vulnerability indicators (CCIEVIs) has been developed, refined, and integrated by Working Group 1 of the "Lancet Countdown: Tracking Progress on Health and Climate Change", an international collaboration across disciplines that include climate, geography, epidemiology, occupation health, and economics. DISCUSSION: This research in practice article is a reflective narrative documenting how we have developed CCIEVIs as a discrete set of quantifiable indicators that are updated annually to provide the most recent picture of climate change's impacts on human health. In our experience, the main challenge was to define globally relevant indicators that also have local relevance and as such can support decision making across multiple spatial scales. We found a hazard, exposure, and vulnerability framework to be effective in this regard. We here describe how we used such a framework to define CCIEVIs based on both data availability and the indicators' relevance to climate change and human health. We also report on how CCIEVIs have been improved and added to, detailing the underlying data and methods, and in doing so provide the defining quality criteria for Lancet Countdown CCIEVIs. CONCLUSIONS: Our experience shows that CCIEVIs can effectively contribute to a world-wide monitoring system that aims to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies. An ongoing challenge is how to improve CCIEVIs so that the description of the linkages between climate change and human health can become more and more comprehensive.

An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles.

BACKGROUND: With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. METHODS AND FINDINGS: We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002-2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6-148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5-80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102-575) than those made with the baseline model (CRPS = 125, 95% CI 120-168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. CONCLUSIONS: This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.

Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.

BACKGROUND: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. METHODS AND FINDINGS: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. CONCLUSIONS: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories.

BACKGROUND: Non-pharmaceutical interventions (NPIs) are used to reduce transmission of SARS coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19). However, empirical evidence of the effectiveness of specific NPIs has been inconsistent. We assessed the effectiveness of NPIs around internal containment and closure, international travel restrictions, economic measures, and health system actions on SARS-CoV-2 transmission in 130 countries and territories. METHODS: We used panel (longitudinal) regression to estimate the effectiveness of 13 categories of NPIs in reducing SARS-CoV-2 transmission using data from January to June 2020. First, we examined the temporal association between NPIs using hierarchical cluster analyses. We then regressed the time-varying reproduction number (Rt) of COVID-19 against different NPIs. We examined different model specifications to account for the temporal lag between NPIs and changes in Rt, levels of NPI intensity, time-varying changes in NPI effect, and variable selection criteria. Results were interpreted taking into account both the range of model specifications and temporal clustering of NPIs. RESULTS: There was strong evidence for an association between two NPIs (school closure, internal movement restrictions) and reduced Rt. Another three NPIs (workplace closure, income support, and debt/contract relief) had strong evidence of effectiveness when ignoring their level of intensity, while two NPIs (public events cancellation, restriction on gatherings) had strong evidence of their effectiveness only when evaluating their implementation at maximum capacity (e.g. restrictions on 1000+ people gathering were not effective, restrictions on < 10 people gathering were). Evidence about the effectiveness of the remaining NPIs (stay-at-home requirements, public information campaigns, public transport closure, international travel controls, testing, contact tracing) was inconsistent and inconclusive. We found temporal clustering between many of the NPIs. Effect sizes varied depending on whether or not we included data after peak NPI intensity. CONCLUSION: Understanding the impact that specific NPIs have had on SARS-CoV-2 transmission is complicated by temporal clustering, time-dependent variation in effects, and differences in NPI intensity. However, the effectiveness of school closure and internal movement restrictions appears robust across different model specifications, with some evidence that other NPIs may also be effective under particular conditions. This provides empirical evidence for the potential effectiveness of many, although not all, actions policy-makers are taking to respond to the COVID-19 pandemic.

Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project.

BACKGROUND: Adaptive designs offer great promise in improving the efficiency and patient-benefit of clinical trials. An important barrier to further increased use is a lack of understanding about which additional resources are required to conduct a high-quality adaptive clinical trial, compared to a traditional fixed design. The Costing Adaptive Trials (CAT) project investigated which additional resources may be required to support adaptive trials. METHODS: We conducted a mock costing exercise amongst seven Clinical Trials Units (CTUs) in the UK. Five scenarios were developed, derived from funded clinical trials, where a non-adaptive version and an adaptive version were described. Each scenario represented a different type of adaptive design. CTU staff were asked to provide the costs and staff time they estimated would be needed to support the trial, categorised into specified areas (e.g. statistics, data management, trial management). This was calculated separately for the non-adaptive and adaptive version of the trial, allowing paired comparisons. Interviews with 10 CTU staff who had completed the costing exercise were conducted by qualitative researchers to explore reasons for similarities and differences. RESULTS: Estimated resources associated with conducting an adaptive trial were always (moderately) higher than for the non-adaptive equivalent. The median increase was between 2 and 4% for all scenarios, except for sample size re-estimation which was 26.5% (as the adaptive design could lead to a lengthened study period). The highest increase was for statistical staff, with lower increases for data management and trial management staff. The percentage increase in resources varied across different CTUs. The interviews identified possible explanations for differences, including (1) experience in adaptive trials, (2) the complexity of the non-adaptive and adaptive design, and (3) the extent of non-trial specific core infrastructure funding the CTU had. CONCLUSIONS: This work sheds light on additional resources required to adequately support a high-quality adaptive trial. The percentage increase in costs for supporting an adaptive trial was generally modest and should not be a barrier to adaptive designs being cost-effective to use in practice. Informed by the results of this research, guidance for investigators and funders will be developed on appropriately resourcing adaptive trials.

Effects of Hot Nights on Mortality in Southern Europe.

BACKGROUND: There is strong evidence concerning the impact of heat stress on mortality, particularly from high temperatures. However, few studies to our knowledge emphasize the importance of hot nights, which may prevent necessary nocturnal rest. OBJECTIVES: In this study, we use hot-night duration and excess to predict daily cause-specific mortality in summer, using multiple cities across Southern Europe. METHODS: We fitted time series regression models to summer cause-specific mortality, including natural, respiratory, and cardiovascular causes, in 11 cities across four countries. We included a distributed lag nonlinear model with lags up to 7 days for hot night duration and excess adjusted by daily mean temperature. We summarized city-specific associations as overall-cumulative exposure-response curves at the country level using meta-analysis. RESULTS: We found positive but generally nonlinear associations between relative risk (RR) of cause-specific mortality and duration and excess of hot nights. RR of duration associated with nonaccidental mortality in Portugal was 1.29 (95% confidence interval [CI] = 1.07, 1.54); other associations were imprecise, but we also found positive city-specific estimates for Rome and Madrid. Risk of hot-night excess ranged from 1.12 (95% CI = 1.05, 1.20) for France to 1.37 (95% CI = 1.26, 1.48) for Portugal. Risk estimates for excess were consistently higher than for duration. CONCLUSIONS: This study provides new evidence that, over a wider range of locations, hot night indices are strongly associated with cause-specific deaths. Modeling the impact of thermal characteristics during summer nights on mortality could improve decisionmaking for preventive public health strategies.

Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15.

Real-time forecasts based on mathematical models can inform critical decision-making during infectious disease outbreaks. Yet, epidemic forecasts are rarely evaluated during or after the event, and there is little guidance on the best metrics for assessment. Here, we propose an evaluation approach that disentangles different components of forecasting ability using metrics that separately assess the calibration, sharpness and bias of forecasts. This makes it possible to assess not just how close a forecast was to reality but also how well uncertainty has been quantified. We used this approach to analyse the performance of weekly forecasts we generated in real time for Western Area, Sierra Leone, during the 2013-16 Ebola epidemic in West Africa. We investigated a range of forecast model variants based on the model fits generated at the time with a semi-mechanistic model, and found that good probabilistic calibration was achievable at short time horizons of one or two weeks ahead but model predictions were increasingly unreliable at longer forecasting horizons. This suggests that forecasts may have been of good enough quality to inform decision making based on predictions a few weeks ahead of time but not longer, reflecting the high level of uncertainty in the processes driving the trajectory of the epidemic. Comparing forecasts based on the semi-mechanistic model to simpler null models showed that the best semi-mechanistic model variant performed better than the null models with respect to probabilistic calibration, and that this would have been identified from the earliest stages of the outbreak. As forecasts become a routine part of the toolkit in public health, standards for evaluation of performance will be important for assessing quality and improving credibility of mathematical models, and for elucidating difficulties and trade-offs when aiming to make the most useful and reliable forecasts.

Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors.

BACKGROUND: Malaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions. METHODS: Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socio-economic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with district-level variation in clinical malaria rates in Malawi, and visualized using maps. RESULTS: Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi. CONCLUSION: The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

Intranasal Glucagon: A New Way to Treat Hypoglycemic Emergencies.

Objective: To review the safety, efficacy, and administration of intranasal (IN) glucagon for the management of hypoglycemia. Data Source: A literature search of PubMed/MEDLINE (1995 to November 2019) using the terms intranasal glucagon, nasal glucagon, glucagon, hypoglycemia treatment, and hypoglycemia management was completed. Study Selection and Data Extraction: English-language studies evaluating IN glucagon were evaluated. Data Synthesis: IN glucagon is a newly approved product for the treatment of hypoglycemia in patients with diabetes, 4 years and older. Administered as a 3-mg dose, it was shown to be noninferior to intramuscular (IM) glucagon. In comparison trials, more than 98% of hypoglycemic events were treated successfully with IN glucagon in both pediatric and adult patients. In simulated and real-world studies, IN glucagon was administered in less than a minute for the majority of scenarios. IM glucagon took longer to administer, ranging from 1 to 4 minutes, and often, patients did not receive the intended full dose. Nausea and vomiting, known adverse events for glucagon, as well as local adverse events were most commonly reported with IN glucagon. Relevance to Patient Care and Clinical Practice: IN glucagon is safe, effective, easy to use, and does not require reconstitution prior to use, which can lead to faster delivery in a severe hypoglycemic event. It does not require age- or weight-based dosing. This delivery method offers an option for someone who fears needles or is uncomfortable with injections. Conclusion: IN glucagon is a safe, effective, easy to use, needle-free treatment option for severe hypoglycemia.

Climate factors and the East Asian summer monsoon may drive large outbreaks of dengue in China.

OBJECTIVE: To investigate the relationship between climate variables, East Asian summer monsoon (EASM) and large outbreaks of dengue in China. METHODS: We constructed ecological niche models (ENMs) to analyse the influence of climate factors on dengue occurrence and predict dengue outbreak areas in China. Furthermore, we formulated a generalised additive model (GAM) to quantify the impact of the EASM on dengue occurrence in mainland China from 1980 to 2016. RESULTS: Mean Temperature of Coldest Quarter had a 62.6% contribution to dengue outbreaks. Southern China including Guangdong, Guangxi, Fujian and Yunnan provinces are more vulnerable to dengue emergence and resurgence. In addition, we found population density had a 68.7% contribution to dengue widely distribution in China using ENMs. Statistical analysis indicated a dome-shaped association between EASM and dengue outbreak using GAM, with the greatest impact in the South-East of China. Besides, there was a positive nonlinear association between monthly average temperature and dengue occurrence. CONCLUSION: We demonstrated the influence of climate factors and East Asian summer monsoon on dengue outbreaks, providing a framework for future studies on the association between climate change and vector-borne diseases.

Effect of Probiotic Use on Antibiotic Administration Among Care Home Residents: A Randomized Clinical Trial.

Importance: Probiotics are frequently used by residents in care homes (residential homes or nursing homes that provide residents with 24-hour support for personal care or nursing care), although the evidence on whether probiotics prevent infections and reduce antibiotic use in these settings is limited. Objective: To determine whether a daily oral probiotic combination of Lactobacillus rhamnosus GG and Bifidobacterium animalis subsp lactis BB-12 compared with placebo reduces antibiotic administration in care home residents. Design, Setting, and Participants: Placebo-controlled randomized clinical trial of 310 care home residents, aged 65 years and older, recruited from 23 care homes in the United Kingdom between December 2016 and May 2018, with last follow-up on October 31, 2018. Interventions: Study participants were randomized to receive a daily capsule containing a probiotic combination of Lactobacillus rhamnosus GG and Bifidobacterium animalis subsp lactis BB-12 (total cell count per capsule, 1.3 × 1010 to 1.6 × 1010) (n = 155), or daily matched placebo (n = 155), for up to 1 year. Main Outcomes and Measures: The primary outcome was cumulative antibiotic administration days for all-cause infections measured from randomization for up to 1 year. Results: Among 310 randomized care home residents (mean age, 85.3 years; 66.8% women), 195 (62.9%) remained alive and completed the trial. Participant diary data (daily data including study product use, antibiotic administration, and signs of infection) were available for 98.7% randomized to the probiotic group and 97.4% randomized to placebo. Care home residents randomized to the probiotic group had a mean of 12.9 cumulative systemic antibiotic administration days (95% CI, 0 to 18.05), and residents randomized to placebo had a mean of 12.0 days (95% CI, 0 to 16.95) (absolute difference, 0.9 days [95% CI, -3.25 to 5.05]; adjusted incidence rate ratio, 1.13 [95% CI, 0.79 to 1.63]; P = .50). A total of 120 care home residents experienced 283 adverse events (150 adverse events in the probiotic group and 133 in the placebo group). Hospitalizations accounted for 94 of the events in probiotic group and 78 events in the placebo group, and deaths accounted for 33 of the events in the probiotic group and 32 of the events in the placebo group. Conclusions and Relevance: Among care home residents in the United Kingdom, a daily dose of a probiotic combination of Lactobacillus rhamnosus GG and Bifidobacterium animalis subsp lactis BB-12 did not significantly reduce antibiotic administration for all-cause infections. These findings do not support the use of probiotics in this setting. Trial Registration: ISRCTN Identifier:16392920.