National public health emergency operations centers (PHEOCs) serve as hubs for coordinating information and resources for effective emergency management. In the International Health Regulations (IHR 2005) Monitoring and Evaluation Framework, a simulation exercise is 1 of 4 components that can be used to test the functionality of a country's emergency response capabilities in a simulated situation. To test the functionality of PHEOCs in World Health Organization African Region member states, a regional functional exercise simulating an Ebola virus disease outbreak was conducted. The public health actions taken in response to the simulated outbreak were evaluated against the exercise objectives. Thematic analysis was conducted to summarize key strengths and areas for improvement. From December 6 to 7, 2022, more than 1,000 representatives from 36 of the 47 African Region member states participated in the exercise from their respective PHEOCs. Approximately 95% of the 461 participants polled agreed with the positive responses to the postexercise survey. More than half of the PHEOC participants were able to test their existing emergency preparedness and response plans and became familiar with the expected roles to be fulfilled during an event. Of the participants who responded to the survey, over 90% reported that the exercise helped them understand their roles during emergency management. The exercise met its objectives and provided an opportunity to test the functionality of PHEOCs using realistic scenarios, and it helped participants understand existing response systems and procedures. However, the exercise also revealed areas for improvement in terms of the timing and preparation of participants. We recommend conducting functional exercises at the regional and national levels at least once a year, early or midyear, to allow many stakeholders to take part in the exercise. Moreover, there is a need to train country-level evaluators and controllers in designing and conducting functional exercises.

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2(nd) most accurate model measured by WIS in 2021-22 and the 5(th) most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is based on that component’s forecast accuracy in past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

Influenza infects an estimated 9 to 35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multi-institution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the US for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of 7 targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the US, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1, 2 and 3 weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision-making.

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

OBJECTIVE: To assess implementation status of public health emergency operations centres (PHEOCs) in all countries in Africa. DESIGN: Cross-sectional. SETTING: Fifty-four national PHEOC focal points in Africa responded to an online survey between May and November 2021. Included variables aimed to assess capacities for each of the four PHEOC core components. To assess the PHEOCs' functionality, criteria were defined from among the collected variables by expert consensus based on PHEOC operations' prioritisation. We report results of the descriptive analysis, including frequencies of proportions. RESULTS: A total of 51 (93%) African countries responded to the survey. Among these, 41 (80%) have established a PHEOC. Twelve (29%) of these met 80% or more of the minimum requirements and were classified as fully functional. Twelve (29%) and 17 (41%) PHEOCs that met 60%-79% and below 60% the minimum requirements were classified as functional and partially functional, respectively. CONCLUSIONS: Countries in Africa made considerable progress in setting up and improving functioning of PHEOCs. One-third of the responding countries with a PHEOC have one fulfilling at least 80% of the minimum requirements to operate the critical emergency functions. There are still several African countries that either do not have a PHEOC or whose PHEOCs only partially meet these minimal requirements. This calls for significant collaboration across all stakeholders to establish functional PHEOCs in Africa.

Background: Following the declaration of coronavirus disease 2019 (COVID-19) as a pandemic on 11 March 2020, in-person events including trainings were canceled to limit the spread of the pandemic. A virtual learning program was established in May 2020 by Africa Centers for Disease Control and Prevention, the World Health Organization, and other partners to strengthen COVID-19 response coordination through the public health emergency operations centers (PHEOCs). We present a review of the webinar series, the experience, and the lessons learned. Method(s): A data extraction tool was developed to retrieve data from the Africa CDC's webinar data repository. Major findings were synthesized and described per thematic area. Result(s): A total of 12,715 (13% of the 95,230 registrants) attended the 56 PHEOC webinar sessions between June 2020 and December 2021 and 47% of the attendees came from 17 countries. Of those who attended, 8,528 (70%) were from Africa. The webinars provided 97 learning hours with an average length of 1.18 hours per session. On average, there were 235 attendees per session. In addition, there was an average of 26 interactions between participants and facilitators per session. A total of 4,084 (44%) of the participants (9,283) responded to the post-session surveys, with over 95% rating the webinar topics as being relevant to their work, contributed to improving their understanding of PHEOC operationalization, and with extensive ease of comprehension. Conclusion(s): The virtual training served the intended audience given the high number of participants from African member states, with satisfactory feedback on training relevance. We highlighted a just-in-time, progressively adaptive experience in delivering a PHEOC/PHEM virtual learning in Africa with a consequential global audience at the peak of the COVID-19 pandemic. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.

The coronavirus disease 2019 (COVID-19) pandemic disrupted health security program implementation and incremental gains achieved after the West African Ebola outbreak in 2016 across Africa. Following cancellation of in-person events, a multi-faceted intervention program was established in May 2020 by Africa Centres for Disease Control and Prevention (Africa CDC), the World Health Organisation, and partners to strengthen national COVID-19 response coordination through public health emergency operations centres (PHEOC) utilizing continuous learning, mentorship, and networking. We present the lessons learned and reflection points. A multi-partner program coordination group was established to facilitate interventions' delivery including webinars and virtual community of practice (COP). We retrieved data from Africa CDC's program repository, synthesised major findings and describe these per thematic area. The virtual COP recorded 1,968 members and approximately 300 engagements in its initial three months. Fifty-six webinar sessions were held, providing 97 cumulative learning hours to 12,715 unique participants. Zoom data showed a return rate of 85%; 67% of webinar attendees were from Africa, and about 26 interactions occurred between participants and facilitators per session. Of 4,084 (44%) participants responding to post-session surveys, over 95% rated the topics as being relevant to their work and contributing to improving their understanding of PHEOC operationalisation. In addition, 95% agreed that the simplicity of the training delivery encouraged a greater number of public health staff to participate and spread lessons from it to their own networks. This just-in-time, progressively adaptive multi-faceted learning and knowledge management approach in Africa, with a consequential global audience at the peak of the COVID-19 pandemic, served its intended audience, had a high number of participants from Africa and received greatly satisfactory feedback.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Evaluating probabilistic forecasts in the context of a real-time public health surveillance system is a complicated business. We agree with Bracher’s (1) observations that the scores established by the US Centers for Disease Control and Prevention (CDC) and used to evaluate our forecasts of seasonal influenza in the United States are not “proper” by definition (2). We thank him for raising this important issue. | | A key advantage of proper scoring is that it incentivizes forecasters to provide their best probabilistic estimates of the fundamental unit of prediction. In the case of the FluSight competition targets, the units are intervals or bins containing dates or values representing influenza-like illness (ILI) activity. A forecast assigns probabilities to each bin.

The COVID-19 pandemic has made it clear that epidemic models play an important role in how governments and the public respond to infectious disease crises. Early in the pandemic, models were used to estimate the true number of infections. Later, they estimated key parameters, generated short-term forecasts of outbreak trends, and quantified possible effects of interventions on the unfolding epidemic.1,2 In contrast to the coordinating role played by major national or international agencies in weather-related emergencies, pandemic modeling efforts were initially scattered across many research institutions. Differences in modeling approaches led to contrasting results, contributing to confusion in public perception of the pandemic. Efforts to coordinate modeling efforts in so-called hubs have provided governments, healthcare agencies, and the public with assessments and forecasts that reflect the consensus in the modeling community.36 This has been achieved by openly synthesizing uncertainties across different modeling approaches and facilitating comparisons between them.

Laws play an important role in emergency response capacity. During the COVID-19 outbreak, experts have noted both a lack of law where it is needed and a problematic use of laws that exist. To address those challenges, policymakers revising public health emergency laws can examine how existing laws were used during the COVID-19 response to address problems that arose during their application. Judicial opinions can provide a source of data for this review. This study used legal epidemiology methods to perform an environmental scan of global judicial opinions, published from March 1 through August 31, 2020, from 23 countries, related to government-issued COVID-19 mitigation measures. The opinions were coded, and findings categorize the measures based on: (1) the World Health Organization's May 2020 publication, Overview of Public Health and Social Measures in the Context of COVID-19, and (2) related legal challenges brought in courts, including disputes about authority; conflicts of law; rationality, proportionality, or necessity; implementation; and enforcement. The findings demonstrate how judicial review of emergency measures has played a role in the COVID-19 response. In some cases, court rulings required mitigation measures to be amended or stopped. In others, court rulings required the government to issue a measure not yet in place. These findings provide examples for understanding issues related to the application of law during an emergency response.

Genetic diversity of Anaplasma phagocytophilum was assessed in specimens from 16 infected patients and 16 infected Ixodes scapularis ticks. A region immediately downstream of the 16S rRNA gene, which included the gene encoding SdhC, was sequenced. For the A. phagocytophilum strains from patients no sequence differences were detected in this region. In contrast, significantly fewer ticks had a sequence encoding SdhC that was identical to that of the human strains (11/16 vs. 16/16, p = 0.04). This variation is consistent with the premise that not all A. phagocytophilum strains present in nature are able to cause clinical illness in humans. A strain referred to as A. phagocytophilumVariant-1 that is regarded as non-pathogenic for humans was previously described using a different typing method. Data from the current study suggest that both typing methods are identifying the same non-pathogenic strains.

As countries face public health emergencies, building public health capacity to prevent, detect, and respond to threats is a priority. In recent years, national public health institutes (NPHIs) have emerged to play a critical role in strengthening public health systems and to accelerate and achieve implementation of the International Health Regulations (IHR 2005). NPHIs are science-based government institutions that provide national leadership and expertise for the country's efforts to protect and improve health. Providing a Legal Framework for a National Public Health Institute is a recently released Africa CDC publication intended to support NPHI development throughout Africa. Here we present a legal mapping analysis of sampled legal domains for 5 countries, using the "Menu of Considerations for an NPHI Legal Framework." The analysis delineates the types of legal authorities countries may use to establish or enhance NPHIs and demonstrates how legal mapping can be used to review legal instruments for NPHIs. It also demonstrates variability among legal approaches countries take to establish and enable public health functions for NPHIs. This article examines how the legal framework and menu of considerations can help countries understand the nuances around creating and implementing the laws that will govern their organizations and how countries can better engage stakeholders to identify or address potential areas for opportunity where law may be used as a tool to strengthen public health infrastructure.

Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

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.

Influenza infects an estimated 9-35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.

In an increasingly globalized world, countries face infectious disease threats and public health emergencies that transcend borders, making health security of paramount importance. Legal frameworks, at both the international and national levels, can empower governments to strengthen public health and preparedness systems to better detect and respond to infectious disease threats and public health emergencies. The development of the International Health Regulations (IHR) (2005) and the Global Health Security Agenda (GHSA), and the resulting Joint External Evaluation (JEE), are examples of coordinated global efforts to build capacity to prevent, detect, and respond to the international spread of disease. This article uses 3 case studies to describe a role for law in IHR implementation. It highlights the Centers for Disease Control and Prevention's (CDC's) Global Health Security Public Health Law Project and describes how legal mapping data and the resources developed are being used by countries to strengthen health systems and support IHR implementation.

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1. Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from <0.01 to 0.41, peak week ranged from 0.08 to 0.49, and peak intensity ranged from <0.01 to 0.17. The scores for predictions of ILI 1-4 weeks in advance ranged from 0.02-0.38 and was highest 1 week ahead. Forecast skill varied by HHS region. Forecasts can predict epidemic characteristics that inform public health actions. CDC, state and local health officials, and researchers are working together to improve forecasts.

Coexistence of multiple chronic conditions (i.e., multimorbidity) is the most common chronic health problem in adults. However, clinical practice guidelines have primarily focused on patients with a single disease, resulting in uncertainty about the care of patients with multimorbidity. The American Thoracic Society convened a workshop with the goal of establishing a strategy to address multimorbidity within clinical practice guidelines. In this Workshop Report, we describe a framework that addresses multimorbidity in each of the key steps of guideline development: topic selection, panel composition, identifying clinical questions, searching for and synthesizing evidence, rating the quality of that evidence, summarizing benefits and harms, formulating recommendations, and rating the strength of the recommendations. For the consideration of multimorbidity in guidelines to be successful and sustainable, the process must be both feasible and pragmatic. It is likely that this will be achieved best by the step-wise addition and refinement of the various components of the framework.

BACKGROUND: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. METHODS: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). RESULTS: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. CONCLUSION: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts.

BACKGROUND: Rickets and vitamin D deficiency appeared to increase in Alaskan children starting in the 1990s. We evaluated the epidemiology of rickets and vitamin D deficiency in Alaska native (AN) children in 2001-2010. METHODS: We analyzed 2001-2010 visits with rickets or vitamin D deficiency diagnosis for AN and American Indian children and the general US population aged <10 years. We conducted a case-control study of AN rickets/vitamin D deficient cases and age- and region-matched controls. RESULTS: In AN children, annual rickets-associated hospitalization rate (2.23/100,000 children/year) was higher than the general US rate (1.23; 95% CI 1.08-1.39). Rickets incidence increased with latitude. Rickets/vitamin D deficiency cases were more likely to have malnutrition (OR 38.1; 95% CI 4.9-294), had similar breast-feeding prevalence, and were less likely to have received vitamin D supplementation (OR 0.23; 95% CI 0.1-0.87) than controls. CONCLUSIONS: Our findings highlight the importance of latitude, malnutrition, and lack of vitamin D supplementation as risk factors for rickets.

The National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) registries have been a source of biospecimens for cancer research for decades. Recently, registry-based biospecimen studies have become more practical, with the expansion of electronic networks for pathology and medical record reporting. Formalin-fixed paraffin-embedded specimens are now used for next-generation sequencing and other molecular techniques. These developments create new opportunities for SEER biospecimen research. We evaluated 31 research articles published during 2005 to 2013 based on authors' confirmation that these studies involved linkage of SEER data to biospecimens. Rather than providing an exhaustive review of all possible articles, our intent was to indicate the breadth of research made possible by such a resource. We also summarize responses to a 2012 questionnaire that was broadly distributed to the NCI intra- and extramural biospecimen research community. This included responses from 30 investigators who had used SEER biospecimens in their research. The survey was not intended to be a systematic sample, but instead to provide anecdotal insight on strengths, limitations, and the future of SEER biospecimen research. Identified strengths of this research resource include biospecimen availability, cost, and annotation of data, including demographic information, stage, and survival. Shortcomings include limited annotation of clinical attributes such as detailed chemotherapy history and recurrence, and timeliness of turnaround following biospecimen requests. A review of selected SEER biospecimen articles, investigator feedback, and technological advances reinforced our view that SEER biospecimen resources should be developed. This would advance cancer biology, etiology, and personalized therapy research.

For the diagnosis of Lyme disease, the 2-tier serologic testing protocol for Lyme disease has a number of shortcomings including low sensitivity in early disease; increased cost, time, and labor; and subjectivity in the interpretation of immunoblots. In this study, the diagnostic accuracy of a single-tier commercial C6 ELISA kit was compared with 2-tier testing. The results showed that the C6 ELISA was significantly more sensitive than 2-tier testing with sensitivities of 66.5% (95% confidence interval [CI] 61.7-71.1) and 35.2% (95% CI 30.6-40.1), respectively (P < 0.001) in 403 sera from patients with erythema migrans. The C6 ELISA had sensitivity statistically comparable to 2-tier testing in sera from Lyme disease patients with early neurologic manifestations (88.6% versus 77.3%, P = 0.13) or arthritis (98.3% versus 95.6%, P = 0.38). The specificities of C6 ELISA and 2-tier testing in over 2200 blood donors, patients with other conditions, and Lyme disease vaccine recipients were found to be 98.9% and 99.5%, respectively (P < 0.05, 95% CI surrounding the 0.6 percentage point difference of 0.04 to 1.15). In conclusion, using a reference standard of 2-tier testing, the C6 ELISA as a single-step serodiagnostic test provided increased sensitivity in early Lyme disease with comparable sensitivity in later manifestations of Lyme disease. The C6 ELISA had slightly decreased specificity. Future studies should evaluate the performance of the C6 ELISA compared with 2-tier testing in routine clinical practice.

BACKGROUND: Non-fatal health outcomes from diseases and injuries are a crucial consideration in the promotion and monitoring of individual and population health. The Global Burden of Disease (GBD) studies done in 1990 and 2000 have been the only studies to quantify non-fatal health outcomes across an exhaustive set of disorders at the global and regional level. Neither effort quantified uncertainty in prevalence or years lived with disability (YLDs). METHODS: Of the 291 diseases and injuries in the GBD cause list, 289 cause disability. For 1160 sequelae of the 289 diseases and injuries, we undertook a systematic analysis of prevalence, incidence, remission, duration, and excess mortality. Sources included published studies, case notification, population-based cancer registries, other disease registries, antenatal clinic serosurveillance, hospital discharge data, ambulatory care data, household surveys, other surveys, and cohort studies. For most sequelae, we used a Bayesian meta-regression method, DisMod-MR, designed to address key limitations in descriptive epidemiological data, including missing data, inconsistency, and large methodological variation between data sources. For some disorders, we used natural history models, geospatial models, back-calculation models (models calculating incidence from population mortality rates and case fatality), or registration completeness models (models adjusting for incomplete registration with health-system access and other covariates). Disability weights for 220 unique health states were used to capture the severity of health loss. YLDs by cause at age, sex, country, and year levels were adjusted for comorbidity with simulation methods. We included uncertainty estimates at all stages of the analysis. FINDINGS: Global prevalence for all ages combined in 2010 across the 1160 sequelae ranged from fewer than one case per 1 million people to 350,000 cases per 1 million people. Prevalence and severity of health loss were weakly correlated (correlation coefficient -0.37). In 2010, there were 777 million YLDs from all causes, up from 583 million in 1990. The main contributors to global YLDs were mental and behavioural disorders, musculoskeletal disorders, and diabetes or endocrine diseases. The leading specific causes of YLDs were much the same in 2010 as they were in 1990: low back pain, major depressive disorder, iron-deficiency anaemia, neck pain, chronic obstructive pulmonary disease, anxiety disorders, migraine, diabetes, and falls. Age-specific prevalence of YLDs increased with age in all regions and has decreased slightly from 1990 to 2010. Regional patterns of the leading causes of YLDs were more similar compared with years of life lost due to premature mortality. Neglected tropical diseases, HIV/AIDS, tuberculosis, malaria, and anaemia were important causes of YLDs in sub-Saharan Africa. INTERPRETATION: Rates of YLDs per 100,000 people have remained largely constant over time but rise steadily with age. Population growth and ageing have increased YLD numbers and crude rates over the past two decades. Prevalences of the most common causes of YLDs, such as mental and behavioural disorders and musculoskeletal disorders, have not decreased. Health systems will need to address the needs of the rising numbers of individuals with a range of disorders that largely cause disability but not mortality. Quantification of the burden of non-fatal health outcomes will be crucial to understand how well health systems are responding to these challenges. Effective and affordable strategies to deal with this rising burden are an urgent priority for health systems in most parts of the world. FUNDING: Bill & Melinda Gates Foundation.

BACKGROUND: Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. METHODS: We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. FINDINGS: In 2010, the three leading risk factors for global disease burden were high blood pressure (7.0% [95% uncertainty interval 6.2-7.7] of global DALYs), tobacco smoking including second-hand smoke (6.3% [5.5-7.0]), and alcohol use (5.5% [5.0-5.9]). In 1990, the leading risks were childhood underweight (7.9% [6.8-9.4]), household air pollution from solid fuels (HAP; 7.0% [5.6-8.3]), and tobacco smoking including second-hand smoke (6.1% [5.4-6.8]). Dietary risk factors and physical inactivity collectively accounted for 10.0% (95% UI 9.2-10.8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient deficiencies, fell in rank between 1990 and 2010, with unimproved water and sanitation accounting for 0.9% (0.4-1.6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. INTERPRETATION: Worldwide, the contribution of different risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc deficiencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that affect children. FUNDING: Bill & Melinda Gates Foundation.

BACKGROUND: Measuring disease and injury burden in populations requires a composite metric that captures both premature mortality and the prevalence and severity of ill-health. The 1990 Global Burden of Disease study proposed disability-adjusted life years (DALYs) to measure disease burden. No comprehensive update of disease burden worldwide incorporating a systematic reassessment of disease and injury-specific epidemiology has been done since the 1990 study. We aimed to calculate disease burden worldwide and for 21 regions for 1990, 2005, and 2010 with methods to enable meaningful comparisons over time. METHODS: We calculated DALYs as the sum of years of life lost (YLLs) and years lived with disability (YLDs). DALYs were calculated for 291 causes, 20 age groups, both sexes, and for 187 countries, and aggregated to regional and global estimates of disease burden for three points in time with strictly comparable definitions and methods. YLLs were calculated from age-sex-country-time-specific estimates of mortality by cause, with death by standardised lost life expectancy at each age. YLDs were calculated as prevalence of 1160 disabling sequelae, by age, sex, and cause, and weighted by new disability weights for each health state. Neither YLLs nor YLDs were age-weighted or discounted. Uncertainty around cause-specific DALYs was calculated incorporating uncertainty in levels of all-cause mortality, cause-specific mortality, prevalence, and disability weights. FINDINGS: Global DALYs remained stable from 1990 (2.503 billion) to 2010 (2.490 billion). Crude DALYs per 1000 decreased by 23% (472 per 1000 to 361 per 1000). An important shift has occurred in DALY composition with the contribution of deaths and disability among children (younger than 5 years of age) declining from 41% of global DALYs in 1990 to 25% in 2010. YLLs typically account for about half of disease burden in more developed regions (high-income Asia Pacific, western Europe, high-income North America, and Australasia), rising to over 80% of DALYs in sub-Saharan Africa. In 1990, 47% of DALYs worldwide were from communicable, maternal, neonatal, and nutritional disorders, 43% from non-communicable diseases, and 10% from injuries. By 2010, this had shifted to 35%, 54%, and 11%, respectively. Ischaemic heart disease was the leading cause of DALYs worldwide in 2010 (up from fourth rank in 1990, increasing by 29%), followed by lower respiratory infections (top rank in 1990; 44% decline in DALYs), stroke (fifth in 1990; 19% increase), diarrhoeal diseases (second in 1990; 51% decrease), and HIV/AIDS (33rd in 1990; 351% increase). Major depressive disorder increased from 15th to 11th rank (37% increase) and road injury from 12th to 10th rank (34% increase). Substantial heterogeneity exists in rankings of leading causes of disease burden among regions. INTERPRETATION: Global disease burden has continued to shift away from communicable to non-communicable diseases and from premature death to years lived with disability. In sub-Saharan Africa, however, many communicable, maternal, neonatal, and nutritional disorders remain the dominant causes of disease burden. The rising burden from mental and behavioural disorders, musculoskeletal disorders, and diabetes will impose new challenges on health systems. Regional heterogeneity highlights the importance of understanding local burden of disease and setting goals and targets for the post-2015 agenda taking such patterns into account. Because of improved definitions, methods, and data, these results for 1990 and 2010 supersede all previously published Global Burden of Disease results. FUNDING: Bill & Melinda Gates Foundation.

Global control of tuberculosis is increasingly dependent on rapid and accurate genetic typing of Mycobacteriumtuberculosis. Spoligotyping is a first-line genotypic fingerprinting method for M.tuberculosis isolates. An international online database (SpolDB4) of spoligotype patterns has been established wherein a clustered pattern (shared by 2 isolates) is designated a shared international type (SIT). Dual infections of single patients by distinct strains of M. tuberculosis is increasingly reported in high tuberculosis incidence areas, raising the possibility of false composite spoligotype patterns if performed upon mixed strain samples. A computational approach was applied to SpolDB4 and found that of the reported 1939 SITs, 54% could be a composite of two other SITs. Although many of the spoligotypes listed in SpolDB4 may be the product of admixing, the majority of patterns were reported with a corresponding low case frequency and so the effect of misclassification upon database integrity with these is likely minimal. Phylogenetic analysis of the five SITs most prone to be a composite demonstrated that these patterns designate nodes from which the ramifications of large families T, MANU, LAM, and EAI emerged. We illustrate how geographic context may indicate when an observed pattern could be the product of mixed infection. Importantly, when one of the most composite-prone SITs is obtained, further genetic testing by alternate methods is prudent to rule-out mixed infection, especially in high tuberculosis prevalence areas. These findings have broad practical implications for tuberculosis control and surveillance, as well as highlight the utility of a computational approach in providing solutions to biological questions in which the information can be digitalized.

Clinical studies have demonstrated that prophylactic antibiotic treatment of tick bites by Ixodes scapularis in Lyme disease hyperendemic regions in the northeastern United States can be effective in preventing infection with Borrelia burgdorferi sensu stricto, the Lyme disease spirochete. A large clinical trial in Westchester County, NY (USA), demonstrated that treatment of tick bite with 200mg of oral doxycycline was 87% effective in preventing Lyme disease in tick-bite victims (Nadelman, R.B., Nowakowski, J., Fish, D., Falco, R.C., Freeman, K., McKenna, D., Welch, P., Marcus, R., Aguero-Rosenfeld, M.E., Dennis, D.T., Wormser, G.P., 2001. Prophylaxis with single-dose doxycycline for the prevention of Lyme disease after an Ixodes scapularis tick bite. N. Engl. J. Med. 345, 79-84.). Although this excellent clinical trial provided much needed information, the authors enrolled subjects if the tick bite occurred within 3days of their clinical visit, but did not analyze the data based on the exact time between tick removal and delivery of prophylaxis. An animal model allows for controlled experiments designed to determine the point in time after tick bite when delivery of oral antibiotics would be too late to prevent infection with B. burgdorferi. Accordingly, we developed a tick-bite prophylaxis model in mice that gave a level of prophylactic protection similar to what had been observed in clinical trials and then varied the time post tick bite of antibiotic delivery. We found that two treatments of doxycycline delivered by oral gavage to mice on the day of removal of a single potentially infectious nymphal I. scapularis protected 74% of test mice compared to controls. When treatment was delayed until 24h after tick removal, only 47% of mice were protected; prophylactic treatment was totally ineffective when delivered ≥2days after tick removal. Although the dynamics of antibiotic treatment in mice may differ from humans, and translation of animal studies to patient management must be approached with caution, we believe our results emphasize the point that antibiotic prophylactic treatment of tick bite to prevent Lyme disease is more likely to be efficacious if delivered promptly after potentially infectious ticks are removed from patients. There is only a very narrow window for prophylactic treatment to be effective post tick removal.