Expectation management.

As physicians transform observed frequencies from studies into predicted probabilities for a given patient, we generally fail to consider that the predictions we utter about a given therapeutic intervention are themselves part of the intervention.

Make Data Sharing Routine to Prepare for Public Health Emergencies.

Jean-Paul Chretien and colleagues argue that recent Ebola and Zika virus outbreaks highlight the importance of data sharing in scientific research.

Predicting influenza with dynamical methods.

BACKGROUND: Prediction of influenza weeks in advance can be a useful tool in the management of cases and in the early recognition of pandemic influenza seasons. METHODS: This study explores the prediction of influenza-like-illness incidence using both epidemiological and climate data. It uses Lorenz's well-known Method of Analogues, but with two novel improvements. Firstly, it determines internal parameters using the implicit near-neighbor distances in the data, and secondly, it employs climate data (mean dew point) to screen analogue near-neighbors and capture the hidden dynamics of disease spread. RESULTS: These improvements result in the ability to forecast, four weeks in advance, the total number of cases and the incidence at the peak with increased accuracy. In most locations the total number of cases per year and the incidence at the peak are forecast with less than 15 % root-mean-square (RMS) Error, and in some locations with less than 10 % RMS Error. CONCLUSIONS: The use of additional variables that contribute to the dynamics of influenza spread can greatly improve prediction accuracy.

Coming home from war.

Many American military personnel who served in the Iraq and Afghanistan wars will need long-term management of war-related conditions. There is pressing need for expertise in veterans' care outside of the Military Health System (MHS) and Department of Veterans Affairs (VA), as many will seek care elsewhere: Veterans receive free MHS care only while on active duty; enhanced eligibility for VA healthcare ends 5 years after military discharge; many veterans eligible for VA healthcare use non-VA services instead; and the Affordable Care Act will expand Medicaid coverage for uninsured veterans. Families of veterans also may need care for conditions related to war service. Most medical schools lack veteran-focused curricula beyond VA clerkships, which often do not provide specific training on service-related conditions. The VA, Department of Defense (DoD), veterans groups, and medical professional organizations should partner to develop technical competencies in veteran and family health care for clinicians at all career stages, and cultural competencies to ensure contextually appropriate care. National and state licensing boards should assess these competencies formally. Partnerships between VA, DoD, and the community for care delivery can improve transitions and the quality of veterans' post-deployment care.

Prediction of a Rift Valley fever outbreak.

El Niño/Southern Oscillation related climate anomalies were analyzed by using a combination of satellite measurements of elevated sea-surface temperatures and subsequent elevated rainfall and satellite-derived normalized difference vegetation index data. A Rift Valley fever (RVF) risk mapping model using these climate data predicted areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. To our knowledge, this is the first prospective prediction of a RVF outbreak.

The AFHSC-Division of GEIS Operations Predictive Surveillance Program: a multidisciplinary approach for the early detection and response to disease outbreaks.

The Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System Operations (AFHSC-GEIS) initiated a coordinated, multidisciplinary program to link data sets and information derived from eco-climatic remote sensing activities, ecologic niche modeling, arthropod vector, animal disease-host/reservoir, and human disease surveillance for febrile illnesses, into a predictive surveillance program that generates advisories and alerts on emerging infectious disease outbreaks. The program's ultimate goal is pro-active public health practice through pre-event preparedness, prevention and control, and response decision-making and prioritization. This multidisciplinary program is rooted in over 10 years experience in predictive surveillance for Rift Valley fever outbreaks in Eastern Africa. The AFHSC-GEIS Rift Valley fever project is based on the identification and use of disease-emergence critical detection points as reliable signals for increased outbreak risk. The AFHSC-GEIS predictive surveillance program has formalized the Rift Valley fever project into a structured template for extending predictive surveillance capability to other Department of Defense (DoD)-priority vector- and water-borne, and zoonotic diseases and geographic areas. These include leishmaniasis, malaria, and Crimea-Congo and other viral hemorrhagic fevers in Central Asia and Africa, dengue fever in Asia and the Americas, Japanese encephalitis (JE) and chikungunya fever in Asia, and rickettsial and other tick-borne infections in the U.S., Africa and Asia.

Author Correction: Global disease outbreaks associated with the 2015-2016 El Niño Event.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action.

INTRODUCTION: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. METHODS: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. RESULTS: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. CONCLUSIONS: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.

Beyond traditional surveillance: applying syndromic surveillance to developing settings--opportunities and challenges.

BACKGROUND: All countries need effective disease surveillance systems for early detection of outbreaks. The revised International Health Regulations [IHR], which entered into force for all 194 World Health Organization member states in 2007, have expanded traditional infectious disease notification to include surveillance for public health events of potential international importance, even if the causative agent is not yet known. However, there are no clearly established guidelines for how countries should conduct this surveillance, which types of emerging disease syndromes should be reported, nor any means for enforcement. DISCUSSION: The commonly established concept of syndromic surveillance in developed regions encompasses the use of pre-diagnostic information in a near real time fashion for further investigation for public health action. Syndromic surveillance is widely used in North America and Europe, and is typically thought of as a highly complex, technology driven automated tool for early detection of outbreaks. Nonetheless, low technology applications of syndromic surveillance are being used worldwide to augment traditional surveillance. SUMMARY: In this paper, we review examples of these novel applications in the detection of vector-borne diseases, foodborne illness, and sexually transmitted infections. We hope to demonstrate that syndromic surveillance in its basic version is a feasible and effective tool for surveillance in developing countries and may facilitate compliance with the new IHR guidelines.

Online posting of unprofessional content by medical students.

CONTEXT: Web 2.0 applications, such as social networking sites, are creating new challenges for medical professionalism. The scope of this problem in undergraduate medical education is not well-defined. OBJECTIVE: To assess the experience of US medical schools with online posting of unprofessional content by students and existing medical school policies to address online posting. DESIGN, SETTING, AND PARTICIPANTS: An anonymous electronic survey was sent to deans of student affairs, their representatives, or counterparts from each institution in the Association of American Medical Colleges. Data were collected in March and April 2009. MAIN OUTCOME MEASURES: Percentage of schools reporting incidents of students posting unprofessional content online, type of professionalism infraction, disciplinary actions taken, existence of institution policies, and plans for policy development. RESULTS: Sixty percent of US medical schools responded (78/130). Of these schools, 60% (47/78) reported incidents of students posting unprofessional online content. Violations of patient confidentiality were reported by 13% (6/46). Student use of profanity (52%; 22/42), frankly discriminatory language (48%; 19/40), depiction of intoxication (39%; 17/44), and sexually suggestive material (38%; 16/42) were commonly reported. Of 45 schools that reported an incident and responded to the question about disciplinary actions, 30 gave informal warning (67%) and 3 reported student dismissal (7%). Policies that cover student-posted online content were reported by 38% (28/73) of deans. Of schools without such policies, 11% (5/46) were actively developing new policies to cover online content. Deans reporting incidents were significantly more likely to report having such a policy (51% vs 18%; P = .006), believing these issues could be effectively addressed (91% vs 63%; P = .003), and having higher levels of concern (P = .02). CONCLUSION: Many responding schools had incidents of unprofessional student online postings, but they may not have adequate policy in place.

The pandemic influenza policy model: a planning tool for military public health officials.

The Pandemic Influenza Policy Model (PIPM) is a collaborative computer modeling effort between the U.S. Department of Defense (DoD) and the Johns Hopkins University Applied Physics Laboratory. Many helpful computer simulations exist for examining the propagation of pandemic influenza in civilian populations. We believe the mission-oriented nature and structured social composition of military installations may result in pandemic influenza intervention strategies that differ from those recommended for civilian populations. Intervention strategies may differ between military bases because of differences in mission, location, or composition of the population at risk. The PIPM is a web-accessible, user-configurable, installation-specific disease model allowing military planners to evaluate various intervention strategies. Innovations in the PIPM include expanding on the mathematics of prior stochastic models, using military-specific social network epidemiology, utilization of DoD personnel databases to more accurately characterize the population at risk, and the incorporation of possible interventions, e.g., pneumococcal vaccine, not examined in previous models.

A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern.

INTRODUCTION: Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016-2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionable the information produced by these studies was. METHODS: To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE, and grey literature review, we identified studies that forecasted, predicted, or simulated ecological or epidemiological phenomena related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility, and clarity by independent reviewers. RESULTS: 2034 studies were identified, of which n = 73 met the eligibility criteria. Spatial spread, R0 (basic reproductive number), and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%), and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%), and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions, and 54% provided sufficient methodological detail to allow complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median of 119 days sooner than journal publication dates, they were used in only 30% of studies. CONCLUSIONS: Many ZIKV predictions were published during the 2016-2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics, and pandemics.

Global Disease Outbreaks Associated with the 2015-2016 El Niño Event.

Interannual climate variability patterns associated with the El Niño-Southern Oscillation phenomenon result in climate and environmental anomaly conditions in specific regions worldwide that directly favor outbreaks and/or amplification of variety of diseases of public health concern including chikungunya, hantavirus, Rift Valley fever, cholera, plague, and Zika. We analyzed patterns of some disease outbreaks during the strong 2015-2016 El Niño event in relation to climate anomalies derived from satellite measurements. Disease outbreaks in multiple El Niño-connected regions worldwide (including Southeast Asia, Tanzania, western US, and Brazil) followed shifts in rainfall, temperature, and vegetation in which both drought and flooding occurred in excess (14-81% precipitation departures from normal). These shifts favored ecological conditions appropriate for pathogens and their vectors to emerge and propagate clusters of diseases activity in these regions. Our analysis indicates that intensity of disease activity in some ENSO-teleconnected regions were approximately 2.5-28% higher during years with El Niño events than those without. Plague in Colorado and New Mexico as well as cholera in Tanzania were significantly associated with above normal rainfall (p < 0.05); while dengue in Brazil and southeast Asia were significantly associated with above normal land surface temperature (p < 0.05). Routine and ongoing global satellite monitoring of key climate variable anomalies calibrated to specific regions could identify regions at risk for emergence and propagation of disease vectors. Such information can provide sufficient lead-time for outbreak prevention and potentially reduce the burden and spread of ecologically coupled diseases.

Prevalence of seropositivity to spotted fever group rickettsiae and Anaplasma phagocytophilum in a large, demographically diverse US sample.

BACKGROUND: Most epidemiologic studies of tick-borne rickettsial diseases in the United States are small and have limited demographic scope, making broader risk assessment difficult. METHODS: We conducted a seroprevalence study of spotted fever group rickettsiae and Anaplasma phagocytophilum, the agent of human granulocytic anaplasmosis. Specimens were selected randomly from the Department of Defense Serum Repository for 10,000 diverse military personnel at various stages in their careers who were serving with active duty status in 1997. Antibody testing included enzyme-linked immunosorbent assay for Rickettsia rickettsii and A. phagocytophilum, and Western blot confirmation for A. phagocytophilum. Risk factors were assessed using logistic regression. RESULTS: Subjects were mostly male and young and were diverse ethnically and geographically. Spotted fever group rickettsiae seropositivity was 6.0% (95% confidence interval, 5.5%-6.4%). In univariable logistic regression, seroprevalence was significantly higher among older subjects, men (6.5%, compared with 3.3% among women), black individuals (8.7%, compared with 5.6% among white individuals), subjects from states with above-average Rocky Mountain spotted fever incidence, and subjects in ground combat specialties. Associations remained significant in multivariable analysis for age, sex, black versus white race, home state with high incidence, and ground combat specialty. Among 696 subjects with serum samples obtained within 7 days after entering the military, the rate of seropositivity was 3.4% (95% confidence interval, 2.1%-4.8%). Seroprevalence was nonsignificantly lower in men (3.4%, compared with 3.7% in women ) and in black individuals (3.4%, compared with 4.1% in white individuals). A. phagocytophilum seropositivity, as determined by by enzyme-linked immunosorbent assay and Western blot, was 2.6% and 0.11% (95% confidence interval, 0.05%-0.18%), respectively. Western blot seropositivity was not significantly associated with subject characteristics in univariable analysis. CONCLUSIONS: Spotted fever group rickettsiae exposure was common and A. phagocytophilum exposure was rare in a US population with broad demographic diversity.

Differential susceptibility to hypertension is due to selection during the out-of-Africa expansion.

Hypertension is a leading cause of stroke, heart disease, and kidney failure. The genetic basis of blood pressure variation is largely unknown but is likely to involve genes that influence renal salt handling and arterial vessel tone. Here we argue that susceptibility to hypertension is ancestral and that differential susceptibility to hypertension is due to differential exposure to selection pressures during the out-of-Africa expansion. The most important selection pressure was climate, which produced a latitudinal cline in heat adaptation and, therefore, hypertension susceptibility. Consistent with this hypothesis, we show that ecological variables, such as latitude, temperature, and rainfall, explain worldwide variation in heat adaptation as defined by seven functional alleles in five genes involved in blood pressure regulation. The latitudinal cline in heat adaptation is consistent worldwide and is largely unmatched by latitudinal clines in short tandem repeat markers, control single nucleotide polymorphisms, or non-functional single nucleotide polymorphisms within the five genes. In addition, we show that latitude and one of these alleles, GNB3 (G protein beta3 subunit) 825T, account for a major portion of worldwide variation in blood pressure. These results suggest that the current epidemic of hypertension is due to exposures of the modern period interacting with ancestral susceptibility. Modern populations differ in susceptibility to these new exposures, however, such that those from hot environments are more susceptible to hypertension than populations from cold environments. This differential susceptibility is likely due to our history of adaptation to climate.

Drought-associated chikungunya emergence along coastal East Africa.

Epidemics of chikungunya fever, an Aedes spp.-borne viral disease, affected hundreds of thousands of people in western Indian Ocean islands and India during 2005-2006. The initial outbreaks occurred in coastal Kenya (Lamu, then Mombasa) in 2004. We investigated eco-climatic conditions associated with chikungunya fever emergence along coastal Kenya using epidemiologic investigations and satellite data. Unusually dry, warm conditions preceded the outbreaks, including the driest since 1998 for some of the coastal regions. Infrequent replenishment of domestic water stores and elevated temperatures may have facilitated Chikungunya virus transmission. These results suggest that drought-affected populations may be at heightened risk for chikungunya fever, and underscore the need for safe water storage during drought relief operations.

Establishing a malaria diagnostics centre of excellence in Kisumu, Kenya.

BACKGROUND: Malaria microscopy, while the gold standard for malaria diagnosis, has limitations. Efficacy estimates in drug and vaccine malaria trials are very sensitive to small errors in microscopy endpoints. This fact led to the establishment of a Malaria Diagnostics Centre of Excellence in Kisumu, Kenya. The primary objective was to ensure valid clinical trial and diagnostic test evaluations. Key secondary objectives were technology transfer to host countries, establishment of partnerships, and training of clinical microscopists. CASE DESCRIPTION: A twelve-day "long" and a four-day "short" training course consisting of supervised laboratory practicals, lectures, group discussions, demonstrations, and take home assignments were developed. Well characterized slides were developed and training materials iteratively improved. Objective pre- and post-course evaluations consisted of 30 slides (19 negative, 11 positive) with a density range of 50-660 parasites/mul, a written examination (65 questions), a photographic image examination (30 images of artifacts and species specific characteristics), and a parasite counting examination. DISCUSSION AND EVALUATION: To date, 209 microscopists have participated from 11 countries. Seventy-seven experienced microscopists participated in the "long" courses, including 47 research microscopists. Sensitivity improved by a mean of 14% (CI 9-19%) from 77% baseline (CI 73-81 %), while specificity improved by a mean of 17% (CI 11-23%) from 76% (CI 70-82%) baseline. Twenty-three microscopists who had been selected for a four-day refresher course showed continued improvement with a mean final sensitivity of 95% (CI 91-98%) and specificity of 97% (CI 95-100%). Only 9% of those taking the pre-test in the "long" course achieved a 90% sensitivity and 95% specificity, which increased to 61% of those completing the "short" course. All measures of performance improved substantially across each of the five organization types and in each course offered. CONCLUSION: The data clearly illustrated that false positive and negative malaria smears are a serious problem, even with research microscopists. Training dramatically improved performance. Quality microscopy can be provided by the Centre of Excellence concept. This concept can be extended to other diagnostics of public health importance, and comprehensive disease control strategies.

Demographic and occupational predictors of early response to a mailed invitation to enroll in a longitudinal health study.

BACKGROUND: Often in survey research, subsets of the population invited to complete the survey do not respond in a timely manner and valuable resources are expended in recontact efforts. Various methods of improving response have been offered, such as reducing questionnaire length, offering incentives, and utilizing reminders; however, these methods can be costly. Utilizing characteristics of early responders (refusal or consent) in enrollment and recontact efforts may be a unique and cost-effective approach for improving the quality of epidemiologic research. METHODS: To better understand early responders of any kind, we compared the characteristics of individuals who explicitly refused, consented, or did not respond within 2 months from the start of enrollment into a large cohort study of US military personnel. A multivariate polychotomous logistic regression model was used to estimate the effect of each covariate on the odds of early refusal and on the odds of early consent versus late/non-response, while simultaneously adjusting for all other variables in the model. RESULTS: From regression analyses, we found many similarities between early refusers and early consenters. Factors associated with both early refusal and early consent included older age, higher education, White race/ethnicity, Reserve/Guard affiliation, and certain information technology and support occupations. CONCLUSION: These data suggest that early refusers may differ from late/non-responders, and that certain characteristics are associated with both early refusal and early consent to participate. Structured recruitment efforts that utilize these differences may achieve early response, thereby reducing mail costs and the use of valuable resources in subsequent contact efforts.