Comparison of crowd-sourced, electronic health records based, and traditional health-care based influenza-tracking systems at multiple spatial resolutions in the United States of America.

BACKGROUND: Influenza causes an estimated 3000 to 50,000 deaths per year in the United States of America (US). Timely and representative data can help local, state, and national public health officials monitor and respond to outbreaks of seasonal influenza. Data from cloud-based electronic health records (EHR) and crowd-sourced influenza surveillance systems have the potential to provide complementary, near real-time estimates of influenza activity. The objectives of this paper are to compare two novel influenza-tracking systems with three traditional healthcare-based influenza surveillance systems at four spatial resolutions: national, regional, state, and city, and to determine the minimum number of participants in these systems required to produce influenza activity estimates that resemble the historical trends recorded by traditional surveillance systems. METHODS: We compared influenza activity estimates from five influenza surveillance systems: 1) patient visits for influenza-like illness (ILI) from the US Outpatient ILI Surveillance Network (ILINet), 2) virologic data from World Health Organization (WHO) Collaborating and National Respiratory and Enteric Virus Surveillance System (NREVSS) Laboratories, 3) Emergency Department (ED) syndromic surveillance from Boston, Massachusetts, 4) patient visits for ILI from EHR, and 5) reports of ILI from the crowd-sourced system, Flu Near You (FNY), by calculating correlations between these systems across four influenza seasons, 2012-16, at four different spatial resolutions in the US. For the crowd-sourced system, we also used a bootstrapping statistical approach to estimate the minimum number of reports necessary to produce a meaningful signal at a given spatial resolution. RESULTS: In general, as the spatial resolution increased, correlation values between all influenza surveillance systems decreased. Influenza-like Illness rates in geographic areas with more than 250 crowd-sourced participants or with more than 20,000 visit counts for EHR tracked government-lead estimates of influenza activity. CONCLUSIONS: With a sufficient number of reports, data from novel influenza surveillance systems can complement traditional healthcare-based systems at multiple spatial resolutions.

Accurate estimation of influenza epidemics using Google search data via ARGO.

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.

Disparities in digital reporting of illness: A demographic and socioeconomic assessment.

Although digital reports of disease are currently used by public health officials for disease surveillance and decision making, little is known about environmental factors and compositional characteristics that may influence reporting patterns. The objective of this study is to quantify the association between climate, demographic and socio-economic factors on digital reporting of disease at the US county level. We reference approximately 1.5 million foodservice business reviews between 2004 and 2014, and use census data, machine learning methods and regression models to assess whether digital reporting of disease is associated with climate, socio-economic and demographic factors. The results show that reviews of foodservice businesses and digital reports of foodborne illness follow a clear seasonal pattern with higher reporting observed in the summer, when most foodborne outbreaks are reported and to a lesser extent in the winter months. Additionally, factors typically associated with affluence (such as, higher median income and fraction of the population with a bachelor's degrees) were positively correlated with foodborne illness reports. However, restaurants per capita and education were the most significant predictors of illness reporting at the US county level. These results suggest that well-known health disparities might also be reflected in the online environment. Although this is an observational study, it is an important step in understanding disparities in the online public health environment.

Using electronic health records and Internet search information for accurate influenza forecasting.

BACKGROUND: Accurate influenza activity forecasting helps public health officials prepare and allocate resources for unusual influenza activity. Traditional flu surveillance systems, such as the Centers for Disease Control and Prevention's (CDC) influenza-like illnesses reports, lag behind real-time by one to 2 weeks, whereas information contained in cloud-based electronic health records (EHR) and in Internet users' search activity is typically available in near real-time. We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication of CDC's flu reports. METHODS: We extend a method originally designed to track flu using Google searches, named ARGO, to combine information from EHR and Internet searches with historical flu activities. Our regularized multivariate regression model dynamically selects the most appropriate variables for flu prediction every week. The model is assessed for the flu seasons within the time period 2013-2016 using multiple metrics including root mean squared error (RMSE). RESULTS: Our method reduces the RMSE of the publicly available alternative (Healthmap flutrends) method by 33, 20, 17 and 21%, for the four time horizons: real-time, one, two, and 3 weeks ahead, respectively. Such accuracy improvements are statistically significant at the 5% level. Our real-time estimates correctly identified the peak timing and magnitude of the studied flu seasons. CONCLUSIONS: Our method significantly reduces the prediction error when compared to historical publicly available Internet-based prediction systems, demonstrating that: (1) the method to combine data sources is as important as data quality; (2) effectively extracting information from a cloud-based EHR and Internet search activity leads to accurate forecast of flu.

Online surveillance of media health event reporting in Nepal: digital disease detection from a One Health perspective.

BACKGROUND: Traditional media and the internet are crucial sources of health information. Media can significantly shape public opinion, knowledge and understanding of emerging and endemic health threats. As digital communication rapidly progresses, local access and dissemination of health information contribute significantly to global disease detection and reporting. METHODS: Health event reports in Nepal (October 2013-December 2014) were used to characterize Nepal's media environment from a One Health perspective using HealthMap - a global online disease surveillance and mapping tool. Event variables (location, media source type, disease or risk factor of interest, and affected species) were extracted from HealthMap. RESULTS: A total of 179 health reports were captured from various sources including newspapers, inter-government agency bulletins, individual reports, and trade websites, yielding 108 (60%) unique articles. Human health events were reported most often (n = 85; 79%), followed by animal health events (n = 23; 21%), with no reports focused solely on environmental health. CONCLUSIONS: By expanding event coverage across all of the health sectors, media in developing countries could play a crucial role in national risk communication efforts and could enhance early warning systems for disasters and disease outbreaks.

A Platform for Monitoring Regional Antimicrobial Resistance, Using Online Data Sources: ResistanceOpen.

Background: Our understanding of the global burden of antimicrobial resistance is limited. Complementary approaches to antimicrobial resistance surveillance are needed. Methods: We developed a Web-based/mobile platform for aggregating, analyzing, and disseminating regional antimicrobial resistance information. Antimicrobial resistance indices from existing but disparate online sources were identified and abstracted. To validate antimicrobial resistance data, in the absence of regional comparators, US and Canadian indices were aggregated and compared to existing national and state estimates. Measures of variability of antimicrobial susceptibility were determined for the United States and Canada to evaluate magnitudes of differences within countries. Results: Over 850 resistance indices globally were identified and abstracted, totaling >5 million isolates, from 340 unique locations. Resistance index coverage spanned 41 countries, 6 continents, 43 of 50 US states, and 8 of 10 Canadian provinces. When compared to reported values, aggregated resistance values for the United States and Canada during 2013 and 2014 demonstrated agreements ranging from 94% to 97%. For the United States, state-specific resistance estimates demonstrated an agreement of 92%. Large differences in antimicrobial susceptibility were seen within countries. Conclusions: Using existing nontraditional data sources, we have developed a Web-based platform for aggregating antimicrobial resistance indices to support monitoring of regional antimicrobial resistance patterns.

Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection.

BACKGROUND: Social media have transformed the communications landscape. People increasingly obtain news and health information online and via social media. Social media platforms also serve as novel sources of rich observational data for health research (including infodemiology, infoveillance, and digital disease detection detection). While the number of studies using social data is growing rapidly, very few of these studies transparently outline their methods for collecting, filtering, and reporting those data. Keywords and search filters applied to social data form the lens through which researchers may observe what and how people communicate about a given topic. Without a properly focused lens, research conclusions may be biased or misleading. Standards of reporting data sources and quality are needed so that data scientists and consumers of social media research can evaluate and compare methods and findings across studies. OBJECTIVE: We aimed to develop and apply a framework of social media data collection and quality assessment and to propose a reporting standard, which researchers and reviewers may use to evaluate and compare the quality of social data across studies. METHODS: We propose a conceptual framework consisting of three major steps in collecting social media data: develop, apply, and validate search filters. This framework is based on two criteria: retrieval precision (how much of retrieved data is relevant) and retrieval recall (how much of the relevant data is retrieved). We then discuss two conditions that estimation of retrieval precision and recall rely on--accurate human coding and full data collection--and how to calculate these statistics in cases that deviate from the two ideal conditions. We then apply the framework on a real-world example using approximately 4 million tobacco-related tweets collected from the Twitter firehose. RESULTS: We developed and applied a search filter to retrieve e-cigarette-related tweets from the archive based on three keyword categories: devices, brands, and behavior. The search filter retrieved 82,205 e-cigarette-related tweets from the archive and was validated. Retrieval precision was calculated above 95% in all cases. Retrieval recall was 86% assuming ideal conditions (no human coding errors and full data collection), 75% when unretrieved messages could not be archived, 86% assuming no false negative errors by coders, and 93% allowing both false negative and false positive errors by human coders. CONCLUSIONS: This paper sets forth a conceptual framework for the filtering and quality evaluation of social data that addresses several common challenges and moves toward establishing a standard of reporting social data. Researchers should clearly delineate data sources, how data were accessed and collected, and the search filter building process and how retrieval precision and recall were calculated. The proposed framework can be adapted to other public social media platforms.

Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds.

BACKGROUND: Twitter's 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content. OBJECTIVE: The aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content. METHODS: We conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18. RESULTS: Results suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA. CONCLUSIONS: We propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts.

Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection.

The growing field of digital disease detection, or epidemic intelligence, attempts to improve timely detection and awareness of infectious disease (ID) events. Early detection remains an important priority; thus, the next frontier for ID surveillance is to improve the recognition and monitoring of drivers (antecedent conditions) of ID emergence for signals that precede disease events. These data could help alert public health officials to indicators of elevated ID risk, thereby triggering targeted active surveillance and interventions. We believe that ID emergence risks can be anticipated through surveillance of their drivers, just as successful warning systems of climate-based, meteorologically sensitive diseases are supported by improved temperature and precipitation data. We present approaches to driver surveillance, gaps in the current literature, and a scientific framework for the creation of a digital warning system. Fulfilling the promise of driver surveillance will require concerted action to expand the collection of appropriate digital driver data.

Using clinicians' search query data to monitor influenza epidemics.

Search query information from a clinician's database, UpToDate, is shown to predict influenza epidemics in the United States in a timely manner. Our results show that digital disease surveillance tools based on experts' databases may be able to provide an alternative, reliable, and stable signal for accurate predictions of influenza outbreaks.

Do celebrity cancer diagnoses promote primary cancer prevention?

OBJECTIVE: Celebrity cancer diagnoses generate considerable media coverage of and increase interest in cancer screening, but do they also promote primary cancer prevention? METHODS: Daily trends for smoking cessation-related media (information-availability) and Google queries (information-seeking) around Brazilian President and smoker Lula da Silva's laryngeal cancer diagnosis announcements were compared to a typical period and several cessation awareness events. RESULTS: Cessation media coverage was 163% (95% confidence interval, 54-328) higher than expected the week after the announcement but returned to typical levels the second week. Cessation queries were 67% (95% confidence interval, 40-96) greater the week after Lula's announcement, remaining 153% (95% confidence interval, 121-188), 130% (95% confidence interval, 101-163) and 71% (95% confidence interval, 43-100) greater during the second, third, and fourth week after the announcement. There were 1.1 million excess cessation queries the month after Lula's announcement, eclipsing query volumes for the week around New Years Day, World No Tobacco Day, and Brazilian National No Smoking Day. CONCLUSION: Just as celebrity diagnoses promote cancer screening, they may also promote primary prevention. Discovery of this dynamic suggests the public should be further encouraged to consider primary (in addition to the usual secondary) cancer prevention around celebrity diagnoses, though more cases, cancers, and prevention behaviors must be explored.

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data.

Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, conventional practices often entail complex, resource-intensive, and costly procedures. This study introduced a data-driven machine learning (ML) modeling framework for CP-related disease surveillance. It leveraged an integrated dataset of municipal wastewater microbiome (e.g., CP abundance), crowdsourced (CP-related web search keywords), and environmental data. Various optimization strategies, including data integration, data normalization, model selection, and hyperparameter tuning, were implemented to improve the ML modeling performance, leading to enhanced predictions of CP cases over time. Explainable artificial intelligence methods identified CP abundance as the most reliable predictor of CP disease cases. Multi-omics subsequently revealed the presence of CP and its genotypes/toxinotypes in wastewater, validating the utility of microbiome-data-enabled ML surveillance for foodborne diseases. This ML-based framework thus exhibits significant potential for complementing and reinforcing existing disease surveillance systems.

Improvements in Digestive Symptoms After Participation in an App-Based Chronic Digestive Disease Management Program: A Prospective Cohort Evaluation.

BACKGROUND: Fewer than 20% of adults with chronic gastrointestinal (GI) symptoms have accessed care to evaluate or manage their symptoms. We sought to characterize whether adults with chronic GI symptoms would use an app for symptom monitoring and the effects of participation in a digitally delivered GI chronic care program. METHODS: We provided a digital digestive care management app to adults via their employer-sponsored benefits. We evaluated participants' self-reported GI symptoms at baseline and between 30 and 90 days post-registration. GI symptoms (e.g., abdominal pain and constipation) were rated on a scale of 0 (no symptoms) to 4 (very severe symptoms). RESULTS: A total of 1936 participants were enrolled (75% female; 67% White, 11% Asian/Pacific Islander, 6% Hispanic, 7% Black; mean age: 43 years). Their most common GI conditions were irritable bowel syndrome (IBS), gastroesophageal reflux disease (GERD), and acid reflux. Participants of all genders and races reported statistically significant improvements in all symptoms between baseline and the end of the intervention (P < 0.05). At baseline, 79.5% of participants reported at least moderate GI symptom severity for at least one symptom. In contrast, at the end of the intervention, only 47.8% of participants reported moderate or severe symptoms, and 310 (16.0%) participants reported no symptoms. Participants who were scheduled with their care team reported greater symptom improvement than those who were not scheduled (P = 0.004). Participants reported feeling greater control of their health (83%), better management of their digestive symptoms (83%), increased happiness (76%), and greater productivity at work (54%). CONCLUSION: Demographically diverse participants engaged with a digital digestive chronic care program and reported significant improvements in digestive symptom severity.

Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar.

BACKGROUND: Public health threats can significantly impact mass gatherings and enhancing surveillance systems would thus be crucial. Epidemic Intelligence from Open Sources (EIOS) was introduced to Qatar to complement the existing surveillance measures in preparation to the FIFA World Cup Qatar 2022 (FWC22). This study estimated the empirical probability of EIOS detecting signals of public health relevance. It also looked at the factors responsible for discerning a moderate-high risk signal during a mass gathering event. METHODS: This cross-sectional descriptive study used data collected between November 8th and December 25th, 2022, through an EIOS dashboard that filtered open-source articles using specific keywords. Triage criteria and scoring scheme were developed to capture signals and these were maintained in MS Excel. EIOS' contribution to epidemic intelligence was assessed by the empirical probability estimation of relevant public health signals. Chi-squared tests of independence were performed to check for associations between various hazard categories and other independent variables. A multivariate logistic regression evaluated the predictors of moderate-high risk signals that required prompt action. RESULTS: The probability of EIOS capturing a signal relevant to public health was estimated at 0.85 % (95 % confidence interval (CI) [0.82 %-0.88 %]) with three signals requiring a national response. The hazard category of the signal had significant association to the region of occurrence (χ2 (5, N = 2543) = 1021.6, p < .001). The hazard category also showed significant association to its detection during matchdays of the tournament (χ2 (5, N = 2543) = 11.2, p < .05). The triage criteria developed was able to discern between low and moderate-high risk signals with an acceptable discrimination (Area Under the Curve=0.79). CONCLUSION: EIOS proved useful in the early warning of public health threats.

Facilitators and Barriers in the Implementation of a Digital Surveillance and Outbreak Response System in Ghana Before and During the COVID-19 Pandemic: Qualitative Analysis of Stakeholder Interviews.

BACKGROUND: In the past 2 decades, many countries have recognized the use of electronic systems for disease surveillance and outbreak response as an important strategy for disease control and prevention. In low- and middle-income countries, the adoption of these electronic systems remains a priority and has attracted the support of global health players. However, the successful implementation and institutionalization of electronic systems in low- and middle-income countries have been challenged by the local capacity to absorb technologies, decisiveness and strength of leadership, implementation costs, workforce attitudes toward innovation, and organizational factors. In November 2019, Ghana piloted the Surveillance Outbreak Response Management and Analysis System (SORMAS) for routine surveillance and subsequently used it for the national COVID-19 response. OBJECTIVE: This study aims to identify the facilitators of and barriers to the sustainable implementation and operation of SORMAS in Ghana. METHODS: Between November 2021 and March 2022, we conducted a qualitative study among 22 resource persons representing different stakeholders involved in the implementation of SORMAS in Ghana. We interviewed study participants via telephone using in-depth interview guides developed consistent with the model of diffusion of innovations in health service organizations. We transcribed the interviews verbatim and performed independent validation of transcripts and pseudonymization. We performed deductive coding using 7 a priori categories: innovation, adopting health system, adoption and assimilation, diffusion and dissemination, outer context, institutionalization, and linkages among the aspects of implementation. We used MAXQDA Analytics Pro for transcription, coding, and analysis. RESULTS: The facilitators of SORMAS implementation included its coherent design consistent with the Integrated Disease Surveillance and Response system, adaptability to evolving local needs, relative advantages for task performance (eg, real-time reporting, generation of case-base data, improved data quality, mobile offline capability, and integration of laboratory procedures), intrinsic motivation of users, and a smartphone-savvy workforce. Other facilitators were its alignment with health system goals, dedicated national leadership, political endorsement, availability of in-country IT capacities, and financial and technical support from inventors and international development partners. The main barriers were unstable technical interoperability between SORMAS and existing health information systems, reliance on a private IT company for data hosting, unreliable internet connectivity, unstable national power supply, inadequate numbers and poor quality of data collection devices, and substantial dependence on external funding. CONCLUSIONS: The facilitators of and barriers to SORMAS implementation are multiple and interdependent. Important success conditions for implementation include enhanced scope and efficiency of task performance, strong technical and political stewardship, and a self-motivated workforce. Inadequate funding, limited IT infrastructure, and lack of software development expertise are mutually reinforcing barriers to implementation and progress to country ownership. Some barriers are external, relate to the overall national infrastructural development, and are not amenable even to unlimited project funding.

Introducing zanadio-A Digitalized, Multimodal Program to Treat Obesity.

While the prevalence of overweight and obesity has been increasing annually, the accessibility of on-site treatment programs is not rising correspondingly. Digital, evidence-based obesity treatment programs could potentially alleviate this situation. The application zanadio has been developed to enable patients with obesity (BMI 30-45 kg/m2) to participate in a digital, multimodal weight reduction program based on current treatment guidelines. This article is divided into two parts: (I) it introduces zanadio, its aims and therapeutic concept, and (II) provides a first impression and demographic data on more than 11,000 patients from across the country who have used zanadio within the last 16 months, which demonstrates the demand for a digital obesity treatment. zanadio has the potential to partially close the current gap in obesity care. Future work should focus on identifying predictors of successful weight loss to further individualize digital obesity treatment, and an important next step would be to prevent obesity, i.e., to start the treatment at lower BMI levels, and to invent digital treatment programs for children and adolescents.

The Landscape of Participatory Surveillance Systems Across the One Health Spectrum: Systematic Review.

BACKGROUND: Participatory surveillance systems augment traditional surveillance systems through bidirectional community engagement. The digital platform evolution has enabled the expansion of participatory surveillance systems, globally, for the detection of health events impacting people, animals, plants, and the environment, in other words, across the entire One Health spectrum. OBJECTIVE: The aim of this landscape was to identify and provide descriptive information regarding system focus, geography, users, technology, information shared, and perceived impact of ongoing participatory surveillance systems across the One Health spectrum. METHODS: This landscape began with a systematic literature review to identify potential ongoing participatory surveillance systems. A survey was sent to collect standardized data from the contacts of systems identified in the literature review and through direct outreach to stakeholders, experts, and professional organizations. Descriptive analyses of survey and literature review results were conducted across the programs. RESULTS: The landscape identified 60 ongoing single-sector and multisector participatory surveillance systems spanning five continents. Of these, 29 (48%) include data on human health, 26 (43%) include data on environmental health, and 24 (40%) include data on animal health. In total, 16 (27%) systems are multisectoral; of these, 9 (56%) collect animal and environmental health data; 3 (19%) collect human, animal, and environmental health data; 2 (13%) collect human and environmental health data; and 2 (13%) collect human and animal health data. Out of 60 systems, 31 (52%) are designed to cover a national scale, compared to those with a subnational (n=19, 32%) or multinational (n=10, 17%) focus. All systems use some form of digital technology. Email communication or websites (n=40, 67%) and smartphones (n=29, 48%) are the most common technologies used, with some using both. Systems have capabilities to download geolocation data (n=31, 52%), photographs (n=29, 48%), and videos (n=6, 10%), and can incorporate lab data or sample collection (n=15, 25%). In sharing information back with users, most use visualization, such as maps (n=43, 72%); training and educational materials (n=37, 62%); newsletters, blogs, and emails (n=34, 57%); and disease prevention information (n=32, 53%). Out of the 46 systems responding to the survey regarding perceived impacts of their systems, 36 (78%) noted "improved community knowledge and understanding" and 31 (67%) noted "earlier detection." CONCLUSIONS: The landscape demonstrated the breadth of applicability of participatory surveillance around the world to collect data from community members and trained volunteers in order to inform the detection of events, from invasive plant pests to weekly influenza symptoms. Acknowledging the importance of bidirectionality of information, these systems simultaneously share findings back with the users. Such directly engaged community detection systems capture events early and provide opportunities to stop outbreaks quickly.

Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis.

BACKGROUND: Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks by using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. OBJECTIVE: The aim of this study was to assess the variation in the timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability by using the example of seasonal influenza epidemic in 24 countries. METHODS: We obtained influenza-related reports between January 2013 and December 2019 from 2 EBS systems, that is, HealthMap and the World Health Organization Epidemic Intelligence from Open Sources (EIOS), and weekly virological influenza counts for the same period from FluNet as the gold standard. Influenza epidemic periods were detected based on report frequency by using Bayesian change point analysis. Timely sensitivity, that is, outbreak detection within the first 2 weeks before or after an outbreak onset was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. RESULTS: Overall, while monitoring the frequency of EBS reports over 7 years in 24 countries, we detected 175 out of 238 outbreaks (73.5%) but only 22 out of 238 (9.2%) within 2 weeks before or after an outbreak onset; in the best case, while monitoring the frequency of health-related reports, we identified 2 out of 6 outbreaks (33%) within 2 weeks of onset. The positive predictive value varied between 9% and 100% for HealthMap and from 0 to 100% for EIOS, and timeliness of detection ranged from 13% to 94% for HealthMap and from 0% to 92% for EIOS, whereas system specificity was generally high (59%-100%). The number of EBS reports available within a country, the human development index, and the country's geographical location partially explained the high variability in system performance across countries. CONCLUSIONS: We documented the global variation of EBS performance and demonstrated that monitoring the report frequency alone in EBS may be insufficient for the timely detection of outbreaks. In particular, in low- and middle-income countries, low data quality and report frequency impair the sensitivity and timeliness of disease surveillance through EBS. Therefore, advances in the development and evaluation and EBS are needed, particularly in low-resource settings.

Patient engagement with digital disease management and readmission rates: The case of congestive heart failure.

Congestive heart failure (CHF) is one of the most common diagnoses in the elderly United States Medicare (⩾ age 65) population. This patient population has a particularly high readmission rate, with one estimate of the 6-month readmission rate topping 40%. The rapid rise of mobile health (mHealth) presents a promising new pathway for reducing hospital readmissions of CHF, and, more generally, the management of chronic conditions. Using a randomized research design and a multivariate regression model, we evaluated the effectiveness of a hybrid mHealth model-the integration of remote patient monitoring with an applied health technology and digital disease management platform-on 45-day hospital readmissions for patients diagnosed with CHF. We find a 78% decrease in the likelihood of CHF hospital readmission for patients who were assigned to the digital disease management platform as compared to patients assigned to control.