Anchoring the mean generation time in the SEIR to mitigate biases in ℜ0 estimates due to uncertainty in the distribution of the epidemiological delays.

The basic reproduction number, ℜ0, is of paramount importance in the study of infectious disease dynamics. Primarily, ℜ0 serves as an indicator of the transmission potential of an emerging infectious disease and the effort required to control the invading pathogen. However, its estimates from compartmental models are strongly conditioned by assumptions in the model structure, such as the distributions of the latent and infectious periods (epidemiological delays). To further complicate matters, models with dissimilar delay structures produce equivalent incidence dynamics. Following a simulation study, we reveal that the nature of such equivalency stems from a linear relationship between ℜ0 and the mean generation time, along with adjustments to other parameters in the model. Leveraging this knowledge, we propose and successfully test an alternative parametrization of the SEIR model that produces accurate ℜ0 estimates regardless of the distribution of the epidemiological delays, at the expense of biases in other quantities deemed of lesser importance. We further explore this approach's robustness by testing various transmissibility levels, generation times and data fidelity (overdispersion). Finally, we apply the proposed approach to data from the 1918 influenza pandemic. We anticipate that this work will mitigate biases in estimating ℜ0.

An umbrella review of the effectiveness of fiscal and pricing policies on food and non-alcoholic beverages to improve health.

Poor quality diets represent major risk factors for the global burden of disease. Modeling studies indicate a potential for diet-related fiscal and pricing policies (FPs) to improve health. There is real-world evidence (RWE) that such policies can change behavior; however, the evidence regarding health is less clear. We conducted an umbrella review of the effectiveness of FPs on food and non-alcoholic beverages in influencing health or intermediate outcomes like consumption. We considered FPs applied to an entire population within a jurisdiction and included four systematic reviews in our final sample. Quality appraisal, an examination of excluded reviews, and a literature review of recent primary studies assessed the robustness of our results. Taxes and, to some extent, subsidies are effective in changing consumption of taxed/subsidized items; however, substitution is likely to occur. There is a lack of RWE supporting the effectiveness of FPs in improving health but this does not mean that they are ineffective. FPs may be important for improving health but their design is critical. Poorly designed FPs may fail to improve health and could reduce support for such policies or be used to support their repeal. More high-quality RWE on the impact of FPs on health is needed.

An umbrella review of the acceptability of fiscal and pricing policies to reduce diet-related noncommunicable disease.

CONTEXT: Poor diet has been implicated in a range of noncommunicable diseases. Fiscal and pricing policies (FPs) may offer a means by which consumption of food and non-alcoholic beverages with links to such diseases can be influenced to improve public health. OBJECTIVE: To examine the acceptability of FPs to reduce diet-related noncommunicable disease, based on systematic review evidence. DATA SOURCES: MEDLINE, EMBASE, PsychInfo, SCI, SSCI, Web of Science, Scopus, EconLit, the Cochrane Library, Epistemonikos, and the Campbell Collaboration Library were searched for relevant studies published between January 1, 1990 and June 2021. DATA EXTRACTION: The studies included systematic reviews of diet-related FPs and: used real-world evidence; examined real or perceived barriers/facilitators; targeted the price of food or non-alcoholic beverages; and applied to entire populations within a jurisdiction. A total of 9996 unique relevant records were identified, which were augmented by a search of bibliographies and recommendations from an external expert advisory panel. Following screening, 4 systematic reviews remained. DATA ANALYSIS: Quality appraisal was conducted using the AMSTAR 2 tool. A narrative synthesis was undertaken, with outcomes grouped according to the WHO-INTEGRATE criteria. The findings indicated a paucity of high-quality systematic review evidence and limited public support for the use of FPs to change dietary habits. This lack of support was related to a number of factors that included: their perceived potential to be regressive; a lack of transparency, ie, there was mistrust around the use of revenues raised; a paucity of evidence around health benefits; the deliberate choice of rates that were lower than those considered necessary to affect diet; and concerns about the potential of such FPs to harm economic outcomes such as employment. CONCLUSION: The findings underscore the need for high-quality systematic review evidence on this topic, and the importance of responding to public concerns and putting in place mechanisms to address these when implementing FPs. This study was funded by Safefood [02A-2020]. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42021274454.

Using a Safety Planning Mobile App to Address Suicidality in Young People Attending Community Mental Health Services in Ireland: Protocol for a Pilot Randomized Controlled Trial.

BACKGROUND: Over 700,000 people die by suicide annually, making it the fourth leading cause of death among those aged 15-29 years globally. Safety planning is recommended best practice when individuals at risk of suicide present to health services. A safety plan, developed in collaboration with a health care practitioner, details the steps to be taken in an emotional crisis. SafePlan, a safety planning mobile app, was designed to support young people experiencing suicidal thoughts and behaviors and to record their plan in a way that is accessible immediately and in situ. OBJECTIVE: The aim of this study is to assess the feasibility and acceptability of the SafePlan mobile app for patients experiencing suicidal thoughts and behaviors and their clinicians within Irish community mental health services, examine the feasibility of study procedures for both patients and clinicians, and determine if the SafePlan condition yields superior outcomes when compared with the control condition. METHODS: A total of 80 participants aged 16-35 years accessing Irish mental health services will be randomized (1:1) to receive the SafePlan app plus treatment as usual or treatment as usual plus a paper-based safety plan. The feasibility and acceptability of the SafePlan app and study procedures will be evaluated using both qualitative and quantitative methodologies. The primary outcomes are feasibility outcomes and include the acceptability of the app to participants and clinicians, the feasibility of delivery in this setting, recruitment, retention, and app use. The feasibility and acceptability of the following measures in a full randomized controlled trial will also be assessed: the Beck Scale for Suicide Ideation, Columbia Suicide Severity Rating Scale, Coping Self-Efficacy Scale, Interpersonal Needs Questionnaire, and Client Service Receipt Inventory. A repeated measures design with outcome data collected at baseline, post intervention (8 weeks), and at 6-month follow-up will be used to compare changes in suicidal ideation for the intervention condition relative to the waitlist control condition. A cost-outcome description will also be undertaken. Thematic analyses will be used to analyze the qualitative data gathered through semistructured interviews with patients and clinicians. RESULTS: As of January 2023, funding and ethics approval have been acquired, and clinician champions across mental health service sites have been established. Data collection is expected to commence by April 2023. The submission of completed manuscript is expected by April 2025. CONCLUSIONS: The framework for Decision-making after Pilot and feasibility Trials will inform the decision to progress to a full trial. The results will inform patients, researchers, clinicians, and health services of the feasibility and acceptability of the SafePlan app in community mental health services. The findings will have implications for further research and policy regarding the broader integration of safety planning apps. TRIAL REGISTRATION: OSF Registries osf.io/3y54m; https://osf.io/3y54m. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44205.

Inferring the effective reproductive number from deterministic and semi-deterministic compartmental models using incidence and mobility data.

The effective reproduction number (ℜt) is a theoretical indicator of the course of an infectious disease that allows policymakers to evaluate whether current or previous control efforts have been successful or whether additional interventions are necessary. This metric, however, cannot be directly observed and must be inferred from available data. One approach to obtaining such estimates is fitting compartmental models to incidence data. We can envision these dynamic models as the ensemble of structures that describe the disease's natural history and individuals' behavioural patterns. In the context of the response to the COVID-19 pandemic, the assumption of a constant transmission rate is rendered unrealistic, and it is critical to identify a mathematical formulation that accounts for changes in contact patterns. In this work, we leverage existing approaches to propose three complementary formulations that yield similar estimates for ℜt based on data from Ireland's first COVID-19 wave. We describe these Data Generating Processes (DGP) in terms of State-Space models. Two (DGP1 and DGP2) correspond to stochastic process models whose transmission rate is modelled as Brownian motion processes (Geometric and Cox-Ingersoll-Ross). These DGPs share a measurement model that accounts for incidence and transmission rates, where mobility data is assumed as a proxy of the transmission rate. We perform inference on these structures using Iterated Filtering and the Particle Filter. The final DGP (DGP3) is built from a pool of deterministic models that describe the transmission rate as information delays. We calibrate this pool of models to incidence reports using Hamiltonian Monte Carlo. By following this complementary approach, we assess the tradeoffs associated with each formulation and reflect on the benefits/risks of incorporating proxy data into the inference process. We anticipate this work will help evaluate the implications of choosing a particular formulation for the dynamics and observation of the time-varying transmission rate.

Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies.

BACKGROUND: Contact tracing is conducted with the primary purpose of interrupting transmission from individuals who are likely to be infectious to others. Secondary analyses of data on the numbers of close contacts of confirmed cases could also: provide an early signal of increases in contact patterns that might precede larger than expected case numbers; evaluate the impact of government interventions on the number of contacts of confirmed cases; or provide data information on contact rates between age cohorts for the purpose of epidemiological modelling. We analysed data from 140,204 close contacts of 39,861 cases in Ireland from 1st May to 1st December 2020. RESULTS: Negative binomial regression models highlighted greater numbers of contacts within specific population demographics, after correcting for temporal associations. Separate segmented regression models of the number of cases over time and the average number of contacts per case indicated that a breakpoint indicating a rapid decrease in the number of contacts per case in October 2020 preceded a breakpoint indicating a reduction in the number of cases by 11 days. CONCLUSIONS: We found that the number of contacts per infected case was overdispersed, the mean varied considerable over time and was temporally associated with government interventions. Analysis of the reported number of contacts per individual in contact tracing data may be a useful early indicator of changes in behaviour in response to, or indeed despite, government restrictions. This study provides useful information for triangulating assumptions regarding the contact mixing rates between different age cohorts for epidemiological modelling.

Exploring the experiences of mental health professionals engaged in the adoption of mobile health technology in Irish mental health services.

BACKGROUND: The World Health Organization report that an estimated 793,000 people died by suicide in 2016 globally. The use of digital technology has been found to be beneficial in the delivery of Web-based suicide prevention interventions. Research on the integration of digital technology within mental health services has indicated that despite the proliferation of technology, engagement by patients and professionals in adopting such technology can be poor. OBJECTIVES: The current study aims to explore the experiences of 15 mental health professionals involved in integrating mobile health technology into their practice. A secondary aim was to identify the drivers and barriers to the adoption of such technology by mental health professionals, and to consider what theoretical models could best account for the data. METHODS: Semi-structured interviews, conducted from July to October 2019, were used to explore the experiences of mental health professionals engaged in the adoption of mobile health technology within mental health services. Mental Health professionals and clinician managers working in HSE Child and Adolescent Mental Health, Adult Mental Health, and Primary Care Psychology services were recruited for the study. Qualitative interview data was transcribed and analysed using NVivo. Thematic Analysis was used to identify themes. RESULTS: Four major themes were identified: Accessibility, 'Transitional Object', Integration, and Trust. Within these 4 major themes, a total of 9 subthemes were identified: Service Accessibility, Immediate Access, Client Engagement, Adjunct-to-therapy, Therapeutic Relationship, Infrastructural Support, Enhancing Treatment, Trust in the Technology, Trust in the Organisation. CONCLUSIONS: Overall, Diffusion of Innovation Theory provides a useful theoretical framework which is consistent with and can adequately account for many of the Major and Subthemes identified in the data. In addition, 'Transitional Objects', a key concept within Object Relations Theory, could offer a means of better understanding how patients and professionals engage with digital technology within mental health services particularly.

Are People Optimistically Biased about the Risk of COVID-19 Infection? Lessons from the First Wave of the Pandemic in Europe.

Unrealistic optimism, the underestimation of one's risk of experiencing harm, has been investigated extensively to understand better and predict behavioural responses to health threats. Prior to the COVID-19 pandemic, a relative dearth of research existed in this domain regarding epidemics, which is surprising considering that this optimistic bias has been associated with a lack of engagement in protective behaviours critical in fighting twenty-first-century, emergent, infectious diseases. The current study addresses this gap in the literature by investigating whether people demonstrated optimism bias during the first wave of the COVID-19 pandemic in Europe, how this changed over time, and whether unrealistic optimism was negatively associated with protective measures. Taking advantage of a pre-existing international participative influenza surveillance network (n = 12,378), absolute and comparative unrealistic optimism were measured at three epidemic stages (pre-, early, peak), and across four countries-France, Italy, Switzerland and the United Kingdom. Despite differences in culture and health response, similar patterns were observed across all four countries. The prevalence of unrealistic optimism appears to be influenced by the particular epidemic context. Paradoxically, whereas absolute unrealistic optimism decreased over time, comparative unrealistic optimism increased, suggesting that whilst people became increasingly accurate in assessing their personal risk, they nonetheless overestimated that for others. Comparative unrealistic optimism was negatively associated with the adoption of protective behaviours, which is worrying, given that these preventive measures are critical in tackling the spread and health burden of COVID-19. It is hoped these findings will inspire further research into sociocognitive mechanisms involved in risk appraisal.

An evaluation of Hamiltonian Monte Carlo performance to calibrate age-structured compartmental SEIR models to incidence data.

Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo method to estimate unknown quantities through sample generation from a target distribution for which an analytical solution is difficult. The strength of this method lies in its geometrical foundations, which render it efficient for traversing high-dimensional spaces. First, this paper analyses the performance of HMC in calibrating five variants of inputs to an age-structured SEIR model. Four of these variants are related to restriction assumptions that modellers devise to handle high-dimensional parameter spaces. The other one corresponds to the unrestricted symmetric variant. To provide a robust analysis, we compare HMC's performance to that of the Nelder-Mead algorithm (NMS), a common method for non-linear optimisation. Furthermore, the calibration is performed on synthetic data in order to avoid confounding effects from errors in model selection. Then, we explore the variation in the method's performance due to changes in the scale of the problem. Finally, we fit an SEIR model to real data. In all the experiments, the results show that HMC approximates both the synthetic and real data accurately, and provides reliable estimates for the basic reproduction number and the age-dependent transmission rates. HMC's performance is robust in the presence of underreported incidences and high-dimensional complexity. This study suggests that stringent assumptions on age-dependent transmission rates can be lifted in favour of more realistic representations. The supplementary section presents the full set of results.

A Mobile Health Approach for Improving Outcomes in Suicide Prevention (SafePlan).

BACKGROUND: Suicide is a prominent cause of death worldwide, particularly among young people. It was the second leading cause of death among those aged 15-29 years globally in 2016. Treatment for patients with suicidal thoughts or behaviors often includes face-to-face psychological therapy with a mental health professional. These forms of interventions may involve maintaining and updating paper-based reports or worksheets in between sessions. Mobile technology can offer a way to support the implementation of evidence-based psychological techniques and the acquisition of protective coping skills. OBJECTIVE: This study aims to develop a mobile app to facilitate service users' access to mental health support and safety planning. This process involved eliciting expert input from clinicians who are actively engaged in the provision of mental health care. METHODS: A survey was distributed to targeted health care professionals to determine what features should be prioritized in a new mobile app relating to suicide prevention. On the basis of the survey results, a clinical design group, comprising 6 members with experience in fields such as mobile health (mHealth), clinical psychology, and suicide prevention, was established. This group was supplemented with further input from additional clinicians who provided feedback over three focus group sessions. The sessions were centered on refining existing app components and evaluating new feature requests. This process was iterated through regular feedback until agreement was reached on the overall app design and functionality. RESULTS: A fully functional mobile app, known as the SafePlan app, was developed and tested with the input of clinicians through an iterative design process. The app's core function is to provide an interactive safety plan to support users with suicidal thoughts or behaviors as an adjunct to face-to-face therapy. A diary component that facilitates the generalization of skills learned through dialectical behavior therapy was also implemented. Usability testing was carried out on the final prototype by students from a local secondary school, who are representative of the target user population in both age and technology experience. The students were asked to complete a system usability survey (SUS) at the end of this session. The mean overall SUS rating was 71.85 (SD 1.38). CONCLUSIONS: The participatory process involving key stakeholders (clinicians, psychologists, and information technology specialists) has resulted in the creation of an mHealth intervention technology that has the potential to increase accessibility to this type of mental health service for the target population. The app has gone through the initial testing phase, and the relevant recommendations have been implemented, and it is now ready for trialing with both clinicians and their patients.

The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.

BACKGROUND: Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance. OBJECTIVE: This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods. METHODS: A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria. RESULTS: Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias. CONCLUSIONS: IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population's online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

Mobile Health Technology Interventions for Suicide Prevention: Systematic Review.

BACKGROUND: Digital interventions are proposed as one way by which effective treatments for self-harm and suicidal ideation may be improved and their scalability enhanced. Mobile devices offer a potentially powerful medium to deliver evidence-based interventions with greater specificity to the individual when the intervention is needed. The recent proliferation of publicly available mobile apps designed for suicide prevention underlines the need for robust evidence to promote safe practice. OBJECTIVE: This review aimed to examine the effectiveness of currently available mobile health (mHealth) technology tools in reducing suicide-specific outcomes. METHODS: The following databases were searched: Cochrane Central Register of Controlled Trials (The Cochrane Library), MEDLINE, EMBASE, PsycINFO, and relevant sources of gray literature. All published and unpublished randomized controlled trials (RCTs), pseudo-RCTs, and pre-post observational studies that evaluated the effectiveness of mHealth technology in suicide prevention delivered via mobile computing and communication technology were included. Studies were included if they measured at least one suicide outcome variable (ie, suicidal ideation, suicidal intent, nonsuicidal self-injurious behavior, and suicidal behavior). A total of 2 review authors independently extracted data and assessed study suitability, in accordance with the Cochrane Collaboration Risk of Bias Tool, on July 31, 2018. Owing to the heterogeneity of outcomes found across studies, results were not amenable for pooled synthesis, and a meta-analysis was not performed. A narrative synthesis of the available research is presented here. RESULTS: A total of 7 studies met criteria for inclusion . Four published articles that reported on the effectiveness of the following mobile phone apps were included: iBobbly, Virtual Hope Box, BlueIce, and Therapeutic Evaluative Conditioning. Results demonstrated some positive impacts for individuals at elevated risk of suicide or self-harm, including reductions in depression, psychological distress, and self-harm and increases in coping self-efficacy. None of the apps evaluated demonstrated the ability to significantly decrease suicidal ideation compared with a control condition. In addition, 3 unpublished and recently completed trials also met criteria for inclusion in the review. CONCLUSIONS: Further research is needed to evaluate the efficacy of stand-alone mHealth technology-based interventions in suicide prevention. The small number of studies reported in this review tentatively indicate that such tools may have a positive impact on suicide-specific outcomes. Future mHealth intervention evaluations would benefit from addressing the following 3 main methodological limitations : (1) heterogeneity of outcomes: a lack of standardized measurement of suicide outcomes across studies; (2) ecological validity: the tendency to exclude potential participants because of the elevated suicide risk may reduce generalizability within clinical settings; and (3) app regulation and definition: the lack of a standardized classification system for mHealth intervention type points to the need for better definition of the scope of such technologies to promote safe practice. TRIAL REGISTRATION: PROSPERO CRD42017072899; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=72899. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.8635.

The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland.

Annual suicide figures are critical in identifying trends and guiding research, yet challenges arising from significant lags in reporting can delay and complicate real-time interventions. In this paper, we utilized Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland between 2004 and 2015. Official suicide rates are recorded by the Central Statistics Office in Ireland. While similar investigations using Google trends data have been carried out in other jurisdictions (e.g., United Kingdom, United Stated of America), such research had not yet been completed in Ireland. We compiled a collection of suicide- and depression-related search terms suggested by Google Trends and manually sourced from the literature. Monthly search rate terms at different lags were compared with suicide occurrences to determine the degree of correlation. Following two approaches based on vector autoregression and neural network autoregression, we achieved mean absolute error values between 4.14 and 9.61 when incorporating search query data, with the highest performance for the neural network approach. The application of this process to United Kingdom suicide and search query data showed similar results, supporting the benefit of Google Trends, neural network approach, and the applied search terms to forecast suicide risk increase. Overall, the combination of societal data and online behavior provide a good indication of societal risks; building on past research, our improvements led to robust models integrating search query and unemployment data for suicide risk forecasting in Ireland.

Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms.

Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries.

Disease mentions in airport and hospital geolocations expose dominance of news events for disease concerns.

BACKGROUND: In recent years, Twitter has been applied to monitor diseases through its facility to monitor users' comments and concerns in real-time. The analysis of tweets for disease mentions should reflect not only user specific concerns but also disease outbreaks. This requires the use of standard terminological resources and can be focused on selected geographic locations. In our study, we differentiate between hospital and airport locations to better distinguish disease outbreaks from background mentions of disease concerns. RESULTS: Our analysis covers all geolocated tweets over a 6 months time period, uses SNOMED-CT as a standard medical terminology, and explores language patterns (as well as MetaMap) to identify mentions of diseases in reference to the geolocation of tweets. Contrary to our expectation, hospital and airport geolocations are not suitable to collect significant portions of tweets concerned with disease outcomes. Overall, geolocated tweets exposed a large number of messages commenting on disease-related news articles. Furthermore, the geolocated messages exposed an over-representation of non-communicable diseases in contrast to infectious diseases. CONCLUSIONS: Our findings suggest that disease mentions on Twitter not only serve the purpose to share personal statements but also to share concerns about news articles. In particular, our assumption about the relevance of hospital and airport geolocations for an increased frequency of diseases mentions has not been met. To further address the linguistic cues, we propose the study of health forums to understand how a change in medium affects the language applied by the users. Finally, our research on the language use may provide essential clues to distinguish complementary trends in the use of language in Twitter when analysing health-related topics.

Mobile Health Technology Interventions for Suicide Prevention: Protocol for a Systematic Review and Meta-Analysis.

BACKGROUND: Previous research has reported that two of the major barriers to help-seeking for individuals at risk of suicide are stigma and geographical isolation. Mobile technology offers a potential means of delivering evidence-based interventions with greater specificity to the individual, and at the time that it is needed. Despite documented motivation by at-risk individuals to use mobile technology to track mental health and to support psychological interventions, there is a shortfall of outcomes data on the efficacy of mobile health (mHealth) technology on suicide-specific outcomes. OBJECTIVE: The objective of this study is to develop a protocol for a systematic review and meta-analysis that aims to evaluate the effectiveness of mobile technology-based interventions for suicide prevention. METHODS: The search includes the Cochrane Central Register of Controlled Trials (CENTRAL: The Cochrane Library), MEDLINE, Embase, PsycINFO, CRESP and relevant sources of gray literature. Studies that have evaluated psychological or nonpsychological interventions delivered via mobile computing and communication technology, and have suicidality as an outcome measure will be included. Two authors will independently extract data and assess the study suitability in accordance with the Cochrane Collaboration Risk of Bias Tool. Studies will be included if they measure at least one suicide outcome variable (ie, suicidal ideation, suicidal intent, nonsuicidal self-injurious behavior, suicidal behavior). Secondary outcomes will be measures of symptoms of depression. Where studies are sufficiently homogenous and reported outcomes are amenable for pooled synthesis, meta-analysis will be performed. A narrative synthesis will be conducted if the data is unsuitable for a meta-analysis. RESULTS: The review is in progress, with findings expected by summer 2018. CONCLUSIONS: To date, evaluations of mobile technology-based interventions in suicide prevention have focused on evaluating content as opposed to efficacy. Indeed, previous research has identified mobile applications that appear to present harmful content. The current review will address a gap in the literature by evaluating the efficacy of stand-alone mobile technology tools in suicide prevention. It is imperative that research identifies the evidence base for such tools in suicide prevention in order to inform policy, guide clinical practice, inform users and focus future research. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42017072899; https:// www.crd.york.ac.uk/prospero/display_record.asp?ID=CRD42017072899  (Archived by WebCite at http://www.webcitation.org/ 6tZAj0yqJ).

Influenzanet: Citizens Among 10 Countries Collaborating to Monitor Influenza in Europe.

BACKGROUND: The wide availability of the Internet and the growth of digital communication technologies have become an important tool for epidemiological studies and health surveillance. Influenzanet is a participatory surveillance system monitoring the incidence of influenza-like illness (ILI) in Europe since 2003. It is based on data provided by volunteers who self-report their symptoms via the Internet throughout the influenza season and currently involves 10 countries. OBJECTIVE: In this paper, we describe the Influenzanet system and provide an overview of results from several analyses that have been performed with the collected data, which include participant representativeness analyses, data validation (comparing ILI incidence rates between Influenzanet and sentinel medical practice networks), identification of ILI risk factors, and influenza vaccine effectiveness (VE) studies previously published. Additionally, we present new VE analyses for the Netherlands, stratified by age and chronic illness and offer suggestions for further work and considerations on the continuity and sustainability of the participatory system. METHODS: Influenzanet comprises country-specific websites where residents can register to become volunteers to support influenza surveillance and have access to influenza-related information. Participants are recruited through different communication channels. Following registration, volunteers submit an intake questionnaire with their postal code and sociodemographic and medical characteristics, after which they are invited to report their symptoms via a weekly electronic newsletter reminder. Several thousands of participants have been engaged yearly in Influenzanet, with over 36,000 volunteers in the 2015-16 season alone. RESULTS: In summary, for some traits and in some countries (eg, influenza vaccination rates in the Netherlands), Influenzanet participants were representative of the general population. However, for other traits, they were not (eg, participants underrepresent the youngest and oldest age groups in 7 countries). The incidence of ILI in Influenzanet was found to be closely correlated although quantitatively higher than that obtained by the sentinel medical practice networks. Various risk factors for acquiring an ILI infection were identified. The VE studies performed with Influenzanet data suggest that this surveillance system could develop into a complementary tool to measure the effectiveness of the influenza vaccine, eventually in real time. CONCLUSIONS: Results from these analyses illustrate that Influenzanet has developed into a fast and flexible monitoring system that can complement the traditional influenza surveillance performed by sentinel medical practices. The uniformity of Influenzanet allows for direct comparison of ILI rates between countries. It also has the important advantage of yielding individual data, which can be used to identify risk factors. The way in which the Influenzanet system is constructed allows the collection of data that could be extended beyond those of ILI cases to monitor pandemic influenza and other common or emerging diseases.

An mHealth Intervention Using a Smartphone App to Increase Walking Behavior in Young Adults: A Pilot Study.

BACKGROUND: Physical inactivity is a growing concern for society and is a risk factor for cardiovascular disease, obesity, and other chronic diseases. OBJECTIVE: This study aimed to determine the efficacy of the Accupedo-Pro Pedometer mobile phone app intervention, with the goal of increasing daily step counts in young adults. METHODS: Mobile phone users (n=58) between 17-26 years of age were randomized to one of two conditions (experimental and control). Both groups downloaded an app that recorded their daily step counts. Baseline data were recorded and followed-up at 5 weeks. Both groups were given a daily walking goal of 30 minutes, but the experimental group participants were told the equivalent goal in steps taken, via feedback from the app. The primary outcome was daily step count between baseline and follow-up. RESULTS: A significant time x group interaction effect was observed for daily step counts (P=.04). Both the experimental (P<.001) and control group (P=.03) demonstrated a significant increase in daily step counts, with the experimental group walking an additional 2000 steps per day. CONCLUSIONS: The results of this study demonstrate that a mobile phone app can significantly increase physical activity in a young adult sample by setting specific goals, using self-monitoring, and feedback.

Participatory Syndromic Surveillance of Influenza in Europe.

The growth of digital communication technologies for public health is offering an unconventional means to engage the general public in monitoring community health. Here we present Influenzanet, a participatory system for the syndromic surveillance of influenza-like illness (ILI) in Europe. Through standardized online surveys, the system collects detailed profile information and self-reported symptoms volunteered by participants resident in the Influenzanet countries. Established in 2009, it now includes 10 countries representing more than half of the 28 member states of the European Union population. The experience of 7 influenza seasons illustrates how Influenzanet has become an adjunct to existing ILI surveillance networks, offering coherence across countries, inclusion of nonmedically attended ILI, flexibility in case definition, and facilitating individual-level epidemiological analyses generally not possible in standard systems. Having the sensitivity to timely detect substantial changes in population health, Influenzanet has the potential to become a viable instrument for a wide variety of applications in public health preparedness and control.