Patient-reported outcomes as a source of evidence in off-label prescribing: analysis of data from PatientsLikeMe

Online cognitive testing in Parkinson's disease: advantages and challenges

Parkinson's disease (PD) is primarily characterized by motor symptoms. Yet, many people with PD experience cognitive decline, which is often unnoticed by clinicians, although it may have a significant impact on quality of life. For over half a century, traditional in-person PD cognitive assessment lacked accessibility, scalability, and specificity due to its inherent limitations. In this review, we propose that novel methods of online cognitive assessment could potentially address these limitations. We first outline the challenges of traditional in-person cognitive testing in PD. We then summarize the existing literature on online cognitive testing in PD. Finally, we explore the advantages, but also the limitations, of three major processes involved in online PD cognitive testing: recruitment and sampling methods, measurement and participation, and disease monitoring and management. Taking the limitations into account, we aim to highlight the potential of online cognitive testing as a more accessible and efficient approach to cognitive testing in PD.

Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST)

The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app's usability as above satisfactory (i.e., 7.9 on 1-10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1-10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.

Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs

Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.

Online Health Communities: an alternative feasible data registry tool for developing countries

Given the many challenges facing healthcare access in many developing countries and the added limitations observed in emergencies like COVID-19 pandemic, the authors here discuss an alternative and feasible approach to overcome all these limitations.

COVID-19 infodemic on Facebook and containment measures in Italy, United Kingdom and New Zealand

The COVID-19 pandemic has been characterized by a social media “infodemic”: an overabundance of information whose authenticity may not always be guaranteed. With the potential to lead individuals to harmful decisions for the society, this infodemic represents a severe threat to information security, public health and democracy. In this paper, we assess the interplay between the infodemic and specific aspects of the pandemic, such as the number of cases, the strictness of containment measures, and the news media coverage. We perform a comparative study on three countries that employed different managements of the COVID-19 pandemic in 2020—namely Italy, the United Kingdom, and New Zealand. We first analyze the three countries from an epidemiological perspective to characterize the impact of the pandemic and the strictness of the restrictions adopted. Then, we collect a total of 6 million posts from Facebook to describe user news consumption behaviors with respect to the reliability of such posts. Finally, we quantify the relationship between the number of posts published in each of the three countries and the number of confirmed cases, the strictness of the restrictions adopted, and the online news media coverage about the pandemic. Our results show that posts referring to reliable sources are consistently predominant in the news circulation, and that users engage more with reliable posts rather than with posts referring to questionable sources. Furthermore, our modelling results suggest that factors related to the epidemiological and informational ecosystems can serve as proxies to assess the evolution of the infodemic.

A critical analysis of the social media policies in Ontario's healthcare system

Purpose

Social media has made a revolutionary change in the relationship between the customers and business or service providers by enabling customers to publish and share feedback and views about product or service quality. This revolutionary change has not been echoed in some healthcare systems. This study analyses the social media policies of healthcare regulatory authorities in Ontario and explores how these policies encourage or discourage healthcare professionals' use of social media for collecting patient stories and understanding patient experience.

Design/methodology/approach

The study used qualitative content analysis to analyse the policy documents, focusing on the manifest themes in these documents. It used convenient sampling to select 12 organizations, including regulating and licensing bodies and health service delivery organizations in Ontario. The authors collected 24 documents from these organizations, including policies, practice standards and social media learning materials.

Findings

In Ontario's healthcare system, social media is perceived as a source of risks to the healthcare professions and professionals. Healthcare regulators emphasize that the codes of conduct and professional standards extend to social media. The study found no systematic recognition of patient stories on social media as a source of information on healthcare quality that can be useful for healthcare professionals.

Originality/value

The study identifies potential unintended consequences of social media policies in the healthcare system and calls for policy and cultural changes to enable the development of safe social media platforms that can facilitate interaction between healthcare providers and patients, when necessary, without the fear of legal consequences or privacy breaches.

Benefits and harms of patient stories on social media from the perspective of healthcare providers and administrators in Ontario

There has been a growing use of social media by patients to share their healthcare experiences and produce information that can be helpful to other patients seeking healthcare services. These stories can reveal issues in healthcare quality. However, faced with the inherent risks of social media, healthcare providers have been skeptical about the value of these stories, and many healthcare systems have adopted restrictive and protective policies to control the use of social media by healthcare providers. This study explores healthcare providers' and administrators' perspectives on patient stories on social media and whether they can use the stories to evaluate healthcare experiences. Semi-structured interviews (n = 21) were conducted with healthcare providers and administrators, including physicians, nurses, and quality managers in Ontario, Canada, between April 2018 and May 2019. Inductive and data-driven thematic analysis was used to analyze the data. Several barriers prevent healthcare providers from realizing the benefits of social media, including concerns about the quality of patients' feedback, the professional codes of conduct, and the time and effort required to process these stories. The study findings suggest that cultural changes in the healthcare system might be required to foster the use of social media for healthcare quality improvement and enable the development of a safe patient-provider communication environment that facilitates the exchange of constructive feedback between the two parties without the fear of legal consequences, breaches of patient privacy, or violation of professional codes of conduct.

Spontaneously Generated Online Patient Experience of Modafinil: A Qualitative and NLP Analysis

Objective: To compare the findings from a qualitative and a natural language processing (NLP) based analysis of online patient experience posts on patient experience of the effectiveness and impact of the drug Modafinil.

Methods: Posts (n = 260) from 5 online social media platforms where posts were publicly available formed the dataset/corpus. Three platforms asked posters to give a numerical rating of Modafinil. Thematic analysis: data was coded and themes generated. Data were categorized into PreModafinil, Acquisition, Dosage, and PostModafinil and compared to identify each poster's own view of whether taking Modafinil was linked to an identifiable outcome. We classified this as positive, mixed, negative, or neutral and compared this with numerical ratings. NLP: Corpus text was speech tagged and keywords and key terms extracted. We identified the following entities: drug names, condition names, symptoms, actions, and side-effects. We searched for simple relationships, collocations, and co-occurrences of entities. To identify causal text, we split the corpus into PreModafinil and PostModafinil and used n-gram analysis. To evaluate sentiment, we calculated the polarity of each post between −1 (negative) and +1 (positive). NLP results were mapped to qualitative results.

Results: Posters had used Modafinil for 33 different primary conditions. Eight themes were identified: the reason for taking (condition or symptom), impact of symptoms, acquisition, dosage, side effects, other interventions tried or compared to, effectiveness of Modafinil, and quality of life outcomes. Posters reported perceived effectiveness as follows: 68% positive, 12% mixed, 18% negative. Our classification was consistent with poster ratings. Of the most frequent 100 keywords/keyterms identified by term extraction 88/100 keywords and 84/100 keyterms mapped directly to the eight themes. Seven keyterms indicated negation and temporal states. Sentiment was as follows 72% positive sentiment 4% neutral 24% negative. Matching of sentiment between the qualitative and NLP methods was accurate in 64.2% of posts. If we allow for one category difference matching was accurate in 85% of posts.

Conclusions: User generated patient experience is a rich resource for evaluating real world effectiveness, understanding patient perspectives, and identifying research gaps. Both methods successfully identified the entities and topics contained in the posts. In contrast to current evidence, posters with a wide range of other conditions found Modafinil effective. Perceived causality and effectiveness were identified by both methods demonstrating the potential to augment existing knowledge.

Exploring Molecular Approaches in Amyotrophic Lateral Sclerosis: Drug Targets from Clinical and Pre-Clinical Findings

Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron disease (MND) characterized by the death of upper and lower motor neurons (corticospinal tract) in the motor cortex, basal ganglia, brain stem, and spinal cord. The patient experiences the sign and symptoms between 55 to 75 years of age, which include impaired motor movement, difficulty in speaking and swallowing, grip loss, muscle atrophy, spasticity, and sometimes associated with memory and cognitive impairments. Median survival is 3 to 5 years after diagnosis and 5 to 10% of the patients live for more than 10 years. The limited intervention of pharmacologically active compounds, that are used clinically, is majorly associated with the narrow therapeutic index. Pre-clinically established experimental models, where neurotoxin methyl mercury mimics the ALS like behavioural and neurochemical alterations in rodents associated with neuronal mitochondrial dysfunctions and downregulation of adenyl cyclase mediated cAMP/CREB, is the main pathological hallmark for the progression of ALS in central as well in the peripheral nervous system. Despite the considerable investigation into neuroprotection, it still constrains treatment choices to strong care and organization of ALS complications. Therefore, this current review specially targeted the investigation of clinical and pre-clinical features available for ALS to understand the pathogenic mechanisms and to explore the pharmacological interventions associated with the up-regulation of intracellular adenyl cyclase/cAMP/ CREB and activation of mitochondrial-ETC coenzyme-Q10 as a future drug target in the amelioration of ALS mediated motor neuronal dysfunctions.

Application of natural language processing techniques to identify off-label drug usage from various online health communities

Objective

Outcomes mentioned on online health communities (OHCs) by patients can serve as a source of evidence for off-label drug usage evaluation, but identifying these outcomes manually is tedious work. We have built a natural language processing model to identify off-label usage of drugs mentioned in these patient posts.

Materials and Methods

Single patient posts from 4 major OHCs were considered for this study. A text classification model was built to classify the posts as either relevant or not relevant based on patient experience. The relevant posts were passed through a spelling correction tool, CSpell, and then medications and indications from these posts were identified using cTAKES (clinical Text Analysis and Knowledge Extraction System), a named entity recognition tool. Drug and indication pairs were identified using a dependency parser. Finally, if the paired indication was not mentioned on the label of the drug approved by U.S. Food and Drug Administration, it was tagged as off-label use of that drug.

Results

Using this algorithm, we identified 289 off-label indications, achieving a recall of 76%.

Conclusions

The method designed in this study identifies and extracts the semantic relationship between drugs and indications from demotic posts in OHCs. The results demonstrate the feasibility of using natural language processing techniques in identifying off-label drug usage across online health forums for a variety of drugs. Understanding patients’ off-label use of drugs may be able to help manufacturers innovate to better address patients’ needs and assist doctors’ prescribing decisions.

Opportunities and pitfalls of social media research in rare genetic diseases: a systematic review

PURPOSE

Social media may be particularly valuable in research in rare genetic diseases because of the low numbers of patients and the rare disease community's robust online presence. The goal of this systematic review was to understand how social media is currently used in rare disease research and the characteristics of the participants in these studies.

METHODS

We conducted a systematic review of six databases to identify studies published in English between January 2004 and November 2020, of which 120 met inclusion criteria.

RESULTS

Most studies were observational (n = 114, 95.0%) and cross-sectional (n = 107, 89.2%), and more than half (n = 69, 57.5%) utilized only surveys. Only 101 rare diseases were included across all studies. Participant demographics, when reported, were predominantly female (70.1% ± 22.5%) and white (85.0% ± 11.0%) adult patients and caregivers.

CONCLUSION

Despite its potential benefits in rare disease research, the use of social media is still methodologically limited and the participants reached may not be representative of the rare disease population by gender, race, age, or rare disease type. As scholars explore using social media for rare disease research, careful attention should be paid to representativeness when studying this diverse patient community.

Feasibility of an Online Patient Community to Support Older Women With Newly Diagnosed Breast Cancer

BACKGROUND

We conducted a feasibility study of integrating a free, online patient health community, PatientsLikeMe (PLM), into the neo/adjuvant care of older patients recently diagnosed with breast cancer. We assessed whether PLM was an appealing social forum to improve women's treatment experience during this stressful and often isolating time.

PATIENTS AND METHODS

We enrolled women ages ≥60 years with recently diagnosed nonmetastatic invasive breast cancer at a single center. Our primary endpoint was feasibility of patient engagement with PLM, with successful engagement defined as using PLM for ≥50% of the total weeks of one's chemotherapy course or 4 months (if receiving hormonal and/or radiation therapy). Participants were surveyed about their health, social support, and experiences.

RESULTS

During February 8, 2016 to June 25, 2018, 47 women enrolled; 14.9% were ages 71 to 75 years and 8.5% were ages ≥76 years; 63.8% received chemotherapy. Two women withdrew after consent; 45 were included in analyses. Overall, 1 (2.2%) patient met the feasibility endpoint, although 8 (17.8%) met engagement criteria for ≥25% of weeks on study. Over time, women submitted a total of >1200 posts to the "InstantMe" feature (rates how they feel) and >130 "MyFeed" stories. Evaluation of satisfaction with PLM was limited by the small number of end-of-study surveys.

CONCLUSION

Although our online social support intervention did not meet the prespecified criteria for feasibility (required sustained PLM utilization), many engaged with PLM. Future interventions should explore ways we can optimally support older patients, who have an increasing access to technology while facing threats of social isolation.

Global Collaborative Social Network (Share4Rare) to Promote Citizen Science in Rare Disease Research: Platform Development Study

BACKGROUND

Rare disease communities are spread around the globe and segmented by their condition. Little research has been performed on the majority of rare diseases. Most patients who are affected by a rare disease have no research on their condition because of a lack of knowledge due to absence of common groups in the research community.

OBJECTIVE

We aimed to develop a safe and secure community of rare disease patients, without geographic or language barriers, to promote research.

METHODS

Cocreation design methodology was applied to build Share4Rare, with consultation and input through workshops from a variety of stakeholders (patients, caregivers, clinicians, and researchers).

RESULTS

The workshops allowed us to develop a layered version of the platform based on educating patients and caregivers with publicly accessible information, a secure community for the patients and caregivers, and a research section with the purpose of collecting patient information for analysis, which was the core and final value of the platform.

CONCLUSIONS

Rare disease research requires global collaboration in which patients and caregivers have key roles. Collective intelligence methods implemented in digital platforms reduce geographic and language boundaries and involve patients in a unique and universal project. Their contributions are essential to increase the amount of scientific knowledge that experts have on rare diseases. Share4Rare has been designed as a global platform to facilitate the donation of clinical information to foster research that matters to patients with rare conditions. The codesign methods with patients have been essential to create a patient-centric design.

E-health and multiple sclerosis

PURPOSE OF REVIEW

To outline recent applications of e-health data and digital tools for improving the care and management of healthcare for people with multiple sclerosis.

RECENT FINDINGS

The digitization of most clinical data, along with developments in communication technologies, miniaturization of sensors and computational advances are enabling aggregation and clinically meaningful analyses of real-world data from patient registries, digital patient-reported outcomes and electronic health records (EHR). These data are allowing more confident descriptions of prognoses for multiple sclerosis patients and the long-term relative benefits and safety of disease-modifying treatments (DMT). Registries allow detailed, multiple sclerosis-specific data to be shared between clinicians more easily, provide data needed to improve the impact of DMT and, with EHR, characterize clinically relevant interactions between multiple sclerosis and other diseases. Wearable sensors provide continuous, long-term measures of performance dynamics in relevant ecological settings. In conjunction with telemedicine and online apps, they promise a major expansion of the scope for patients to manage aspects of their own care. Advances in disease understanding, decision support and self-management using these Big Data are being accelerated by machine learning and artificial intelligence.

SUMMARY

Both health professionals and patients can employ e-health approaches and tools for development of a more patient-centred learning health system.

Combining Social Media and FDA Adverse Event Reporting System to Detect Adverse Drug Reactions

INTRODUCTION

Adverse drug reactions (ADRs) are unintended reactions caused by a drug or combination of drugs taken by a patient. The current safety surveillance system relies on spontaneous reporting systems (SRSs) and more recently on observational health data; however, ADR detection may be delayed and lack geographic diversity. The broad scope of social media conversations, such as those on Twitter, can include health-related topics. Consequently, these data could be used to detect potentially novel ADRs with less latency. Although research regarding ADR detection using social media has made progress, findings are based on single information sources, and no study has yet integrated drug safety evidence from both an SRS and Twitter.

OBJECTIVE

The aim of this study was to combine signals from an SRS and Twitter to facilitate the detection of safety signals and compare the performance of the combined system with signals generated by individual data sources.

METHODS

We extracted potential drug-ADR posts from Twitter, used Monte Carlo expectation maximization to generate drug safety signals from both the US FDA Adverse Event Reporting System and posts from Twitter, and then integrated these signals using a Bayesian hierarchical model. The results from the integrated system and two individual sources were evaluated using a reference standard derived from drug labels. Area under the receiver operating characteristics curve (AUC) was computed to measure performance.

RESULTS

We observed a significant improvement in the AUC of the combined system when comparing it with Twitter alone, and no improvement when comparing with the SRS alone. The AUCs ranged from 0.587 to 0.637 for the combined SRS and Twitter, from 0.525 to 0.534 for Twitter alone, and from 0.612 to 0.642 for the SRS alone. The results varied because different preprocessing procedures were applied to Twitter.

CONCLUSION

The accuracy of signal detection using social media can be improved by combining signals with those from SRSs. However, the combined system cannot achieve better AUC performance than data from FAERS alone, which may indicate that Twitter data are not ready to be integrated into a purely data-driven combination system.

A machine learning and network framework to discover new indications for small molecules

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts.

Determining the Topic Evolution and Sentiment Polarity for Albinism in a Chinese Online Health Community: Machine Learning and Social Network Analysis

BACKGROUND

There are more than 6000 rare diseases in existence today, with the number of patients with these conditions rapidly increasing. Most research to date has focused on the diagnosis, treatment, and development of orphan drugs, while few studies have examined the topics and emotions expressed by patients living with rare diseases on social media platforms, especially in online health communities (OHCs).

OBJECTIVE

This study aimed to determine the topic categorizations and sentiment polarity for albinism in a Chinese OHC, Baidu Tieba, using multiple methods. The OHC was deeply mined using topic mining, social network analysis, and sentiment polarity analysis. Through these methods, we determined the current situation of community construction, identifying the ongoing needs and problems experienced by people with albinism in their daily lives.

METHODS

We used the albinism community on the Baidu Tieba platform as the data source in this study. Term frequency-inverse document frequency, latent dirichlet allocation models, and naive Bayes were employed to mine the various topic categories. Social network analysis, which was completed using the Gephi tool, was employed to analyze the evolution of the albinism community. Sentiment polarity analysis was performed using a long short-term memory algorithm.

RESULTS

We identified 8 main topics discussed in the community: daily sharing, family, interpersonal communication, social life and security, medical care, occupation and education, beauty, and self-care. Among these topics, daily sharing represented the largest proportion of the discussions. From 2012 to 2019, the average degree and clustering coefficient of the albinism community continued to decline, while the network center transferred from core communities to core users. A total of 68.43% of the corpus was emotional, with 35.88% being positive and 32.55% negative. There were statistically significant differences in the distribution of sentiment polarity between topics (P<.001). Negative emotions were twice as high as positive emotions in the social life and security topic.

CONCLUSIONS

The study reveals insights into the emotions expressed by people with albinism in the Chinese OHC, Baidu Tieba, providing health care practitioners with greater appreciation of the current emotional support needed by patients and the patient experience. Current OHCs do not exert enough influence due to limited effective organization and development. Health care sectors should take greater advantage of OHCs to support vulnerable patients with rare diseases to meet their evidence-based needs.

Concept based auto-assignment of healthcare questions to domain experts in online Q&A communities

BACKGROUND

Healthcare consumers are increasingly turning to the online health Q&A communities to seek answers for their questions because current general search engines are unable to digest complex health-related questions. Q&A communities are platforms where users ask unstructured questions from different healthcare topics.

OBJECTIVES

This study aimed to provide a concept-based approach to automatically assign health questions to the appropriate domain experts.

METHODS

We developed three processes for (1) expert profiling, (2) question analysis and (3) similarity calculation and assignment. Semantic weight of concepts combined with TF-IDF weighting comprised vectors of concepts as expert profiles. Subsequently, the similarity between submitted questions and expert profiles was calculated to find a relevant expert.

RESULTS

We randomly selected 345 questions posted by consumers for 38 experts in 13 health topics from NetWellness as input data. Our results showed the precision and recall of our proposed method for the studied topics were between 63 %-92 % and 61 %-100 %, respectively. The calculated F-measure in selected topics was between 62 % (Addiction and Substance Abuse) and 94 % (Eye and Vision Care) with a combined F-measure of 80 %.

CONCLUSIONS

Concept-based methods using unified medical language system and natural language processing techniques could automatically assign actual health questions in different topics to the relevant domain experts with good performance metrics.

The use or generation of biomedical data and existing medicines to discover and establish new treatments for patients with rare diseases - recommendations of the IRDiRC Data Mining and Repurposing Task Force

The number of available therapies for rare diseases remains low, as fewer than 6% of rare diseases have an approved treatment option. The International Rare Diseases Research Consortium (IRDiRC) set up the multi-stakeholder Data Mining and Repurposing (DMR) Task Force to examine the potential of applying biomedical data mining strategies to identify new opportunities to use existing pharmaceutical compounds in new ways and to accelerate the pace of drug development for rare disease patients. In reviewing past successes of data mining for drug repurposing, and planning for future biomedical research capacity, the DMR Task Force identified four strategic infrastructure investment areas to focus on in order to accelerate rare disease research productivity and drug development: (1) improving the capture and sharing of self-reported patient data, (2) better integration of existing research data, (3) increasing experimental testing capacity, and (4) sharing of rare disease research and development expertise. Additionally, the DMR Task Force also recommended a number of strategies to increase data mining and repurposing opportunities for rare diseases research as well as the development of individualized and precision medicine strategies.

Unlicensed medicines

Richard Griffith, Senior Lecturer in Health Law at Swansea University, discusses the law relating to the supply and administration of unlicensed medicines to meet the special need of a patient.

Profiling off-label prescriptions in cancer treatment using social health networks

Objectives

To investigate using patient posts in social media as a resource to profile off-label prescriptions of cancer drugs.

Methods

We analyzed patient posts from the Inspire health forums (www.inspire.com) and extracted mentions of cancer drugs from the 14 most active cancer-type specific support groups. To quantify drug-disease associations, we calculated information component scores from the frequency of posts in each cancer-specific group with mentions of a given drug. We evaluated the results against three sources: manual review, Wolters-Kluwer Medi-span, and Truven MarketScan insurance claims.

Results

We identified 279 frequently discussed and therefore highly associated drug-disease pairs from Inspire posts. Of these, 96 are FDA approved, 9 are known off-label uses, and 174 do not have records of known usage (potentially novel off-label uses). We achieved a mean average precision of 74.9% in identifying drug-disease pairs with a true indication association from patient posts and found consistent evidence in medical claims records. We achieved a recall of 69.2% in identifying known off-label drug uses (based on Wolters-Kluwer Medi-span) from patient posts.

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well-being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team-based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.

The Missing Reality of Real Life in Real-World Evidence

Reality is defined as a real event, a real thing, or state of affairs. Reality exists in the places where we live our daily lives, in the relationships we have with others, and in our experiences, circumstances, and situations that occur across the lifespan. As the everydayness of our lives becomes increasingly digitized, data generated from the reality that exists outside of our healthcare encounters holds much promise to fill recognized gaps in real‐world evidence (RWE). In the past decade, many factors have converged to uniquely position person‐generated data for use in health care delivery, payment reform, product development, and regulatory decision making. Yet, real‐world data will fall short of its promise to fill gaps in RWE if what we learn does not reflect the real lives of real people from across the spectrum of social, economic, and cultural experiences.

Digital Trespass: Ethical and Terms-of-Use Violations by Researchers Accessing Data From an Online Patient Community

With the expansion and popularity of research on websites such as Facebook and Twitter, there has been increasing concern about investigator conduct and social media ethics. The availability of large data sets has attracted researchers who are not traditionally associated with health data and its associated ethical considerations, such as computer and data scientists. Reliance on oversight by ethics review boards is inadequate and, due to the public availability of social media data, there is often confusion between public and private spaces. In addition, social media participants and researchers may pay little attention to traditional terms of use. In this paper, we review four cases involving ethical and terms-of-use violations by researchers seeking to conduct social media studies in an online patient research network. These violations involved unauthorized scraping of social media data, entry of false information, misrepresentation of researcher identities of participants on forums, lack of ethical approval and informed consent, use of member quotations, and presentation of findings at conferences and in journals without verifying accurate potential biases and limitations of the data. The correction of these ethical lapses often involves much effort in detecting and responding to violators, addressing these lapses with members of an online community, and correcting inaccuracies in the literature (including retraction of publications and conference presentations). Despite these corrective actions, we do not regard these episodes solely as violations. Instead, they represent broader ethical issues that may arise from potential sources of confusion, misinformation, inadequacies in applying traditional informed consent procedures to social media research, and differences in ethics training and scientific methodology across research disciplines. Social media research stakeholders need to assure participants that their studies will not compromise anonymity or lead to harmful outcomes while preserving the societal value of their health-related studies. Based on our experience and published recommendations by social media researchers, we offer potential directions for future prevention-oriented measures that can be applied by data producers, computer/data scientists, institutional review boards, research ethics committees, and publishers.

The history and future of digital health in the field of behavioral medicine

Since its earliest days, the field of behavioral medicine has leveraged technology to increase the reach and effectiveness of its interventions. Here, we highlight key areas of opportunity and recommend next steps to further advance intervention development, evaluation, and commercialization with a focus on three technologies: mobile applications (apps), social media, and wearable devices. Ultimately, we argue that future of digital health behavioral science research lies in finding ways to advance more robust academic-industry partnerships. These include academics consciously working towards preparing and training the work force of the twenty first century for digital health, actively working towards advancing methods that can balance the needs for efficiency in industry with the desire for rigor and reproducibility in academia, and the need to advance common practices and procedures that support more ethical practices for promoting healthy behavior.

User Perspectives of a Web-Based Data-Sharing Platform (Open Humans) on Ethical Oversight in Participant-Led Research: Protocol for a Quantitative Study

BACKGROUND

Advances in medicine rely to a great extent on people's willingness to share their data with researchers. With increasingly widespread use of digital technologies, several Web-based communities have emerged aiming to enable their users to share large amounts of data, some of which can possibly be employed for research purposes by scientists, or to conduct participant-led research (PLR). Scholarship has recently addressed the necessity of interrogating how existing ethical standards can and should be applied and adapted in view of the specificities of such Web-based activities. So far, no study has explored participants' beliefs about and attitudes toward ethical oversight when it comes to platforms that involve medical data sharing.

OBJECTIVE

This paper presents the protocol for a survey study aimed at understanding users' beliefs about Web-based data-sharing platforms regarding how research ethics principles should be applied in such a setting. Furthermore, the study aims at quantitatively assessing the relationship between participants' perspectives on ethical oversight and other variables such as previous participation in research, beliefs about data sharing, and attitudes toward self-experimentation.

METHODS

We are conducting a Web-based survey with users of a popular Web-based data-sharing platform, Open Humans. The survey has been sent to approximately 4640 users registered for the Open Humans newsletter. To fill out the survey, participants need to have an account on Open Humans. We expect a 5%-10% response rate (between 200 and 400 completed surveys out of approximately 4000 survey invitations sent). Independent variables include past data-sharing behavior and intention, beliefs about data sharing, past participation in research, attitudes toward self-experimentation, perceived knowledge of the platform's guidelines and terms, perceived importance of having transparent guidelines, and governance-related beliefs. The main dependent variable is participants' expectations regarding who should ensure that ethical requirements are met within research projects conducted on open data-sharing platforms, based on Emanuel et al's ethical framework. We will use chi-square tests to assess the relationship between participants' expectations regarding ethical oversight and their past behavior, future intentions, beliefs, attitudes, and knowledge.

RESULTS

Data collection started on June 13, 2018. A reminder to fill out the survey was sent to participants in mid-July. We expect to gain insights on users' perspectives on the ethical oversight of Web-based data-sharing platforms and on the associated experiences, beliefs, and sociodemographic characteristics.

CONCLUSIONS

When digital tools allow people to engage in PLR including medical data, understanding how people interpret and envision the ethical oversight of their data-sharing practices is crucial. This will be the first study to explore users' perspectives on ethical oversight of Web-based data-sharing platforms. The results will help inform the development of a framework that can be employed for platforms hosting various kinds of research projects to accommodate participants' ethical oversight needs.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/10939.

Use of Social Media in the Assessment of Relative Effectiveness: Explorative Review With Examples From Oncology

Background

An element of health technology assessment constitutes assessing the clinical effectiveness of drugs, generally called relative effectiveness assessment. Little real-world evidence is available directly after market access, therefore randomized controlled trials are used to obtain information for relative effectiveness assessment. However, there is growing interest in using real-world data for relative effectiveness assessment. Social media may provide a source of real-world data.

Objective

We assessed the extent to which social media-generated health data has provided insights for relative effectiveness assessment.

Methods

An explorative literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify examples in oncology where health data were collected using social media. Scientific and grey literature published between January 2010 and June 2016 was identified by four reviewers, who independently screened studies for eligibility and extracted data. A descriptive qualitative analysis was performed.

Results

Of 1032 articles identified, eight were included: four articles identified adverse events in response to cancer treatment, three articles disseminated quality of life surveys, and one study assessed the occurrence of disease-specific symptoms. Several strengths of social media-generated health data were highlighted in the articles, such as efficient collection of patient experiences and recruiting patients with rare diseases. Conversely, limitations included validation of authenticity and presence of information and selection bias.

Conclusions

Social media may provide a potential source of real-world data for relative effectiveness assessment, particularly on aspects such as adverse events, symptom occurrence, quality of life, and adherence behavior. This potential has not yet been fully realized and the degree of usefulness for relative effectiveness assessment should be further explored.

Developing a Mental Health eClinic to Improve Access to and Quality of Mental Health Care for Young People: Using Participatory Design as Research Methodologies

Background

Each year, many young Australians aged between 16 and 25 years experience a mental health disorder, yet only a small proportion access services and even fewer receive timely and evidence-based treatments. Today, with ever-increasing access to the Internet and use of technology, the potential to provide all young people with access (24 hours a day, 7 days a week) to the support they require to improve their mental health and well-being is promising.

Objective

The aim of this study was to use participatory design (PD) as research methodologies with end users (young people aged between 16 and 25 years and youth health professionals) and our research team to develop the Mental Health eClinic (a Web-based mental health clinic) to improve timely access to, and better quality, mental health care for young people across Australia.

Methods

A research and development (R&D) cycle for the codesign and build of the Mental Health eClinic included several iterative PD phases: PD workshops; translation of knowledge and ideas generated during workshops to produce mockups of webpages either as hand-drawn sketches or as wireframes (simple layout of a webpage before visual design and content is added); rapid prototyping; and one-on-one consultations with end users to assess the usability of the alpha build of the Mental Health eClinic.

Results

Four PD workshops were held with 28 end users (young people n=18, youth health professionals n=10) and our research team (n=8). Each PD workshop was followed by a knowledge translation session. At the conclusion of this cycle, the alpha prototype was built, and one round of one-on-one end user consultation sessions was conducted (n=6; all new participants, young people n=4, youth health professionals n=2). The R&D cycle revealed the importance of five key components for the Mental Health eClinic: a home page with a visible triage system for those requiring urgent help; a comprehensive online physical and mental health assessment; a detailed dashboard of results; a booking and videoconferencing system to enable video visits; and the generation of a personalized well-being plan that includes links to evidence-based, and health professional–recommended, apps and etools.

Conclusions

The Mental Health eClinic provides health promotion, triage protocols, screening, assessment, a video visit system, the development of personalized well-being plans, and self-directed mental health support for young people. It presents a technologically advanced and clinically efficient system that can be adapted to suit a variety of settings in which there is an opportunity to connect with young people. This will enable all young people, and especially those currently not able or willing to connect with face-to-face services, to receive best practice clinical services by breaking down traditional barriers to care and making health care more personalized, accessible, affordable, and available.

Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach

BACKGROUND

Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help.

OBJECTIVE

The aim of this study was to use text mining on material from an online forum exploring patients' concerns about treatment (antidepressants and anxiolytics).

METHODS

Concerns about treatment were collected from discussion titles in patients' online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum.

RESULTS

The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients' concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs.

CONCLUSIONS

Patients' expression on the Internet is a potential additional resource in addressing patients' concerns about treatment. Patient profiles are close to that of patients treated in psychiatry.

Individual Prognosis of Symptom Burden and Functioning in Chronic Diseases: A Generic Method Based on Patient-Reported Outcome (PRO) Measures

Background

Information to the patient about the long-term prognosis of symptom burden and functioning is an integrated part of clinical practice, but relies mostly on the clinician’s personal experience. Relevant prognostic models based on patient-reported outcome (PRO) data with repeated measurements are rarely available.

Objective

The aim was to describe a generic method for individual long-term prognosis of symptom burden and functioning that implied few statistical presumptions, to evaluate an implementation for prognosis of depressive symptoms in stroke patients and to provide open access to a Web-based prototype of this implementation for individual use.

Methods

The method used to describe individual prognosis of a PRO outcome was based on the selection of a specific subcohort of patients who have the same score as the patient in question at the same time (eg, after diagnosis or treatment start), plus or minus one unit of minimal clinically important difference. This subcohort’s experienced courses were then used to provide quantitative measures of prognosis over time. A cohort of 1404 stroke patients provided data for a simulation study and a prototype for individual use. Members of the cohort answered questionnaires every 6 months for 3.5 years. Depressive symptoms were assessed by the Hospital Anxiety and Depression Scale (HADS) and a single item from the SF-12 (MH4) health survey. Four approaches were compared in a simulation study in which the prognosis for each member of the cohort was individually assessed.

Results

The mean standard deviations were 40% to 70% higher in simulated scores. Mean errors were close to zero, and mean absolute errors were between 0.46 and 0.66 SD in the four approaches. An approach in which missing HADS scores were estimated from the single-item SF-12 MH4 performed marginally better than methods restricted to questionnaires with a genuine HADS score, which indicates that data collected with shorter questionnaires (eg, in clinical practice) may be used together with longer versions with the full scale, given that the design includes at least two simultaneous measurements of the full scale and the surrogate measure.

Conclusions

This is the first description and implementation of a nonparametric method for individual PRO-based prognosis. Given that relevant PRO data have been collected longitudinally, the method may be applied to other patient groups and to any outcome related to symptom burden and functioning. This initial implementation has been deliberately made simple, and further elaborations as well as the usability and clinical validity of the method will be scrutinized in clinical practice. An implementation of the prototype is available online at www.prognosis.dk.

Exploring targets and therapies for amyotrophic lateral sclerosis: current insights into dietary interventions

A growing number of preclinical and human studies demonstrate a disease-modifying effect of nutritional state in amyotrophic lateral sclerosis (ALS). The management of optimal nutrition in ALS is complicated, as physiological, physical, and psychological effects of the disease need to be considered and addressed accordingly. In this regard, multidisciplinary care teams play an integral role in providing dietary guidance to ALS patients and their carers. However, with an increasing research focus on the use of dietary intervention strategies to manage disease symptoms and improve prognosis in ALS, many ALS patients are now seeking or are actively engaged in using complementary and alternative therapies that are dietary in nature. In this article, we review the aspects of appetite control, energy balance, and the physiological effects of ALS relative to their impact on overall nutrition. We then provide current insights into dietary interventions for ALS, considering the mechanisms of action of some of the common dietary interventions used in ALS, discussing their validity in the context of clinical trials.

Flavonoid rich dark cocoa may improve fatigue in people with multiple sclerosis, yet has no effect on glycaemic response: An exploratory trial

CONTEXT

Current research suggests that dark cocoa may reduce fatigue; however, the effect on fatigue in people with MS (pwMS) has never been established. The objective of this feasibility study was to explore the acute effect of high flavonoid cocoa on measures of fatigue and glycaemic response.

METHODS

This was a randomised crossover participant blind exploratory study in 12 participants (2 male and 10 female) with MS-related fatigue (>4 on the Fatigue Severity Scale; FSS). After fasting overnight, participants consumed the high flavonoid cocoa drink (350 mg gallic acid equivalents {GAE}/g) or a low flavonoid cocoa control (120 mg GAE/g), consuming the alternative drink on the next visit. Fatigue was self-reported on a 100 mm visual analogue scale at 30-min time intervals for 2 h post cocoa consumption and every 2 h for the rest of the day. Fatigability was monitored using a 6 min walk test (6MWT) at the end of the visit (2 h), and activity monitors worn for 24 h commencing at 12 noon on the day of testing. The feasibility of performing the trial including outcome measures was documented.

RESULTS

A moderate effect was found in self-reported fatigue throughout the day in favour of the high flavonoid group (Cohen's d 0.32, 95% non-central t CI -0.57 to 1.20). Fatigability measures did not change. Participants consumed and enjoyed the cocoa, all participants completed the study and outcome measures were accepted.

CONCLUSION

The results of this study support further trials to investigate the feasibility and efficacy of pure cocoa as a dietary supplement for fatigue in pwMS.

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations

BACKGROUND

Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner.

OBJECTIVE

This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed.

METHODS

We produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines.

RESULTS

The resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes.

CONCLUSIONS

We present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP.

How to Become a Smart Patient in the Era of Precision Medicine?

The objective of this paper is to define the definition of smart patients, summarize the existing foundation, and explore the approaches and system participation model of how to become a smart patient. Here a thorough review of the literature was conducted to make theory derivation processes of the smart patient; "data, information, knowledge, and wisdom (DIKW) framework" was performed to construct the model of how smart patients participate in the medical process. The smart patient can take an active role and fully participate in their own health management; DIKW system model provides a theoretical framework and practical model of smart patients; patient education is the key to the realization of smart patients. The conclusion is that the smart patient is attainable and he or she is not merely a patient but more importantly a captain and global manager of one's own health management, a partner of medical practitioner, and also a supervisor of medical behavior. Smart patients can actively participate in their healthcare and assume higher levels of responsibility for their own health and wellness which can facilitate the development of precision medicine and its widespread practice.

Estimating the Duration of Public Concern After the Fukushima Dai-ichi Nuclear Power Station Accident From the Occurrence of Radiation Exposure-Related Terms on Twitter: A Retrospective Data Analysis

Background

After the Fukushima Dai-ichi Nuclear Power Station accident in Japan on March 11, 2011, a large number of comments, both positive and negative, were posted on social media.

Objective

The objective of this study was to clarify the characteristics of the trend in the number of tweets posted on Twitter, and to estimate how long public concern regarding the accident continued. We surveyed the attenuation period of the first term occurrence related to radiation exposure as a surrogate endpoint for the duration of concern.

Methods

We retrieved 18,891,284 tweets from Twitter data between March 11, 2011 and March 10, 2012, containing 143 variables in Japanese. We selected radiation, radioactive, Sievert (Sv), Becquerel (Bq), and gray (Gy) as keywords to estimate the attenuation period of public concern regarding radiation exposure. These data, formatted as comma-separated values, were transferred into a Statistical Analysis System (SAS) dataset for analysis, and survival analysis methodology was followed using the SAS LIFETEST procedure. This study was approved by the institutional review board of Hokkaido University and informed consent was waived.

Results

A Kaplan-Meier curve was used to show the rate of Twitter users posting a message after the accident that included one or more of the keywords. The term Sv occurred in tweets up to one year after the first tweet. Among the Twitter users studied, 75.32% (880,108/1,168,542) tweeted the word radioactive and 9.20% (107,522/1,168,542) tweeted the term Sv. The first reduction was observed within the first 7 days after March 11, 2011. The means and standard errors (SEs) of the duration from the first tweet on March 11, 2011 were 31.9 days (SE 0.096) for radioactive and 300.6 days (SE 0.181) for Sv. These keywords were still being used at the end of the study period. The mean attenuation period for radioactive was one month, and approximately one year for radiation and radiation units. The difference in mean duration between the keywords was attributed to the effect of mass media. Regularly posted messages, such as daily radiation dose reports, were relatively easy to detect from their time and formatted contents. The survival estimation indicated that public concern about the nuclear power plant accident remained after one year.

Conclusions

Although the simple plot of the number of tweets did not show clear results, we estimated the mean attenuation period as approximately one month for the keyword radioactive, and found that the keywords were still being used in posts at the end of the study period. Further research is required to quantify the effect of other phrases in social media data. The results of this exploratory study should advance progress in influencing and quantifying the communication of risk.

Meeting the special medicinal needs of patients

The clinical needs of patients can often be met by prescribing and administering a licensed medicine. District nurses will have some cases where a medicine cannot be used because the patient is unable to take it in its licensed form. In such situations, district nurses may be able to arrange for the patient to be supplied with an unlicensed medicine specifically made to meet that patient's special need. This article discusses the options available to district nurses to lawfully supply patients who have a special medication need due to, for example, an allergy to a particular ingredient or an inability to swallow solid oral medicines, such as tablets or capsules.

Challenges in developing drugs for pediatric CNS disorders: A focus on psychopharmacology

Many psychiatric and behavioral disorders manifest in childhood (attention deficit hyperactivity disorder, obsessive compulsive disorder, anxiety, depression, schizophrenia, autism spectrum disorder, etc.) and the opportunity for intervening early may attenuate full development of the disorder and lessen long term disability. Yet, pediatric drug approvals for CNS indications are limited, and pediatric testing generally occurs only after establishing adult efficacy, more as an afterthought rather than with the initial goal of developing the medication for a pediatric CNS indication. With pharmaceutical companies decreasing funding of their neuroscience research divisions overall, the prospects for moving promising investigational drugs forward into pediatrics will only decline. The goal of this review is to highlight important challenges around pediatric drug development for psychiatric disorders, specifically during clinical development, and to present opportunities for filling these gaps, using new strategies for de-risking investigational drugs in new clinical trial designs/models. We will first present the current trends in pediatric drug efficacy testing in academic research and in industry trials, we will then discuss the regulatory landscape of pediatric drug testing, including policies intended to support and encourage more testing. Obstacles that remain will then be presented, followed by new designs, funding opportunities and considerations for testing investigational drugs safely.

Capturing the patient perspective: patient-reported outcomes as clinical trial endpoints

Just as clinical trial design and rigor have evolved with improvements in methods and processes, so too have methods for capturing patient data in clinical trials. Substantial evidence suggests that standard physician reporting of symptoms for which we lack objective diagnostics (e.g., pain) is often discordant with patient self-report. Current reporting using the National Cancer Institute Common Terminology Criteria for Adverse Events (CTC[AE]) for symptom capture relies on a filtering system, from patient to physician to medical record to medical record abstraction to data entry, with each step requiring interpretation and the possibility of error. In contrast, patient-reported outcomes (PROs) eliminate the filter and rely on direct report. Furthermore, the lack of validation and training in use of the CTC(AE) creates an inadequate data capture system. Inadequacies might be observed as underreporting or overreporting symptom prevalence and severity compared with patient self-report. Inaccuracies in symptom reporting can lead to missing important prognostic information, lack of understanding of patient adherence with therapies, and lack of information for patient decision making. They can also lead to opportunities lost in terms of labeling claims and comparative effectiveness analyses. New developments in patient-reported outcome (PRO) reporting, including the PRO-CTC(AE) and models for incorporation of PROs in clinical trials, might facilitate routine PRO reporting complementary to CTC(AE) in clinical trials. In addition, the cadre of validated PRO instruments already in existence allows for more in-depth, hypothesis-driven evaluations. For standard toxicity reporting, the time has come for mandatory routine PRO symptom reporting complementary to the CTC(AE).

A novel web informatics approach for automated surveillance of cancer mortality trends

Cancer surveillance data are collected every year in the United States via the National Program of Cancer Registries (NPCR) and the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute (NCI). General trends are closely monitored to measure the nation's progress against cancer. The objective of this study was to apply a novel web informatics approach for enabling fully automated monitoring of cancer mortality trends. The approach involves automated collection and text mining of online obituaries to derive the age distribution, geospatial, and temporal trends of cancer deaths in the US. Using breast and lung cancer as examples, we mined 23,850 cancer-related and 413,024 general online obituaries spanning the timeframe 2008-2012. There was high correlation between the web-derived mortality trends and the official surveillance statistics reported by NCI with respect to the age distribution (ρ=0.981 for breast; ρ=0.994 for lung), the geospatial distribution (ρ=0.939 for breast; ρ=0.881 for lung), and the annual rates of cancer deaths (ρ=0.661 for breast; ρ=0.839 for lung). Additional experiments investigated the effect of sample size on the consistency of the web-based findings. Overall, our study findings support web informatics as a promising, cost-effective way to dynamically monitor spatiotemporal cancer mortality trends.

The Impact of Online Social Networks on Health and Health Systems: A Scoping Review and Case Studies

Interaction through online social networks potentially results in the contestation of prevailing ideas about health and health care, and to mass protest where health is put at risk or health care provision is wanting. Through a review of the academic literature and case studies of four social networking health sites (PatientsLikeMe, Mumsnet, Treatment Action Campaign, and My Pro Ana), we establish the extent to which this phenomenon is documented, seek evidence of the prevalence and character of health-related networks, and explore their structure, function, participants, and impact, seeking to understand how they came into being and how they sustain themselves. Results indicate mass protest is not arising from these established health-related networking platforms. There is evidence of changes in policy following campaigning activity prompted by experiences shared through social networking such as improved National Health Service care for miscarriage (a Mumsnet campaign). Platform owners and managers have considerable power to shape these campaigns. Social networking is also influencing health policy indirectly through increasing awareness and so demand for health care. Transient social networking about health on platforms such as Twitter were not included as case studies but may be where the most radical or destabilizing influence on health care policy might arise.

The utility of web mining for epidemiological research: studying the association between parity and cancer risk

BACKGROUND

The World Wide Web has emerged as a powerful data source for epidemiological studies related to infectious disease surveillance. However, its potential for cancer-related epidemiological discoveries is largely unexplored.

METHODS

Using advanced web crawling and tailored information extraction procedures, the authors automatically collected and analyzed the text content of 79 394 online obituary articles published between 1998 and 2014. The collected data included 51 911 cancer (27 330 breast; 9470 lung; 6496 pancreatic; 6342 ovarian; 2273 colon) and 27 483 non-cancer cases. With the derived information, the authors replicated a case-control study design to investigate the association between parity (i.e., childbearing) and cancer risk. Age-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for each cancer type and compared to those reported in large-scale epidemiological studies.

RESULTS

Parity was found to be associated with a significantly reduced risk of breast cancer (OR = 0.78, 95% CI, 0.75-0.82), pancreatic cancer (OR = 0.78, 95% CI, 0.72-0.83), colon cancer (OR = 0.67, 95% CI, 0.60-0.74), and ovarian cancer (OR = 0.58, 95% CI, 0.54-0.62). Marginal association was found for lung cancer risk (OR = 0.87, 95% CI, 0.81-0.92). The linear trend between increased parity and reduced cancer risk was dramatically more pronounced for breast and ovarian cancer than the other cancers included in the analysis.

CONCLUSION

This large web-mining study on parity and cancer risk produced findings very similar to those reported with traditional observational studies. It may be used as a promising strategy to generate study hypotheses for guiding and prioritizing future epidemiological studies.

Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review

OBJECTIVE

Research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals' ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions: Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health?Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes?Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = 15), Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n = 10), Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10).

CONCLUSIONS

The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.

Systematic review on the prevalence, frequency and comparative value of adverse events data in social media

AIM

The aim of this review was to summarize the prevalence, frequency and comparative value of information on the adverse events of healthcare interventions from user comments and videos in social media.

METHODS

A systematic review of assessments of the prevalence or type of information on adverse events in social media was undertaken. Sixteen databases and two internet search engines were searched in addition to handsearching, reference checking and contacting experts. The results were sifted independently by two researchers. Data extraction and quality assessment were carried out by one researcher and checked by a second. The quality assessment tool was devised in-house and a narrative synthesis of the results followed.

RESULTS

From 3064 records, 51 studies met the inclusion criteria. The studies assessed over 174 social media sites with discussion forums (71%) being the most popular. The overall prevalence of adverse events reports in social media varied from 0.2% to 8% of posts. Twenty-nine studies compared the results from searching social media with using other data sources to identify adverse events. There was general agreement that a higher frequency of adverse events was found in social media and that this was particularly true for 'symptom' related and 'mild' adverse events. Those adverse events that were under-represented in social media were laboratory-based and serious adverse events.

CONCLUSIONS

Reports of adverse events are identifiable within social media. However, there is considerable heterogeneity in the frequency and type of events reported, and the reliability or validity of the data has not been thoroughly evaluated.

Development of the National Healthy Sleep Awareness Project Sleep Health Surveillance Questions

OBJECTIVES

For the first time ever, as emphasized by inclusion in the Healthy People 2020 goals, sleep health is an emphasis of national health aims. The National Healthy Sleep Awareness Project (NHSAP) was tasked to propose questions for inclusion in the next Behavioral Risk Factor Surveillance System (BRFSS), a survey that includes a number of questions that target behaviors thought to impact health, as a means to measure community sleep health. The total number of questions could not exceed five, and had to include an assessment of the risk for obstructive sleep apnea (OSA).

METHODS

An appointed workgroup met via teleconference and face-to-face venues to develop an inventory of published survey questions being used to identify sleep health, to develop a framework on which to analyze the strengths and weaknesses of current survey questions concerning sleep, and to develop recommendations for sleep health and disease surveillance questions going forward.

RESULTS

The recommendation was to focus on certain existing BRFSS questions pertaining to sleep duration, quality, satisfaction, daytime alertness, and to add to these other BRFSS existing questions to make a modified STOP-BANG questionnaire (minus the N for neck circumference) to assess for risk of OSA.

CONCLUSIONS

Sleep health is an important dimension of health that has previously received less attention in national health surveys. We believe that 5 questions recommended for the upcoming BRFSS question banks will assist as important measures of sleep health, and may help to evaluate the effectiveness of interventions to improve sleep health in our nation.

Mental disorder recovery correlated with centralities and interactions on an online social network

Recent research has established both a theoretical basis and strong empirical evidence that effective social behavior plays a beneficial role in the maintenance of physical and psychological well-being of people. To test whether social behavior and well-being are also associated in online communities, we studied the correlations between the recovery of patients with mental disorders and their behaviors in online social media. As the source of the data related to the social behavior and progress of mental recovery, we used PatientsLikeMe (PLM), the world’s first open-participation research platform for the development of patient-centered health outcome measures. We first constructed an online social network structure based on patient-to-patient ties among 200 patients obtained from PLM. We then characterized patients’ online social activities by measuring the numbers of “posts and views” and “helpful marks” each patient obtained. The patients’ recovery data were obtained from their self-reported status information that was also available on PLM. We found that some node properties (in-degree, eigenvector centrality and PageRank) and the two online social activity measures were significantly correlated with patients’ recovery. Furthermore, we re-collected the patients’ recovery data two months after the first data collection. We found significant correlations between the patients’ social behaviors and the second recovery data, which were collected two months apart. Our results indicated that social interactions in online communities such as PLM were significantly associated with the current and future recoveries of patients with mental disorders.