Online discussion of drug side effects and discontinuation among breast cancer survivors

Dual-target inhibitors based on ERα: Novel therapeutic approaches for endocrine resistant breast cancer

Estrogen receptor alpha (ERα), a nuclear transcription factor, is a well-validated therapeutic target for more than 70% of all breast cancers (BCs). Antagonizing ERα either by selective estrogen receptor modulators (SERMs) or selective estrogen receptor degraders (SERDs) forms the foundation of endocrine therapy and has achieved great success in the treatment of ERα positive (ERα+) BCs. Unfortunately, despite initial effectiveness, endocrine resistance eventually emerges in up to 30% of ERα+ BC patients and remains a significant medical challenge. Several mechanisms implicated in endocrine resistance have been extensively studied, including aberrantly activated growth factor receptors and downstream signaling pathways. Hence, the crosstalk between ERα and another oncogenic signaling has led to surge of interest to develop combination therapies and dual-target single agents. This review briefly introduces the synergisms between ERα and another anticancer target and summarizes the recent advances of ERα-based dual-targeting inhibitors from a medicinal chemistry perspective. Accordingly, their rational design strategies, structure-activity relationships (SARs) and biological activities are also dissected to provide some perspectives on future directions for ERα-based dual target drug discovery in BC therapy.

Priorities for alleviating menopausal symptoms after cancer

OBJECTIVE

To determine treatment priorities in women cancer patients attending a dedicated Menopausal Symptoms After Cancer service.

METHODS

Cancer type and stage were abstracted from medical records. Women ranked up to three symptoms as treatment priorities from the list "hot flushes/night sweats," "mood changes," "vaginal dryness or soreness," "sleep disturbances," "feeling tired or worn out (fatigue)," "sexual problems and/or pain with intercourse," "joint pain," and "something else" with free-text response. For each prioritized symptom, patients completed standardized patient-reported outcome measures to determine symptom severity and impact.

RESULTS

Of 189 patients, most had breast cancer (48.7%, n = 92), followed by hematological (25.8%, n = 49), gynecological (18.0%, n = 34), or colorectal (2.6%, n = 5). The highest (first-ranked) treatment priority was vasomotor symptoms (33.9%, n = 64), followed by fatigue (18.0%, n = 34), vaginal dryness/soreness (9.5%, n = 18), and sexual problems/pain with intercourse (9.5%, n = 18). Symptoms most often selected in the top three ("prioritized") were fatigue (57.7%, n = 109), vasomotor symptoms (57.1%, n = 108), and sleep disturbance (49.2%, n = 93). In patients who prioritized vasomotor symptoms, medians on the "problem," "distress," and "interference" dimensions of the Hot Flash Related Daily Interference Scale were, respectively, 6.0 (interquartile range [IQR], 5.0-8.0), 5.5 (IQR, 3.0-8.0), and 5.0 (IQR, 3.-7.0), indicating moderate severity. In patients who prioritized fatigue, the median Fatigue Scale score was 28 (IQR, 19-36), 37% worse than general population.

CONCLUSIONS

Vasomotor symptoms, fatigue, sexual problems, and vaginal dryness/soreness were the leading priorities for treatment. Understanding symptom severity and patient priorities will inform better care for this growing population.

Sample Bias in Web-Based Patient-Generated Health Data of Dutch Patients With Gastrointestinal Stromal Tumor: Survey Study

BACKGROUND

Increasingly, social media is being recognized as a potential resource for patient-generated health data, for example, for pharmacovigilance. Although the representativeness of the web-based patient population is often noted as a concern, studies in this field are limited.

OBJECTIVE

This study aimed to investigate the sample bias of patient-centered social media in Dutch patients with gastrointestinal stromal tumor (GIST).

METHODS

A population-based survey was conducted in the Netherlands among 328 patients with GIST diagnosed 2-13 years ago to investigate their digital communication use with fellow patients. A logistic regression analysis was used to analyze clinical and demographic differences between forum users and nonusers.

RESULTS

Overall, 17.9% (59/328) of survey respondents reported having contact with fellow patients via social media. Moreover, 78% (46/59) of forum users made use of GIST patient forums. We found no statistically significant differences for age, sex, socioeconomic status, and time since diagnosis between forum users (n=46) and nonusers (n=273). Patient forum users did differ significantly in (self-reported) treatment phase from nonusers (P=.001). Of the 46 forum users, only 2 (4%) were cured and not being monitored; 3 (7%) were on adjuvant, curative treatment; 19 (41%) were being monitored after adjuvant treatment; and 22 (48%) were on palliative treatment. In contrast, of the 273 patients who did not use disease-specific forums to communicate with fellow patients, 56 (20.5%) were cured and not being monitored, 31 (11.3%) were on curative treatment, 139 (50.9%) were being monitored after treatment, and 42 (15.3%) were on palliative treatment. The odds of being on a patient forum were 2.8 times as high for a patient who is being monitored compared with a patient that is considered cured. The odds of being on a patient forum were 1.9 times as high for patients who were on curative (adjuvant) treatment and 10 times as high for patients who were in the palliative phase compared with patients who were considered cured. Forum users also reported a lower level of social functioning (84.8 out of 100) than nonusers (93.8 out of 100; P=.008).

CONCLUSIONS

Forum users showed no particular bias on the most important demographic variables of age, sex, socioeconomic status, and time since diagnosis. This may reflect the narrowing digital divide. Overrepresentation and underrepresentation of patients with GIST in different treatment phases on social media should be taken into account when sourcing patient forums for patient-generated health data. A further investigation of the sample bias in other web-based patient populations is warranted.

Attitudes and Expectations of Clinical Research Participants Toward Digital Health and Mobile Dietary Assessment Tools: Cross-Sectional Survey Study

Background

The adoption of health technologies is key to empower research participants and collect quality data. However, the acceptance of health technologies is usually evaluated in patients or healthcare practitioners, but not in clinical research participants.

Methods

A 27-item online questionnaire was provided to the 11,695 members of a nutrition clinical research participant database from the Nantes area (France), to assess (1) participants' social and demography parameters, (2) equipment and usage of health apps and devices, (3) expectations in research setting and (4) opinion about the future of clinical research. Each item was described using frequency and percentage overall and by age classes. A global proportion comparison was performed using chi-square or Fisher-exact tests.

Results

A total of 1529 respondents (81.0% women, 19.0% men) completed the survey. Main uses of health apps included physical activity tracking (54.7%, age-related group difference, p < 0.001) and food quality assessment (45.7%, unrelated to age groups). Overall, 20.4% of respondents declared owning a connected wristband or watch. Most participants (93.8%) expected the use of connected devices in research. However, protection of personal data (37.5%), reliability (35.5%) and skilled use of devices (28.5%) were perceived as the main barriers. Most participants (93.3%) would agree to track their food intake using a mobile app, and 80.5% would complete it for at least a week while taking part in a clinical study. Only 13.2% would devote more than 10 min per meal to such record. A majority (60.4%) of respondents would accept to share their social media posts in an anonymous way and most (82.2%) of them would accept to interact with a chatbot for research purposes.

Conclusions

Our cross-sectional study suggests that clinical study participants are enthusiastic about all forms of digital health technologies and participant-centered studies but remain concerned about the use of personal data. Repeated assessments are suggested to evaluate the research participant's interest in technologies following the increase in use and demand for innovative health services during the pandemic of COVID-19.

Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning

Social forums offer a lot of new channels for collecting patients’ opinions to construct predictive models of adverse drug reactions (ADRs) for post-marketing surveillance. However, due to the characteristics of social posts, there are many challenges still to be solved when deriving such models, mainly including problems caused by data sparseness, data features with a high-dimensionality, and term diversity in data. To tackle these crucial issues related to identifying ADRs from social posts, we perform data analytics from the perspectives of data balance, feature selection, and feature learning. Meanwhile, we design a comprehensive experimental analysis to investigate the performance of different data processing techniques and data modeling methods. Most importantly, we present a deep learning-based approach that adopts the BERT (Bidirectional Encoder Representations from Transformers) model with a new batch-wise adaptive strategy to enhance the predictive performance. A series of experiments have been conducted to evaluate the machine learning methods with both manual and automated feature engineering processes. The results prove that with their own advantages both types of methods are effective in ADR prediction. In contrast to the traditional machine learning methods, our feature learning approach can automatically achieve the required task to save the manual effort for the large number of experiments.

Applications of quantitative social media listening to patient-centric drug development

Social media listening has been increasingly acknowledged as a tool with applications in many stages of the drug development process. These applications were created to meet the need for patient-centric therapies that are fit-for-purpose and meaningful to patients. Such applications, however, require the leverage of new quantitative approaches and analytical methods that draw from developments in artificial intelligence and real-world data (RWD) analysis. Here, we review the state-of-the-art in quantitative social media listening (QSML) methods applied to drug discovery from the perspective of the pharmaceutical industry.

Information Patients With Melanoma Spontaneously Report About Health-Related Quality of Life on Web-Based Forums: Case Study

Background

There is a general agreement on the importance of health-related quality of life (HRQoL). This type of information is becoming increasingly important for the value assessment of health technology assessment agencies in evaluating the benefits of new health technologies, including medicines. However, HRQoL data are often limited, and additional sources that provide this type of information may be helpful.

Objective

We aim to identify the HRQoL topics important to patients with melanoma based on web-based discussions on public social media forums.

Methods

We identified 3 public web-based forums from the United States and the United Kingdom, namely the Melanoma Patient Information Page, the Melanoma International Forum, and MacMillan. Their posts were randomly selected and coded using qualitative methods until saturation was reached.

Results

Of the posts assessed, 36.7% (150/409) of posts on Melanoma International Forum, 45.1% (198/439) on MacMillan, and 35.4% (128/362) on Melanoma Patient Information Page focused on HRQoL. The 2 themes most frequently mentioned were mental health and (un)certainty. The themes were constructed based on underlying and more detailed codes. Codes related to fear, worry and anxiety, uncertainty, and unfavorable effects were the most-often discussed ones.

Conclusions

Web-based forums are a valuable source for identifying relevant HRQoL aspects in patients with a given disease. These aspects could be cross-referenced with existing tools and they might improve the content validity of patient-reported outcome measures, including HRQoL questionnaires. In addition, web-based forums may provide health technology assessment agencies with a more holistic understanding of the external aspects affecting patient HRQoL. These aspects might support the value assessment of new health technologies and could therefore help inform topic prioritization as well as the scoping phase before any value assessment.

Active neural networks to detect mentions of changes to medication treatment in social media

Objective

We address a first step toward using social media data to supplement current efforts in monitoring population-level medication nonadherence: detecting changes to medication treatment. Medication treatment changes, like changes to dosage or to frequency of intake, that are not overseen by physicians are, by that, nonadherence to medication. Despite the consequences, including worsening health conditions or death, 50% of patients are estimated to not take medications as indicated. Current methods to identify nonadherence have major limitations. Direct observation may be intrusive or expensive, and indirect observation through patient surveys relies heavily on patients’ memory and candor. Using social media data in these studies may address these limitations.

Methods

We annotated 9830 tweets mentioning medications and trained a convolutional neural network (CNN) to find mentions of medication treatment changes, regardless of whether the change was recommended by a physician. We used active and transfer learning from 12 972 reviews we annotated from WebMD to address the class imbalance of our Twitter corpus. To validate our CNN and explore future directions, we annotated 1956 positive tweets as to whether they reflect nonadherence and categorized the reasons given.

Results

Our CNN achieved 0.50 F₁-score on this new corpus. The manual analysis of positive tweets revealed that nonadherence is evident in a subset with 9 categories of reasons for nonadherence.

Conclusion

We showed that social media users publicly discuss medication treatment changes and may explain their reasons including when it constitutes nonadherence. This approach may be useful to supplement current efforts in adherence monitoring.

The impact of medication side effects on adherence and persistence to hormone therapy in breast cancer survivors: A qualitative systematic review and thematic synthesis

BACKGROUND

Hormone Therapy (HT) reduces the risk of breast cancer recurrence and mortality in women with breast cancer. Despite these clinical benefits, rates of HT non-adherence and non-persistence are high. Research suggests this may be due to the impact of HT side effects. However, little research has explored the individual contribution of side effects to non-adherence and non-persistence behaviours, thereby hindering the implementation of targeted intervention strategies. Our aim is to review the published literature on breast cancer survivors' lived experiences of HT side effects and explore how these may be related to non-adherence and non-persistence behaviour.

METHODS

Electronic searches were conducted from inception to May 2020, utilising Cochrane CENTRAL, Medline, Embase, Web of Science and PsycINFO databases. Searches included a combination of terms related to breast cancer, adherence, hormone therapy and side effects.

RESULTS

Sixteen eligible papers were identified, and study quality was high. Data were thematically synthesised into four analytical themes, which encompassed 13 descriptive sub-themes: 'Daily impact of side-effects', 'Role of Health Care Professionals', 'Managing HT side-effects', and 'Weighing up the pros and cons'.

CONCLUSIONS

HT side effects significantly impact breast cancer survivor's quality of life. A lack of support from healthcare providers leads to self-management strategies, which negatively affects adherence and persistence behaviour.

Therapeutic potential of thymoquinone in combination therapy against cancer and cancer stem cells

The long-term success of standard anticancer monotherapeutic strategies has been hampered by intolerable side effects, resistance to treatment and cancer relapse. These monotherapeutic strategies shrink the tumor bulk but do not effectively eliminate the population of self-renewing cancer stem cells (CSCs) that are normally present within the tumor. These surviving CSCs develop mechanisms of resistance to treatment and refuel the tumor, thus causing cancer relapse. To ensure durable tumor control, research has moved away from adopting the monotreatment paradigm towards developing and using combination therapy. Combining different therapeutic modalities has demonstrated significant therapeutic outcomes by strengthening the anti-tumor potential of monotreatment against cancer and cancer stem cells, mitigating their toxic adverse effects, and ultimately overcoming resistance. Recently, there has been growing interest in combining natural products from different sources or with clinically used chemotherapeutics to further improve treatment efficacy and tolerability. Thymoquinone (TQ), the main bioactive constituent of Nigella sativa, has gained great attention in combination therapy research after demonstrating its low toxicity to normal cells and remarkable anticancer efficacy in extensive preclinical studies in addition to its ability to target chemoresistant CSCs. Here, we provide an overview of the therapeutic responses resulting from combining TQ with conventional therapeutic agents such as alkylating agents, antimetabolites and antimicrotubules as well as with topoisomerase inhibitors and non-coding RNA. We also review data on anticancer effects of TQ when combined with ionizing radiation and several natural products such as vitamin D3, melatonin and other compounds derived from Chinese medicinal plants. The focus of this review is on two outcomes of TQ combination therapy, namely eradicating CSCs and treating various types of cancers. In conclusion, the ability of TQ to potentiate the anticancer activity of many chemotherapeutic agents and sensitize cancer cells to radiotherapy makes it a promising molecule that could be used in combination therapy to overcome resistance to standard chemotherapeutic agents and reduce their associated toxicities.

Extending Adjuvant Endocrine Therapy for 10 Years: A Mixed-Methods Analysis of Women's Decision Making in an Online Breast Cancer Forum

An additional 5 years of treatment with adjuvant hormonal therapy, to complete 10 years of medication, is recommended to reduce the risk of breast cancer recurrence. Yet professionals and patients should balance this benefit against side effects and toxicities. Little is known about women’s decision making regarding persistence with extended endocrine therapy. In this study, we collected data from a UK online breast cancer forum to analyse patterns of persistence and its associated factors. A mixed-methods exploratory sequential design was used, with a qualitative analysis of text (n = 61 individuals) informing the development of a quantitative instrument to statistically analyse the prevalence of the findings (n = 130). Our findings identified three different groups of women who had to make decisions regarding persistence with treatment: those about to complete 5 years of therapy, those who decided to extend treatment, and those who were initially prescribed 10 years. Factors affecting persistence were, lack of self-efficacy in managing side effects, lack of reassurance about individual risk of recurrence, and impact on quality of life. Interventions such as training of healthcare professionals including risk communication, medication reviews by clinical pharmacists, and re-planning of services in follow-up care, should better support women’s needs in extended hormonal therapy.

Mapping and Modeling of Discussions Related to Gastrointestinal Discomfort in French-Speaking Online Forums: Results of a 15-Year Retrospective Infodemiology Study

BACKGROUND

Gastrointestinal (GI) discomfort is prevalent and known to be associated with impaired quality of life. Real-world information on factors of GI discomfort and solutions used by people is, however, limited. Social media, including online forums, have been considered a new source of information to examine the health of populations in real-life settings.

OBJECTIVE

The aims of this retrospective infodemiology study are to identify discussion topics, characterize users, and identify perceived determinants of GI discomfort in web-based messages posted by users of French social media.

METHODS

Messages related to GI discomfort posted between January 2003 and August 2018 were extracted from 14 French-speaking general and specialized publicly available online forums. Extracted messages were cleaned and deidentified. Relevant medical concepts were determined on the basis of the Medical Dictionary for Regulatory Activities and vernacular terms. The identification of discussion topics was carried out by using a correlated topic model on the basis of the latent Dirichlet allocation. A nonsupervised clustering algorithm was applied to cluster forum users according to the reported symptoms of GI discomfort, discussion topics, and activity on online forums. Users' age and gender were determined by linear regression and application of a support vector machine, respectively, to characterize the identified clusters according to demographic parameters. Perceived factors of GI discomfort were classified by a combined method on the basis of syntactic analysis to identify messages with causality terms and a second topic modeling in a relevant segment of phrases.

RESULTS

A total of 198,866 messages associated with GI discomfort were included in the analysis corpus after extraction and cleaning. These messages were posted by 36,989 separate web users, most of them being women younger than 40 years. Everyday life, diet, digestion, abdominal pain, impact on the quality of life, and tips to manage stress were among the most discussed topics. Segmentation of users identified 5 clusters corresponding to chronic and acute GI concerns. Diet topic was associated with each cluster, and stress was strongly associated with abdominal pain. Psychological factors, food, and allergens were perceived as the main causes of GI discomfort by web users.

CONCLUSIONS

GI discomfort is actively discussed by web users. This study reveals a complex relationship between food, stress, and GI discomfort. Our approach has shown that identifying web-based discussion topics associated with GI discomfort and its perceived factors is feasible and can serve as a complementary source of real-world evidence for caregivers.

A Comparative View of Reported Adverse Effects of Statins in Social Media, Regulatory Data, Drug Information Databases and Systematic Reviews

INTRODUCTION

There are few studies assessing how data on adverse drug events from consumers on social media compare with other sources.

AIM

The aim of this study was to assess the consistency of adverse event data of statin medications from social media as compared with other sources.

METHODS

We collected data on the adverse events of statins from Twitter, the US FDA Adverse Event Reporting System (FAERS), the UK Medicines and Healthcare products Regulatory Agency (MHRA), drug information databases (DIDs) and systematic reviews. We manually annotated 12,649 tweets collected between June 2013 and August 2018. We collected 45,447 reports from FAERS, 10,415 from MHRA, identified 17 systematic reviews with relevant data and extracted data from Facts and Comparisons^® and Clinical Pharmacology^®. We compared the proportion, relative frequencies and rank of each category of adverse event from each source using MedDRA^® primary System Organ Class codes.

RESULTS

Compared with other sources, patients on social media are proportionally far more likely to complain about musculoskeletal symptoms than other adverse events. Most adverse events showed a high level of agreement between Twitter and regulatory data. DIDs tend to demonstrate similar patterns but not as strongly. Systematic reviews tend to examine pre-specified adverse events or those reported by trial investigators.

CONCLUSIONS

Combining the data from multiple sources, albeit challenging, may provide a broader safety profile of any medication. Systematically collected social media reports may be able to contribute information on the most pertinent adverse effects to patients.

Exploring the Health-Related Quality of Life of Patients Treated With Immune Checkpoint Inhibitors: Social Media Study

BACKGROUND

Immune checkpoint inhibitors (ICIs) are increasingly used to treat several types of tumors. Impact of this emerging therapy on patients' health-related quality of life (HRQoL) is usually collected in clinical trials through standard questionnaires. However, this might not fully reflect HRQoL of patients under real-world conditions. In parallel, users' narratives from social media represent a potential new source of research concerning HRQoL.

OBJECTIVE

The aim of this study is to assess and compare coverage of ICI-treated patients' HRQoL domains and subdomains in standard questionnaires from clinical trials and in real-world setting from social media posts.

METHODS

A retrospective study was carried out by collecting social media posts in French language written by internet users mentioning their experiences with ICIs between January 2011 and August 2018. Automatic and manual extractions were implemented to create a corpus where domains and subdomains of HRQoL were classified. These annotations were compared with domains covered by 2 standard HRQoL questionnaires, the EORTC QLQ-C30 and the FACT-G.

RESULTS

We identified 150 users who described their own experience with ICI (89/150, 59.3%) or that of their relative (61/150, 40.7%), with 137 users (91.3%) reporting at least one HRQoL domain in their social media posts. A total of 8 domains and 42 subdomains of HRQoL were identified: Global health (1 subdomain; 115 patients), Symptoms (13; 76), Emotional state (10; 49), Role (7; 22), Physical activity (4; 13), Professional situation (3; 9), Cognitive state (2; 2), and Social state (2; 2). The QLQ-C30 showed a wider global coverage of social media HRQoL subdomains than the FACT-G, 45% (19/42) and 29% (12/42), respectively. For both QLQ-C30 and FACT-G questionnaires, coverage rates were particularly suboptimal for Symptoms (68/123, 55.3% and 72/123, 58.5%, respectively), Emotional state (7/49, 14% and 24/49, 49%, respectively), and Role (17/22, 77% and 15/22, 68%, respectively).

CONCLUSIONS

Many patients with cancer are using social media to share their experiences with immunotherapy. Collecting and analyzing their spontaneous narratives are helpful to capture and understand their HRQoL in real-world setting. New measures of HRQoL are needed to provide more in-depth evaluation of Symptoms, Emotional state, and Role among patients with cancer treated with immunotherapy.

Topic Modeling and User Network Analysis on Twitter during World Lupus Awareness Day

Twitter is increasingly used by individuals and organizations to broadcast their feelings and practices, providing access to samples of spontaneously expressed opinions on all sorts of themes. Social media offers an additional source of data to unlock information supporting new insights disclosures, particularly for public health purposes. Systemic lupus erythematosus (SLE) is a complex, systemic autoimmune disease that remains a major challenge in therapeutic diagnostic and treatment management. When supporting patients with such a complex disease, sharing information through social media can play an important role in creating better healthcare services. This study explores the nature of topics posted by users and organizations on Twitter during world Lupus day to extract latent topics that occur in tweet texts and to identify what information is most commonly discussed among users. We identified online influencers and opinion leaders who discussed different topics. During this analysis, we found two different types of influencers that employed different narratives about the communities they belong to. Therefore, this study identifies hidden information for healthcare decision-makers and provides a detailed model of the implications for healthcare organizations to detect, understand, and define hidden content behind large collections of text.

Pharmacokinetic and pharmacodynamic considerations in developing a response to the opioid epidemic

Introduction: Opioids continue to be used widely for pain management. Widespread availability of prescription opioids has led to opioid abuse and addiction. Besides steps to reduce inappropriate prescribing, exploiting opioid pharmacology to make their use safer is important.Areas covered: This article discusses the pathology and factors underlying opioid abuse. Pharmacokinetic and pharmacodynamic properties affecting abuse liability of commonly abused opioids have been highlighted. These properties inform the development of ideal abuse deterrent products. Mechanisms and cost-effectiveness of available abuse deterrent products have been reviewed in addition to the pharmacology of medications used to treat addiction.Expert opinion: The opioid crisis presents unique challenges to managing pain effectively given the limited repertoire of strong analgesics. The 5-point strategy to combat the opioid crisis calls for better preventive, treatment, and recovery services, better data, better pain management, better availability of overdose-reversing drugs and better research. There is an urgent need to decrease the cost of abuse deterrent opioids which deters their cost-effectiveness. In addition, discovery of novel analgesics, further insight into central and peripheral pain mechanisms, understanding genomic risk profiles for efficient targeted efforts, and education will be key to winning this fight against the opioid crisis.

Breast cancer patients' experiences of adherence and persistence to oral endocrine therapy: A qualitative evidence synthesis

PURPOSE

Adjuvant endocrine therapy (AET) significantly reduces the risk of breast cancer recurrence and mortality in women with hormone receptor (HR+) breast cancer. Despite the documented survival benefits with AET, non-adherence and non-persistence remains a significant problem. This systematic review of qualitative research aimed to synthesise breast cancer patients' experiences of adherence and persistence to oral endocrine therapy.

METHODS

The ENTREQ guidelines were followed. A systematic search strategy was performed across eleven electronic databases (Embase, Cinahl, Pubmed, Psychinfo, Proquest, Lenus, Scopus, Web of Science, Rian.ie, EThOS e-theses online, DART Europe). Thomas and Harden's three-stage approach to thematic analysis was undertaken on the findings of all included studies. Confidence in the findings were reviewed using GRADE-CERQual.

RESULTS

Twenty-four qualitative studies were included in the synthesis. Three analytic themes were identified (We don't have an option; the side effects are worse than the disease; help us with information and support). Adherence was often driven by women feeling they had no option and a fear of cancer recurrence. Persistence was helped with support and information. Non-adherence and non-persistence were associated with debilitating side effects, inadequate information and lack of support.

CONCLUSIONS

Adherence and persistence to AET was often suboptimal among breast cancer patients. Women commonly felt isolated and neglected as a result of insufficient information and support from healthcare professionals. If women are to persist with AET, primary care providers should be aware of the facilitators and barriers to adherence, and they should be knowledgeable in symptom management strategies.

Genetic Variants Associated with Cancer Pain and Response to Opioid Analgesics: Implications for Precision Pain Management

OBJECTIVE

To review the current knowledge on the association of genetic variants with cancer pain.

DATA SOURCES

Data-based publications and review articles retrieved from PubMed, CINAHL, and Web of Science, as well as an additional search in Google Scholar.

CONCLUSION

Genetic variability can influence differential pain perception and response to opioids in cancer patients, which will have implications in the optimal personalized treatment of cancer pain. More studies are warranted to replicate findings.

IMPLICATIONS FOR NURSING PRACTICE

Nurses are poised to educate patients on biomarker testing and interpretation and to use precision pain management strategies based on this information.

Nanoparticle-mediated targeted drug delivery for breast cancer treatment

Breast cancer (BC) is the most common malignancy in women worldwide, and one of the deadliest after lung cancer. Currently, standard methods for cancer therapy including BC are surgery followed by chemotherapy or radiotherapy. However, both chemotherapy and radiotherapy often fail to treat BC due to the side effects that these therapies incur in normal tissues and organs. In recent years, various nanoparticles (NPs) have been discovered and synthesized to be able to selectively target tumor cells without causing any harm to the healthy cells or organs. Therefore, NPs-mediated targeted drug delivery systems (DDS) have become a promising technique to treat BC. In addition to their selectivity to target tumor cells and reduce side effects, NPs have other unique properties which make them desirable for cancer treatment such as low toxicity, good compatibility, ease of preparation, high photoluminescence (PL) for bioimaging in vivo, and high loadability of drugs due to their tunable surface functionalities. In this study, we summarize with a critical analysis of the most recent therapeutic studies involving various NPs-mediated DDS as alternatives for the traditional treatment approaches for BC. It will shed light on the significance of NPs-mediated DDS and serve as a guide to seeking for the ideal methodology for future targeted drug delivery for an efficient BC treatment.

Social media as a tool for assessing patient perspectives on quality of life in metastatic melanoma: a feasibility study

Purpose

Development of innovative drugs for melanoma is occurring rapidly. Incremental gains in overall survival amongst innovative products may be difficult to measure in clinical trials, and their use may be associated with increased toxicity profiles. Therefore, HTA agencies increasingly require information on HRQoL for the assessment of such drugs. This study explored the feasibility of social media to assess patient perspectives on HRQoL in melanoma, and whether current cancer- and melanoma-specific HRQoL questionnaires represent these perspectives.

Methods

A survey was distributed on the social media channels of Melanoma Patient Network Europe to assess melanoma patients’ perspectives regarding HRQoL. Two researchers independently conducted content analysis to identify key themes, which were subsequently compared to questions from one current cancer-specific and two melanoma-specific HRQoL questionnaires (i.e. EORTC QLQ-C30, EORTC QLQ-MEL38, FACT-M).

Results

In total, 72 patients and 17 carers completed the survey. Patients indicated that family, having a normal life, and enjoying life were the three most important aspects of HRQoL for them. Carers indicated that being capable, having manageable adverse events, and being pain-free were the three most important aspects of HRQoL for patients. Respondents seem to find some questions from HRQoL questionnaires relevant (e.g. ‘Have you felt able to carry on with things as normal?’) and others less relevant (e.g. ‘Have you had swelling near your melanoma site?’). Additionally, wording may differ between patients and HRQoL questionnaires, whereby patients generally use a more positive tone.

Conclusions

Social media may provide a valuable tool in assessing patient perspectives regarding HRQoL. However, differences seem to emerge between patient and carer perspectives. Additionally, patient perspectives did not seem to fully correlate to questions posed in cancer- (i.e. EORTC QLQ-C30) and melanoma-specific (i.e. EORTC QLQ-MEL38, FACT-M) HRQoL questionnaires examined.

Electronic supplementary material

The online version of this article (10.1186/s12955-018-1047-z) contains supplementary material, which is available to authorized users.

Authenticity and credibility aware detection of adverse drug events from social media

OBJECTIVES

Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media.

METHODS

Detecting ADEs whose drug and AE may be reported in different posts of a user leads to major concerns regarding the content authenticity and user credibility, which have not been addressed in previous studies. Content authenticity concerns whether a post mentions drugs or adverse events that are actually consumed or experienced by the writer. User credibility indicates the degree to which chronological evidence from a user's sequence of posts should be trusted in the ADE detection. We propose AC-SPASM, a Bayesian model for the authenticity and credibility aware detection of ADEs from social media. The model exploits the interaction between content authenticity, user credibility and ADE signal quality. In particular, we argue that the credibility of a user correlates with the user's consistency in reporting authentic content.

RESULTS

We conduct experiments on a real-world Twitter dataset containing 1.2 million posts from 13,178 users. Our benchmark set contains 22 drugs and 8089 AEs. AC-SPASM recognizes authentic posts with F₁ - the harmonic mean of precision and recall of 80%, and estimates user credibility with precision@10 = 90% and NDCG@10 - a measure for top-10 ranking quality of 96%. Upon validation against known ADEs, AC-SPASM achieves F₁ = 91%, outperforming state-of-the-art baseline models by 32% (p < 0.05). Also, AC-SPASM obtains precision@456 = 73% and NDCG@456 = 94% in detecting and prioritizing unknown potential ADE signals for further investigation. Furthermore, the results show that AC-SPASM is scalable to large datasets.

CONCLUSIONS

Our study demonstrates that taking into account the content authenticity and user credibility improves the detection of ADEs from social media. Our work generates hypotheses to reduce experts' guesswork in identifying unknown potential ADEs.

Authenticity and credibility aware detection of adverse drug events from social media

OBJECTIVES

Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media.

METHODS

Detecting ADEs whose drug and AE may be reported in different posts of a user leads to major concerns regarding the content authenticity and user credibility, which have not been addressed in previous studies. Content authenticity concerns whether a post mentions drugs or adverse events that are actually consumed or experienced by the writer. User credibility indicates the degree to which chronological evidence from a user's sequence of posts should be trusted in the ADE detection. We propose AC-SPASM, a Bayesian model for the authenticity and credibility aware detection of ADEs from social media. The model exploits the interaction between content authenticity, user credibility and ADE signal quality. In particular, we argue that the credibility of a user correlates with the user's consistency in reporting authentic content.

RESULTS

We conduct experiments on a real-world Twitter dataset containing 1.2 million posts from 13,178 users. Our benchmark set contains 22 drugs and 8089 AEs. AC-SPASM recognizes authentic posts with F₁ - the harmonic mean of precision and recall of 80%, and estimates user credibility with precision@10 = 90% and NDCG@10 - a measure for top-10 ranking quality of 96%. Upon validation against known ADEs, AC-SPASM achieves F₁ = 91%, outperforming state-of-the-art baseline models by 32% (p < 0.05). Also, AC-SPASM obtains precision@456 = 73% and NDCG@456 = 94% in detecting and prioritizing unknown potential ADE signals for further investigation. Furthermore, the results show that AC-SPASM is scalable to large datasets.

CONCLUSIONS

Our study demonstrates that taking into account the content authenticity and user credibility improves the detection of ADEs from social media. Our work generates hypotheses to reduce experts' guesswork in identifying unknown potential ADEs.

Oral Endocrine Therapy Nonadherence, Adverse Effects, Decisional Support, and Decisional Needs in Women With Breast Cancer

BACKGROUND

Oral endocrine therapy (OET) such as tamoxifen or aromatase inhibitors reduces recurrence and mortality for the 75% of breast cancer survivors (BCSs) with a diagnosis of estrogen receptor-positive breast cancer. Because many BCSs decide not take OET as recommended because of adverse effects, understanding BCSs' decisional supports and needs is foundational to supporting quality OET decision making about whether to adhere to OET.

OBJECTIVE

The aim of this study was to examine literature pertaining to OET nonadherence and adverse effects using the Ottawa Decision Support Framework categories of decisional supports and decisional needs because these factors potentially influence OET use.

METHODS

A systematic literature search was performed in PubMed and CINAHL using combined search terms "aromatase inhibitors and adherence" and "tamoxifen and adherence." Studies that did not meet criteria were excluded. Relevant data from 25 publications were extracted into tables and reviewed by 2 authors.

RESULTS

Findings identified the impact of adverse effects on OET nonadherence, an absence of decisional supports provided to or available for BCSs who are experiencing OET adverse effects, and the likelihood of unmet decisional needs related to OET.

CONCLUSIONS

Adverse effects contribute to BCSs decisions to stop OET, yet there has been little investigation of the process through which that occurs. This review serves as a call to action for providers to provide support to BCSs experiencing OET adverse effects and facing decisions related to nonadherence.

IMPLICATIONS FOR PRACTICE

Findings suggest BCSs prescribed OET have unmet decisional needs, and more decisional supports are needed for BCSs experiencing OET adverse effects.

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks

BACKGROUND

Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and pharmacovigilance. A number of machine learning methods have been proposed for chemical-induced disease (CID) extraction due to some publicly available annotated corpora. Most of them suffer from time-consuming feature engineering except deep learning methods. In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction.

RESULTS

Experimental results on a benchmark dataset, the CDR (Chemical-induced Disease Relation) dataset of the BioCreative V challenge for CID extraction show that the highest precision, recall and F-score of our RPCNN-based CID extraction system are 65.24, 77.21 and 70.77%, which is competitive with other state-of-the-art systems.

CONCLUSIONS

A novel deep learning method is proposed for document-level CID extraction, where domain knowledge, piecewise strategy, attention mechanism, and multi-instance learning are combined together. The effectiveness of the method is proved by experiments conducted on a benchmark dataset.

A conceptual model of social networks and mechanisms of cancer mortality, and potential strategies to improve survival

Women with larger personal social networks have better breast cancer survival and a lower risk of mortality. However, little work has examined the mechanisms through which social networks influence breast cancer outcomes and cancer outcomes more generally, potentially limiting the development of feasible, clinically effective interventions. In fact, much of the emphasis in cancer research regarding the influence of social relationships on cancer outcomes has focused on the benefits of the provision of social support to patients, especially through peer support groups, and only more recently through patient navigation. Though critically important, there are other ways through which social relationships might influence outcomes, around which interventions might be developed. In addition to social support, these include social resources, social norms, social contagion, social roles, and social burdens and obligations. This narrative review addresses how social networks may influence cancer outcomes and discusses potential strategies for improving outcomes given these relationships. The paper (a) describes background and limitations of previous research, (b) outlines terms and provides a conceptual model that describes interrelationships between social networks and relevant variables and their hypothesized influence on cancer outcomes, (c) clarifies social and psychosocial mechanisms through which social networks affect downstream factors, (d) describes downstream behavioral, treatment, and physiological factors through which these subsequently influence recurrence and mortality, and (e) describes needed research and potential opportunities to enhance translation. Though most literature in this area pertains to breast cancer, this review has substantial relevance for cancer outcomes generally. Further clarification and research regarding potential mechanisms are needed to translate epidemiological findings on social networks into clinical and community strategies to improve cancer outcomes.

Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review

BACKGROUND

A scoping review to characterize the literature on the use of conversations in social media as a potential source of data for detecting adverse events (AEs) related to health products.

METHODS

Our specific research questions were (1) What social media listening platforms exist to detect adverse events related to health products, and what are their capabilities and characteristics? (2) What is the validity and reliability of data from social media for detecting these adverse events? MEDLINE, EMBASE, Cochrane Library, and relevant websites were searched from inception to May 2016. Any type of document (e.g., manuscripts, reports) that described the use of social media data for detecting health product AEs was included. Two reviewers independently screened citations and full-texts, and one reviewer and one verifier performed data abstraction. Descriptive synthesis was conducted.

RESULTS

After screening 3631 citations and 321 full-texts, 70 unique documents with 7 companion reports available from 2001 to 2016 were included. Forty-six documents (66%) described an automated or semi-automated information extraction system to detect health product AEs from social media conversations (in the developmental phase). Seven pre-existing information extraction systems to mine social media data were identified in eight documents. Nineteen documents compared AEs reported in social media data with validated data and found consistent AE discovery in all except two documents. None of the documents reported the validity and reliability of the overall system, but some reported on the performance of individual steps in processing the data. The validity and reliability results were found for the following steps in the data processing pipeline: data de-identification (n = 1), concept identification (n = 3), concept normalization (n = 2), and relation extraction (n = 8). The methods varied widely, and some approaches yielded better results than others.

CONCLUSIONS

Our results suggest that the use of social media conversations for pharmacovigilance is in its infancy. Although social media data has the potential to supplement data from regulatory agency databases; is able to capture less frequently reported AEs; and can identify AEs earlier than official alerts or regulatory changes, the utility and validity of the data source remains under-studied.

TRIAL REGISTRATION

Open Science Framework ( https://osf.io/kv9hu/ ).

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.

Descriptions of Adverse Drug Reactions Are Less Informative in Forums Than in the French Pharmacovigilance Database but Provide More Unexpected Reactions

Background: Social media have drawn attention for their potential use in Pharmacovigilance. Recent work showed that it is possible to extract information concerning adverse drug reactions (ADRs) from posts in social media. The main objective of the Vigi4MED project was to evaluate the relevance and quality of the information shared by patients on web forums about drug safety and its potential utility for pharmacovigilance.

Methods: After selecting websites of interest, we manually evaluated the relevance of the content of posts for pharmacovigilance related to six drugs (agomelatine, baclofen, duloxetine, exenatide, strontium ranelate, and tetrazepam). We compared forums to the French Pharmacovigilance Database (FPVD) to (1) evaluate whether they contained relevant information to characterize a pharmacovigilance case report (patient’s age and sex; treatment indication, dose and duration; time-to-onset (TTO) and outcome of the ADR, and drug dechallenge and rechallenge) and (2) perform impact analysis (nature, seriousness, unexpectedness, and outcome of the ADR).

Results: The cases in the FPVD were significantly more informative than posts in forums for patient description (age, sex), treatment description (dose, duration, TTO), and outcome of the ADR, but the indication for the treatment was more often found in forums. Cases were more often serious in the FPVD than in forums (46% vs. 4%), but forums more often contained an unexpected ADR than the FPVD (24% vs. 17%). Moreover, 197 unexpected ADRs identified in forums were absent from the FPVD and the distribution of the MedDRA System Organ Classes (SOCs) was different between the two data sources.

Discussion: This study is the first to evaluate if patients’ posts may qualify as potential and informative case reports that should be stored in a pharmacovigilance database in the same way as case reports submitted by health professionals. The posts were less informative (except for the indication) and focused on less serious ADRs than the FPVD cases, but more unexpected ADRs were presented in forums than in the FPVD and their SOCs were different. Thus, web forums should be considered as a secondary, but complementary source for pharmacovigilance.

Efficacy of a biobehavioral intervention for hot flashes: a randomized controlled pilot study

OBJECTIVE

The need for effective nonhormonal treatments for hot flash management without unwanted side effects continues. The primary aim of this pilot study was to evaluate the effect of combining a nonhormonal pharmacologic agent with a behavioral treatment for hot flash reduction.

METHODS

Seventy-one postmenopausal women were randomized to one of four groups: venlafaxine 75 mg + hypnosis (VH) versus venlafaxine 75 mg + sham hypnosis (VSH) versus a placebo pill + hypnosis (PH) versus placebo pill + sham hypnosis (PSH). Women recorded hot flash severity and frequency in a daily diary, in real time. The intrapatient difference in hot flash score (frequency × severity) at 8 weeks was analyzed using a General Estimating Equation model, using VSH as the referent arm, controlling for baseline hot flashes.

RESULTS

The active arms including PH or VH were not statistically significantly different than VSH (P = 0.34, P = 0.05, respectively). Women in each active arm reported hot flash reductions of about 50%, with the PSH group reporting a 25% reduction. Women receiving the PSH reported statistically significantly smaller reductions in hot flash score than women in the referent VSH arm (P = 0.001). There were no significant negative side effects during the course of the study.

CONCLUSIONS

Hypnosis alone reduced hot flashes equal to venlafaxine alone, but the combination of hypnosis and venlafaxine did not reduce hot flashes more than either treatment alone. More research is needed to clarify whether combining hypnosis with a different antidepressant would provide synergistic benefits.

Talking About My Care: Detecting Mentions of Hormonal Therapy Adherence Behavior in an Online Breast Cancer Community

Hormonal therapy adherence is challenging for many patients with hormone-receptor-positive breast cancer. Gaining intuition into their adherence behavior would assist in improving outcomes by pinpointing, and eventually addressing, why patients fail to adhere. While traditional adherence studies rely on survey-based methods or electronic medical records, online health communities provide a supplemental data source to learn about such behavior and often on a much larger scale. In this paper, we focus on an online breast cancer discussion forum and propose a framework to automatically extract hormonal therapy adherence behavior (HTAB) mentions. The framework compares medical term usage when describing when a patient is taking hormonal therapy medication and interrupting their treatment (e.g., stop/pause taking medication). We show that by using shallow neural networks, in the form of wordlvec, the learned features can be applied to build efficient HTAB mention classifiers. Through medical term comparison, we find that patients who exhibit an interruption behavior are more likely to mention depression and their care providers, while patients with continuation behavior are more likely to mention common side effects (e.g., hot flashes, nausea and osteoporosis), vitamins and exercise.

Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach

Background

Medication nonadherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying noncompliance to treatment may help health professionals to address it. Patients use peer-to-peer virtual communities and social media to share their experiences regarding their treatments and diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of noncompliance.

Objective

The aim of this study was to detect messages describing patients’ noncompliant behaviors associated with a drug of interest. Thus, the objective was the clustering of posts featuring a homogeneous vocabulary related to nonadherent attitudes.

Methods

We focused on escitalopram and aripiprazole used to treat depression and psychotic conditions, respectively. We implemented a probabilistic topic model to identify the topics that occurred in a corpus of messages mentioning these drugs, posted from 2004 to 2013 on three of the most popular French forums. Data were collected using a Web crawler designed by Kappa Santé as part of the Detec’t project to analyze social media for drug safety. Several topics were related to noncompliance to treatment.

Results

Starting from a corpus of 3650 posts related to an antidepressant drug (escitalopram) and 2164 posts related to an antipsychotic drug (aripiprazole), the use of latent Dirichlet allocation allowed us to model several themes, including interruptions of treatment and changes in dosage.

The topic model approach detected cases of noncompliance behaviors with a recall of 98.5% (272/276) and a precision of 32.6% (272/844).

Conclusions

Topic models enabled us to explore patients’ discussions on community websites and to identify posts related with noncompliant behaviors. After a manual review of the messages in the noncompliance topics, we found that noncompliance to treatment was present in 6.17% (276/4469) of the posts.

HARNESSING SOCIAL MEDIA FOR HEALTH INFORMATION MANAGEMENT

The remarkable upsurge of social media has dramatic impacts on health care research and practice in the past decade. Social media are reshaping health information management in a variety of ways, ranging from providing cost-effective ways to improve clinician-patient communication and exchange health-related information and experience, to enabling the discovery of new medical knowledge and information. Despite some demonstrated initial success, social media use and analytics for improving health as a research field is still at its infancy. Information systems researchers can potentially play a key role in advancing the field. This study proposes a conceptual framework for social media-based health information management by drawing on multi-disciplinary research. With the guidance of the framework, this research presents related research challenges, identifies important yet under-explored research issues, and discusses promising directions for future research.

Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts

INTRODUCTION

The rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data.

OBJECTIVE

Our objective was to examine whether specific product-adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS).

METHODS

A retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug-event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product-event pair were compiled. Automated classifiers were used to identify each 'post with resemblance to an adverse event' (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms. Drug safety physicians conducted a manual review to determine causality using World Health Organization-Uppsala Monitoring Centre (WHO-UMC) assessment criteria. Cases were also compared with those reported in FAERS.

FINDINGS

A total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product-event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product-event associations: dronedarone-vasculitis and Banana Boat Sunscreen--skin burns. No product-event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS.

CONCLUSIONS

An efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.

How often people google for vaccination: Qualitative and quantitative insights from a systematic search of the web-based activities using Google Trends

Nowadays, more and more people surf the Internet seeking health-related information. Information and communication technologies (ICTs) can represent an important opportunities in the field of Public Health and vaccinology. The aim of our current research was to investigate a) how often people search the Internet for vaccination-related information, b) if this search is spontaneous or induced by media, and c) which kind of information is in particular searched. We used Google Trends (GT) for monitoring the interest for preventable infections and related vaccines. When looking for vaccine preventable infectious diseases, vaccine was not a popular topic, with some valuable exceptions, including the vaccine against Human Papillomavirus (HPV). Vaccines-related queries represented approximately one third of the volumes regarding preventable infections, greatly differing among the vaccines. However, the interest for vaccines is increasing throughout time: in particular, users seek information about possible vaccine-related side-effects. The five most searched vaccines are those against 1) influenza; 2) meningitis; 3) diphtheria, pertussis (whooping cough), and tetanus; 4) yellow fever; and 5) chickenpox. ICTs can have a positive influence on parental vaccine-related knowledge, attitudes, beliefs and vaccination willingness. GT can be used for monitoring the interest for vaccinations and the main information searched.

Social Networks Across Common Cancer Types: The Evidence, Gaps, and Areas of Potential Impact

Although the association between social context and health has been demonstrated previously, much less is known about network interactions by gender, race/ethnicity, and sociodemographic characteristics. Given the variability in cancer outcomes among groups, research on these relationships may have important implications for addressing cancer health disparities. We examined the literature on social networks and cancer across the cancer continuum among adults. Relevant studies (N=16) were identified using two common databases: PubMed and Google Scholar. Most studies used a prospective cohort study design (n=9), included women only (n=11), and were located in the United States (n=14). Seventy-five percent of the studies reviewed used a validated scale or validated items to measure social networks (n=12). Only one study examined social network differences by race, 57.1% (n=8) focused on breast cancer alone, 14.3% (n=2) explored colorectal cancer or multiple cancers simultaneously, and 7.1% (n=1) only prostate cancer. More than half of the studies included multiple ethnicities in the sample, while one study included only low-income subjects. Despite findings of associations between social networks and cancer survival, risk, and screening, none of the studies utilized social networks as a mechanism for reducing health disparities; however, such an approach has been utilized for infectious disease control. Social networks and the support provided within these networks have important implications for health behaviors and ultimately cancer disparities. This review serves as the first step toward dialog on social networks as a missing component in the social determinants of cancer disparities literature that could move the needle upstream to target adverse cancer outcomes among vulnerable populations.

Lessons Learned Designing and Using an Online Discussion Forum for Care Coordinators in Primary Care

While an increasing number of researchers are using online discussion forums for qualitative research, few authors have documented their experiences and lessons learned to demonstrate this method's viability and validity in health services research. We comprehensively describe our experiences, from start to finish, of designing and using an asynchronous online discussion forum for collecting and analyzing information elicited from care coordinators in Patient-Centered Medical Homes across the United States. Our lessons learned from each phase, including planning, designing, implementing, using, and ending this private online discussion forum, provide some recommendations for other health services researchers considering this method. An asynchronous online discussion forum is a feasible, efficient, and effective method to conduct a qualitative study, particularly when subjects are health professionals.

Perceptions of Oncologists, Healthcare Policy Makers, Patients and the General Population on the Value of Pharmaceutical Treatments in Oncology

Introduction

The purpose of this study was to explore the main factors explaining the relative weight of the different attributes that determine the value of oncologic treatments from the different perspectives of healthcare policy makers (HCPM), oncologists, patients and the general population in Spain.

Methods

Structured interviews were conducted to assess: (1) the importance of the attributes on treatment choice when comparing a new cancer drug with a standard cancer treatment; (2) the importance of survival, quality of life (QoL), costs and innovation in cancer; and (3) the most worrying side effects related to cancer drugs.

Results

A total of 188 individuals participated in the study. For all participants, when choosing treatments, the best rated characteristics were greater efficacy, greater safety, treatment adaptation to patients’ individual requirements and the rapid reincorporation of patients to their daily activities. There were important differences among participants in their opinion about survival, QoL and cost. In general, oncologists, patients, and the general population gave greater value to gains in QoL than healthcare policy makers. Compared to other participants healthcare policy makers gave greater importance to the economic impact related to oncology treatments.

Conclusions

Gains in QoL, survival, safety, cost and innovation are perceived differently by different groups of stakeholders. It is recommended to consider the perspective of different stakeholders in the assessment of a new cancer drugs to obtain more informed decisions when deciding on the most appropriate treatment to use.

Funding

Eli Lilly & Co, Madrid (Spain).

Oral Endocrine Therapy Nonadherence, Adverse Effects, Decisional Support, and Decisional Needs in Women With Breast Cancer

BACKGROUND

Oral endocrine therapy (OET) such as tamoxifen or aromatase inhibitors reduces recurrence and mortality for the 75% of breast cancer survivors (BCSs) with a diagnosis of estrogen receptor-positive breast cancer. Because many BCSs decide not take OET as recommended because of adverse effects, understanding BCSs' decisional supports and needs is foundational to supporting quality OET decision making about whether to adhere to OET.

OBJECTIVE

The aim of this study was to examine literature pertaining to OET nonadherence and adverse effects using the Ottawa Decision Support Framework categories of decisional supports and decisional needs because these factors potentially influence OET use.

METHODS

A systematic literature search was performed in PubMed and CINAHL using combined search terms "aromatase inhibitors and adherence" and "tamoxifen and adherence." Studies that did not meet criteria were excluded. Relevant data from 25 publications were extracted into tables and reviewed by 2 authors.

RESULTS

Findings identified the impact of adverse effects on OET nonadherence, an absence of decisional supports provided to or available for BCSs who are experiencing OET adverse effects, and the likelihood of unmet decisional needs related to OET.

CONCLUSIONS

Adverse effects contribute to BCSs decisions to stop OET, yet there has been little investigation of the process through which that occurs. This review serves as a call to action for providers to provide support to BCSs experiencing OET adverse effects and facing decisions related to nonadherence.

IMPLICATIONS FOR PRACTICE

Findings suggest BCSs prescribed OET have unmet decisional needs, and more decisional supports are needed for BCSs experiencing OET adverse effects.

An ensemble method for extracting adverse drug events from social media

OBJECTIVE

Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large data source for information on ADEs. The objective of this study is to develop a relation extraction system that uses natural language processing techniques to effectively distinguish between ADEs and non-ADEs in informal text on social media.

METHODS AND MATERIALS

We develop a feature-based approach that utilizes various lexical, syntactic, and semantic features. Information-gain-based feature selection is performed to address high-dimensional features. Then, we evaluate the effectiveness of four well-known kernel-based approaches (i.e., subset tree kernel, tree kernel, shortest dependency path kernel, and all-paths graph kernel) and several ensembles that are generated by adopting different combination methods (i.e., majority voting, weighted averaging, and stacked generalization). All of the approaches are tested using three data sets: two health-related discussion forums and one general social networking site (i.e., Twitter).

RESULTS

When investigating the contribution of each feature subset, the feature-based approach attains the best area under the receiver operating characteristics curve (AUC) values, which are 78.6%, 72.2%, and 79.2% on the three data sets. When individual methods are used, we attain the best AUC values of 82.1%, 73.2%, and 77.0% using the subset tree kernel, shortest dependency path kernel, and feature-based approach on the three data sets, respectively. When using classifier ensembles, we achieve the best AUC values of 84.5%, 77.3%, and 84.5% on the three data sets, outperforming the baselines.

CONCLUSIONS

Our experimental results indicate that ADE extraction from social media can benefit from feature selection. With respect to the effectiveness of different feature subsets, lexical features and semantic features can enhance the ADE extraction capability. Kernel-based approaches, which can stay away from the feature sparsity issue, are qualified to address the ADE extraction problem. Combining different individual classifiers using suitable combination methods can further enhance the ADE extraction effectiveness.

CD-REST: a system for extracting chemical-induced disease relation in literature

Mining chemical-induced disease relations embedded in the vast biomedical literature could facilitate a wide range of computational biomedical applications, such as pharmacovigilance. The BioCreative V organized a Chemical Disease Relation (CDR) Track regarding chemical-induced disease relation extraction from biomedical literature in 2015. We participated in all subtasks of this challenge. In this article, we present our participation system Chemical Disease Relation Extraction SysTem (CD-REST), an end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug-disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request. The web services can be accessed fromhttp://clinicalnlptool.com/cdr The online CD-REST demonstration system is available athttp://clinicalnlptool.com/cdr/cdr.html. Database URL:http://clinicalnlptool.com/cdr;http://clinicalnlptool.com/cdr/cdr.html.

Overcoming Organizational Challenges of Integrating Patient-Reported Outcomes in Oncology Clinical Trials

Patients with cancer frequently experience multiple symptoms that may cause significant distress and may impair physical, emotional, and social functioning and health-related quality of life. Drug development in oncology is characterized by a high attrition rate of new compounds, faster development times encouraged by the regulatory process, studies that are often open and single-arm, and emphasis on survival-related endpoints, creating unique challenges for the inclusion of patient reported outcomes (PROs). These challenges to include PRO-related endpoints in oncology research are further exacerbated by downward pressure on budget and resources and also an overly rigorous application of the US Food and Drug Administration's PRO guidance, which can in turn prevent study teams from optimally including PROs in oncology clinical trials. With increasing calls for demonstration of value of new cancer drugs from payers, patients, and their caregivers, study teams should consider the utility of PROs beyond regulatory needs. Optimal implementation of a PRO strategy in oncology research can be achieved by applying the PRO guidance to the greatest extent possible, making use of off-the-shelf PRO measures to capture concepts of interest, discussing plans with the regulatory bodies early in the process, and treating PRO-related endpoints with the same level of rigor as other endpoints.

A research framework for pharmacovigilance in health social media: Identification and evaluation of patient adverse drug event reports

Social media offer insights of patients' medical problems such as drug side effects and treatment failures. Patient reports of adverse drug events from social media have great potential to improve current practice of pharmacovigilance. However, extracting patient adverse drug event reports from social media continues to be an important challenge for health informatics research. In this study, we develop a research framework with advanced natural language processing techniques for integrated and high-performance patient reported adverse drug event extraction. The framework consists of medical entity extraction for recognizing patient discussions of drug and events, adverse drug event extraction with shortest dependency path kernel based statistical learning method and semantic filtering with information from medical knowledge bases, and report source classification to tease out noise. To evaluate the proposed framework, a series of experiments were conducted on a test bed encompassing about postings from major diabetes and heart disease forums in the United States. The results reveal that each component of the framework significantly contributes to its overall effectiveness. Our framework significantly outperforms prior work.