Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators.

Patients, life science industry and regulatory authorities are united in their goal to reduce the disease burden of patients by closing remaining unmet needs. Patients have, however, not always been systematically and consistently involved in the drug development process. Recognizing this gap, regulatory bodies worldwide have initiated patient-focused drug development (PFDD) initiatives to foster a more systematic involvement of patients in the drug development process and to ensure that outcomes measured in clinical trials are truly relevant to patients and represent significant improvements to their quality of life. As a source of real-world evidence (RWE), social media has been consistently shown to capture the first-hand, spontaneous and unfiltered disease and treatment experience of patients and is acknowledged as a valid method for generating patient experience data by the Food and Drug Administration (FDA). While social media listening (SML) methods are increasingly applied to many diseases and use cases, a significant piece of uncertainty remains on how evidence derived from social media can be used in the drug development process and how it can impact regulatory decision making, including legal and ethical aspects. In this policy paper, we review the perspectives of three key stakeholder groups on the role of SML in drug development, namely patients, life science companies and regulators. We also carry out a systematic review of current practices and use cases for SML and, in particular, highlight benefits and drawbacks for the use of SML as a way to identify unmet needs of patients. While we find that the stakeholders are strongly aligned regarding the potential of social media for PFDD, we identify key areas in which regulatory guidance is needed to reduce uncertainty regarding the impact of SML as a source of patient experience data that has impact on regulatory decision making.

Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium.

OBJECTIVE: The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results. METHODS: The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of 5 tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events). RESULTS: In total, 29 teams registered, representing 17 countries. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora. CONCLUSION: To facilitate future work, the datasets-a total of 61 353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.

Overview of the 8th Social Media Mining for Health Applications (#SMM4H) Shared Tasks at the AMIA 2023 Annual Symposium.

The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of five tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events). In total, 29 teams registered, representing 18 countries. In this paper, we present the annotated corpora, a technical summary of the systems, and the performance results. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora. To facilitate future work, the datasets-a total of 61,353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.

Promoting engagement with quality communication in social media.

The COVID-19 pandemic made explicit the issues of communicating science in an information ecosystem dominated by social media platforms. One of the fundamental communication challenges of our time is to provide the public with reliable content and contrast misinformation. This paper investigates how social media can become an effective channel to promote engagement and (re)build trust. To measure the social response to quality communication, we conducted an experimental study to test a set of science communication recommendations on Facebook and Twitter. The experiment involved communication practitioners and social media managers from select countries in Europe, applying and testing such recommendations for five months. Here we analyse their feedback in terms of adoption and show that some differences emerge across platforms, topics, and recommendation categories. To evaluate these recommendations' effect on users, we measure their response to quality content, finding that the median engagement is generally higher, especially on Twitter. The results indicate that quality communication strategies may elicit positive feedback on social media. A data-driven and co-designed approach in developing counter-strategies is thus promising in tackling misinformation.

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.

Human mobility in response to COVID-19 in France, Italy and UK.

The COVID-19 pandemic is one of the defining events of our time. National Governments responded to the global crisis by implementing mobility restrictions to slow down the spread of the virus. To assess the impact of those policies on human mobility, we perform a massive comparative analysis on geolocalized data from 13 M Facebook users in France, Italy, and the UK. We find that lockdown generally affects national mobility efficiency and smallworldness-i.e., a substantial reduction of long-range connections in favor of local paths. The impact, however, differs among nations according to their mobility infrastructure. We find that mobility is more concentrated in France and UK and more distributed in Italy. In this paper we provide a framework to quantify the substantial impact of the mobility restrictions. We introduce a percolation model mimicking mobility network disruption and find that node persistence in the percolation process is significantly correlated with the economic and demographic characteristics of countries: areas showing higher resilience to mobility disruptions are those where Value Added per Capita and Population Density are high. Our methods and findings provide important insights to enhance preparedness for global critical events and to incorporate resilience as a relevant dimension to estimate the socio-economic consequences of mobility restriction policies.

The COVID-19 social media infodemic.

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.

Economic and social consequences of human mobility restrictions under COVID-19.

In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near-real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown.

Selective exposure shapes the Facebook news diet.

The social brain hypothesis approximates the total number of social relationships we are able to maintain at 150. Similar cognitive constraints emerge in several aspects of our daily life, from our mobility to the way we communicate, and might even affect the way we consume information online. Indeed, despite the unprecedented amount of information we can access online, our attention span still remains limited. Furthermore, recent studies have shown that online users are more likely to ignore dissenting information, choosing instead to interact with information adhering to their own point of view. In this paper, we quantitatively analyse users' attention economy in news consumption on social media by analysing 14 million users interacting with 583 news outlets (pages) on Facebook over a time span of six years. In particular, we explore how users distribute their activity across news pages and topics. On the one hand, we find that, independently of their activity, users show a tendency to follow a very limited number of pages. On the other hand, users tend to interact with almost all the topics presented by their favoured pages. Finally, we introduce a taxonomy accounting for users' behaviour to distinguish between patterns of selective exposure and interest. Our findings suggest that segregation of users in echo chambers might be an emerging effect of users' activity on social media and that selective exposure-i.e. the tendency of users to consume information adhering to their preferred narratives-could be a major driver in their consumption patterns.

Polarization of the vaccination debate on Facebook.

BACKGROUND: Vaccine hesitancy has been recognized as a major global health threat. Having access to any type of information in social media has been suggested as a potential influence on the growth of anti-vaccination groups. Recent studies w.r.t. other topics than vaccination show that access to a wide amount of content through the Internet without intermediaries resolved into major segregation of the users in polarized groups. Users select information adhering to theirs system of beliefs and tend to ignore dissenting information. OBJECTIVES: The goal was to assess whether users' attitudes are polarized on the topic of vaccination on Facebook and how this polarization develops over time. METHODS: We perform a thorough quantitative analysis by studying the interaction of 2.6 M users with 298,018 Facebook posts over a time span of seven years and 5 months. We applied community detection algorithms to automatically detect the emergence of communities accounting for the users' activity on the pages. Also, we quantified the cohesiveness of these communities over time. RESULTS: Our findings show that the consumption of content about vaccines is dominated by the echo chamber effect and that polarization increased over the years. Well-segregated communities emerge from the users' consumption habits i.e., the majority of users consume information in favor or against vaccines, not both. CONCLUSION: The existence of echo chambers may explain why social-media campaigns that provide accurate information have limited reach and be effective only in sub-groups, even fomenting further opinion polarization. The introduction of dissenting information into a sub-group is disregarded and can produce a backfire effect, thus reinforcing the pre-existing opinions within the sub-group. Public health professionals should try to understand the contents of these echo chambers, for example by getting passively involved in such groups. Only then it will be possible to find effective ways of countering anti-vaccination thinking.

Anatomy of news consumption on Facebook.

The advent of social media and microblogging platforms has radically changed the way we consume information and form opinions. In this paper, we explore the anatomy of the information space on Facebook by characterizing on a global scale the news consumption patterns of 376 million users over a time span of 6 y (January 2010 to December 2015). We find that users tend to focus on a limited set of pages, producing a sharp community structure among news outlets. We also find that the preferences of users and news providers differ. By tracking how Facebook pages "like" each other and examining their geolocation, we find that news providers are more geographically confined than users. We devise a simple model of selective exposure that reproduces the observed connectivity patterns.

Measuring adherence to antiretroviral treatment: the role of pharmacy records of drug withdrawals.

This study aimed to evaluate adherence to antiretroviral treatment (ART) among HIV + adults, assess its association with HIV viral load (VL) and identify factors associated to adherence. A survey involving a random sample of adults followed at a HIV/AIDS reference center in São Paulo city, Brazil, from 2007 to 2009 was done. A questionnaire was applied and data were retrieved from the pharmacy and medical records. The study involved 292 subjects: 70.2% men; median age: 43 years; median duration of ART: 8 years. 89.3% self-reported taken all prescribed pills in the last 3 days but only 39.3% picked up ≥95% of the prescribed ART from the pharmacy in the last 12 months. At the multivariate analysis having symptoms prior to ART, taking fewer ART pills, and not missing medical appointments were independently associated to higher adherence. Adherence was strongly associated with undetectable HIV VL. Rates of undetectable HIV VL did not differ from 80 to ≥95% of adherence.

Ascariasis in the subdistrict of Cavacos, municipality of Alterosa (MG), Brazil: effect of mass treatment with albendazole on the intensity of infection

Em amostra aleatoria e estratificada da populacao do subdistrito de Cavacos, no municipio de Alterosa (Minas Gerais, Brasil) estudaram-se os aspectos clinicos e epidemiologicos da infeccao por Ascaris lumbricoides. Avaliou-se, tambem, na mesma amostra, seis meses mais tarde, o efeito do tratamento em massa com albendazol sobre a prevalencia e intensidade de infeccao por esse nematoide. Na primeira fase do estudo, realizou-se inquerito em 248 individuos, utilizando questionario que investigava aspectos relativos a condicoes socioeconomicas, sanitarias e clinicas. Foram, tambem, examinadas 230 amostras de fezes pela tecnica de Kato-Katz, visando determinacao da prevalencia e intensidade de infeccao por A. lumbricoides. Ao mesmo tempo, 202 individuos foram submetidos a micro-hematocrito e em 70 criancas com idade menor ou igual a 12 anos efetuou-se avaliacao do estado nutricional. Determinou-se, ainda, a presenca de ovos de A. lumbricoides e outros helmintos em 22 amostras de solo colhidas na zona urbana de Cavacos...