Dataset Information

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.

ABSTRACT: This research characterized how Facebook deals with rare diseases. This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks. This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextBlob Python library for sentiment analysis. The Facebook analysis shows that posts with photos and positive comments have the highest engagement. We also observed that words related to diseases, attention, disability and services have a lot of presence in the decalogue of priorities (which serves for all associations to work on the same objectives and provides the lines of action to be followed by political decision makers) and little on Facebook, and words of gratitude are more present on Facebook than in the decalogue. Finally, the temporal analysis shows that there is a high variation between the polarity average and the hour of the day.

SUBMITTER: Subirats L

PROVIDER: S-EPMC6163744 | biostudies-other | 2018 Aug

REPOSITORIES: biostudies-other

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Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.

Subirats Laia L Reguera Natalia N Bañón Antonio Miguel AM Gómez-Zúñiga Beni B Minguillón Julià J Armayones Manuel M

International journal of environmental research and public health 20180830 9

This research characterized how Facebook deals with rare diseases. This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks. This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextB ...[more]

PMID: 30200209

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Project description:BackgroundLoneliness, social isolation, and feeling disconnected from society are commonly experienced by parents of children with rare diseases and are, among others, important reasons for special supportive care needs. Social networking platforms are increasingly used for health communication, information exchange, and support. In the field of rare pediatric diseases, qualitative studies have shown that Facebook online support groups are utilized by and beneficial for persons affected by rare pediatric diseases. Nonetheless, the extent of this usage has not been investigated.ObjectiveThis study aims to provide a comprehensive quantitative analysis of the extent of Facebook usage as a tool for rare pediatric disease support groups and to explore factors that influence a disease's representation on Facebook. These results potentially offer important insights for future public health initiatives and give direction to further research that can give much needed support to parents of children with rare diseases.MethodsWe determined rare pediatric diseases using the inventory of the online portal Orphanet. Facebook support groups were identified by searching 5 synonymous disease descriptions using the group category search bar. Disease- and group-describing parameters were statistically analyzed using standard descriptive statistical methods.Results6398 Facebook support groups, representing 826 diseases (19.5% of all searched diseases), were found. 69% are private groups. Group type, size, activity (sum of posts, comments, and reactions calculated by Facebook), new memberships, and language varied largely between groups (member count: minimum 1, maximum 23,414; activity last 30 days: minimum 0, maximum 3606). The highest percentage of awareness and information groups was found for teratogenic diseases (18/68, 26%). The odds of finding a Facebook group increased according to the level of information available about the disease: known prevalence (odds ratio [OR] 3.98, 95% CI 3.39-4.66, P<.001), known disease type (OR 3.15, 95% CI 2.70-3.68, P<.001), and known inheritance mode (OR 2.06, 95% CI 1.68-2.52, P<.001) were all associated with higher odds of finding a Facebook group, as was dominant compared to nondominant inheritance (OR 2.05, 95% CI 1.74-3.42, P<.001). The number of groups per disease increased with higher prevalence.ConclusionsFacebook is widely used as a tool for support groups for rare pediatric diseases and continues to be relevant. Two-thirds of the groups are private groups, indicating group participants' need for privacy, which should be further explored. The advantages and limitations of Facebook as a tool for support groups in the field of rare diseases should be further investigated as it will allow health professionals to use Facebook more meaningfully in their counseling and guidance of affected individuals and their family members.

Project description:BackgroundAccording to the International Rare Diseases Research Consortium (IRDiRC), a known rare disease (RD) should be diagnosable within a year. This study sought: firstly, to ascertain how long it takes to obtain the diagnosis of a RD in Spain, along with its associated time trend; and secondly, to identify and measure diagnostic delay (defined by the IRDiRC as any period exceeding a year) by reference to the characteristics of RDs and the persons affected by them.MethodsUsing data sourced from the Spanish Rare Diseases Patient Registry, we performed a descriptive analysis of the time elapsed between symptom onset and diagnosis of each RD, by sex, age and date of symptom onset, and type of RD. We analysed the time trend across the period 1960-2021 and possible change points, using a Joinpoint regression model and assuming a Poisson distribution. The multivariate analysis was completed with backward stepwise logistic regression.ResultsDetailed information was obtained on 3304 persons with RDs: 56.4% had experienced delay in diagnosis of their RDs, with the mean time taken being 6.18 years (median = 2; IQR 0.2-7.5). Both the percentage of patients with diagnostic delay and the average time to diagnosis underwent a significant reduction across the study period (p < 0.001). There was a higher percentage of diagnostic delays: in women (OR 1.25; 95% CI 1.07-1.45); in cases with symptom onset at age 30-44 years (OR 1.48; 95% CI 1.19-1.84): and when analysed by type of RD, in mental and behavioural disorders (OR 4.21; 95% CI 2.26-7.85), followed by RDs of the nervous system (OR 1.39; 95% CI 1.02-1.88).ConclusionsThis is the first study to quantify time to diagnosis of RDs in Spain, based on data from a national registry open to any RD. Since over half of all persons affected by RDs experience delay in diagnosis, new studies are needed to ascertain the factors associated with this delay and the implications this has on the lives of patients and their families.

Project description:IntroductionThe Chinese government has made significant strides in addressing the needs of individuals affected by rare diseases in recent years. This paper aims to provide a comprehensive analysis of national rare disease policies in China from 2009 to 2022, using a mixed-methods approach.MethodsA two-dimensional analytical framework, which includes policy tools and policy themes, is introduced to analyze the rare disease policies comprehensively. Drawing on the policy tools theory proposed by Rothwell and Zegveld, this paper evaluates the tools used in rare disease policies. Co-word analyses and network analyses are employed to identify key themes in rare disease policies and collaboration among government departments.ResultsThe rare disease policy landscape in China is undergoing rapid growth, with an increasing number of government departments involved in policy formulation. However, further collaboration between departments is needed to strengthen these policies. Environment-based and supply-based tools are preferred in rare disease policies. The policy themes can be grouped into four categories: (1) Registration, Approval and Supply of Rare Disease Drugs, (2) Construction of Diagnosis and Treatment System for Rare Diseases, (3) Development and Genericization of Rare Disease Drugs, and (4) Social Security for Patients with Rare Diseases.DiscussionThe study provides valuable insights into the current state of rare disease policies in China and offers suggestions for policy improvement. The results show that the Chinese government has made efforts to address the needs of individuals affected by rare diseases, but there is still room for improvement. The collaboration between government departments needs to be strengthened to achieve better rare disease policies. The findings of this study have implications for other countries with similar healthcare systems and can contribute to a better understanding of the impact of rare disease policies on public health.

Dataset Information

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.

Publications

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.

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