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Neurodegenerative Diseases: Quantifying Patient and Caregiver Insights from Self-Reported and Specialized Social Media

Abstract

Analyzing patient and caregiver reported opinions, perspectives, and decision-making from specialized social media sources was used to augment the understanding of unmet patient and caregiver needs that may not be captured using traditional methods such as prospective surveys.  Natural Language Processing (NLP) was used to aggregate and analyze the most frequently reported issues reported online by patients and caregivers living with neurodegenerative diseases. Using Real-Life Science’s proprietary SPEC-R categorization framework, the data yielded that individuals register a whole spectrum of observations, perspectives, and complaints that are not readily captured by traditional disease instruments and may not be visible or well understood by clinicians, HCPs, and/or researchers.

Introduction

For neurodegenerative diseases, the best opportunity for patients and caregivers to benefit from available treatments, enroll in clinical trials, and/or express their wishes is in the early stages of the disease – hence the importance of early detection. Social media represents an opportunity to passively observe and analyze patients in the earliest stages of the disease, in addition to potential pre-disease patient candidates, even before they are ‘captured’ by the traditional healthcare system mechanisms.

This study aimed to capture a better understanding of the disease burden across all stages of diseases by capturing online patient and caregiver self-reported narratives using social media and mapping them to clinical assessment instruments commonly used in neurodegenerative research and patient screening.

We aimed to evaluate what burdensome functional impairments and symptoms can be extracted from real-world, unguided narratives, with an initial focus on specialized social media, and established the applicability of using software-driven Natural Language Processing and data mining approaches to enhance research into self-reported issues.

Data Collection and Specialized Social Media Sources

We collected patient and caregiver narratives from a wide range of generalist and specialist social media sources – Disease-focused forums contain the most engaged reporters made up of patients and caregivers.

Social Media SourceTotal NarrativesTotal (%)
Disease Focused Forums e.g. Alzheimer's Forum83,89874.6%
General Social Media e.g. Twitter, Reddit10,9099.7%
General Health Forums e.g. Healthboards10,0098.9%
General Health Social Networks e.g. Dailystrength6,1865.5%
Treatment Review Websites e.g. AskaPatient1,4631.3%
Total112,464

Real Life Sciences’ SPEC-F Data Classification and Modeling

Natural Language Processing (NLP) was used in combination with manual curation of the data to codify verbatim reports of symptoms and impairments. Reports were classified against a series of standard medical taxonomies such as the WHO’s International Classification of Functioning, Disability, and Health (WHO-ICF) and Medical Dictionary for Regulatory Activities Terminology (MedDRA), and further into the following categorizations: Social, Physical, Emotional, Cognitive, and Financial (SPEC-F).  Verbatim curated narratives 

were matched to corresponding sub-concepts: Social Interactions, Asthenia, and Fatigue. Anxiety, Conversation Issues, and Driving Impairment.  Sub concepts were then matched with SPEC-F categories as follows:

Social = Social Interactions

Physical = Asthenia and Fatigue

Emotional = Anxiety

Cognitive = Conversation Issues

Financial = Cost of Care Concern

Evaluation of Patient Burden Using Online Social Media in Cognitive Impairment

Detection of cognitive impairment can be challenging due to the subtlety of changes in the early stages of disease progression. Our goal was to demonstrate how social media can be used to capture the patient burden within this patient population.

Cognitive issues such as memory impairments and comprehension-related issues were the most frequently reported by both patient and caregiver groups, however, patients reported at a higher rate.

Patients’ higher incidence of reporting may suggest a deeper concern with losing cognitive ability felt by patients themselves at an earlier disease stage.  This is further supported by a higher reporting rate of anxiety and depressive disorders among patients relative to caregivers.

In contrast, certain role activities, physical and social issues such as driving, fatigue, walking and mobility, relationships with spouse or partner are reported by caregivers at a higher rate than patients.  The higher rate of caregiver reporting could suggest that these issues may have a greater impact on the daily lives of caregivers or maybe more readily recognizable than cognitive issues.

Concepts associated with reported domains were also qualitatively assessed to develop a more granular understanding of concepts such as Anxiety. Anxiety is the most frequently reported emotional issue and is commonly reported in conjunction with the themes listed below. 

#1 Future - worrying about what is ahead, losing control, and the desire to ‘live again’ (caregiver)

#2 Feeling Guilty - putting loved ones into assisted living, focusing on themselves (caregivers)

#3 Loneliness - losing a life partner, breadwinner (caregiver)

Furthermore, there is significant reporting around these factors getting progressively worse over time.

Understanding the Evolution of Patient Burden across the Severity Stages of Neurodegenerative Diseases Using Online Social Media

We evaluated differences across patient and caregiver reporting groups across the progressive stages to inform how to tailor patient-centric diagnostic, disease, and lifestyle management approaches throughout the disease.

Our sample size totaled 10,886 self-reported narratives.  Caregiver narratives totaled 10,174 while 692 patient narratives were collected and analyzed. Patients’ cognitive issues were classified as mild, early, or moderate-severe. Among patients with mild and their caregivers, 95.5% patients and 83.0% caregivers discussed cognitive issues, e.g. memory impairments.  While 61.1% of patients and 51.4% of caregivers shared emotional issues, e.g. anxiety and depression.

Among patients with Early and their caregivers, emotional issues were the most frequently discussed by both patients (93.6%) and caregivers (53.5%), e.g. anxiety, depression, and other emotional experiences

Cognitive issues followed and were shared by 86.9% of patients and 39.6% of caregivers. Among caregivers for patients with Moderate-Severe, 42.7% reported on emotional issues and 20.7% on physical issues, e.g. fatigue and agitation.

Congruence of Clinical Assessment Instruments with Online Narratives from Specialized Social Media by Patients with Neurodegenerative Diseases and Caregivers

We evaluated whether concepts within clinical assessment instruments commonly used in neurodegenerative research (e.g. CDR-SB) align with the self-reported concepts on social media; identify any deficits and potential optimizations.

Research may need to employ multiple clinical instruments to properly capture key domains of disease impact on patients with various stages of severity.

This study aimed to better understand disease burden by capturing online patient and caregiver narratives over social media and mapping them to clinical assessment instruments.

Cognitive deficiencies were well captured, however, patient burden with Emotional, Physical, Social, and Role Activity challenges was only partially represented in the selected instruments. The study has also identified additional areas of disease burden that are not currently represented in clinical research and practice. This points to the need for further development of clinical instruments to assess the full impact of degenerative diseases on patients and caregivers.

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