Dataset Information

Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis.

ABSTRACT:

Objective

The objective of this study is to understand the primary topics of consumer discussion on Twitter associated with telehealth for mental health or substance abuse for prepandemic versus during-pandemic time-periods, using a state-of-the-art machine learning (ML) natural language processing (NLP) method.

Materials and methods

The primary methodological phases of this project were: (1) collecting, cleaning, and filtering data (tweets) from January 2014 to June 2021, (2) describing the final corpus, (3) running and optimizing Bidirectional Encoder Representations from Transformers (BERT; using BERTopic in Python) models, and (4) human refinement of topic model results and thematic classification of topics.

Results

The number of tweets in this context increased by 4 times during the pandemic (2017 tweets prepandemic vs 8672 tweets during the pandemic). During the pandemic topics were more frequently mental health related than substance abuse related. Top during-pandemic topics were therapy, suicide, pain (associated with burnout and drinking), and mental health diagnoses such as ADHD and autism. Anxiety was a key topic of discussion both pre- and during the pandemic.

Discussion

Telehealth for mental health and substance abuse is being discussed more frequently online, which implies growing demand. Given the topics extracted as proxies for demand, the most demand is currently for telehealth for mental health primarily, especially for children, parents, and therapy for those with anxiety or depression, and substance abuse secondarily.

Conclusions

Scarce telehealth resources can be allocated more efficiently if topics of consumer discussion are included in resource allocation decision- and policy-making processes.

SUBMITTER: Baird A

PROVIDER: S-EPMC9047171 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis.

Baird Aaron A Xia Yusen Y Cheng Yichen Y

JAMIA open 20220427 2

<h4>Objective</h4>The objective of this study is to understand the primary topics of consumer discussion on Twitter associated with telehealth for mental health or substance abuse for prepandemic versus during-pandemic time-periods, using a state-of-the-art machine learning (ML) natural language processing (NLP) method.<h4>Materials and methods</h4>The primary methodological phases of this project were: (1) collecting, cleaning, and filtering data (tweets) from January 2014 to June 2021, (2) des ...[more]

PMID: 35495736

Similar Datasets

Project description:BackgroundAddiction is one of the most rapidly growing epidemics that currently plagues nations around the world. In the United States, it has cost the government more than US $700 billion a year in terms of health care and other associated costs and is also associated with serious social, physical, and mental consequences. Increasing efforts have been made to tackle this issue at different levels, from primary prevention to rehabilitation across the globe. With the use of digital technology rapidly increasing, an effort to leverage the consumer health information technologies (CHITs) to combat the rising substance abuse epidemic has been underway. CHITs are identified as patient-focused technological platforms aimed to improve patient engagement in health care and aid them in navigating the complex health care system.ObjectiveThis review aimed to provide a holistic and overarching view of the breadth of research on primary prevention of substance abuse using CHIT conducted over nearly past five decades. It also aimed to map out the changing landscape of CHIT over this period.MethodsWe conducted a scoping review using the Arksey and O'Malley's modified methodological framework. We searched 4 electronic databases (PubMed, Cochrane, Scopus, and EMBASE). Papers were included if the studies addressed the use of CHIT for primary prevention of substance abuse and were published in English between 1809 and 2018. Studies that did not focus solely on primary prevention or assessed additional comorbid conditions were eliminated.ResultsForty-two papers that met our inclusion criteria were included in the review. These studies were published between 1970 and 2018 and were not restricted by geography, age, race, or sex. The review mapped studies using the most commonly used CHIT platforms for substance abuse prevention from mass media in the 1970s to mobile and social media in 2018. Moreover, 191 studies that were exclusively focused on alcohol prevention were excluded and will be addressed in a separate paper. The studies included had diverse research designs although the majority were randomized controlled trials (RCT) or review papers. Many of the RCTs used interventions based on different behavioral theories such as family interactions, social cognitive theories, and harm-minimization framework.ConclusionsThis review found CHIT platforms to be efficacious and cost-effective in the real-world settings. We also observed a gradual shift in the types and use of CHIT platforms over the past few decades and mapped out their progression. In addition, the review detected a shift in consumer preferences and behaviors from face-to-face interactions to technology-based platforms. However, the studies included in this review only focused on the aspect of primary prevention. Future reviews could assess the effectiveness of platforms for secondary prevention and for prevention of substance abuse among comorbid populations.

Project description:BackgroundLow back pain (LBP) remains the leading cause of disability worldwide. A better understanding of the beliefs regarding LBP and impact of LBP on the individual is important in order to improve outcomes. Although personal experiences of LBP have traditionally been explored through qualitative studies, social media allows access to data from a large, heterogonous, and geographically distributed population, which is not possible using traditional qualitative or quantitative methods. As data on social media sites are collected in an unsolicited manner, individuals are more likely to express their views and emotions freely and in an unconstrained manner as compared to traditional data collection methods. Thus, content analysis of social media provides a novel approach to understanding how problems such as LBP are perceived by those who experience it and its impact.ObjectiveThe objective of this study was to identify contextual variables of the LBP experience from a first-person perspective to provide insights into individuals' beliefs and perceptions.MethodsWe analyzed 896,867 cleaned tweets about LBP between January 1, 2014, and December 31, 2018. We tested and compared latent Dirichlet allocation (LDA), Dirichlet multinomial mixture (DMM), GPU-DMM, biterm topic model, and nonnegative matrix factorization for identifying topics associated with tweets. A coherence score was determined to identify the best model. Two domain experts independently performed qualitative content analysis of the topics with the strongest coherence score and grouped them into contextual categories. The experts met and reconciled any differences and developed the final labels.ResultsLDA outperformed all other algorithms, resulting in the highest coherence score. The best model was LDA with 60 topics, with a coherence score of 0.562. The 60 topics were grouped into 19 contextual categories. "Emotion and beliefs" had the largest proportion of total tweets (157,563/896,867, 17.6%), followed by "physical activity" (124,251/896,867, 13.85%) and "daily life" (80,730/896,867, 9%), while "food and drink," "weather," and "not being understood" had the smallest proportions (11,551/896,867, 1.29%; 10,109/896,867, 1.13%; and 9180/896,867, 1.02%, respectively). Of the 11 topics within "emotion and beliefs," 113,562/157,563 (72%) had negative sentiment.ConclusionsThe content analysis of tweets in the area of LBP identified common themes that are consistent with findings from conventional qualitative studies but provide a more granular view of individuals' perspectives related to LBP. This understanding has the potential to assist with developing more effective and personalized models of care to improve outcomes in those with LBP.

Project description:BackgroundNonpharmaceutical interventions (NPIs) (such as wearing masks and social distancing) have been implemented by governments around the world to slow the spread of COVID-19. To promote public adherence to these regimes, governments need to understand the public perceptions and attitudes toward NPI regimes and the factors that influence them. Twitter data offer a means to capture these insights.ObjectiveThe objective of this study is to identify tweets about COVID-19 NPIs in six countries and compare the trends in public perceptions and attitudes toward NPIs across these countries. The aim is to identify factors that influenced public perceptions and attitudes about NPI regimes during the early phases of the COVID-19 pandemic.MethodsWe analyzed 777,869 English language tweets about COVID-19 NPIs in six countries (Australia, Canada, New Zealand, Ireland, the United Kingdom, and the United States). The relationship between tweet frequencies and case numbers was assessed using a Pearson correlation analysis. Topic modeling was used to isolate tweets about NPIs. A comparative analysis of NPIs between countries was conducted.ResultsThe proportion of NPI-related topics, relative to all topics, varied between countries. The New Zealand data set displayed the greatest attention to NPIs, and the US data set showed the lowest. The relationship between tweet frequencies and case numbers was statistically significant only for Australia (r=0.837, P<.001) and New Zealand (r=0.747, P<.001). Topic modeling produced 131 topics related to one of 22 NPIs, grouped into seven NPI categories: Personal Protection (n=15), Social Distancing (n=9), Testing and Tracing (n=10), Gathering Restrictions (n=18), Lockdown (n=42), Travel Restrictions (n=14), and Workplace Closures (n=23). While less restrictive NPIs gained widespread support, more restrictive NPIs were perceived differently across countries. Four characteristics of these regimes were seen to influence public adherence to NPIs: timeliness of implementation, NPI campaign strategies, inconsistent information, and enforcement strategies.ConclusionsTwitter offers a means to obtain timely feedback about the public response to COVID-19 NPI regimes. Insights gained from this analysis can support government decision making, implementation, and communication strategies about NPI regimes, as well as encourage further discussion about the management of NPI programs for global health events, such as the COVID-19 pandemic.

Project description:ObjectivesDespite increases in global health actors and funding levels, health inequities persist. We empirically tested whether global health governance (GHG) operates under the rational actor model (RAM) and characterised GHG power dynamics.DesignWe collected approximately 75 000 tweets of 20 key global health actors, between 2016 and 2020, using Twitter API. We generated priorities from tweets collected using topic modelling. Priorities from tweets were compared with stated priorities from content analyses of policy documents and with revealed priorities from network analyses of development assistance for health funding data. Comparing priorities derived from Twitter, policy documents and funding data, we can test whether GHG operates under RAM and characterise power dynamics in GHG.Participants20 global health actors were identified based on a consensus of three peer-reviewed articles mapping global health networks. All tweets of each actor were collected in 3-month intervals from November 2016 to May 2020. Policy documents and developmental assistance for health (DAH) financial data for each actor were collected for the same period.ResultsWe find all 20 actors and the global health system collectively fulfil the three conditions of RAM based on stated and revealed priorities. We also find compulsory and institutional power asymmetries in GHG. Funding organisations have compulsory power over channels of DAH and implementing institutions they directly fund. Funding organisations also have transitive influence over implementing institutions receiving DAH funding.ConclusionsWe find that there is a correlation between the priorities of large funders and the priorities of health actors. This correlation in conjunction with GHG operating under the RAM and the asymmetric power held by funders raises issues. GHG under the RAM grants large funders majority of the power to determine global health priorities and ultimately influencing outcomes while implementing organisations, especially those that work closest with populations, have little to limited influence in priority-setting.

Project description:BackgroundReducing the burden of pediatric mental illness requires greater knowledge of mental health and substance abuse (MHSA) outcomes in children who are at an increased risk of primary psychiatric illness. National data on hospital care for psychiatric illness in children with epilepsy are limited.MethodsWe used the Kids' Inpatient Database (KID), the Healthcare Cost and Utilization Project (HCUP), and the Agency for Healthcare Research and Quality from 2003 to 2009 to examine MHSA hospitalization patterns in children with comorbid epilepsy. Nonparametric and regression analyses determined the association of comorbid epilepsy with specific MHSA diagnoses and examined the impact of epilepsy on length of stay (LOS) for such MHSA diagnoses while controlling for demographic, payer, and hospital characteristics.ResultsWe observed 353,319 weighted MHSA hospitalizations of children ages 6-20; 3280 of these involved a child with epilepsy. Depression was the most common MHSA diagnosis in the general population (39.5%) whereas bipolar disorder was the most common MHSA diagnosis among children with epilepsy (36.2%). Multivariate logistic regression models revealed that children with comorbid epilepsy had greater adjusted odds of bipolar disorder (AOR: 1.17, 1.04-1.30), psychosis (AOR: 1.78, 1.51-2.09), sleep disorder (AOR: 5.90, 1.90-18.34), and suicide attempt/ideation (AOR: 3.20, 1.46-6.99) compared to the general MHSA inpatient population. Epilepsy was associated with a greater LOS and a higher adjusted incidence rate ratio (IRR) for prolonged LOS (IRR: 1.12, 1.09-1.17), particularly for suicide attempt/ideation (IRR: 3.74, 1.68-8.34).ConclusionsChildren with epilepsy have distinct patterns of hospital care for mental illness and substance abuse and experience prolonged hospitalization for MHSA conditions. Strategies to reduce psychiatric hospitalizations in this population may require disease-specific approaches and should measure disease-relevant outcomes. Hospitals caring for large numbers of children with neurological disease (such as academic centers) may have inaccurate measurements of mental health-care quality unless the impact of key comorbid conditions such as epilepsy is considered.

Project description:ObjectiveThe objective of this study was to examine mental health conditions among hospitalized individuals with Parkinson's disease in the United States.MethodsThis was a serial cross-sectional study of hospitalizations of individuals aged ≥60 identified in the Nationwide Inpatient Sample dataset from 2000 to 2010. We identified all hospitalizations with a diagnosis of PD, alcohol abuse, anxiety, bipolar disorder, depression, impulse control disorders, mania, psychosis, substance abuse, and attempted suicide/suicidal ideation. National estimates of each mental health condition were compared between hospitalized individuals with and without PD. Hierarchical logistic regression models determined which inpatient mental health diagnoses were associated with PD, adjusting for demographic, payer, geographic, and hospital characteristics.ResultsWe identified 3,918,703 mental health and substance abuse hospitalizations. Of these, 2.8% (n = 104, 437) involved a person also diagnosed with PD. The majority of mental health and substance abuse patients were white (86.9% of PD vs 83.3% of non-PD). Women were more common than men in both groups (male:female prevalence ratio, PD: 0.78, 0.78-0.79, non-PD: 0.58, 0.57-0.58). Depression (adjusted odds ratio 1.32, 1.31-1.34), psychosis (adjusted odds ratio 1.25, 1.15-1.33), bipolar disorder (adjusted odds ratio 2.74, 2.69-2.79), impulse control disorders (adjusted odds ratio 1.51, 1.31-1.75), and mania (adjusted odds ratio 1.43, 1.18-1.74) were more likely among PD patients, alcohol abuse was less likely (adjusted odds ratio 0.26, 0.25-0.27). We found no PD-associated difference in suicide-related care.ConclusionsPD patients have unique patterns of acute care for mental health and substance abuse. Research is needed to guide PD treatment in individuals with pre-existing psychiatric illnesses, determine cross provider reliability of psychiatric diagnoses in PD patients, and inform efforts to improve psychiatric outcomes. © 2016 International Parkinson and Movement Disorder Society.

Dataset Information

Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis.

Objective

Materials and methods

Results

Discussion

Conclusions

Publications

Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets