Applying the Health Belief Model to Characterize Racial/Ethnic Differences in Digital Conversations Related to Depression Pre- and Mid-COVID-19: Descriptive Analysis

Abstract

Background The prevalence of depression in the United States is >3 times higher mid-COVID-19 versus prepandemic. Racial/ethnic differences in mindsets around depression and the potential impact of the COVID-19 pandemic are not well characterized. Objective This study aims to describe attitudes, mindsets, key drivers, and barriers related to depression pre- and mid-COVID-19 by race/ethnicity using digital conversations about depression mapped to health belief model (HBM) concepts. Methods Advanced search, data extraction, and artificial intelligence–powered tools were used to harvest, mine, and structure open-source digital conversations of US adults who engaged in conversations about depression pre- (February 1, 2019-February 29, 2020) and mid-COVID-19 pandemic (March 1, 2020-November 1, 2020) across the internet. Natural language processing, text analytics, and social data mining were used to categorize conversations that included a self-identifier into racial/ethnic groups. Conversations were mapped to HBM concepts (ie, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy). Results are descriptive in nature. Results Of 2.9 and 1.3 million relevant digital conversations pre- and mid-COVID-19, race/ethnicity was determined among 1.8 million (62.2%) and 979,000 (75.3%) conversations, respectively. Pre-COVID-19, 1.3 million (72.1%) conversations about depression were analyzed among non-Hispanic Whites (NHW), 227,200 (12.6%) among Black Americans (BA), 189,200 (10.5%) among Hispanics, and 86,800 (4.8%) among Asian Americans (AS). Mid-COVID-19, a total of 736,100 (75.2%) conversations about depression were analyzed among NHW, 131,800 (13.5%) among BA, 78,300 (8.0%) among Hispanics, and 32,800 (3.3%) among AS. Conversations among all racial/ethnic groups had a negative tone, which increased pre- to mid-COVID-19; finding support from others was seen as a benefit among most groups. Hispanics had the highest rate of any racial/ethnic group of conversations showing an avoiding mindset toward their depression. Conversations related to external barriers to seeking treatment (eg, stigma, lack of support, and lack of resources) were generally more prevalent among Hispanics, BA, and AS than among NHW. Being able to benefit others and building a support system were key drivers to seeking help or treatment for all racial/ethnic groups. Conclusions There were considerable racial/ethnic differences in drivers and barriers to seeking help and treatment for depression pre- and mid-COVID-19. As expected, COVID-19 has made conversations about depression more negative and with frequent discussions of barriers to seeking care. Applying concepts of the HBM to data on digital conversation about depression allowed organization of the most frequent themes by race/ethnicity. Individuals of all groups came online to discuss their depression. These data highlight opportunities for culturally competent and targeted approaches to addressing areas amenable to change that might impact the ability of people to ask for or receive mental health help, such as the constructs that comprise the HBM.

Patient perceptions of disease burden and treatment of myasthenia gravis based on sentiment analysis of digital conversations

“…Sources of conversations included message boards (online discussion sites where conversations take the form of posted messages, e.g., Reddit), topical sites (e.g., MGFA), social media networks (e.g., Facebook, Instagram, X [formerly Twitter]), content-sharing sites (e.g., TikTok, YouTube), blogs, and comments. Topical data were extracted and tagged by origin and user based on self-identification in the conversations or in public profiles using the CulturIntel™ methodology, which has been described previously 7 , 8 . Each unique relevant digital conversation was included in the analysis, forming a large unstructured dataset.…”

“…Themes such as symptoms, diagnostic journey, lived patient experiences, clinical and socioeconomic burden of disease, and treatment patterns and preferences can be identified easily via machine-learning techniques and quantified to reflect the frequency, and hence importance, of these discussions among patients 3 – 6 . The methodology has already been used to analyze attitudes toward depression among different ethnic groups 7 , 8 , barriers to breast cancer treatments 9 , and perceptions about suicidality among patients with epilepsy 10 .…”

Selection Bias in Digital Conversations on Depression Before and During COVID-19

“…We read with great interest the article, “Applying the Health Belief Model to Characterize Racial/Ethnic Differences in Digital Conversations Related to Depression Pre- and Mid-COVID-19: Descriptive Analysis” by Castilla-Puentes et al [ 1 ]. The authors’ aim was to use digital conversations obtained by CulturIntel to describe the mentality, key drivers, and obstacles related to depression before and during the COVID-19 pandemic mapped to health belief model (HBM) concepts.…”