JMIR Publications

ABSTRACT

With great interest we read 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 author’s aim was to use digital conversations obtained by CulturIntel to describe the mentality, key drivers, and obstacles related to depression during pre- and mid-COVID-19 times mapped to Health Belief Models (HBM). The article concluded that the conversion of digital conversations into HBM can impact individuals across races/ethnicities in asking for or receiving help for mental health. We applaud the monumental task of evaluating large data points and categorizing them to their respective population. However, even though the authors acknowledge the limitations of only using digital conversations, we wish to address two distinct issues related to demographics, particularly difficulties with addressing older populations as well as identification of racial/cultural grouping. First, sole use of digital conversations results in a selection bias against older residents. According to Pew Research reported in 2021, only 45% of those who are 65 and older used social media sites compared to 84% of those aged 18 to 29 and 81% of those aged 30 to 49 [2]. Other methods should be implemented to capture senior residents’ health beliefs conversations about depression, to capture a more accurate representation of the population to create an HBM. Furthermore, the 2019 US Census Bureau reported that there were 54.1 million residents 65= years and older in the US [3]. These data points highlight the need for diverse methods to numerically capture this important segment of society. Second, despite categorizing data into different races/ethnicities, it did not identify residency status. There is a difference between U.S born, naturalized, and non-citizen immigrants. Asian immigrants who are in the process of applying for citizenship or are ineligible in applying for citizenship are considerably more depressed than naturalized citizens, due to the fear of possible deportation [4]. The rapid growth of various Asian subgroups, each with different cultural acclimatization needs, highlights the importance of cultural awareness regarding mental health needs. Further categorization of Asian subgroups revealed that non-citizens, as well as naturalized citizens both reported worse overall mental health, as well as health in general, compared to U.S.-born counterparts. The differences are attributable to multiple socioeconomic factors including education levels, employment, insurance, and access to health care [5]. The novel way of using language processing to categorize racial/ethnic groups unfortunately is not capable at this point in separating out differences between cultural generations. It is recommended to develop and use methods that can capture specific Asian languages and ways to identify residency status to help identify generational and cultural differences. Implementing methods to cover the points mentioned can strengthen future applications of the study of the health belief model. In conclusion, we believe that adjusting for the above concerns will create an enhanced culturally competent HBM model capable of identifying specific populations as well as needs for mental health support. We look forward to future advancements in this field.