Stigma Scale Adaptation and Validation for Measuring COVID-19 Stigma.

BACKGROUND: The current coronavirus disease 2019 (COVID-19) pandemic has been found to be associated with increased psychosocial problems such as depression, anxiety, stress, and stigma. Many health-related stigma instruments that have been developed are condition-specific; these should be adapted and validated for generic use, across different health conditions. This study was conducted to measure stigma, stress, anxiety, and depression using the COVID-19 Stigma Scale-Modified (CSS-M), a modified version of the HIV Stigma Scale, among the Indian population. METHODS: A weblink-based online survey was conducted using the adapted CSS-M, along with the Depression, Anxiety, and Stress Scale-21. Collected data were analyzed with correlation analysis, reliability analysis, exploratory factor analysis, and convergent and divergent validity. RESULTS: With a sample size of 375, the modified scale for COVID-19 stigma showed internal consistency and a good inter-item correlation (Cronbach's alpha 0.821). Principal axis factoring with varimax rotation along with alternative parallel analysis established the two factorial structure and had valid composite reliability, discriminate validity, and partial convergent validity. CONCLUSION: We found that COVID-19 Stigma Scale-Modified is a valid measure to assess COVID-19-related stigma. The scale was found to be internally consistent with a good inter-item correlation, composite reliability, valid discriminate validity, and partial convergent validity. Specific COVID-related validated scales for stigma should be developed in the future.

Excessive and Unreliable Health Information and Its Predictability for Anxiety: A Cross-Sectional Observational Study.

INTRODUCTION: Being ignorant or unaware is not expected in a situation like the pandemic of COVID-19 with modern internet connectivity and the era of social media. However, information overload may itself lead to health anxiety. AIMS AND OBJECTIVES: This study investigated the predictability of health anxiety with information overload and sociodemographic profiles during the COVID-19 pandemic. MATERIALS AND METHODS: A cross-sectional study was done among 400 caretakers of non-covid patients in a tertiary healthcare medical college. The consenting participants provided their sociodemographic details and responded to the short health anxiety inventory (SHAI), Beck anxiety inventory (BAI) and Information overload scale (IOS) for COVID-19. RESULTS: A total number of 400 participants aged 35.58 ± 10.57 years participated and out of which 88.2% acknowledged health-related anxiety and 56.8% for excessive use of social media. BAI measured anxiety was mild for 19.8%, moderate for 3.5% and severe for 3%. The linear regression analysis predicted health anxiety by three variables only: total anxiety as measured by the Beck anxiety inventory [ß = 0.416, t = 9.318, p = 0.000], information overload (rejection of information) [ß = 0.171, t = 3.126, p = 0.002], and excessive use of social media [ß = 0.124, t = 2.888, p = 0.004]. CONCLUSION: Information overload, its rejection and excessive use of social media were found to be predictive of health-related anxiety.