Search

Evolution of family leisure time have increased the demand for attractive destinations. In tourism, destination choice (DC) is considered a critical, complex and contextual decision. This, first of its kind, comprehensive, mix method bibliometric analysis of 627 papers from Scopus database spanning 25 years, is aimed to identify and review the most influential DC literature. It shows that DC is an interdisciplinary and evolving subject. Five themes, namely DC conceptualization, DC decision-making, DC modelling, DC internal and external factors are prevalent. DC conceptualization is explained by modified TPB and other models. DC decision-making process is hierarchical, sequential, multistage and varies across psychological, demographic and social profiles. Multiple choice, econometric and utility maximization models along with push–pull internal and external motivational factors have been extensively published. Growing academic interest in DC is evident from a threefold jump in average number of articles post-2007, 2500+ citations in 2019 out of 21,664 across 25 years, a growing trend of cross-country collaboration, rising number of publications in mainstream journals and 9 out of top 10 authors getting published in the short period of 2008–2016. Future research themes include empirical investigations of models, non-linear multifaceted decision-making and the pandemic's impact on motivational factors. [ABSTRACT FROM AUTHOR]

Objective: To compare rates and risk factors of severe COVID-19-related outcomes between American Indian/Alaska Native (AI/AN) and non-Hispanic White people (NHW). Methods: Aggregate Social Vulnerability Index (SVI), COVID-19-related risk factor, hospitalization, and mortality data were obtained from 16 states for January 1, 2020-March 31, 2021. Generalized estimating equation Poisson regression models calculated age-adjusted cumulative incidences, incidence ratios (IR), and 95% confidence intervals (CI) comparing AI/AN and NHW persons by age, sex, and county-level SVI status. Results: Race data were missing for 42.7% of COVID-19 cases, 24.7% of hospitalizations, and 10.1% of deaths. Risk of AI/AN COVID-19 mortality was 2.6 times that of NHW persons (IR 2.6, 95% CI: 1.7 - 3.4); risk of COVID-19-related hospitalization among AI/AN persons was 3.5 times that of NHW (IR: 3.5, 95% CI: 2.7 - 4.3). Severe COVID-19 outcomes were significantly higher for AI/AN persons compared to NHW persons across all age and sex groups. There was no statistically significant difference in COVID-19 outcomes by SVI status. Associations between severe COVID-19 outcomes and co-morbid risk factors were inconsistent. Conclusions: Results describe increased risk of severe COVID-19 outcomes for AI/AN persons compared to NHW persons despite quality issues in public health surveillance data. Data linkages and improved ascertainment reduce race/ethnicity misclassification and improve data quality. COVID-19-related health burdens among AI/AN persons warrant improved access for AI/AN communities to medical countermeasures and healthcare resources. [ABSTRACT FROM AUTHOR]