Introduction: Mental disorders represent serious public health concerns in the U.S. Compared with Whites, racial/ethnic minority adolescents are more likely to be affected by mental disorders but less likely to use mental health services. This systematic review aimed to summarize factors related to mental health service use among minority adolescents in the U.S. as identified in previous research.Methodology: Following the PRISMA guideline, we systematically searched seven databases for peer reviewed articles related to barriers and facilitators of mental health service use among racial/ethnic minority adolescents.Results: Thirty-two quantitative studies met our inclusion criteria, among which 12 studies (37.5%) sampled mostly Blacks or African Americans, 6 studies (18.7%) focused primarily on Hispanics or Latin/a/x, including Mexican Americans and Puerto Ricans, and 4 studies (12.5%) were mostly Asian Americans (e.g., Chinese, Vietnamese). Based on the socio-ecological framework, 21 studies (65.6%) identified adolescent-related barriers and facilitators of mental health service use, including biological (e.g., age, gender), clinical (e.g., symptom severity), behavioral (e.g., drug/alcohol use), and psychological characteristics (e.g., internal asset) of minority youth. Ten studies (31.3%) identified parents-related factors that influenced minority adolescent mental health service use, including parental perceptions and beliefs, family and parenting issues, and demographic characteristics. Primary factors at the therapist level included ethnic match between patient and practitioner, relationship with healthcare practitioners, and patient-therapist co-endorsement of etiological beliefs. Fifteen studies (46.9%) identified factors influencing minority adolescent mental health service use at the contextual/structural level, including household income, insurance status, and family structure. Lastly, acculturation and school experiences were major factors at the social/cultural level that influence minority adolescent service use.Conclusion: More empirical studies are needed to understand the mechanism underlying minority adolescents' unmet mental health service needs. Culturally competent interventions are warranted to engage minority adolescents with mental disorders into treatment.
Quantifying the timing and content of policy changes affecting international travel and immigration is key to ongoing research on the spread of SARS-CoV-2 and the socioeconomic impacts of border closures. The COVID Border Accountability Project (COBAP) provides a hand-coded dataset of >1000 policies systematized to reflect a complete timeline of country-level restrictions on movement across international borders during 2020. Trained research assistants used pre-set definitions to source, categorize and verify for each new border policy: start and end dates, whether the closure is “complete” or “partial”, which exceptions are made, which countries are banned, and which air/land/sea borders were closed. COBAP verified the database through internal and external audits from public health experts. For purposes of further verification and future data mining efforts of pandemic research, the full text of each policy was archived. The structure of the COBAP dataset is designed for use by social and biomedical scientists. For broad accessibility to policymakers and the public, our website depicts the data in an interactive, user-friendly, time-based map.
To examine COVID-19 vaccination barriers in the US, this study drew on publicly available county-level data (n = 3130) to investigate the impact of vaccine hesitancy on the relationship between county-level social/structural barriers and vaccine coverage. A hierarchical regression was performed to establish the relationship between the COVID-19 Vaccine Coverage Index (CVAC) and vaccine coverage, assess the moderating effect of vaccine hesitancy on this relationship, and explore the influence of ethno-racial composition on vaccine coverage. A significant, negative relationship (r2 = 0.11, f2 = 0.12) between CVAC and vaccine coverage by county was established (step 1). When vaccine hesitancy was introduced as a moderator (step 2), the model significantly explained additional variance in vaccine coverage (r2 = 0.21, f2 = 0.27). Simple slopes analysis indicated a significant interaction effect, whereby the CVAC–vaccine coverage relationship was stronger in low hesitancy counties as compared with high hesitancy counties. Counties with low social/structural barriers (CVAC) but high hesitancy were projected to have 14% lower vaccine coverage. When county-level ethno-racial composition was introduced (step 3), higher proportions of white residents in a county predicted decreased vaccination rates (p < 0.05). Findings indicate that CVAC should be paired with vaccine hesitancy measures to better predict vaccine uptake. Moreover, counties with higher proportions of white residents led to decreases in vaccine uptake, suggesting that future intervention strategies should also target whites to reach herd immunity. We conclude that public health leaders and practitioners should address both social/structural and psychological barriers to vaccination to maximize vaccine coverage, with a particular focus on vaccine hesitancy in communities with minimal social/structural barriers.
Funded in 2021 by the Centers for Disease Control and Prevention, Communities RISE Together (RISE) aims to increase the reach and effectiveness of messages to address vaccine hesitancy to further health equity. Twitter is a predominant social media source used by communities to share messaging and factual local information with constituents. We looked at the Twitter accounts of the organizations in 10 regional communities to examine social media communication patterns to guide how to increase messaging engagement. Specifically, we focused on Twitter post content, likes, and retweets. Our findings identified certain words-such as "food," "older adults," "equity," and "covid"-that were most associated with increased likes and retweets on the platform. However, the strongest predictor of receiving likes and retweets is the number of followers. Sentiment was a significant, but not meaningful, predictor of tweet engagement.
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