Methodologies used in studies examining substance abuse, violence and HIV/AIDS (SAVA) constructs using a syndemic framework: a scoping review.
Jennifer V ChavezPiao WangMichaela E LarsonVicky VazquezMario R De La RosaVictoria Behar-ZusmanPublished in: AIDS care (2023)
The syndemic theoretical framework has been used in health disparities research to explain several co-occurring epidemics, particularly in populations facing disparate health conditions. A prominent example of this is seen in Singer's Substance Abuse, Violence and HIV/AIDS (SAVA) syndemic theory. However, even though numerous studies support some of the theoretical underpinnings of the SAVA syndemic, the empirical applications of the theory remain methodologically underdeveloped. The current review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review (PRISMA-ScR), to present the state of the science of methodologies examining SAVA constructs using the syndemic framework. Seven bibliographic databases were searched with no language or date restrictions. Studies were synthesized by author, year of publication, study location, total sample size, study population, SAVA outcomes, analytic method of SAVA measurement, intervention type, level of influence, disease interaction and concentration, main findings of the study, and possible future areas of research. Our search yielded a total of 967 articles, and 123 were included in the review. Methodologic and statistical innovation is needed to elevate the impact of syndemic theory for elucidating the synergistic effects of determinants leading to health disparities.
Keyphrases
- randomized controlled trial
- meta analyses
- hiv aids
- public health
- mental health
- healthcare
- systematic review
- antiretroviral therapy
- type diabetes
- emergency department
- autism spectrum disorder
- metabolic syndrome
- social media
- machine learning
- human immunodeficiency virus
- insulin resistance
- climate change
- hepatitis c virus
- health promotion
- deep learning
- drug induced
- health insurance