TY - JOUR
T1 - Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response
AU - Rucinski, Katherine
AU - Knight, Jesse
AU - Willis, Kalai
AU - Wang, Linwei
AU - Rao, Amrita
AU - Roach, Mary Anne
AU - Phaswana-Mafuya, Refilwe
AU - Bao, Le
AU - Thiam, Safiatou
AU - Arimi, Peter
AU - Mishra, Sharmistha
AU - Baral, Stefan
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/8
Y1 - 2024/8
N2 - Purpose of Review: Big Data Science can be used to pragmatically guide the allocation of resources within the context of national HIV programs and inform priorities for intervention. In this review, we discuss the importance of grounding Big Data Science in the principles of equity and social justice to optimize the efficiency and effectiveness of the global HIV response. Recent Findings: Social, ethical, and legal considerations of Big Data Science have been identified in the context of HIV research. However, efforts to mitigate these challenges have been limited. Consequences include disciplinary silos within the field of HIV, a lack of meaningful engagement and ownership with and by communities, and potential misinterpretation or misappropriation of analyses that could further exacerbate health inequities. Summary: Big Data Science can support the HIV response by helping to identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. However, in the absence of a guiding framework for equity, alongside meaningful collaboration with communities through balanced partnerships, a reliance on big data could continue to reinforce inequities within and across marginalized populations.
AB - Purpose of Review: Big Data Science can be used to pragmatically guide the allocation of resources within the context of national HIV programs and inform priorities for intervention. In this review, we discuss the importance of grounding Big Data Science in the principles of equity and social justice to optimize the efficiency and effectiveness of the global HIV response. Recent Findings: Social, ethical, and legal considerations of Big Data Science have been identified in the context of HIV research. However, efforts to mitigate these challenges have been limited. Consequences include disciplinary silos within the field of HIV, a lack of meaningful engagement and ownership with and by communities, and potential misinterpretation or misappropriation of analyses that could further exacerbate health inequities. Summary: Big Data Science can support the HIV response by helping to identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. However, in the absence of a guiding framework for equity, alongside meaningful collaboration with communities through balanced partnerships, a reliance on big data could continue to reinforce inequities within and across marginalized populations.
KW - Big Data Science
KW - Community HIV response
KW - Explanatory modeling
KW - HIV transmission dynamics
KW - Health equity
KW - Key populations
KW - Predictive modeling
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U2 - 10.1007/s11904-024-00702-3
DO - 10.1007/s11904-024-00702-3
M3 - Review article
C2 - 38916675
AN - SCOPUS:85196851356
SN - 1548-3568
VL - 21
SP - 208
EP - 219
JO - Current HIV/AIDS reports
JF - Current HIV/AIDS reports
IS - 4
ER -