ArticleBMJ open2019
Using big data analytics to improve HIV medical care utilisation in South Carolina: A study protocol.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed.
- Prior-Year Substance Use Disorders and Subsequent HIV Retention in Care Among Men Living with HIV Who Have Sex with Men.AIDS and behavior · 2026Article
- Geospatial Accessibility Factors Influencing HIV Care Continuum Outcomes in South Carolina.AIDS and behavior · 2026Article
- Temporal network analysis of comorbidities among people with HIV in South Carolina.HIV medicine · 2026Article
- Leveraging Electronic Health Records and Claims Data to Improve HIV and Comorbidity Care Trajectories: A Scoping Review.Current HIV/AIDS reports · 2025Article
- Longitudinal assessments of viral rebound among people with HIV in South Carolina: a population-based cohort study.AIDS care · 2025Article
- AI applications in HIV research: advances and future directions.Frontiers in microbiology · 2025Review
- Longitudinal Viral Load Clustering for People With HIV Using Functional Principal Component Analysis.AIDS research and treatment · 2025Article
- Article
- The Impacts of HIV-Related Service Interruptions During the COVID-19 Pandemic: Protocol of a Mixed Methodology Longitudinal Study.AIDS and behavior · 2024Article
- Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction.Biometrical journal. Biometrische Zeitschrift · 2024Article
- The dynamic risk factors of cardiovascular disease among people living with HIV: a real-world data study.BMC public health · 2024Article
- Retrospective Review of Complications and Revision Rates Between Isolated Talonavicular vs Talonavicular and Subtalar (Double) Arthrodesis vs Triple Arthrodesis.Foot & ankle orthopaedics · 2024Article
- Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience.Learning health systems · 2024Article
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- Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol.BMC infectious diseases · 2022Article
- Moderation effect of community health on the relationship between racial/ethnic residential segregation and HIV viral suppression in South Carolina: A county-level longitudinal study from 2013 to 2018.Frontiers in public health · 2022Article
- Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case.JMIR medical informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
introductionLinkage and retention in HIV medical care remains problematic in the USA. Extensive health utilisation data collection through electronic health records (EHR) and claims data represent new opportunities for scientific discovery. Big data science (BDS) is a powerful tool for investigating HIV care utilisation patterns. The South Carolina (SC) office of Revenue and Fiscal Affairs (RFA) data warehouse captures individual-level longitudinal health utilisation data for persons living with HIV (PLWH). The data warehouse includes EHR, claims and data from private institutions, housing, prisons, mental health, Medicare, Medicaid, State Health Plan and the department of health and human services. The purpose of this study is to describe the process for creating a comprehensive database of all SC PLWH, and plans for using BDS to explore, identify, characterise and explain new predictors of missed opportunities for HIV medical care utilisation. METHODS AND ANALYSIS: This project will create person-level profiles guided by the Gelberg-Andersen Behavioral Model and describe new patterns of HIV care utilisation. The population for the comprehensive database comes from statewide HIV surveillance data (2005-2016) for all SC PLWH (N≈18000). Surveillance data are available from the state health department's enhanced HIV/AIDS Reporting System (e-HARS). Additional data pulls for the e-HARS population will include Ryan White HIV/AIDS Program Service Reports, Health Sciences SC data and Area Health Resource Files. These data will be linked to the RFA data and serve as sources for traditional and vulnerable domain Gelberg-Anderson Behavioral Model variables. The project will use BDS techniques such as machine learning to identify new predictors of HIV care utilisation behaviour among PLWH, and 'missed opportunities' for re-engaging them back into care. ETHICS AND DISSEMINATION: The study team applied for data from different sources and submitted individual Institutional Review Board (IRB) applications to the University of South Carolina (USC) IRB and other local authorities/agencies/state departments. This study was approved by the USC IRB (#Pro00068124) in 2017. To protect the identity of the persons living with HIV (PLWH), researchers will only receive linked deidentified data from the RFA. Study findings will be disseminated at local community forums, community advisory group meetings, meetings with our state agencies, local partners and other key stakeholders (including PLWH, policy-makers and healthcare providers), presentations at academic conferences and through publication in peer-reviewed articles. Data security and patient confidentiality are the bedrock of this study. Extensive data agreements ensuring data security and patient confidentiality for the deidentified linked data have been established and are stringently adhered to. The RFA is authorised to collect and merge data from these different sources and to ensure the privacy of all PLWH. The legislatively mandated SC data oversight council reviewed the proposed process stringently before approving it. Researchers will get only the encrypted deidentified dataset to prevent any breach of privacy in the data transfer, management and analysis processes. In addition, established secure data governance rules, data encryption and encrypted predictive techniques will be deployed. In addition to the data anonymisation as a part of privacy-preserving analytics, encryption schemes that protect running prediction algorithms on encrypted data will also be deployed. Best practices and lessons learnt about the complex processes involved in negotiating and navigating multiple data sharing agreements between different entities are being documented for dissemination.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.