Beyond binary retention in HIV care

Lee, Hana; Wu, Xiaotian K.; Genberg, Becky L.; Mugavero, Michael J.; Cole, Stephen R.; Lau, Bryan; Hogan, Joseph W.

doi:10.1097/qad.0000000000001936

Cited by 36 publications

(18 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These patients can benefit from tailored support services. Research on patterns of care over time could provide a better understanding of factors associated with patient dropout from care ( 15 ). Patients might respond well to knowledge of the personal and preventive benefits of treatment.…”

Section: Discussionmentioning

confidence: 99%

Vital Signs:HIV Transmission Along the Continuum of Care — United States, 2016

Purcell

Sansom

et al. 2019

MMWR Morb. Mortal. Wkly. Rep.

218

176

View full text Add to dashboard Cite

Background In 2016, an estimated 1.1 million persons had human immunodeficiency virus (HIV) infection in the United States; 38,700 were new infections. Knowledge of HIV infection status, behavior change, and antiretroviral therapy (ART) all prevent HIV transmission. Persons who achieve and maintain viral suppression (achieved by most persons within 6 months of starting ART) can live long, healthy lives and pose effectively no risk of HIV transmission to their sexual partners. Methods A model was used to estimate transmission rates in 2016 along the HIV continuum of care. Data for sexual and needle-sharing behaviors were obtained from National HIV Behavioral Surveillance. Estimated HIV prevalence, incidence, receipt of care, and viral suppression were obtained from National HIV Surveillance System data. Results Overall, the HIV transmission rate was 3.5 per 100 person-years in 2016. Along the HIV continuum of care, the transmission rates from persons who were 1) acutely infected and unaware of their infection, 2) non-acutely infected and unaware, 3) aware of HIV infection but not in care, 4) receiving HIV care but not virally suppressed, and 5) taking ART and virally suppressed were 16.1, 8.4, 6.6, 6.1, and 0 per 100 person-years, respectively. The percentages of all transmissions generated by each group were 4.0%, 33.6%, 42.6%, 19.8%, and 0%, respectively. Conclusion Approximately 80% of new HIV transmissions are from persons who do not know they have HIV infection or are not receiving regular care. Going forward, increasing the percentage of persons with HIV infection who have achieved viral suppression and do not transmit HIV will be critical for ending the HIV epidemic in the United States.

show abstract

Section: Discussionmentioning

confidence: 99%

Vital Signs:HIV Transmission Along the Continuum of Care — United States, 2016

Purcell

Sansom

et al. 2019

MMWR Morb. Mortal. Wkly. Rep.

218

176

View full text Add to dashboard Cite

show abstract

“…Furthermore, while most prior literature regarding retention in care examines factors associated with retention at a single point in time, our model dynamically predicts retention longitudinally. Patients' appointment attendance patterns change over time, with patients often transitioning in and out of care 41 . The method we developed provides a retention risk score at the visit level and recalculates the score at each subsequent visit, incorporating new data that becomes available as well as characteristics that change over time (e.g., prior appointment attendance, HIV viral load, substance use patterns, change of address, etc.…”

Section: Discussionmentioning

confidence: 99%

Predictive Analytics for Retention in Care in an Urban HIV Clinic

Ramachandran

Kumar

Koenig

et al. 2020

Sci Rep

View full text Add to dashboard Cite

consistent medical care among people living with HiV is essential for both individual and public health. HIV-positive individuals who are 'retained in care' are more likely to be prescribed antiretroviral medication and achieve HIV viral suppression, effectively eliminating the risk of transmitting HIV to others. However, in the United States, less than half of HIV-positive individuals are retained in care. Interventions to improve retention in care are resource intensive, and there is currently no systematic way to identify patients at risk for falling out of care who would benefit from these interventions. We developed a machine learning model to identify patients at risk for dropping out of care in an urban HIV care clinic using electronic medical records and geospatial data. The machine learning model has a mean positive predictive value of 34.6% [SD: 0.15] for flagging the top 10% highest risk patients as needing interventions, performing better than the previous state-of-the-art logistic regression model (PPV of 17% [SD: 0.06]) and the baseline rate of 11.1% [SD: 0.02]. Machine learning methods can improve the prediction ability in HIV care clinics to proactively identify patients at risk for not returning to medical care. Consistent medical care is essential for the health of people living with HIV. HIV-positive individuals who receive regular medical care are more likely to receive antiretroviral therapy, less likely to develop Acquired Immune Deficiency Syndrome (AIDS), and have improved survival rates compared to HIV-positive individuals who do not receive regular medical care 1-3. In the field of HIV medicine, patients who receive regular medical care are considered 'retained in care. ' Retention in care is not only important for the individual health of people living with HIV, but also for public health. HIV-positive individuals who are retained in care and taking antiretroviral therapy are able to suppress the HIV viral level in their serum to undetectable levels, effectively eliminating the risk of transmitting HIV to others. Accordingly, retention in care is a critical pillar of public health agency plans to eliminate HIV transmission in the United States 4-6. Despite the clear benefits of retention in care for individual and public health, less than half of individuals living with HIV in the U.S. are retained in care. Lack of access to healthcare is one reason that patients may not be retained in care 7. However, for patients who lack health insurance, state and federal programs such as the Ryan White HIV/AIDS Program provide funding to pay for HIV care visits and antiretroviral medications. Despite these programs, many patients living with HIV still do not regularly attend medical appointments. Additional barriers to retention in care remain, including mental illness, substance use, insecure housing, poverty, neighborhood violence, and stigma 8-16. Interventions that are effective for improving retention in care include intensive case management, peer navigation, and multi-faceted outreach progra...

show abstract

“…On the other hand, retention was defined as a contact with health services in specific time intervals after linkage in most studies, but a considerable number of studies defined it as a contact with health services after linkage without further requirements. There are [19] The authors presented the dynamic patterns of care and retention using multistate methodology (Method described in: Lee H, et al, A state transition framework for patient-level modeling of engagement and retention in HIV care using longitudinal cohort data. Statistics in Medicine, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…A graph illustrating this is presented in the Supplement. Recent studies have highlighted the importance of investigating, in addition to the standard cross-sectional CoC, time spend in each of the CoC stages [19,24,34]. Among the reviewed articles, a relatively small number have recently taken efforts to construct longitudinal CoC, most of them focusing on the time to reaching the next CoC stages and risk factors for delays between stages (Supplementary Table ).…”

Section: Discussionmentioning

confidence: 99%

HIV continuum of care: expanding scope beyond a cross-sectional view to include time analysis: a systematic review

et al. 2021

View full text Add to dashboard Cite

Introduction The continuum of care (CoC) model has been used to describe the main pillars of HIV care. This study aims to systematically review methods and elucidate gaps in the CoC analyses, especially in terms of the timing of the progression through steps, recognized nowadays as a critical parameter for an effective response to the epidemic. Methods A PubMed and EMBASE databases search up to December 2019 resulted in 1918 articles, of which 209 were included in this review; 84 studies presented in major HIV conferences were also included. Studies that did not provide explicit definitions, modelling studies and those reporting only on metrics for subpopulations or factors affecting a CoC stage were excluded. Included articles reported results on 1 to 6 CoC stages. Results Percentage treated and virally suppressed was reported in 78%, percentage diagnosed and retained in care in 58%, percentage linked to care in 54% and PLHIV in 36% of the articles. Information for all stages was provided in 23 studies. Only 6 articles use novel CoC estimates: One presents a dynamic CoC based on multistate analysis techniques, two base their time-to-next-stage estimates on a risk estimation method based on the cumulative incidence function, weighted for confounding and censoring and three studies estimated the HIV infection time based on mathematical modelling. Conclusion A limited number of studies provide elaborated time analyses of the CoC. Although time analyses lack the straightforward interpretation of the cross-sectional CoC, they provide valuable insights for the timely response to the HIV epidemic. A future goal would be to develop a model that retains the simplicity of the cross-sectional CoC but also incorporates timing between stages.

show abstract

Beyond binary retention in HIV care

Cited by 36 publications

References 27 publications

Vital Signs:HIV Transmission Along the Continuum of Care — United States, 2016

Vital Signs:HIV Transmission Along the Continuum of Care — United States, 2016

Predictive Analytics for Retention in Care in an Urban HIV Clinic

HIV continuum of care: expanding scope beyond a cross-sectional view to include time analysis: a systematic review

Contact Info

Product

Resources

About