The individual, interactive, and syndemic effect of substance use, depression, education, and ethnicity on retention in HIV care

Psychosocial Syndemic Burden, Sexual Behaviors, and Engagement in HIV Prevention Care Among Sexual and Gender Minority Youths: United States, 2022

Objectives. To examine linear and nonlinear associations between psychosocial syndemic factors and HIV risk and engagement in HIV prevention care among sexual and gender minority (SGM) youths. Methods. Between February and October 2022, we recruited 17 578 SGM youths aged 13 to 18 years in the United States for an online survey. We examined the relationship of syndemics (i.e., binge drinking, drug use, sexual victimization, and anti-lesbian, gay, bisexual, and transgender discrimination) with sexual behaviors (i.e., sexual initiation, condomless anal or vaginal sex, and number of sexual partners) and HIV prevention care (i.e., HIV testing, preexposure prophylaxis awareness and utilization) using regression. Results. Psychosocial syndemic burden (number of syndemic factors reported) was linearly and cubically associated with engagement in sexual behaviors. Psychosocial syndemic burden was linearly associated with higher HIV testing and preexposure prophylaxis awareness and cubically associated with higher preexposure prophylaxis utilization. Conclusions. Our findings are evidence of synergism across psychosocial syndemic factors regarding HIV risk and engagement in HIV prevention care among SGM youths in the United States. Public Health Implications. Multicomponent interventions may help reduce HIV risk and promote access to HIV prevention services among SGM individuals aged 13 to 18 years. (Am J Public Health. 2024;114(9):892-902. https://doi.org/10.2105/AJPH.2024.307753).

The intersecting effects of race, wealth, and education on AIDS incidence, mortality, and case-fatality rate: a Brazilian cohort study of 28.3 million individuals

The relationships between race, education, wealth, their intersections and AIDS morbidity/mortality were analyzed in retrospective cohort of 28.3 million individuals followed for 9 years (2007-2015). Together with several sensitivity analyses, a wide range of interactions on additive and multiplicative scales were estimated. Race, education, and wealth were each strongly associated with all of the AIDS-related outcomes, and the magnitude of the associations increased as intersections were included. A significantly higher risk of illness (aRR: 3.07, 95%CI:2.67-3.53) and death (aRR: 4.96, 95%CI:3.99-6.16) from AIDS was observed at the intersection of Black race, lower educational attainment, and less wealth. A higher case-fatality rate (aRR: 1.62, 95%CI:1.18-2.21) was also seen for the same intersectional group. Historically oppressed groups lying at the intersections of race, education, and wealth, had a considerably higher risk of illness and death from AIDS. AIDS-related interventions will require the implementation of comprehensive intersectoral policies that follow an intersectionality perspective.

Syndemic of Substance Abuse, Depression, and Type 2 Diabetes and Their Association with Retention in Care Among People Living with HIV

Substance abuse (SA), depression, and type 2 diabetes (DM2) often co-occur among people living with HIV (PLHIV). Guided by a syndemic framework, this cross-sectional retrospective study examined the cumulative and interaction effects of SA, depression, and DM2 on retention in HIV care (RIC) among 621 PLHIV receiving medical care in central Pennsylvania. We performed logistic regression analysis to test the associations between SA, depression, and DM2 and RIC. To test the "syndemic" model, we assessed additive and multiplicative interactions. In an unadjusted model, a dose-response pattern between the syndemic index (total number of health conditions) and RIC was detected (OR for 1 syndemic factor vs. none: 1.01, 95% CI: 0.69-1.47; 2 syndemic factors: 1.59, 0.89-2.84; 3 syndemic factors: 1.62, 0.44-5.94), but no group reached statistical significance. Interactions on both additive and multiplicative scales were not significant, demonstrating no syndemic effect of SA, depression, and DM2 on RIC among our study sample. Our findings highlight that comorbid conditions may, in some populations, facilitate RIC rather than act as barriers, which may be due to higher levels of engagement with medical care.

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation

BACKGROUND

Reducing care lapses for people living with HIV is critical to ending the HIV epidemic and beneficial for their health. Predictive modeling can identify clinical factors associated with HIV care lapses. Previous studies have identified these factors within a single clinic or using a national network of clinics, but public health strategies to improve retention in care in the United States often occur within a regional jurisdiction (eg, a city or county).

OBJECTIVE

We sought to build predictive models of HIV care lapses using a large, multisite, noncurated database of electronic health records (EHRs) in Chicago, Illinois.

METHODS

We used 2011-2019 data from the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a database including multiple health systems, covering the majority of 23,580 people with an HIV diagnosis living in Chicago. CAPriCORN uses a hash-based data deduplication method to follow people across multiple Chicago health care systems with different EHRs, providing a unique citywide view of retention in HIV care. From the database, we used diagnosis codes, medications, laboratory tests, demographics, and encounter information to build predictive models. Our primary outcome was lapses in HIV care, defined as having more than 12 months between subsequent HIV care encounters. We built logistic regression, random forest, elastic net logistic regression, and XGBoost models using all variables and compared their performance to a baseline logistic regression model containing only demographics and retention history.

RESULTS

We included people living with HIV with at least 2 HIV care encounters in the database, yielding 16,930 people living with HIV with 191,492 encounters. All models outperformed the baseline logistic regression model, with the most improvement from the XGBoost model (area under the receiver operating characteristic curve 0.776, 95% CI 0.768-0.784 vs 0.674, 95% CI 0.664-0.683; P<.001). Top predictors included the history of care lapses, being seen by an infectious disease provider (vs a primary care provider), site of care, Hispanic ethnicity, and previous HIV laboratory testing. The random forest model (area under the receiver operating characteristic curve 0.751, 95% CI 0.742-0.759) revealed age, insurance type, and chronic comorbidities (eg, hypertension), as important variables in predicting a care lapse.

CONCLUSIONS

We used a real-world approach to leverage the full scope of data available in modern EHRs to predict HIV care lapses. Our findings reinforce previously known factors, such as the history of prior care lapses, while also showing the importance of laboratory testing, chronic comorbidities, sociodemographic characteristics, and clinic-specific factors for predicting care lapses for people living with HIV in Chicago. We provide a framework for others to use data from multiple different health care systems within a single city to examine lapses in care using EHR data, which will aid in jurisdictional efforts to improve retention in HIV care.

Multicenter Development and Validation of a Model for Predicting Retention in Care Among People with HIV

Predictive analytics can be used to identify people with HIV currently retained in care who are at risk for future disengagement from care, allowing for prioritization of retention interventions. We utilized machine learning methods to develop predictive models of retention in care, defined as no more than a 12 month gap between HIV care appointments in the Center for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort. Data were split longitudinally into derivation and validation cohorts. We created logistic regression (LR), random forest (RF), and gradient boosted machine (XGB) models within a discrete-time survival analysis framework and compared their performance to a baseline model that included only demographics, viral suppression, and retention history. 21,267 Patients with 507,687 visits from 2007 to 2018 were included. The LR model outperformed the baseline model (AUC 0.68 [0.67-0.70] vs. 0.60 [0.59-0.62], P < 0.001). RF and XGB models had similar performance to the LR model. Top features in the LR model included retention history, age, and viral suppression.

Context, COVID-19 and comorbidities: exploring emergent directions in syndemics and HIV research

PURPOSE OF REVIEW

The purpose of this review is to identify themes across articles that aimed to explore HIV-related syndemics in 2020 and 2021 and to discuss their implications for research on syndemics.

RECENT FINDINGS

We identified 189 articles on syndemics between 2020 and 2021. Key themes across studies included COVID-19; mental health and psychosocial challenges; substance use; socio-structural factors; protective factors; and methodological approaches. COVID-19's implications for HIV syndemic research were discussed. Mental health and substance use research largely examined linkages with sexual practices or reduced HIV care retention. Researchers examined associations between socio-structural variables (e.g. poverty) and elevated HIV exposure, reduced HIV testing and poorer health. Concepts of water insecurity and 'ecosyndemics' were also raised, as was the importance of attending to noncommunicable diseases and comorbidities. Most studies did not assess interactions between health conditions, signalling the need for methodological grounding in the foundational concepts of syndemic theory.

SUMMARY

Most studies recommended that HIV prevention and care research attend to the interplay between poor mental health, substance use and multidimensional violence. Increased attention to structural factors, particularly exacerbated poverty in the COVID-19 pandemic, is required. Research can identify protective factors to harness to advance HIV prevention and care.