Objectives: Social determinants of health (SDHs) are the complex, structural, and societal factors that are responsible for most health inequities. Since 2003, the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) has researched how SDHs place communities at risk for communicable diseases and poor adolescent health. We described the frequency and types of SDHs discussed in articles authored by NCHHSTP. Methods: We used the MEDLINE/PubMed search engine to systematically review the frequency and type of SDHs that appeared in peer-reviewed publications available in PubMed from January 1, 2009, through December 31, 2014, with a NCHHSTP affiliation. We chose search terms to identify articles with a focus on the following SDH categories: income and employment, housing and homelessness, education and schooling, stigma or discrimination, social or community context, health and health care, and neighborhood or built environment. We classified articles based on the depth of topic coverage as “substantial” (ie, one of ≤3 foci of the article) or “minimal” (ie, one of ≥4 foci of the article). Results: Of 862 articles authored by NCHHSTP, 366 (42%) addressed the SDH factors of interest. Some articles addressed >1 SDH factor (366 articles appeared 568 times across the 7 categories examined), and we examined them for each category that they addressed. Most articles that addressed SDHs (449/568 articles; 79%) had a minimal SDH focus. SDH categories that were most represented in the literature were health and health care (190/568 articles; 33%) and education and schooling (118/568 articles; 21%). Conclusions: This assessment serves as a baseline measurement of inclusion of SDH topics from NCHHSTP authors in the literature and creates a methodology that can be used in future assessments of this topic.

Since the onset of the COVID-19 pandemic, it has been unclear how vulnerable people with HIV (PwH) are to SARS-CoV-2 infection. We sought to determine if PwH are more likely to test positive for SARS-CoV-2 than people without HIV, and to identify risk factors associated with SARS-CoV-2 positivity among PwH. We conducted a cross-sectional study in which we collected electronic medical record data for all patients who underwent SARS-CoV-2 PCR testing at an academic medical center. Presence of HIV and other chronic diseases were based on the presence of ICD-10 diagnosis codes. We calculated the percent positivity for SARS-CoV-2 among PwH and among people without HIV. Among PwH, we compared demographic factors, comorbidities, HIV viral load, CD4 T-cell count, and antiretroviral therapy (ART) regimens between those who tested positive for SARS-CoV-2 and those who tested negative. Comparisons were made using chi squared tests or Wilcoxon rank sum tests. Multivariate models were created using logistic regression. Among 69,763 people tested for SARS-CoV-2, 0.6% (431) were PwH. PwH were not significantly more likely to test positive for SARS-CoV-2 than people without HIV (7.2% (31/431) vs 8.4% (5820/69763), p = 0.35), but were more likely to be younger, Black, and male (p-values < .0001). There were no significant differences in HIV clinical factors, chronic diseases, or ART regimens among PwH testing positive for SARS-CoV-2 versus those testing negative. In our sample, PwH were not more likely to contract SARS-CoV-2, despite being more likely to be members of demographic groups known to be at higher risk for infection. Differences between PwH who tested positive for SARS-CoV-2 and those who tested negative were only seen in Hispanic/Latino ethnicity (non-Hispanic or Latino vs unknown Hispanic or Latino ethnicity (OR 0.2 95% CI (0.6, 0.9)) and site of testing(inpatient vs outpatient OR 3.1 95% CI (1.3, 7.4)).

Background: Black cisgender women (hereafter referred to as “women”) experience one of the highest incidences of HIV among all populations in the United States. Pre-exposure prophylaxis (PrEP) is an effective biomedical HIV prevention option, but uptake among women is low. Despite tailored strategies for certain populations, including men who have sex with men and transgender women, Black women are frequently overlooked in HIV prevention efforts. Strategies to increase PrEP awareness and use among Black women are needed at multiple levels (ie, community, system or clinic, provider, and individual or patient). Objective: This study aimed to identify barriers and facilitators to PrEP uptake and persistence among Black cisgender women and to map implementation strategies to identified barriers using the CFIR (Consolidated Framework for Implementation Research)-ERIC (Expert Recommendations for Implementing Change) Implementation Strategy Matching Tool. Methods: We conducted a secondary analysis of previous qualitative studies completed by a multidisciplinary team of HIV physicians, implementation scientists, and epidemiologists. Studies involved focus groups and interviews with medical providers and women at a federally qualified health center in Chicago, Illinois. Implementation science frameworks such as the CFIR were used to investigate determinants of PrEP use among Black women. In this secondary analysis, data from 45 total transcripts were analyzed. We identified barriers and facilitators to PrEP uptake and persistence among cisgender women across each CFIR domain. The CFIR-ERIC Implementation Strategy Matching Tool was used to map appropriate implementation strategies to address barriers and increase PrEP uptake among Black women. Results: Barriers to PrEP uptake were identified across the CFIR domains. Barriers included being unaware that PrEP was available (characteristics of individuals), worrying about side effects and impacts on fertility and pregnancy (intervention characteristics), and being unsure about how to pay for PrEP (outer setting). Providers identified lack of training (characteristics of individuals), need for additional clinical support for PrEP protocols (inner setting), and need for practicing discussions about PrEP with women (intervention characteristics). ERIC mapping resulted in 5 distinct implementation strategies to address barriers and improve PrEP uptake: patient education, provider training, PrEP navigation, clinical champions, and electronic medical record optimization. Conclusions: Evidence-based implementation strategies that address individual, provider, and clinic factors are needed to engage women in the PrEP care continuum. Tailoring implementation strategies to address identified barriers increases the probability of successfully improving PrEP uptake. Our results provide an overview of a comprehensive, multilevel implementation strategy (ie, “POWER Up”) to improve PrEP uptake among women. International Registered Report Identifier (IRRID): RR2-10.1371/journal.pone.0285858

Introduction: Researchers in the United States have created several models to predict persons most at risk for HIV. Many of these predictive models use data from all persons newly diagnosed with HIV, the majority of whom are men, and specifically men who have sex with men (MSM). Consequently, risk factors identified by these models are biased toward features that apply only to men or capture sexual behaviours of MSM. We sought to create a predictive model for women using cohort data from two major hospitals in Chicago with large opt-out HIV screening programs. Methods: We matched 48 newly diagnosed women to 192 HIV-negative women based on number of previous encounters at University of Chicago or Rush University hospitals. We examined data for each woman for the two years prior to either their HIV diagnosis or their last encounter. We assessed risk factors including demographic characteristics and clinical diagnoses taken from patient electronic medical records (EMR) using odds ratios and 95% confidence intervals. We created a multivariable logistic regression model and measured predictive power with the area under the curve (AUC). In the multivariable model, age group, race, and ethnicity were included a priori due to increased risk for HIV among specific demographic groups. Results: The following clinical diagnoses were significant at the bivariate level and were included in the model: pregnancy (OR 1.96 (1.00, 3.84)), hepatitis C (OR 5.73 (1.24, 26.51)), substance use (OR 3.12 (1.12, 8.65)) and sexually transmitted infections (STIs) chlamydia, gonorrhoea, or syphilis. We also a priori included demographic factors that are associated with HIV. Our final model had an AUC of 0.74 and included healthcare site, age group, race, ethnicity, pregnancy, hepatitis C, substance use, and STI diagnosis. Conclusions: Our predictive model showed acceptable discrimination between those who were and were not newly diagnosed with HIV. We identified risk factors such as recent pregnancy, recent hepatitis C diagnosis, and substance use in addition to the traditionally used recent STI diagnosis that can be incorporated by health systems to detect women who are vulnerable to HIV and would benefit from preexposure prophylaxis (PrEP).

Background: Regular medical care is important for people living with HIV. A no-show predictive model among people with HIV could improve clinical care by allowing providers to proactively engage patients at high risk of missing appointments. Epic, a major provider of electronic medical record systems, created a model that predicts a patient’s probability of being a no-show for an outpatient health care appointment; however, this model has not been externally validated in people with HIV. Objective: We examined the performance of Epic’s no-show model among people with HIV at an academic medical center and assessed whether the performance was impacted by the addition of demographic and HIV clinical information. Methods: We obtained encounter data from all in-person appointments among people with HIV from January 21 to March 30, 2022, at the University of Chicago Medicine. We compared the predicted no-show probability at the time of the encounter to the actual outcome of these appointments. We also examined the performance of the Epic model among people with HIV for only HIV care appointments in the infectious diseases department. We further compared the no-show model among people with HIV for HIV care appointments to an alternate random forest model we created using a subset of seven readily accessible features used in the Epic model and four additional features related to HIV clinical care or demographics. Results: We identified 674 people with HIV who contributed 1406 total scheduled in-person appointments during the study period. Of those, we identified 331 people with HIV who contributed 440 HIV care appointments. The performance of the Epic model among people with HIV for all appointments in any outpatient clinic had an area under the receiver operating characteristic curve (AUC) of 0.65 (95% CI 0.63-0.66) and for only HIV care appointments had an AUC of 0.63 (95% CI 0.59-0.67). The alternate model we created for people with HIV attending HIV care appointments had an AUC of 0.78 (95% CI 0.75-0.82), a significant improvement over the Epic model restricted to HIV care appointments (P<.001). Features identified as important in the alternate model included lead time, appointment length, HIV viral load >200 copies per mL, lower CD4 T cell counts (both 50 to <200 cells/mm3 and 200 to <350 cells/mm3), and female sex. Conclusions: For both models among people with HIV, performance was significantly lower than reported by Epic. The improvement in the performance of the alternate model over the proprietary Epic model demonstrates that, among people with HIV, the inclusion of demographic information may enhance the prediction of appointment attendance. The alternate model further reveals that the prediction of appointment attendance in people with HIV can be improved by using HIV clinical information such as CD4 count and HIV viral load test results as features in the model.