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.

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.

Background: Current guidelines recommend concurrent screening for HIV and syphilis with gonorrhea and chlamydia testing. Despite this, many patients are still not screened. This study describes trends in demographics and encounter locations associated with missed opportunities for HIV and syphilis screening among patients tested for gonorrhea or chlamydia. Methods: This is a retrospective review of all encounters with gonorrhea or chlamydia testing in a large, urban hospital from November 1, 2018, to July 31, 2021. Demographic information and encounter location were extracted from the medical record. Encounters were categorized as including both HIV and syphilis (complete) screening, HIV screening only, or neither. Logistic regression was used to examine associations between demographics and encounter location and likelihood of complete screening. Results: There were 42,791 patient encounters, of which 40.2% had complete screening, 6.2% had concurrent HIV screening only, and 53.6% had no concurrent screening. Increasing age, female sex (adjusted odds ratio [aOR], 0.58; 95% confidence interval [CI], 0.55–0.61; P < 0.01), non-Hispanic Black race (aOR, 0.52; 95% CI, 0.49–0.55; P < 0.01), and public insurance (aOR, 0.72; 95% CI, 0.69–0.75; P < 0.01) were associated with lower odds of complete screening. Emergency department (ED) encounters were most likely to include complete screening (aOR, 3.11; 95% CI, 2.96–3.26; P < 0.01). Conclusions: This study found that a large proportion of patients tested for gonorrhea and chlamydia had missed opportunities for HIV and syphilis screening. Significant demographic disparities were found. The emergency department was most likely to screen for both HIV and syphilis. Decreasing disparities in screening could have profound effects on the HIV and syphilis epidemics.

Despite representing a disproportionately high percentage of new HIV diagnoses in France annually, women who have migrated from Sub-Saharan Africa (WMSSA) remain underserved by HIV prevention strategies, including Pre-Exposure Prophylaxis (PrEP). This study aimed to understand healthcare providers’ experiences and attitudes toward PrEP delivery to WMSSA within family planning centers (FPCs) of the Paris region in France. We conducted a web-based cross-sectional survey from February to June 2024 to explore the knowledge, attitudes, and experiences of providers in FPCs in Paris and Seine-Saint-Denis (SSD) County. The survey link was emailed to FPC providers via their departmental mailing lists. Of the 284 providers who were contacted, 64 completed the survey (response rate of 23%). Respondents were predominantly women (95%), with a median age of 44 (IQR 35–53) and a median of 17.5 (IQR 10–26) years of professional experience. They worked as physicians (44%), midwives (34%), or nurses (22%), primarily in FPCs within SSD County (77%). All providers had heard of PrEP; 42% had already discussed it with a client; 28% reported PrEP prescriptions being offered in their FPC; and 21% had already prescribed it for a woman. Among participants, 42% had received PrEP training, and 53% rated their overall PrEP knowledge as good or very good. About one-third of providers reported feeling uncomfortable discussing or prescribing PrEP to women. The top three barriers to PrEP implementation were the lack of PrEP awareness among clients (32%), inadequate provider training (21%), and the limited number of PrEP prescribers in FPCs (21%). Providers endorsed multiple interventions to increase PrEP delivery, including PrEP training, educational materials, and policy shifts to broaden prescriber roles. FPC providers in Paris and SSD County have limited experience in delivering PrEP to women. Several facilitators were identified to inform PrEP implementation strategies at the provider, client and structural levels.

Background: Preexposure prophylaxis (PrEP) is a medication that prevents HIV infection. However, PrEP is not equally used by all who would benefit. We examined PrEP disparities by comparing the number of sexually transmitted infections (STIs) and PrEP prescriptions across sex, race, and ethnic groups from 2 federally qualified health centers (FQHCs) in Pinellas County, Florida, and Chicago, Illinois. Methods: We obtained limited electronic medical record data from January 1, 2023, to December 31, 2023, and identified STI diagnoses (gonorrhea, chlamydia, or syphilis) and PrEP initiation prescriptions. We calculated the PrEP initiation rate for different sex, race, and ethnicity groups; the rate difference in PrEP initiations for each demographic group compared with that in White men; and the ratio of PrEP initiations to STI diagnoses in each group. Results: Out of a total of 63,591 patients, 1208 (1.9%) were diagnosed with an STI and 88 (0.1%) had PrEP initiation prescriptions. There was no rate difference per 100 people in PrEP initiation among Black and White men, but Hispanic men (−0.11), Black women (−0.14), White women (−0.24), and Hispanic women (−0.25) demonstrated PrEP disparities. When examining the ratio of PrEP initiations to STI diagnoses in each group, White men had (0.42) PrEP initiations per STI diagnosis, with much lower ratios seen for Hispanic men (0.2), Black men (0.13), Black women (0.04), White women (0.03), and Hispanic women (0.02). Conclusions: White men had the highest rate of initial PrEP prescriptions. Men overall received PrEP between 3 and 21 times more frequently per STI diagnosis than women.

This study assessed the performance of syphilis serology alone versus the gold standard of a reactive rapid plasma reagin plus clinical history for identifying active syphilis infections in a universal emergency department screening setting, an approach that could be helpful for researchers analyzing large datasets or clinicians working in limited resource settings.