HIV and Syphilis Co-Screening Rates Among Patients Tested for Gonorrhea and Chlamydia at a Large, Urban Hospital.

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 (aOR 0.58, 95% 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 a large proportion of patients tested for gonorrhea and chlamydia had missed opportunities for HIV and syphilis screening. Significant demographic disparities were found. The ED was most likely to screen for both HIV and syphilis. Decreasing disparities in screening could have profound effects on the HIV and syphilis epidemics.

Data to Care Pilot Program in Chicago: Experience, Outcomes, and Direction for the Future.

CONTEXT: Data to Care (D2C) involves sharing HIV surveillance data between health care facilities and health departments to improve continuity of care for people living with HIV (PLWH). The Chicago Department of Public Health (CDPH) initiated a D2C pilot program at the University of Chicago Medicine (UCM) from June 2016 to September 2019. OBJECTIVES: To describe the proportion of patients reported by UCM as not in care who were able to be matched to CDPH enhanced HIV/AIDS Reporting System (eHARS) and to report the individual-level factors associated with matching and viral suppression. DESIGN: Retrospective program evaluation. SETTING: UCM, an academic health care center that provides HIV care to adults via a Ryan White clinic on the south side of Chicago. PARTICIPANTS: Adult PLWH who had received care at UCM but did not have current documented HIV care visit(s). MAIN OUTCOME MEASURE: Proportion of matched patients; factors associated with matching and viral suppression. RESULTS: Overall, 72.4% (n = 813/1123) of patients reported by UCM were matched by CDPH to eHARS. Individuals aged 40 to 49 years (odds ratio [OR] = 1.99; 95% confidence interval [CI], 1.10-3.62), 50 to 59 years (OR = 2.47; 95% CI, 1.37-4.47), and 60 years or older (OR = 6.18; 95% CI, 3.18-12.32) were more likely to match in eHARS. People who lived outside of Chicago (OR = 0.09; 95% CI, 0.05-0.15) or with unknown zip codes (OR = 0.08; 95% CI, 0.05-0.12) were less likely to match. Men who have sex with men and persons older than 50 years were more likely to be virally suppressed. CONCLUSIONS: D2C is an evidence-based strategy for reengagement of PLWH; however, program implementation relies on successful data matching. We found that a large proportion of patients from UCM were not matched, particularly those who were younger or lived outside of Chicago. Additional research is needed to understand ways to improve data matching to facilitate reengagement in HIV care.

Measuring Retention in HIV Care in the First Year of the COVID-19 Pandemic: The Impact of Telehealth.

It is unknown how the COVID-19 pandemic impacted traditional measures of retention in HIV care. We calculated six different retention measures at an urban HIV care clinic for two time periods: pre-pandemic, and during the first year of the COVID-19 pandemic, with and without inclusion of telehealth appointments. Spearman rank correlation was used to assess correlation between different measures of retention. For both the pre-pandemic and pandemic time periods, there was strong correlation among measures of missed visits (range 0.857-0.957). More patients were considered retained in care during the pandemic when telehealth appointments were included in the analysis.

Development of a predictive model for identifying women vulnerable to HIV in Chicago.

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).

No-show Prediction Model Performance Among People With HIV: External Validation Study.

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.

Assessing Patient Acceptance of an Automated Algorithm to Identify Ciswomen for HIV Pre-Exposure Prophylaxis.

The use of HIV pre-exposure prophylaxis (PrEP) in cisgender women (ciswomen) lags far behind their need. Data elements from the electronic medical record (EMR), including diagnosis of a sexually transmitted infection (STI), can be incorporated into automated algorithms for identifying clients who are most vulnerable to HIV and would benefit from PrEP. However, it is unknown how women feel about the use of such technology. In this study, we assessed women's attitudes and opinions about an automated EMR-based HIV risk algorithm and determined if their perspectives varied by level of HIV risk. Respondents were identified using best practice alerts or referral to a clinic for STI symptoms from January to December 2021 in Chicago, IL. Participants were asked about HIV risk factors, their self-perceived HIV risk, and their thoughts regarding an algorithm to identify ciswomen who could benefit from PrEP. Most of the 112 women who completed the survey (85%) thought they were at low risk for HIV, despite high rates of STI diagnoses. The majority were comfortable with the use of this algorithm, but their comfort level dropped when asked about the algorithm identifying them specifically. Ciswomen had mixed feelings about the use of an automated HIV risk algorithm, citing it as a potentially helpful and empowering tool for women, yet raising concerns about invasion of privacy and potential racial bias. Clinics must balance the benefits of using an EMR-based algorithm for ciswomen with their concerns about privacy and bias to improve PrEP uptake among particularly vulnerable women.

Trends in STI testing and diagnosis rates during the COVID-19 pandemic at a large urban tertiary care center, and the role of the emergency department in STI care.

Introduction: The COVID-19 pandemic has had profound effects on access to care, including outpatient sexually transmitted infection (STI) testing and treatment. Many vulnerable populations already relied on the emergency department (ED) for much of their care prior to the pandemic. This study examines trends in STI testing and positivity before and during the pandemic at a large urban medical center and evaluates the role of the ED in providing STI care. Methods: This is a retrospective review of all gonorrhea, chlamydia, and trichomonas tests from November 1, 2018, through July 31, 2021. Demographic information and location and results of STI testing were extracted from the electronic medical record. Trends in STI testing and positivity were examined for 16 month periods before and after the COVID-19 pandemic started (March 15, 2020), with the latter divided into the early pandemic period (EPP: March 15 -July 31, 2020) and late pandemic period (LPP: August 1, 2020 - July 31, 2021). Results: Tests per month decreased by 42.4% during the EPP, but rebounded by July 2020. During the EPP, the proportion of all STI testing originating in the ED increased from 21.4% pre-pandemic to 29.3%, and among pregnant women from 45.2% to 51.5%. Overall STI positivity rate increased from 4.4% pre-pandemic to 6.2% in the EPP. Parallel trends were observed for gonorrhea and chlamydia individually. The ED represented 50.5% of overall positive tests, and as much as 63.1% of positive testing during the EPP. The ED was the source of 73.4% of positive tests among pregnant women, which increased to 82.1% during the EPP. Conclusions: STI trends from this large urban medical center paralleled national trends, with an early decrease in positive cases followed by a rebound by the end of May 2020. The ED represented an important source of testing for all patients, and especially for pregnant patients, throughout the study period, but even more so early in the pandemic. This suggests that more resources should be directed towards STI testing, education, and prevention in the ED, as well as to support linkage to outpatient primary and obstetric care during the ED visit.

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.

PrEP Persistence Support and Monitoring in Areas of High HIV Burden in the Midwestern United States.

Monitoring pre-exposure prophylaxis (PrEP) metrics can guide service delivery yet does not occur routinely. We developed a survey to understand current practices for monitoring PrEP at PrEP-providing organizations in Illinois and Missouri. The survey was distributed from September through November 2020; 26 organizations participated. Most respondents indicated ongoing efforts to screen for PrEP eligibility (66.7%), link to care (87.5%), and retain clients in care (70.8%); 70.8% reported collecting data on PrEP initiation, 41.7% on retention in care, and 37.5% on missed visits. Barriers to monitoring PrEP metrics included lack of IT support (69.6%), manual processes (69.6%), and lack of staff resources (65.2%). Most respondents offered clients support for PrEP retention and adherence and wanted to expand interventions for PrEP persistence, yet fewer monitored corresponding metrics. To enhance PrEP implementation, organizations should improve monitoring and evaluation of PrEP metrics along the entire continuum and respond with appropriate services to support clients.

Comparison of algorithms for identifying people with HIV from electronic medical records in a large, multi-site database.

Objective: As electronic medical record (EMR) data are increasingly used in HIV clinical and epidemiologic research, accurately identifying people with HIV (PWH) from EMR data is paramount. We sought to evaluate EMR data types and compare EMR algorithms for identifying PWH in a multicenter EMR database. Materials and Methods: We collected EMR data from 7 healthcare systems in the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) including diagnosis codes, anti-retroviral therapy (ART), and laboratory test results. Results: In total, 13 935 patients had a positive laboratory test for HIV; 33 412 patients had a diagnosis code for HIV; and 17 725 patients were on ART. Only 8576 patients had evidence of HIV-positive status for all 3 data types (laboratory results, diagnosis code, and ART). A previously validated combination algorithm identified 22 411 patients as PWH. Conclusion: EMR algorithms that combine laboratory results, administrative data, and ART can be applied to multicenter EMR data to identify PWH.

Critical transitions in Chinese dunes during the past 12,000 years.

Dune systems can have alternative stable states that coexist under certain environmental conditions: a vegetated, stabilized state and a bare active state. This behavior implies the possibility of abrupt transitions from one state to another in response to gradual environmental change. Here, we synthesize stratigraphic records covering 12,000 years of dynamics of this system at 144 localities across three dune fields in northern China. We find side-by-side coexistence of active and stabilized states, and occasional sharp shifts in time between those contrasting states. Those shifts occur asynchronously despite the fact that the entire landscape has been subject to the same gradual changes in monsoon rainfall and other conditions. At larger scale, the spatial heterogeneity in dune dynamics averages out to produce relatively smooth change. However, our results do show different paths of recovery and collapse of vegetation at system-wide scales, implying that hysteretic behavior occurs in spatially extended systems.

Large wind shift on the great plains during the medieval warm period.

Spring-summer winds from the south move moist air from the Gulf of Mexico to the Great Plains. Rainfall in the growing season sustains prairie grasses that keep large dunes in the Nebraska Sand Hills immobile. Longitudinal dunes built during the Medieval Warm Period (800 to 1000 years before the present) record the last major period of sand mobility. These dunes are oriented NW-SE and are composed of cross-strata with bipolar dip directions. The trend and structure of the dunes record a drought that was initiated and sustained by a historically unprecedented shift of spring-summer atmospheric circulation over the Plains: Moist southerly flow was replaced by dry southwesterly flow.