Racial/ethnic and HIV risk category disparities in preexposure prophylaxis discontinuation among patients in publicly funded primary care clinics.

OBJECTIVE: Dissemination of preexposure prophylaxis (PrEP) is a priority for reducing new HIV infections, especially among vulnerable populations. However, there are limited data available on PrEP discontinuation following initiation, an important component of the PrEP cascade. DESIGN: Patients receiving PrEP within the San Francisco Department of Public Health Primary Care Clinics (SFPCC) are included in a PrEP registry if they received a PrEP prescription, were not receiving postexposure prophylaxis, and not known to be HIV-positive. METHODS: We calculated PrEP discontinuation for patients initiating PrEP at any time from January 2012 to July 2017 and evaluated their association with demographic and risk variables using Cox regression analysis. RESULTS: Overall, 348 patients received PrEP over the evaluation period. The majority (84%) were men, and the cohort was racially/ethnically diverse. The median duration of PrEP use was 8.3 months. In adjusted analysis, PrEP discontinuation was lower among older patients (aHR 0.89; 95% CI 0.80-0.99; P = 0.03); but higher among black patients (compared with white patients; aHR 1.87; 95% CI 1.27-2.74; P = 0.001), patients who inject drugs (aHR 4.80; 95% CI 2.66-8.67; P < 0.001), and transgender women who have sex with men (compared with MSM; aHR 1.94; 95% CI 1.36-2.77; P < 0.001). CONCLUSION: Age, racial/ethnic, and risk category disparities in PrEP discontinuation were identified among patients in a public health-funded primary care setting. Further efforts are needed to understand and address PrEP discontinuation among priority populations to maximize the preventive impact of PrEP, and reverse HIV-related disparities at a population level.

Multivariate Risk Adjustment of Primary Care Patient Panels in a Public Health Setting: A Comparison of Statistical Models.

We compared prospective risk adjustment models for adjusting patient panels at the San Francisco Department of Public Health. We used 4 statistical models (linear regression, two-part model, zero-inflated Poisson, and zero-inflated negative binomial) and 4 subsets of predictor variables (age/gender categories, chronic diagnoses, homelessness, and a loss to follow-up indicator) to predict primary care visit frequency. Predicted visit frequency was then used to calculate patient weights and adjusted panel sizes. The two-part model using all predictor variables performed best (R = 0.20). This model, designed specifically for safety net patients, may prove useful for panel adjustment in other public health settings.

Difference in Survival between Housed and Homeless individuals with HIV, San Francisco, 2002-2011.

San Francisco (SF), a city with large HIV-infected and homeless populations, expanded supportive housing for HIV-infected people in 2007. We used the SF HIV/AIDS registry to compare survival between people who were homeless and who were housed at time of HIV diagnosis from 2002 through 2011. Housing status was obtained from medical records and deaths from local, state, and national vital registration. Survival was estimated using the Kaplan-Meier product-limit method. Ten percent of the 5,474 cases were homeless. Among people diagnosed between 2002 and 2006, the five-year survival was worse for people who were homeless at HIV diagnosis than for housed individuals (79% vs. 92%, p<.0001), but not for those diagnosed between 2007 and 2011 (92% vs. 93%, p=.3938). The improved survival among HIV-infected homeless people occurred during the time of increased supportive housing for this population. Our findings support including housing as an essential component of HIV care.

Assessing and increasing patient panel size in the public sector.

CONTEXT: Panel management is a central component of the primary care medical home, but faces numerous challenges in the safety net setting. In the San Francisco Department of Public Health, many of our community-based primary care clinics have difficulty accommodating all patients seeking care. OBJECTIVE: We evaluated patient panel size in our 7 clinics providing cradle-to-grave primary care services to more than 25,000 active patients. DESIGN: We adjusted panel size for age, gender, diagnoses, homelessness, and substance abuse; set related policies; and assessed the effects on our clinics. On the basis of our previous data and targets set by other safety net providers, we established a minimum of 1125 patients per full-time paid primary care provider (ie, full-time equivalent [FTE]) in April 2009. We calculated the target panel size each clinic would have if all their providers reached the minimum panel size goal and compared it with the panel size attained by the clinic. RESULTS: Nine months after establishing panel size policy, providers reached 82% of the aggregate target panel size. Five of the 7 clinics increased their adjusted panel size per FTE in the range of 2% to 23%. Two data-oriented and innovative clinics with some of the highest adjusted panel sizes per FTE largely maintained their panel size. Two clinics that had the lowest adjusted panel size per FTE realized a 23% and 8% respective gain; both clinics reduced barriers to new patient appointments. Two clinics acquired new providers and experienced a concomitant drop in panel size per FTE while the new clinicians expanded their panels. One of these clinics had difficulty managing high no-show rates and creating effective appointment templates. CONCLUSIONS: Routine data generation, review of data with administrators and providers, data-driven policies and panel size standards, and interventions to bolster team-based care are important tools for increasing capacity at our primary care clinics.

Creating a medical home in the San Francisco department of public health: establishing patient panels.

Patients with a medical home tend to fare better. One of the first steps toward establishing a medical home is to create panels by designating a clinic responsible for each patient. In 2006, we defined active clinic panels (all patients assigned to a clinic and seen there for one or more outpatient medical visits during the past 2 years) for the San Francisco Department of Public Health's 13 community- and four public hospital-based primary care clinics and began automatically assigning previously unassigned patients to clinics based on utilization. In 2007, we created a Web-based user interface for managing panels from within the electronic medical record. Providers and medical directors can now view and verify their panels and link to patient demographic and utilization data. Through April 2008, 14 508 patients have been auto-assigned to a clinic; on average 320 patients were assigned monthly. A total of 82,637 primary care patients were on a clinic panel, and 73.6 percent of them were active. Patient demographics, panel size, and productivity vary considerably by clinic. By establishing active panels and providing Web-based access to panel data, we can systematically assign patients a clinical home; enable providers to manage their panels; accurately measure utilization, capacity, and productivity; assess patient characteristics; and generate clinical quality indicators based on an accurate denominator. These management tools will allow us to set policies and work toward our goal of establishing a medical home for San Franciscans who rely on publicly funded care.