The prevalence and demographic correlates of stress, anxiety, and depression among veterinary students in the Southeastern United States.

It is no secret that the rigors of professional medical programs are creating an immense strain on mental health, and studies show that students are coping poorly. It is becoming more widely known that this problem especially exists in veterinary medicine. Veterinary colleges are starting to make changes to address the mental health crisis among their student (and practitioner) population, however, in order to solve a problem you must first understand your audience. There are still questions regarding who is more often affected and why? In this study, the prevalence and correlates of stress, anxiety, and depression among veterinary students in the Southeastern US is the primary focus. Three hundred and forty two participants answered survey questions addressing socio-demographics, as well as, completed a perceived stress scale and patient health questionnaire (PHQ-4) to measure anxiety and depression. Chi-square, independent samples t-test, and ANOVA were conducted to assess demographic correlates of stress, anxiety, and depression. High levels of stress, anxiety, and depression were identified among veterinary students participating in the study. Stress level was significantly associated with sex and Grade Point Average (GPA): females and those with GPA <3.0 exhibited higher levels of stress. While it is good that veterinary colleges are already making changes, the question remains if these changes are significant enough. Based on this study's results, transitioning to a pass/fail grading system and implementing regular mindfulness practice are proposed changes that may aid in promoting a positive mental health culture for students and future veterinary professionals.

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.

Increasing Colorectal Cancer Screening at Community-Based Primary Care Clinics in San Francisco.

CONTEXT: Adult colorectal cancer screening (CRCS) can lower disease incidence and mortality. However, widespread implementation is inconsistent, especially in the public sector. While specific interventions to increase CRCS have been identified, firsthand accounts of CRCS improvement efforts using multiple techniques in public sector settings are lacking. OBJECTIVE: A program evaluation was conducted to assess the effect of implementing a culture of continuous quality improvement (QI) on CRCS practices and prevalence. A multipronged incremental effort over more than a decade to increase CRCS at the San Francisco Department of Public Health is described. SETTING: Community-based primary care clinics. PARTICIPANTS: Departmental activities and 5 clinics providing full-scope primary care to CRCS-eligible adults who participated in departmental activities and outreach interventions were assessed. MAIN OUTCOME MEASURES: Departmental and clinic-specific CRCS activities and prevalence. RESULTS: Efforts included departmental prioritization; data-driven QI incorporating routine data sharing (monthly reports and data walls); departmental and clinic-specific QI committees; panel management (a team approach to generation of eligibility lists prior to scheduled visits, routinely offering screening during appointments or mailing test kits for patients without appointments); and departmental mail and phone outreach events. Screening ranged from 36.6% to 54.4% in 2010; in 2013, it ranged from 43.6% to 70.2%. Increases occurred consistently over that time in 3 of the 5 clinics and ranged from 1.1% to 14.5%; decreases occurred during 2 intervals in 2 clinics and ranged from 2.3% to 4.3%. CONCLUSION: CRCS prevalence can be markedly improved in the public sector with a data-driven panel management approach supported by departmental and clinic-specific QI committees and group outreach events. Continued prioritization of and focus on CRCS is required to ensure long-term success. Even small increases will result in avoidable morbidity and mortality associated with this highly preventable disease.

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.

Rhesus macaques (Macaca mulatta) are not neophobic toward novel food with a high sugar content.

While we know that food neophobia in nonhuman primates is affected by social factors, little is known about how palatability, and specifically sugar content, might ameliorate a monkey's hesitancy to eat a novel food. It is likely that an innate preference for sweet substances would alter an animal's typical neophobic response. To test this, I presented nine male rhesus macaques with novel foods that contained a low, high, or no amount of sugar. As expected, the monkeys exhibited a neophobic response to the no-sugar novel foods but not to the high-sugar novel foods. Previous research on food neophobia may need to be reevaluated in light of the effects of sugar content on the neophobic response.