Angstman, Kurt B

The optimal patient panel size (PPS) in primary care and the factors determining it remain unclear. We conducted a meta-narrative review of the literature to evaluate factors influencing PPS and assess its association with patient outcomes. A comprehensive search of electronic databases was performed from inception through December 2023, focusing on original studies reporting factors used to determine PPS and related outcomes (eg, clinical outcomes, process measures, and resource utilization). A total of 48 studies were included, identifying 7 key factors influencing PPS. Smaller panels were associated with improved patient satisfaction, continuity of care, and health promotion, while clinical outcomes, utilization, and costs showed minimal impact by PPS. Panel size was primarily associated with patient age, sex, comorbidities, and practice type and structure. Community-based centers typically managed smaller panels, often staffed by female clinicians and serving socioeconomically disadvantaged populations with greater health needs than hospital-based practices. Female clinicians were also independently associated with managing smaller panels, higher quality care indicators, fewer emergency department visits, and improved patient satisfaction. Determining the ideal PPS is a multifaceted process influenced by practice setting, patient demographics, and clinician characteristics. While practice-related factors showed limited association with PPS, patient-reported outcomes were more closely linked to it. Primary care practices should tailor panel sizes to their patient populations, emphasizing a patient-centered approach and ensuring adequate infrastructure support to optimize care delivery.

Background: Depression is common in the primary care setting and tobacco use is more prevalent among individuals with depression. Recent research has linked smoking to poorer outcomes of depression treatment. We hypothesized that in adult primary care patients with the diagnosis of depression, current smoking would have a negative impact on clinical outcomes, regardless of treatment type (usual primary care [UC] vs collaborative care management [CCM]). Methods: A retrospective chart review study of 5155 adult primary care patients with depression in a primary care practice in southeast Minnesota was completed. Variables obtained included age, gender, marital status, race, smoking status, initial Patient Health Questionnaire–9 (PHQ-9), and 6-month PHQ-9. Clinical remission (CR) was defined as 6-month PHQ-9 <5. Persistent depressive symptoms (PDS) were defined as PHQ-9 ≥10 at 6 months. Treatment in both CCM and UC were compared. Results: Using intention to treat analysis, depressed smokers treated with CCM were 4.60 times as likely (95% CI 3.24-6.52, P < .001) to reach CR and were significantly less likely to have PDS at 6 months (adjusted odds ratio [AOR] 0.19, 95% CI 0.14-0.25, P < .001) compared with smokers in UC. After a 6-month follow-up, depressed smokers treated with CCM were 1.75 times as likely (95% CI 1.18-2.59, P = .006) to reach CR and were significantly less likely to have PDS (AOR 0.45, 95% CI 0.31-0.64, P < .001) compared with smokers in UC. Conclusions: CCM significantly improved depression outcomes for smokers at 6 months compared with UC. However, in the UC group, smoking outcomes were not statistically different at 6 months for either remission or PDS. Also, nonsmokers in CCM had the best clinical outcomes at 6 months in both achieving clinical remission and reduction of PDS when compared with smokers in UC as the reference group.

Background: Previous studies have suggested that having a comorbid personality disorder (PD) along with major depression is associated with poorer depression outcomes relative to those without comorbid PD. However, few studies have examined the influence of specific PD cluster types. The purpose of the current study is to compare depression outcomes between cluster A, cluster B, and cluster C PD patients treated within a collaborative care management (CCM), relative to CCM patients without a PD diagnosis. The overarching goal was to identify cluster types that might confer a worse clinical prognosis. Methods: This retrospective chart review study examined 2826 adult patients with depression enrolled in CCM. The cohort was divided into 4 groups based on the presence of a comorbid PD diagnosis (cluster A/nonspecified, cluster B, cluster C, or no PD). Baseline clinical and demographic variables, along with 6-month follow-up Patient Health Questionnaire–9 (PHQ-9) scores were obtained for all groups. Depression remission was defined as a PHQ-9 score <5 at 6 months, and persistent depressive symptoms (PDS) was defined as a PHQ-9 score ≥10 at 6 months. Adjusted odds ratios (AORs) were determined for both remission and PDS using logistic regression modeling for the 6-month PHQ-9 outcome, while retaining all study variables. Results: A total of 59 patients (2.1%) had a cluster A or nonspecified PD diagnosis, 122 patients (4.3%) had a cluster B diagnosis, 35 patients (1.2%) had a cluster C diagnosis, and 2610 patients (92.4%) did not have any PD diagnosis. The presence of a cluster A/nonspecified PD diagnosis was associated with a 62% lower likelihood of remission at 6 months (AOR = 0.38; 95% CI 0.20-0.70). The presence of a cluster B PD diagnosis was associated with a 71% lower likelihood of remission at 6 months (AOR = 0.29; 95% CI 0.18-0.47). Conversely, having a cluster C diagnosis was not associated with a significantly lower likelihood of remission at 6 months (AOR = 0.83; 95% CI 0.42-1.65). Increased odds of having PDS at 6-month follow-up were seen with cluster A/nonspecified PD patients (AOR = 3.35; 95% CI 1.92-5.84) as well as cluster B patients (AOR = 3.66; 95% CI 2.45-5.47). However, cluster C patents did not have significantly increased odds of experiencing persistent depressive symptoms at 6-month follow-up (AOR = 0.95; 95% CI 0.45-2.00). Conclusions: Out of the 3 clusters, the presence of a cluster B PD diagnosis was most significantly associated with poorer depression outcomes at 6-month follow-up, including reduced remission rates and increased risk for PDS. The cluster A/nonspecified PD group also showed poor outcomes; however, the heterogeneity of this subgroup with regard to PD features must be noted. The development of novel targeted interventions for at-risk clusters may be warranted in order to improve outcomes of these patients within the CCM model of care.

Background: The use of a collaborative care management (CCM) model can dramatically improve short- and long-term treatment outcomes for patients with major depressive disorder (MDD). Patients with comorbid personality disorder (PD) may experience poorer treatment outcomes for MDD. Our study seeks to examine the differences in MDD treatment outcomes for patients with comorbid PD when using a CCM approach rather than usual care (UC). Methods: In our retrospective cohort study, we reviewed the records of 9614 adult patients enrolled in our depression registry with the clinical diagnosis MDD and the diagnosis of PD (Yes/No). Clinical outcomes for depression were measured with Patient Health Questionnaire–9 (PHQ-9) scores at 6 months. Results: In our study cohort, 59.4% of patients (7.1% of which had comorbid PD) were treated with CCM, as compared with 40.6% (6.8% with PD) treated with UC. We found that the presence of a PD adversely affected clinical outcomes of remission within both groups, however, at 6 months patients with PD had significantly lower MDD remission rates when treated with UC as compared with those treated with CCM (11.5% vs 25.2%, P = .002). Patients with PD in the UC group were also noted to have an increased rate of persistent depressive symptoms (PHQ-9 score ≥10) at 6 months as compared with those in the CCM group (67.7% vs 51.7%, P = .004). Conclusions: In patients with comorbid MDD and PD, clinical outcomes at 6 months were significantly improved when treated with CCM compared with UC. This finding is encouraging and supports the idea that CCM is an effective model for caring for patients with behavioral concerns, and it may be of even greater benefit for those patients being treated for comorbid behavioral health conditions.