Journal of Primary Care and Community Health最新文献

Background: Despite increased access to HIV pre-exposure prophylaxis (PrEP) in Canada, familiarity and experience among primary care providers (PCPs)-including family doctors and those working with key populations-remains limited. To understand the barriers and facilitators of PrEP familiarity and experience, we conducted a situational analysis in PCPs in sub-urban and rural Ontario.

Methods: We surveyed a non-probabilistic sample of PCPs using an online questionnaire, designed with the Consolidated Framework for Implementation Research (CFIR). Poisson regressions with robust variance were used to assess the relationship between CFIR domains, sociodemographic, and practice characteristics on both PrEP familiarity and experience.

Results: A total of 54 PCPs participated (6% response rate), comprising 80% physicians and 20% nurses. Nearly 30% of the sample worked with key populations, including sexual health clinics and community care centers, 18% of respondents reported high familiarity with PrEP, and 44% reported PrEP experience (referred, started a conversation, or prescribed). PrEP familiarity and experience were associated with working in an organization serving key populations, working with gender minorities, and having colleagues providing PrEP. Providers with a positive perception of PrEP and its necessity for populations at risk were more likely to have PrEP-related experience. Higher familiarity and experience were reported by PCPs with specific clinical skills related to PrEP, and with the perception that PrEP was compatible with their practice as primary provider.

Conclusions: Our findings suggest that organizational support, and additional training and education would facilitate PrEP provision by PCPs in suburban/rural Ontario.

Introduction/objectives: Nurse practitioners (NPs) improve access to care in community-based primary care. Determining an appropriate workload for NPs is complex. The number of patients seen by NPs represents an important consideration. We sought to determine the feasibility, acceptability and appropriateness of implementing the online NP workload measurement index (NP-WI).

Methods: Feasibility study supported by the Theoretical Framework of Acceptability, conducted across 3 health regions in Québec, Canada. Data were collected from January to July 2024 using the online NP-WI (n = 66), 8-item acceptability questionnaire (n = 47), weekly implementation team meetings with NPs and decision-makers (n = 11), field notes and interviews (n = 13). Data analysis completed using descriptive statistics and content analysis, with data integration using joint displays.

Results: NPs indicated that the NP-WI was easy to use. Acceptability scores were positively rated. Daily data entry took 5 to 7 min to complete. NPs deemed a 4-week collection period sufficient to capture a representative workload sample. The NP-WI captured patient, provider and organizational characteristics and the number of patients seen by NPs.

Conclusions: NP-WI implementation was feasible. The instrument can support healthcare workforce planning with more adequate estimations of NP workload in community-based primary care, and provide greater equity in resource allocation and distribution of NP workload.

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: The purpose of this study is to examine workplace violence (WPV) toward primary care physicians (PCPs), including prevalence, associated factors, impacts, and response to WPV in Chengdu, China.

Methods: We used an online cross-sectional design to collect data from October to November 2022 with a structured self-administered questionnaire from a purposive sample of 568 PCPs in Chengdu city.

Results: Among the 490 valid questionnaires, 44.3% of respondents reported at least encountered 1 type of WPV in the preceding year, with 38.0% experiencing emotional abuse, 24.5% threats, 9.8% experiencing physical assault, 5.5% verbal sexual harassment, and 1.6% sexual abuse. The common perpetrators of WPV were patients (81.6%) and their relatives (44.2%). A multilevel analysis showed that PCPs without night shift had lower odds of experiencing WPV (odds ratios [OR] = 0.461, 95% confidence interval [CI] = 0.299-0.700, P = .0004), while higher education levels were associated with increased odds (OR = 1.835, 95% CI = 1.126-3.005, P = .015). The common reasons of causing WPV perceived by PCPs were "rejected unreasonable demands" (81.6%), "unmet the expectations" (51.2%), and "waiting too long" (47.9%). In response to WPV, 80.6% of PCPs had communicated positively with patients, 61.8% informed superiors, and 23.5% called security guards/police officers, but still 10.6% took no action. Most respondents (82.9%) did not receive training on how to deal with WPV. After experiencing WPV, PCPs reported low work efficiency (56.2%), decreased patient trust (32.7%), and anxious feelings (32.7%).

Conclusions: This study revealed a moderate prevalence of violence against PCPs in primary care settings. Most PCPs lack coping strategies to handle WPV and suffer many negative effects. It is imperative that PCPs receive formal training in WPV management, and our study provides evidence to support such training programs.

Introduction/objectives: Vaccination rates in the United States have seen a concerning decline in recent years. Primary care, as the front line of healthcare for most individuals, is critical in reversing this trend. This study investigates trends in vaccine delivery amongst healthcare providers before and after Coronavirus Disease 2019 (COVID-19).

Methods: The delivery of vaccinations were enumerated by provider type using a combination of 2017 to 2021 Medicare Part B Fee-For-Service claims and the 2013 to 2021 Medical Expenditure Panel Survey (MEPS).

Results: Our findings reveal a notable decline in primary care physician (PCP)-administered vaccinations, with mass immunizers and non-physician providers increasingly assuming this role. Medicare claims showed that the vaccine delivery by PCPs decreased over time, from 46.2% in 2017 to 32.3% in 2021, while the vaccine delivery by mass immunizers increased over time, from 45.2% in 2017 to 60.5% in 2021. Similarly, MEPS data showed that PCPs accounted for 53.6% of vaccination visits in the pre-pandemic period but only 22.9% during the pandemic.

Conclusions: The COVID-19 pandemic profoundly disrupted vaccine delivery, reshaping the roles of PCPs and other providers. This study highlights the need to better integrate primary care into future vaccine distribution frameworks to ensure broad and equitable access in the post-pandemic era.

Introduction/objectives: Little is known about the prevalence of patient-reported social risk factors and the use of the HOUSES Index, a simple, reliable method of assessing socioeconomic status (SES) based on publicly available housing data, in a predominantly rural, primary care population.

Methods: We conducted a cross-sectional analysis of adult patients paneled to family medicine clinicians in a US Midwest health system as of December 31, 2022. Patients' listed address determined HOUSES Index as quartile rank (Q1 lowest SES) and rural/urban status. Social risk data including housing, food, transportation, finances, and violence were collected from health record questionnaires. A mixed effect model was used to assess associations between social risk, HOUSES Index, and rurality.

Results: Of the 352 355 patients included, rural patients were more likely than urban patients to report all social risk factors and had lower SES as measured by HOUSES quartiles. In the mixed effects analysis, HOUSES quartile was independently predictive of reporting an at-risk social risk factor (Q1 vs Q4 OR = 2.27, 95% CI = 2.19-2.37), but rurality was not (OR = 1.02, 95% CI = 0.97-1.07) after adjusting for HOUSES.

Conclusions: The increased prevalence of social risk factors among rural residents is largely explained by individual SES measured by HOUSES Index.

Maternal mortality in the U.S. remains a pressing public health issue, with African American and Indigenous populations experiencing significantly higher rates. In Arizona, maternal mortality reached 30.0 deaths per 100,000 births in 2023, well above the national average. To address persistent maternal and child health (MCH) inequities, the Arizona Department of Health Services, in collaboration with a university and other strategic partners, conducted the 2025 Arizona Statewide Title V Maternal and Child Health Needs Assessment using community-engaged research (CeNR) approaches. Qualitative methods, including the River of Life (ROL) and Community Forums (CF), were employed to elevate diverse community voices, particularly from underserved rural, border, and minoritized populations. Key priorities identified included improving access to mental health care, addressing childcare shortages, expanding transportation and healthcare access, and strengthening culturally and linguistically appropriate services. The ROL approach revealed 8 social drivers of health, while CFs highlighted actionable solutions rooted in lived experience. Findings underscore the need for upstream policy investments and community-informed strategies to reduce maternal health disparities and improve statewide MCH outcomes. This participatory process demonstrates the value of engaging historically marginalized communities in shaping programs and policies that directly impact their health and well-being.

Introduction/objectives: The purpose of this descriptive study was to strengthen understanding of the human-animal bond and the impact of One Health Clinics (OHCs) on the communities they serve. We aimed to assess how joint access to veterinary care and human health services enables community members to engage with healthcare for themselves.

Methods: Individuals attending 2 OHCs in the United States were surveyed to gain insight into reasons for attending OHCs, attitudes on the human-animal bond, healthcare access and utilization, and pet owner satisfaction and trust toward medical and veterinary professionals. Both clinics operated in areas with limited medical and veterinary healthcare access, but varied in clinic structure and availability of human healthcare volunteers.

Results: A total of 175 surveys were analyzed. Most participants attended primarily for veterinary services (Clinic A: 91%, Clinic B: 75%). However, a significantly higher proportion of Clinic B participants received health screenings (91% vs 32%, P < .0001), learned new health information (48% vs 31%, P = .0129), and were provided with follow-up health resources (84% vs 51%, P = .000007). Across both sites, the majority expressed high trust in medical and veterinary providers (Clinic A: 84%-95%, Clinic B: 90%-98%) and reported high satisfaction with the care received. Most participants considered their pets vital family members (Clinic A: 81%, Clinic B: 93%), and many credited their pets with supporting physical activity and reducing feelings of depression and loneliness. Importantly, a majority of respondents at both clinics indicated they were more likely to seek healthcare for themselves if veterinary services were also available (Clinic A: 56%, Clinic B: 72%).

Conclusions: These findings suggest that OHCs have the potential to enhance access to human healthcare in underserved communities by utilizing veterinary services as a point of engagement. Participants reported a strong bond with their pet. Based on reported levels of trust and satisfaction, the OHC model may help strengthen relationships between underserved communities and human healthcare and veterinary professionals.

Background: Diabetes remains a major public health concern in the United States, particularly in Tennessee, where prevalence rates exceed national averages. Traditional statistical approaches may not fully capture the non-linear interactions among predictors. This study applied both traditional approaches and machine learning (ML) techniques to predict and identify key contributing factors associated with self-reported diabetes using the 2023 Behavioral Risk Factor Surveillance System (BRFSS) dataset.

Methods: A cross-sectional analysis was conducted on 5634 (weighted population 5 614 486) adults from the Tennessee BRFSS dataset. Sociodemographic, behavioral, and health-related variables were analyzed. Data processing, exploratory analysis, and modeling were performed in Python using Pandas, NumPy, Scikit-learn, and SHAP. Seven algorithms were tested: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, and XGBoost, with stratified 5-fold cross-validation. Models were evaluated using accuracy, precision, recall, balanced accuracy, F1-score, AUROC, and PR-AUC.

Results: The Gradient Boosting model demonstrated the best overall performance, achieving an accuracy of 82%, precision of 48%, recall of 32%, F1-score of 37%, AUROC of 0.80, and PR-AUC of 0.45. Key predictors included high blood pressure, high cholesterol, body mass index, comorbidity burden, and physical inactivity. SHAP analysis revealed that both clinical factors and social determinants substantially influenced diabetes risk.

Conclusion: This study highlights the strong potential of machine learning, particularly Gradient Boosting, in predicting self-reported diabetes. Integrating SHAP analysis enhanced interpretability by revealing how the above factors interact to influence diabetes risk, underscoring the value of explainable AI for precision public health and targeted prevention strategies.