Survivorship Care for Women Living With Ovarian Cancer: Protocol for a Randomized Controlled Trial.

BACKGROUND: Ovarian cancer ranks 12th in cancer incidence among women in the United States and 5th among causes of cancer-related death. The typical treatment of ovarian cancer focuses on disease management, with little attention given to the survivorship needs of the patient. Qualitative work alludes to a gap in survivorship care; yet, evidence is lacking to support the delivery of survivorship care for individuals living with ovarian cancer. We developed the POSTCare survivorship platform with input from survivors of ovarian cancer and care partners as a means of delivering patient-centered survivorship care. This process is framed by the chronic care model and relevant behavioral theory. OBJECTIVE: The overall goal of this study is to test processes of care that support quality of life (QOL) in survivorship. The specific aims are threefold: first, to test the efficacy of the POSTCare platform in supporting QOL, reducing depressive symptom burden, and reducing recurrence worry. In our second aim, we will examine factors that mediate the effect of the intervention. Our final aim focuses on understanding aspects of care platform design and delivery that may affect the potential for dissemination. METHODS: We will enroll 120 survivors of ovarian cancer in a randomized controlled trial and collect data at 12 and 24 weeks. Each participant will be randomized to either the POSTCare platform or the standard of care process for survivorship. Our population will be derived from 3 clinics in Texas; each participant will have received some combination of treatment modalities; continued maintenance therapy is not exclusionary. RESULTS: We will examine the impact of the POSTCare-O platform on QOL at 12 weeks after intervention as the primary end point. We will look at secondary outcomes, including depressive symptom burden, recurrence anxiety, and physical symptom burden. We will identify mediators important to the impact of the intervention to inform revisions of the intervention for subsequent studies. Data collection was initiated in November 2023 and will continue for approximately 2 years. We expect results from this study to be published in early 2026. CONCLUSIONS: This study will contribute to the body of survivorship science by testing a flexible platform for survivorship care delivery adapted for the specific survivorship needs of patients with ovarian cancer. The completion of this project will contribute to the growing body of science to guide survivorship care for persons living with cancer. TRIAL REGISTRATION: ClinicalTrials.gov NCT05752448; https://clinicaltrials.gov/study/NCT05752448. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48069.

Coping During the COVID-19 Pandemic: Experiences of Complex Cancer Survivors.

Patients with cancer and multiple chronic conditions (complex cancer survivors) are vulnerable to the negative impacts of COVID-19. However, their experiences and coping strategies during the COVID-19 pandemic have not been e.

How Does Prior Experience Pay Off in Large-Scale Quality Improvement Initiatives?

INTRODUCTION: To examine the association of prior investment on the effectiveness of organizations delivering large-scale external support to improve primary care. METHODS: Mixed-methods study of 7 EvidenceNOW grantees (henceforth, Cooperatives) and their recruited practices (n = 1720). Independent Variable: Cooperatives's experience level prior to EvidenceNOW, defined as a sustained track record in delivering large-scale quality improvement (QI) to primary care practices (high, medium, or low). Dependent Variables: Implementation of external support, measured as facilitation dose; effectiveness at improving (1) clinical quality, measured as practices' performance on Aspirin, Blood Pressure, Cholesterol, and Smoking (ABCS); and (2) practice capacity, measured using the Adaptive Reserve (AR) score and Change Process Capacity Questionnaire (CPCQ). Data were analyzed using multivariable linear regressions and a qualitative inductive approach. RESULTS: Cooperatives with High (vs low) levels of prior experience with and investment in large-scale QI before EvidenceNOW recruited more geographically dispersed and diverse practices, with lower baseline ABCS performance (differences ranging from 2.8% for blood pressure to 41.5% for smoking), delivered more facilitation (mean=+20.3 hours, P = .04), and made greater improvements in practices' QI capacity (CPCQ: +2.04, P < .001) and smoking performance (+6.43%, P = .003). These Cooperatives had established networks of facilitators at the start of EvidenceNOW and leadership experienced in supporting this workforce, which explained their better recruitment, delivery of facilitation, and improvement in outcomes. DISCUSSION: Long-term investment that establishes regionwide organizations with infrastructure and experience to support primary care practices in QI is associated with more consistent delivery of facilitation support, and greater improvement in practice capacity and some clinical outcomes.

Effective Facilitator Strategies for Supporting Primary Care Practice Change: A Mixed Methods Study.

PURPOSE: Practice facilitation is an evidence-informed implementation strategy to support quality improvement (QI) and aid practices in aligning with best evidence. Few studies, particularly of this size and scope, identify strategies that contribute to facilitator effectiveness. METHODS: We conducted a sequential mixed methods study, analyzing data from EvidenceNOW, a large-scale QI initiative. Seven regional cooperatives employed 162 facilitators to work with 1,630 small or medium-sized primary care practices. Main analyses were based on facilitators who worked with at least 4 practices. Facilitators were defined as more effective if at least 75% of their practices improved on at least 1 outcome measure-aspirin use, blood pressure control, smoking cessation counseling (ABS), or practice change capacity, measured using Change Process Capability Questionnaire-from baseline to follow-up. Facilitators were defined as less effective if less than 50% of their practices improved on these outcomes. Using an immersion crystallization and comparative approach, we analyzed observational and interview data to identify strategies associated with more effective facilitators. RESULTS: Practices working with more effective facilitators had a 3.6% greater change in the mean percentage of patients meeting the composite ABS measure compared with practices working with less effective facilitators (P <.001). More effective facilitators cultivated motivation by tailoring QI work and addressing resistance, guided practices to think critically, and provided accountability to support change, using these strategies in combination. They were able to describe their work in detail. In contrast, less effective facilitators seldom used these strategies and described their work in general terms. Facilitator background, experience, and work on documentation did not differentiate between more and less effective facilitators. CONCLUSIONS: Facilitation strategies that differentiate more and less effective facilitators have implications for enhancing facilitator development and training, and can assist all facilitators to more effectively support practice changes.

How Does Prior Experience Pay Off in Large-Scale Quality Improvement Initiatives?

INTRODUCTION: To examine the association of prior investment on the effectiveness of organizations delivering large-scale external support to improve primary care. METHODS: Mixed-methods study of 7 EvidenceNOW grantees (henceforth, Cooperatives) and their recruited practices (n = 1720). Independent Variable: Cooperatives's experience level prior to EvidenceNOW, defined as a sustained track record in delivering large-scale quality improvement (QI) to primary care practices (high, medium, or low). Dependent Variables: Implementation of external support, measured as facilitation dose; effectiveness at improving (1) clinical quality, measured as practices' performance on Aspirin, Blood Pressure, Cholesterol, and Smoking (ABCS); and (2) practice capacity, measured using the Adaptive Reserve (AR) score and Change Process Capacity Questionnaire (CPCQ). Data were analyzed using multivariable linear regressions and a qualitative inductive approach. RESULTS: Cooperatives with High (vs low) levels of prior experience with and investment in large-scale QI before EvidenceNOW recruited more geographically dispersed and diverse practices, with lower baseline ABCS performance (differences ranging from 2.8% for blood pressure to 41.5% for smoking), delivered more facilitation (mean=+20.3 hours, P = .04), and made greater improvements in practices' QI capacity (CPCQ: +2.04, P < .001) and smoking performance (+6.43%, P = .003). These Cooperatives had established networks of facilitators at the start of EvidenceNOW and leadership experienced in supporting this workforce, which explained their better recruitment, delivery of facilitation, and improvement in outcomes. DISCUSSION: Long-term investment that establishes regionwide organizations with infrastructure and experience to support primary care practices in QI is associated with more consistent delivery of facilitation support, and greater improvement in practice capacity and some clinical outcomes.

Cardiovascular Disease Preventive Services Among Smaller Primary Care Practices.

INTRODUCTION: Cardiovascular disease preventive services (aspirin use, blood pressure control, and smoking-cessation support) are crucial to controlling cardiovascular diseases. This study draws from 1,248 small-to-medium-sized primary care practices participating in the EvidenceNOW Initiative from 2015-2016 across 12 states to provide practice-level aspirin use, blood pressure control, and smoking-cessation support estimates; report the percentage of practices that meet Million Hearts targets; and identify the practice characteristics associated with better performance. METHODS: This cross-sectional study utilized linear regression modeling (analyzed in 2020-2021) to examine the association of aspirin use, blood pressure control, and smoking-cessation support performance with practice characteristics that included structural attributes (e.g., size, ownership, rurality), practice capacity and contextual characteristics, health information technology, and patient panel demographics. RESULTS: On average, practice performance on aspirin use, blood pressure control, and smoking-cessation support quality measures was 64% for aspirin, 63% for blood pressure, and 62% for smoking-cessation support. The 2012 Million Hearts goal of achieving the rates of 70% was achieved by 52% (aspirin), 32% (blood pressure), and 54% (smoking) of practices. Practice characteristics associated with aspirin use, blood pressure control, and smoking-cessation support performance included ownership (hospital/health system-owned practices had 11% higher aspirin performance than clinician-owned practices [p=0.001]), rurality (rural practices had lower performance than urban practices in all aspirin use, blood pressure control, and smoking-cessation support quality metrics [difference in aspirin=11.1%, p=0.001; blood pressure=4.2%, p=0.022; smoking=14.4%, p=0.009]), and disruptions (practices that experienced >1 major disruption showed lower aspirin performance [-7.1%, p<0.001]). CONCLUSIONS: Achieving the Million Hearts targets may be assisted by collecting and reporting practice-level performance, which can promote change at the practice level and identify areas where additional support is needed to achieve initiative goals.

Use of Quality Improvement Strategies Among Small to Medium-Size US Primary Care Practices.

PURPOSE: Improving primary care quality is a national priority, but little is known about the extent to which small to medium-size practices use quality improvement (QI) strategies to improve care. We examined variations in use of QI strategies among 1,181 small to medium-size primary care practices engaged in a national initiative spanning 12 US states to improve quality of care for heart health and assessed factors associated with those variations. METHODS: In this cross-sectional study, practice characteristics were assessed by surveying practice leaders. Practice use of QI strategies was measured by the validated Change Process Capability Questionnaire (CPCQ) Strategies Scale (scores range from -28 to 28, with higher scores indicating more use of QI strategies). Multivariable linear regression was used to examine the association between practice characteristics and the CPCQ strategies score. RESULTS: The mean CPCQ strategies score was 9.1 (SD = 12.2). Practices that participated in accountable care organizations and those that had someone in the practice to configure clinical quality reports from electronic health records (EHRs), had produced quality reports, or had discussed clinical quality data during meetings had higher CPCQ strategies scores. Health system-owned practices and those experiencing major disruptive changes, such as implementing a new EHR system or clinician turnover, had lower CPCQ strategies scores. CONCLUSION: There is substantial variation in the use of QI strategies among small to medium-size primary care practices across 12 US states. Findings suggest that practices may need external support to strengthen their ability to do QI and to be prepared for new payment and delivery models.

Secondhand Smoke Exposure and Smoke-free Policy in Philadelphia Public Housing.

OBJECTIVES: Multi-unit housing environments remain significant sources of secondhand smoke (SHS) exposure, especially for vulnerable populations in subsidized housing. In Philadelphia, the largest US housing authority to implement smoke-free policies, we measured baseline resident smoking-related behaviors and attitudes, and longitudinal exposures to airborne nicotine, during policy development and implementation. METHODS: In 4 communities, we collected data in 2013, 2014, and 2016, before and after introduction of comprehensive smoke-free policies, interviewing persons in 172 households, and monitoring air-borne nicotine in non-smoking homes and public areas. Average nicotine level differences across years were estimated with multi-level models. RESULTS: Fifty-six percent of respondents smoked. Only 37% of households were smoke-free, with another 41% restricting smoking by area or time of day. The number of locations with detectable nicotine did not differ before and after policy implementation, with approximately 20% of non-smoking homes and 70%-80% of public areas having detectable nicotine. However, public area nicotine levels were lower in 2016, after policy implementation, than in 2013 and 2014 (-0.19 µg/m3, p = .03). CONCLUSIONS: Findings suggest that initial policy implementation was associated with reduced SHS exposure in Philadelphia. As HUD strengthens smoke-free policies, SHS monitoring can be useful to educate stakeholders and build support for policy enforcement.

Childhood Obesity in an Inner-City Primary Care Population: A Longitudinal Study.

BACKGROUND: Understanding the dynamics of obesity among children and adolescents in high-risk, low-income patient populations is critical to guide and evaluate appropriate clinical and public health interventions. METHODS: We identified a cohort of 472 predominantly low-income, minority pediatric patients aged 3-18 years with baseline measurements in 2010 and analyzed follow-up data through September 2013. Weight status at baseline and end of follow-up were ascertained. RESULTS: The prevalence of obesity was 25% (95% confidence interval [CI] 21%-29%) at baseline and 24% (95% CI 20%-28%) after an average of 2.3 years follow-up. Among the 353 subjects who were not obese at baseline, the cumulative incidence of obesity was 8% (95% CI 5%-11%). Those who were normal weight at baseline had an incidence of 3% (1%-6%); those who were overweight had an incidence of 22% (95% CI 14%-32%). Among the 119 subjects who were obese at baseline, 29% (95% CI 21%-38%) were not obese at the end of follow-up. Remission of obesity among those who were severely obese was only 12% (95% CI 4%-26%); among other obese patients remission was 38% (95% CI 28%-50%). CONCLUSION: The prevalence of obesity did not change substantially during follow-up. The cumulative incidence of obesity was 8%, and most of the incidence was among children who were overweight at baseline. Remission was common, especially among those who were not severely obese at baseline. Understanding and addressing determinants of obesity over the lifecourse is critical to the long-term health of children in the United States.

Recognition and Management of Elevated Blood Pressure in Pediatric Patients: Challenges and Disparities in Community Health Centers.

The prevalence of childhood elevated blood pressure (EBP)-a single blood pressure recording above the normal range-is increasing in the United States. Recognizing childhood EBP is difficult because classification is a function of age, sex, and height. We assessed the frequency of clinical recognition of EBP and follow-up care in a sample of pediatric patients seen in 2010 and followed up through September 2013 in a network of 8 urban health centers. Of 754 patients with BP measurements, 261 (35 %) had at least 1 EBP reading during the study period. Of those with an EBP reading, 52 (20 %) had at least 1 EBP reading noted in their medical record. Clinicians were more likely to recognize EBP in overweight/obese [OR 3.27 (95 % confidence interval (CI) 1.64-6.51)] and male [OR 2.83 (95 % CI 1.64-4.42)] children. Strategies to support routine monitoring of BP status could improve identification and management of pediatric EBP.