Barriers to and facilitators of implementing colorectal cancer screening evidence-based interventions in federally qualified health centers: a qualitative study.

BACKGROUND: There is an urgent need to increase colorectal cancer screening (CRCS) uptake in Texas federally qualified health centers (FQHCs), which serve a predominantly vulnerable population with high demands. Empirical support exists for evidence-based interventions (EBIs) that are proven to increase CRCS; however, as with screening, their use remains low in FQHCs. This study aimed to identify barriers to and facilitators of implementing colorectal cancer screening (CRCS) evidence-based interventions (EBIs) in federally qualified health centers (FQHCs), guided by the Consolidated Framework for Implementation Research (CFIR). METHODS: We recruited employees involved in implementing CRCS EBIs (e.g., physicians) using data from a CDC-funded program to increase the CRCS in Texas FQHCs. Through 23 group interviews, we explored experiences with practice change, CRCS promotion and quality improvement initiatives, organizational readiness, the impact of COVID-19, and the use of CRCS EBIs (e.g., provider reminders). We used directed content analysis with CFIR constructs to identify the critical facilitators and barriers. RESULTS: The analysis revealed six primary CFIR constructs that influence implementation: information technology infrastructure, innovation design, work infrastructure, performance measurement pressure, assessing needs, and available resources. Based on experiences with four recommended EBIs, participants described barriers, including data limitations of electronic health records and the design of reminder alerts targeted at deliverers and recipients of patient or provider reminders. Implementation facilitators include incentivized processes to increase provider assessment and feedback, existing clinic processes (e.g., screening referrals), and available resources to address patient needs (e.g., transportation). Staff buy-in emerged as an implementation facilitator, fostering a conducive environment for change within clinics. CONCLUSIONS: Using CFIR, we identified barriers, such as the burden of technology infrastructure, and facilitators, such as staff buy-in. The results, which enhance our understanding of CRCS EBI implementation in FQHCs, provide insights into designing nuanced, practical implementation strategies to improve cancer control in a critical setting.

Intersecting social determinants of health among patients with childcare needs: a cross-sectional analysis of social vulnerability.

INTRODUCTION: Access to childcare is an understudied social determinant of health (SDOH). Our health system established a childcare facility for patients to address childcare barriers to healthcare. Recognizing that social risk factors often co-exist, we sought to understand intersecting social risk factors among patients with childcare needs who utilized and did not utilize the childcare facility and identify residual unmet social needs alongside childcare needs. METHODS: We conducted a cross-sectional analysis of patients who enrolled in the childcare facility from November 2020 to October 2022 to compare parameters of the Social Vulnerability Index (SVI) associated with the census tract extracted from electronic medical record (EMR) data among utilizers and non-utilizers of the facility. Overall SVI and segmentation into four themes of vulnerability (socioeconomic status, household characteristics, racial/ethnic minority status, and housing type/transportation) were compared across utilizers and utilizers. Number of 90th percentile indicators were also compared to assess extreme levels of vulnerability. A sample of utilizers additionally received a patient-reported social needs screening questionnaire administered at the childcare facility. RESULTS: Among 400 enrollees in the childcare facility, 70% utilized childcare services and 30% did not. Utilizers and non-utilizers were demographically similar, though utilizers were more likely to speak Spanish (34%) compared to non-utilizers (22%). Mean SVI was similar among utilizers and non-utilizers, but the mean number of 90th percentile indicators were higher for non-utilizers compared to utilizers (4.3 ± 2.7 vs 3.7 ± 2.7, p = 0.03), primarily driven by differences in the housing type/transportation theme (p = 0.01). Non-utilizers had a lower rate of healthcare utilization compared to utilizers (p = 0.02). Among utilizers who received patient-reported screening, 84% had one unmet social need identified, of whom 62% agreed for additional assistance. Among social work referrals, 44% were linked to social workers in their medical clinics, while 56% were supported by social work integrated in the childcare facility. CONCLUSIONS: This analysis of SDOH approximated by SVI showed actionable differences, potentially transportation barriers, among patients with childcare needs who utilized a health system-integrated childcare facility and patients who did not utilize services. Furthermore, residual unmet social needs among patients who utilized the facility demonstrate the multifactorial nature of social risk factors experienced by patients with childcare needs and opportunities to address intersecting social needs within an integrated intervention. Intersecting social needs require holistic examination and multifaceted interventions.

Factors influencing implementation of a care coordination intervention for cancer survivors with multiple comorbidities in a safety-net system: an application of the Implementation Research Logic Model.

BACKGROUND: Under- and uninsured cancer survivors have significant medical, social, and economic complexity. For these survivors, effective care coordination between oncology and primary care teams is critical for high-quality, comprehensive care. While evidence-based interventions exist to improve coordination between healthcare teams, testing implementation of these interventions for cancer survivors seen in real-world safety-net settings has been limited. This study aimed to (1) identify factors influencing implementation of a multicomponent care coordination intervention (nurse coordinator plus patient registry) focused on cancer survivors with multiple comorbidities in an integrated safety-net system and (2) identify mechanisms through which the factors impacted implementation outcomes. METHODS: We conducted semi-structured interviews (patients, providers, and system leaders), structured observations of primary care and oncology operations, and document analysis during intervention implementation between 2016 and 2020. The practice change model (PCM) guided data collection to identify barriers and facilitators of implementation; the PCM, Consolidated Framework for Implementation Research, and Implementation Research Logic Model guided four immersion/crystallization data analysis and synthesis cycles to identify mechanisms and assess outcomes. Implementation outcomes included appropriateness, acceptability, adoption, and penetration. RESULTS: The intervention was appropriate and acceptable to primary care and oncology teams based on reported patient needs and resources and the strength of the evidence supporting intervention components. Active and sustained partnership with system leaders facilitated these outcomes. There was limited adoption and penetration early in implementation because the study was narrowly focused on just breast and colorectal cancer patients. This created barriers to real-world practice where patients with all cancer types receive care. Over time, flexibility intentionally designed into intervention implementation facilitated adoption and penetration. Regular feedback from system partners and rapid cycles of implementation and evaluation led to real-time adaptations increasing adoption and penetration. DISCUSSION: Evidence-based interventions to coordinate care for underserved cancer survivors across oncology and primary care teams can be implemented successfully when system leaders are actively engaged and with flexibility in implementation embedded intentionally to continuously facilitate adoption and penetration across the health system.

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.

Differences in Use of Clinical Decision Support Tools and Implementation of Aspirin, Blood Pressure Control, Cholesterol Management, and Smoking Cessation Quality Metrics in Small Practices by Race and Sex.

Importance: Practice-level evidence is needed to clarify the value of population-based clinical decision support (CDS) tools in reducing racial and sex disparities in cardiovascular care. Objective: To evaluate the association between CDS tools and racial and sex disparities in the aspirin use, blood pressure control, cholesterol management, and smoking cessation (ABCS) care quality metrics among smaller primary care practices. Design, Setting, and Participants: This cross-sectional study used practice-level data from the Agency for Healthcare Research and Quality-funded EvidenceNOW initiative. The national initiative from May 1, 2015, to April 30, 2021, spanned 12 US states and focused on improving cardiovascular preventive care by providing quality improvement support to smaller primary care practices. A total of 576 primary care practices in EvidenceNOW submitted both survey data and electronic health record (EHR)-derived ABCS data stratified by race and sex. Main Outcomes and Measures: Practice-level estimates of disparities between Black and White patients and between male and female patients were calculated as the difference in proportions of eligible patients within each practice meeting ABCS care quality metrics. The association between CDS tools (EHR prompts, standing orders, and clinical registries) and disparities was evaluated by multiply imputed multivariable models for each CDS tool, adjusted for practice rurality, ownership, and size. Results: Across the 576 practices included in the analysis, 219 (38.0%) had patient panels that were more than half White and 327 (56.8%) had panels that were more than half women. The proportion of White compared with Black patients meeting metrics for blood pressure (difference, 5.16% [95% CI, 4.29%-6.02%]; P < .001) and cholesterol management (difference, 1.49% [95% CI, 0.04%-2.93%] P = .04) was higher; the proportion of men meeting metrics for aspirin use (difference, 4.36% [95% CI, 3.34%-5.38%]; P < .001) and cholesterol management (difference, 3.88% [95% CI, 3.14%-4.63%]; P < .001) was higher compared with women. Conversely, the proportion of women meeting practice blood pressure control (difference, -1.80% [95% CI, -2.32% to -1.28%]; P < .001) and smoking cessation counseling (difference, -1.67% [95% CI, -2.38% to -0.95%]; P < .001) metrics was higher compared with men. Use of CDS tools was not associated with differences in race or sex disparities except for the smoking metric. Practices using CDS tools showed a higher proportion of men meeting the smoking counseling metric than women (coefficient, 3.82 [95% CI, 0.95-6.68]; P = .009). Conclusions and Relevance: The findings of this cross-sectional study suggest that practices using CDS tools had small disparities that were not statistically significant, but CDS tools were not associated with reductions in disparities. More research is needed on effective practice-level interventions to mitigate disparities.

Polypharmacy and prescription medication use in a population-based sample of adolescent and young adult cancer survivors.

PURPOSE: We examined prescription medication use and identified correlates of polypharmacy-taking multiple medications-in adolescent and young adult cancer survivors (AYAs), who experience early-onset chronic conditions. METHODS: Our cross-sectional study pooled data (2008-2017) from the national Medical Expenditure Panel Survey. We estimated prevalence of polypharmacy (≥ 5 unique prescription medications over an approximate 1-year period) in AYAs (age 18-39 years with a history of cancer) and age- and sex-matched controls, overall and by sociodemographics, clinical factors, and health indicators. We compared survivors' and controls' medication use across therapeutic classes. To identify correlates of polypharmacy among AYAs, we included factors with p < 0.20 in bivariable analysis in a multivariable logistic regression model. RESULTS: AYAs (n = 601) had a higher prevalence of polypharmacy than controls (n = 2,402), overall (31.5% vs. 15.9%, p < .01) and by all sociodemographics, clinical factors, and health indicators. A majority of AYAs with multiple chronic conditions (58.8%, 95% CI 47.3-70.4) or disability (61.3%, 95% CI 52.6-70.0) had polypharmacy. Patterns of AYAs' medication use across therapeutic classes were consistent with their chronic conditions. Nearly one-third used opioid/narcotic analgesics (32.2% vs. 13.7% of controls, p < 0.01). Among AYAs, multiple chronic conditions (aOR 4.68, 95% CI 2.23-9.83) and disability (aOR 3.70, 95% CI 2.23-6.14) were correlated with polypharmacy. CONCLUSIONS: Chronic conditions and disabilities, including aftereffects of cancer treatment, may drive polypharmacy in AYAs. Future research should examine adverse outcomes of polypharmacy and opioid/narcotic use in AYAs. IMPLICATIONS FOR CANCER SURVIVORS: AYAs with chronic conditions or disabilities should be monitored for polypharmacy.

Polypharmacy and medication fill nonadherence in a population-based sample of adolescent and young adult cancer survivors, 2008-2017.

PURPOSE: We examined the association between polypharmacy-an established risk factor for nonadherence in the elderly-and medication fill nonadherence in a large national sample of adolescent and young adult cancer survivors (AYAs) in the USA. METHODS: We pooled data (2008-2017) from the Medical Expenditure Panel Survey. We defined polypharmacy as ≥ 3 unique medications prescribed, based on self-report and pharmacy data, and medication fill nonadherence as self-reported delay or inability to obtain a necessary medication. We estimated prevalence of medication fill nonadherence among AYAs (age 18-39 years with a cancer history). We used logistic regression to estimate the association between (1) polypharmacy and medication fill nonadherence in AYAs, and (2) total number of medications prescribed and medication fill nonadherence, controlling for sex, number of chronic conditions, disability, and survey year. RESULTS: AYAs (n = 598) were predominantly female (76.2%), age 30-39 years (64.9%), and non-Hispanic White (72.1%). Nearly half were poor (19.0%) or near-poor/low income (21.6%). One in ten AYAs reported medication fill nonadherence (9.75%). Of these, more than 70% cited cost-related barriers as the reason. AYAs with polypharmacy had 2.49 times higher odds of medication fill nonadherence (95%CI 1.11-5.59), compared to those without polypharmacy. Odds of medication fill nonadherence increased by 16% with each additional medication prescribed (AOR 1.16, 95% CI 1.07-1.25). CONCLUSIONS: Polypharmacy may be an important risk factor for medication fill nonadherence in AYAs in the USA. IMPLICATIONS FOR CANCER SURVIVORS: Improving AYAs' medication adherence requires eliminating cost-related barriers, particularly for those with polypharmacy.

Importance of primary care for underserved cancer patients with multiple chronic conditions.

PURPOSE: To understand the impact of pre-existing conditions on healthcare utilization among under- and uninsured patients in the transition from cancer treatment to post-treatment survivorship. METHODS: Using electronic health record data, we constructed a cohort of patients seen in an integrated county health system between 1/1/2010 and 12/31/2016. Six hundred thirty-one adult patients diagnosed with non-metastatic breast or colorectal cancer during this period (cases) were matched 1:1 on sex and Charlson comorbidity index to non-cancer patients who had at least two chronic conditions and with at least one visit to the health system during the study period (controls). Conditional fixed effects Poisson regression models compared number of primary care and emergency department (ED) visits and completed [vs. no show or missed] appointments between cancer and non-cancer patients. RESULTS: Cancer patients had significantly lower number of visits compared with non-cancer patients (N = 46,965 vs. 85,038). Cancer patients were less likely to have primary care (IRR = 0.25; 95% CI: 0.24, 0.27) and ED visits (IRR = 0.57; 95% CI: 0.50, 0.64) but more likely to complete a scheduled appointment (AOR = 4.83; 95% CI: 4.32, 5.39) compared with non-cancer patients. Cancer patients seen in primary care at a higher rate were more likely to visit the ED (IRR = 2.06; 95% CI: 1.52, 2.80) than those seen in primary care at a lower rate. CONCLUSION: Health systems need to find innovative, effective solutions to increase primary care utilization among cancer patients with chronic care conditions to ensure optimal management of both chronic conditions and cancer. IMPLICATIONS FOR CANCER SURVIVORS: Maintaining regular connections with primary care providers during active cancer treatment should be promoted.

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.

Improving Delivery of Cardiovascular Disease Preventive Services in Small-to-Medium Primary Care Practices.

BACKGROUND: The EvidenceNOW initiative provided smaller primary care practices with external support interventions to implement quality improvement strategies focused on cardiovascular disease prevention. This manuscript reports effectiveness of EvidenceNOW interventions in improving quality metrics. METHODS: Seven regional Cooperatives delivered external support interventions (practice facilitation, health information technology support to assist with audit and feedback, performance benchmarking, learning collaboratives, and establishing community linkages) to 1278 smaller primary care practices. Outcomes included proportion of eligible patients meeting Centers for Medicaid and Medicare Services-specified ABCS metrics, that is, Aspirin for those at risk of ischemic vascular disease; achieving target Blood pressure among hypertensives; prescribing statin for those with elevated Cholesterol, diabetes, or increased cardiovascular disease risk; and screening for Smoking and providing cessation counseling. An event study compared prepost changes in outcomes among intervention practices and a difference-in-differences design compared intervention practices to 688 external comparison practices. RESULTS: Mean baseline outcomes ranged from 61.5% (cholesterol) to 64.9% (aspirin). In the event study, outcomes improved significantly (aspirin: +3.39 percentage points, 95% CI, 0.61-6.17; blood pressure: +1.59, 95% CI, 0.12-3.06; cholesterol: +4.43, 95% CI, 0.33-8.53; smoking: +7.33, 95% CI, 4.70-9.96). Difference-in-differences estimates were similar in magnitude but statistically significant for smoking alone. Preintervention trends were significant for smoking, but parallel-trends tests were not significant. CONCLUSIONS: EvidenceNOW Cooperatives improved cardiovascular prevention quality metrics among small and medium sized primary care practices across the US. While estimated improvements were small, they reflected average changes across a large and diverse sample of practices.

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.

The Effects of Major Disruptions on Practice Participation in Facilitation During a Primary Care Quality Improvement Initiative.

BACKGROUND: Disruptions in primary care practices, like ownership change, clinician turnover, and electronic health record system implementation, can stall quality improvement (QI) efforts. However, little is known about the relationship between these disruptions and practice participation in facilitated QI. METHODS: We explore this relationship using data collected from EvidenceNOW in a mixed-methods convergent design. EvidenceNOW was a large-scale facilitation-based QI initiative in small and medium primary care practices. Data included practice surveys, facilitator time logs, site visit field notes, and interviews with facilitators and practices. Using multivariate regression, we examined associations between disruptions during interventions and practice participation in facilitation, measured by in-person facilitator hours in 987 practices. We analyzed qualitative data on 40 practices that described disruptions. Qualitative and quantitative teams iterated analyses based on each other's emergent findings. RESULTS: Many practices (51%) reported experiencing 1 or more disruptions during the 3- to 15-month interventions. Loss of clinicians (31.6%) was most prevalent. In adjusted analyses, disruptions were not significantly associated with participation in facilitation. Qualitative data revealed that practices that continued active participation were motivated, had some QI infrastructure, and found value in working with their facilitators. Facilitators enabled practice participation by doing EHR-related work for practices, adapting work for available staff, and helping address needs beyond the explicit aims of EvidenceNOW. CONCLUSIONS: Disruptions are prevalent in primary care, but practices can continue participating in QI interventions, particularly when supported by a facilitator. Facilitators may benefit from additional training in approaches for helping practices attenuate the effects of disruptions and adapting strategies to help interventions work to continue building QI capacity.

Oropharyngeal Cancer Incidence and Mortality Trends in All 50 States in the US, 2001-2017.

Importance: Oropharyngeal cancer (OPC) incidence is rising among men in the US. Comprehensive assessments of nationwide trends in OPC incidence and mortality by demographics, tumor characteristics at diagnosis, and geography are lacking. Objective: We examined secular trends in OPC incidence and mortality rates in all 50 US states and the District of Columbia (DC). Design, Setting, and Participants: In this cross-sectional study, we used the US Cancer Statistics data set to examine OPC incidence trends from 2001 through 2017. Observed and incidence-based mortality trends were evaluated using data from the National Center for Health Statistics and Surveillance Epidemiology and End Results program, respectively. Data analysis was conducted from January to April 2021. Results: Nationwide, 260 182 OPC cases were identified; 209 297 (80%) occurred in men, 168 674 (65%) with regional stage, and 142 068 (55%) in the Southeast and Midwest regions, during 2001 to 2017. Incidence of OPC increased nationally 2.7% per year among men, with a notable (over 3% per year) rise among non-Hispanic White men and in men aged 65 years and older. Overall, among women, the annual percentage change was 0.5% (95% CI, -0.28% to 1.22%). Among men, with a 3.1% per year rise (95% CI, 2.4% to 3.8%), regional-stage OPC incidence increased nearly 2-fold. Among women, regional-stage OPC incidence increased 1.0% per year (95% CI, 0.3% to 1.7%). Among men, OPC incidence increased in all states and regions except Alaska, DC, and Wyoming. Among men, the most pronounced increases (more than 3.5% per year) were clustered in the Southeast and Midwest regions. Among women, a rise of more than 2% per year was also concentrated in the Southeast and Midwest regions. Among men, OPC incidence-based mortality increased 2.1% per year (95% CI, 1.0% to 3.2%) overall in recent years (from 2006 to 2017). In contrast, among women, the annual percentage change in OPC incidence-based mortality was -1.2% (95% CI, -2.5% to 0.1%). Conclusions and Relevance: The findings of this cross-sectional study suggest that the incidence of OPC has continued to increase nationally among men in the US, with rapid increases among the elderly population. The notable rise in regional-stage OPC and the concurrent recent rise in mortality among men is troubling and calls for urgent improvements in prevention. Distinct geographic patterns with notable rises in the Midwest and Southeast regions imply the need for improved and targeted prevention as well as future studies to understand etiological reasons for geographic disparities.

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.

Understanding the Delivery of Patient-Centered Survivorship Care Planning: An Exploratory Interview Study With Complex Cancer Survivors.

INTRODUCTION: Understanding key elements of the survivorship care planning process, such as patient-centered communication (PCC) and health self-efficacy, are critical for delivering patient-centered survivorship care to cancer survivors with multiple chronic conditions ("complex cancer survivors"). Building upon our team's recent research efforts to examine the survivorship care planning process from a patient-centered lens, this exploratory study leveraged an ongoing quasi-experimental trial to elucidate the experience of complex cancer survivors with survivorship care planning and post-treatment management. METHODS: We conducted a hypothesis-generating thematic content analysis on 8 interview transcripts. RESULTS: Survivors reported positive experiences communicating with their oncology care team but the presence of multiple chronic conditions in addition to cancer creates additional barriers to patient-centered survivorship care. CONCLUSION: These findings support the need for further in-depth research aimed at improving PCC across all care teams and enabling self-management by delivering more personalized survivorship care planning that aligns with survivor's needs, values, and preferences.

Thematic Analysis of Challenges of Care Coordination for Underinsured and Uninsured Cancer Survivors With Chronic Conditions.

Importance: Although a majority of underinsured and uninsured patients with cancer have multiple comorbidities, many lack consistent connections with a primary care team to manage chronic conditions during and after cancer treatment. This presents a major challenge to delivering high-quality comprehensive and coordinated care. Objective: To describe challenges and opportunities for coordinating care in an integrated safety-net system for patients with both cancer and other chronic conditions. Design, Setting, and Participants: This multimodal qualitative study was conducted from May 2016 to July 2019 at a county-funded, vertically integrated safety-net health system including ambulatory oncology, urgent care, primary care, and specialty care. Participants were 93 health system stakeholders (clinicians, leaders, clinical, and administrative staff) strategically and snowball sampled for semistructured interviews and observation during meetings and daily processes of care. Data collection and analysis were conducted iteratively using a grounded theory approach, followed by systematic thematic analysis to organize data, review, and interpret comprehensive findings. Data were analyzed from March 2019 to March 2020. Main Outcomes and Measures: Multilevel factors associated with experiences of coordinating care for patients with cancer and chronic conditions among oncology and primary care stakeholders. Results: Among interviews and observation of 93 health system stakeholders, system-level factors identified as being associated with care coordination included challenges to accessing primary care, lack of communication between oncology and primary care clinicians, and leadership awareness of care coordination challenges. Clinician-level factors included unclear role delineation and lack of clinician knowledge and preparedness to manage the effects of cancer and chronic conditions. Conclusions and Relevance: Primary care may play a critical role in delivering coordinated care for patients with cancer and chronic diseases. This study's findings suggest a need for care delivery strategies that bridge oncology and primary care by enhancing communication, better delineating roles and responsibilities across care teams, and improving clinician knowledge and preparedness to care for patients with cancer and chronic conditions. Expanding timely access to primary care is also key, albeit challenging in resource-limited safety-net settings.

Email Patient-Provider Communication and Cancer Screenings Among US Adults: Cross-sectional Study.

BACKGROUND: The growth of electronic medical records and use of patient portals have allowed for patients and health care providers to communicate via email and direct messaging between health care visits. Email patient-provider communication (PPC) may enhance traditional face-to-face PPC by allowing patients to ask questions, receive clear explanations, engage in shared decision-making, and confirm their understanding between in-person visits. Despite increasing trends in the use of email PPC since the early 2000s, few studies have evaluated associations between email PPC and the uptake of preventive services. OBJECTIVE: The objective of this study was to determine associations between the use of email PPC and the likelihood of undergoing breast, cervical, and colon cancer screenings among adults who have received health care in the past 12 months. METHODS: Secondary, cross-sectional data from the 2011-2015 National Health Interview Survey were combined and analyzed. For each cancer screening, inclusion criteria were based on the age of screening recommendations and prior history of cancer diagnosis (n=35,912 for breast, n=48,512 for cervical, and n=45,884 for colon). The independent variable was whether adults used email PPC in the past 12 months (yes or no). The dependent variables were whether (1) women (aged ≥40 years) received a mammogram in the past 12 months; (2) women (aged 21-65 years) received a Pap test in the past 12 months; and (3) individuals (aged ≥50 years) received a colon cancer screening in the past 12 months. Bivariate and multivariable logistic regression analyses were conducted. RESULTS: Adults who reported receiving all three cancer screenings in the past 12 months were more likely to be non-Hispanic White; be married or living with a partner; have a bachelor's degree or higher education level; have health insurance coverage; and perceive their health as excellent, very good, or good (all P<.001). Men were more likely to receive colon cancer screenings than women (P<.001). Multivariable logistic regression models showed women who used email to communicate with their health care providers had greater odds of receiving breast (odds ratio [OR] 1.32, 95% CI 1.20-1.44) and cervical (OR 1.11, 95% CI 1.02-1.20) cancer screenings than women who did not use email PPC. Adults who used email to communicate with their health care providers had 1.55 times greater odds (95% CI 1.42-1.69) of receiving a colon cancer screening than those who did not use email PPC. CONCLUSIONS: Our results demonstrate that email PPC is a marker of increased likelihood of adults completing age-appropriate cancer screenings, particularly breast, cervical, and colon cancer screenings. More research is needed to examine other factors related to the reasons for and quality of email PPC between patients and health care providers and determine avenues for health education and intervention to further explore this association.