Population Health Management

Colorectal cancer (CRC) is a leading contributor to morbidity and mortality in the United States, with documented disparities in screening rates across racial lines. This study examined the intersection of race and social vulnerability in CRC screening rates. We examined 117,424 patients eligible for CRC screening in New York State. We assessed CRC screening among individuals aged 45–80 based on Healthcare Effectiveness Data and Information Set guidelines. We used Social Vulnerability Indices (SVIs) geocoded to each patient’s census track. We employed log-binomial multivariate regression models with interaction terms to evaluate the effects of race and social vulnerability on CRC screening rates, while controlling for age, sex, marital status, and community type. Among 117,424 patients eligible for CRC screening, 75.2% of patients (n = 88,276) were effectively screened. Screening rates varied based on race (White 76.2%; Black 64.4%; P < 0.0001). CRC screening rates declined with increasing social vulnerability among both Black and White patients. Among patients in the most socially vulnerable groups, the adjusted rate of screening was 3.8%–8.4% lower for Black patients compared to White patients. In contrast, within the least socially vulnerable (most affluent) group, there was no statistical difference in the rate of CRC screening by race (adjusted risk ratio = 0.981; P = 0.201). The impact of social vulnerability was experienced disproportionately by Black patients. Among patients living in the most vulnerable census tracts, Black patients were significantly less likely to be screened for CRC compared to White patients facing similar social vulnerability. In contrast, we did not observe a difference in screening rates by race in the most affluent (least socially vulnerable) group.

Although patients with uncontrolled diabetes (A1C ≥ 9%) frequently visit their primary care physicians’ offices, their diabetes medication is not always changed or intensified at those visits, a pattern described as “therapeutic inertia.” One potential solution is the use of point-of-care fingerstick hemoglobin A1C (A1C), which provides results in minutes, rather than venous A1C, which takes days. However, point-of-care A1C is not consistently used in current practice. The authors sought to determine the effectiveness of a systematic nurse-based intervention for implementing point-of-care A1C testing among patients with uncontrolled diabetes. They conducted a before-and-after experimental trial to determine the effect of point-of-care A1C testing on therapeutic inertia in a hospital-based clinic in New York, NY, in 2024–2025. They compared the frequency of any clinical action taken and the time to communication of results to patients before vs. after intervention implementation, using chi-squared and t-tests, respectively. The study included 203 patients, of whom 40.4% were Black or African American and 38.9% were Hispanic. Systematic collection of point-of-care A1C did not change the frequency of clinical actions taken overall (79.0% pre-intervention vs. 73.8% post-intervention, P = 0.38). However, the intervention resulted in a significantly shorter time from test to communication of results to patients, compared to usual care (0.6 days vs. 7.4 days, P = 0.03). A systematic intervention to increase the utilization of point-of-care A1C is effective in facilitating faster delivery of results to patients, which enables more counseling for patients with uncontrolled diabetes during their visit.

This study examines the relationship between continuity of care with a family doctor and the usual place of care for minor health problems among Canadian seniors, with a focus on differences between immigrants and the native-born. Data come from the Canadian Health Survey on Seniors (n = 40,848). The outcome identifies respondents’ usual source of immediate care for minor problems: a doctor’s office, hospital outpatient/community clinic/walk-in clinic, emergency department (ED), or another location. Multinomial logistic regression models estimate associations between continuity of care—measured as duration of attachment to a family doctor—and place of care, with and without interaction terms between continuity and immigrant status, adjusting for demographic, socioeconomic, health, and access-related factors. Seniors attached to a regular family doctor for more than 2 years are significantly less likely to rely on the ED as their usual source of care (OR = 0.89, P < 0.05). Immigrants are also less likely than native-born seniors to use the ED (OR = 0.69, P < 0.001). However, interaction models show that immigrants with short-term attachment (less than 1 year) have higher odds of ED use than native-born seniors with similar attachment (OR = 2.33, P = 0.031). Poor self-rated health, lower income, and access barriers are associated with greater ED reliance. These findings highlight the importance of sustained primary care relationships, particularly for newly attached and immigrant seniors, to reduce avoidable ED use and promote equitable access to community-based care in Canada.

Community Health Needs Assessments (CHNAs), mandated by the Affordable Care Act for tax-exempt hospitals, represent an underutilized yet rich data source for disease-specific advocacy. This commentary proposes a novel framework in which disease advocacy organizations—such as Alzheimer’s Los Angeles, the American Heart Association, and the National Alliance on Mental Illness—deploy artificial intelligence (AI) agents to systematically analyze CHNAs, identify gaps in condition-specific care, generate personalized outreach to hospital leadership, and publicly score health systems on their responsiveness to identified needs. Using Alzheimer’s disease and dementia care in Los Angeles County as a primary case example, this article describes how AI-driven automation of data collection, natural language processing of CHNA documents, and coordinated advocacy campaigns can transform the current passive CHNA cycle into an active mechanism for population health improvement. The framework combines reputational accountability through public scorecards with constructive, evidence-based recommendations, creating a “carrot-and-stick” dynamic that existing literature on public performance reporting suggests can achieve engagement rates of 40%–70% and meaningful institutional change in 30%–60% of targeted systems. This approach is adaptable across chronic conditions and disease advocacy organizations, wherever publicly reported community needs data intersect with organized patient advocacy. Implications for population health management, health system quality improvement, and the responsible integration of AI in public health advocacy are discussed.

Public health professionals have long understood the link between social determinants of health, health-related social needs (HRSN), and health outcomes. Recently, the integration of social care to address HRSN within the health care environment has increased, spurred by payers and health systems recognizing that they will be unable to improve health equity without addressing the underlying nonclinical causes of illness. However, the integration of social care and health care is complex, involving many stakeholders within health care and social care systems, as well as the community. From September 2021 to September 2022, the Institute for Healthcare Improvement, with funding from Pfizer, supported a Learning and Action Network (LAN) involving three integrated delivery networks (IDNs) from across the United States. During the LAN, the IDNs tested social care integration strategies within their patient populations and shared results both with one another and stakeholders in their own systems to enhance the rate of learning and improvement. Detailed here are practical examples from the LAN and learnings from the participating IDNs, with a focus on HRSN screening. This paper presents the quality improvement framework for the LAN, including the use of a three-part data review process and Plan-Do-Study-Act cycles, and documents the successes and challenges faced by each of the IDNs, which include dramatic improvements in HRSN screening rates. Success in this work can be realized with intentional engagement with health care system leadership and the community, closely monitoring implementation, and by evaluating what really matters at the core of this work—achieving health equity.

Prior work has shown that persons with disabilities have higher rates of poor cardiovascular outcomes than persons without disabilities. Little research has evaluated this relationship in persons with single and dual sensory loss. This study aimed to examine the association between single and dual sensory loss and cardiovascular disease (CVD). Data were obtained from 2021 Behavioral Risk Factor Surveillance System, a representative household survey of 234,394 noninstitutionalized civilian adults. The independent variable was sensory loss (neither, vision loss only, hearing loss only, both vision and hearing loss). The outcomes were self-reported angina/coronary heart disease, stroke, heart attack, and CVD (composite variable). Covariates were sex, age, race/ethnicity, education, employment, marital status, insurance, income, metropolitan status, body mass index, blood pressure, cholesterol, diabetes, depression, exercise, fruit and vegetable intake, smoking, and alcohol risk. Weighted multivariable logistic regression was used to evaluate the relationship between sensory loss and CVD, adjusting for covariates. In weighted and fully adjusted models, persons with vision loss only, hearing loss only, and dual sensory loss had increased odds of heart attack, stroke, angina/coronary heart disease, and CVD in comparison with persons without sensory loss. Additional research is needed to examine the underlying mechanisms influencing increased risk for CVD in persons with sensory loss. Initiatives are needed that aim to reduce the burden of CVD among persons with single and dual sensory loss.

Transitions of care from hospital to home are a vulnerable time for patients, particularly so for those with social determinants of health (SDOH) needs that may impact their ability to manage their health in the outpatient setting. Traditional interventions focus on medication, continuity of care, health information, and red flags and do not necessarily address patients’ SDOH needs. The objective was to evaluate the incorporation of an SDOH screening and intervention into transitions of care encounters (referred to here as CTI+). This is a retrospective analysis of electronic health record data of patients discharged from a large urban health system with a transition of care encounter between May 2022 and April 2024. Demographic characteristics of patients who participate in CTI + are presented, as well as the prevalence of screening positive for issues with health literacy, medical transportation, food insecurity, and financial resource strain. The 30-day readmission rates for patients with SDOH factors and those without were compared, as well as readmission rates in the year prior to and following implementation of CTI+. Of 5942 encounters, 31.9% screened positive for at least one SDOH. 25.6% reported issues with health literacy. Readmission rates rose slightly for each additional positive SDOH factor. After implementation of CTI+, readmissions dropped from 11.7% to 9.8%. These findings highlight the need to address health literacy and demonstrate that incorporating SDOH screening and interventions into transitions of care may help mitigate the effect of SDOH on readmissions.

To examine the associations of sleep quality, sleep duration, and their trajectories with the risk of incident arthritis in middle-aged and older adults in the United Kingdom. Data from waves 4 to 8 of the English Longitudinal Study of Aging were used. We included 3028 participants aged ≥50 years without arthritis at baseline, followed for ∼8 years. Sleep quality was assessed using the Jenkins Sleep Scale (JSS), categorized as good, intermediate, or poor. Sleep duration was divided into tertiles. Incident arthritis was self-reported and physician-diagnosed. Associations were estimated using multivariable logistic regression and stratified analyses, including restricted cubic spline (RCS) models to explore dose–response relationships. Overall, 559 cases of incident arthritis were identified. Both intermediate and good sleep quality were associated with a lower risk of arthritis compared to poor sleep (odds ratio [OR] intermediate = 0.71, OR good = 0.55, P for trend < 0.001). The longest sleep tertile (Q3) showed a modest protective effect (OR = 0.72). RCS analysis revealed a significant linear dose–response relationship between JSS scores and arthritis risk. Sensitivity analyses of sleep-quality trajectories showed lower arthritis risk in all but the stable poor pattern, with the lowest risk in the stable good group (OR = 0.44). Poor subjective sleep quality is a more consistent predictor of arthritis than short-sleep duration alone, with a clear dose–response gradient. Improving poor sleep quality may be key to reducing arthritis risk.