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.

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.

The association between pediatric care coordination (PCC) and the prevalence of oral health problems (OHP) among children and youths in the United States was investigated. Using data from the 2022 to 23 National Survey of Children's Health for participants ≤17 years, multivariable Poisson regression models were employed to estimate the association between effective PCC and OHP (the presence of ≥1 parent-reported case of dental caries, gum bleeding, or dental pain). The study estimated the population attributable risk (PAR) to quantify the proportion of potentially avoidable OHP with effective PCC. All models were weighted to account for the complex survey design and adjusted for sociodemographic, health insurance, and geographic covariates. The analytical sample comprised 56,287 participants, representing an estimated target population of 34 million children ≤17 years of age who needed PCC. After controlling for covariates, the difference in the prevalence of OHP between children who received effective PCC (13.1%) and those who did not (17.5%) was statistically significant (adjusted prevalence ratio: 1.33, 95% CI:1.22, 1.44). The PAR was 9.9%, indicating OHP was 9.9% more prevalent among children who lacked effective PCC. Not receiving needed effective PCC was associated with a greater burden of OHP among U.S. children and adolescents. This finding highlights the importance of intentional organization of health services among relevant stakeholders to support optimal oral health.

Preventable readmissions represent a significant opportunity to improve quality and reduce healthcare costs, with approximately 26% of Medicare medicine readmissions considered preventable. However, evidence on the effectiveness of post-discharge interventions at scale remains mixed, and implementing evidence-based practices consistently across large, diverse health systems is a challenge. To address these concerns, the Mass General Brigham Population Health Services Organization (MGB PHSO) developed and implemented a novel, multidisciplinary, system-wide post-discharge intervention aimed at reducing 30-day readmissions within its Medicare Shared Savings Program (MSSP) Accountable Care Organization (ACO). It was hypothesized that standardizing delivery through a high-fidelity workflow would reduce readmissions. A standardized, multidisciplinary program was created involving: (1) a coordinator conducting chart review and obtaining records; (2) a pharmacist performing medication reconciliation; and (3) a registered nurse completing a post-discharge assessment. A prospective cohort study was conducted comparing the outcomes of patients at pilot intervention sites with those of a propensity-matched control group. The intervention cohort showed a directional reduction in 30-day readmission rates compared to the matched controls (13.5% vs. 16.3%, P = 0.07) but no significant difference in 30-day emergency department presentations. The intervention group also had a significantly higher rate of 14-day follow-up appointments (70.0% vs. 65.3%, P = 0.025). These findings support the effectiveness of a centralized, standardized post-discharge strategy for reducing readmissions within an ACO setting. This study demonstrates that structured, system-level interventions can improve care transitions and outcomes in value-based care models.

Type 2 diabetes mellitus (T2D) is a prevalent metabolic disorder with significant health and economic burdens worldwide. The relationship between inflammation-related indicators and the risk of developing new-onset T2D remains underexplored. This study aims to identify and validate an interpretable predictive model for incident T2D using inflammation-related indicators. We analyzed data from 220,937 participants free of diabetes at baseline in the UK Biobank. Six machine learning algorithms were employed to construct predictive models. Feature selection was performed using Least Absolute Shrinkage and Selection Operator regression. SHapley Additive exPlanations (SHAP) were used to interpret the best-performing model. A genetic risk score (GRS, an aggregate measure of genetic susceptibility to T2D) was constructed, and multivariate Cox regression assessed the combined effects of genetic and inflammatory factors on T2D incidence. The Extreme Gradient Boosting model demonstrated the best performance (training set AUC = 0.863, testing set AUC = 0.838). Key predictors included body mass index, cholesterol, age, alanine aminotransferase, high-density lipoprotein, and Prognostic Nutritional Index (a marker predictive of inflammation and nutritional outcomes). SHAP analysis revealed significant contributions from these features. C-reactive protein and white blood cell count showed strong associations with future T2D risk. Integrating the GRS significantly improved the model's predictive performance (ΔAUC = +0.025, P < 0.05 via DeLong's test). This study presents an interpretable machine learning model for new-onset T2D risk prediction, emphasizing the role of inflammation and genetic factors. The findings provide a valuable tool for early T2D prevention and intervention, offering insights into the complex interplay between inflammation and diabetes development.

Cognitive decline and late-life depression are intertwined public health challenges for aging populations globally. To inform effective prevention, the current study investigated the dynamic temporal associations between multidimensional cognitive functions and depressive symptoms. Using four waves of longitudinal data (2013-2020) from a large panel study of older adults, the current study employed an integrated framework combining optimized dynamic time warping, cross-lagged panel models, and network analysis to model complex, lagged relationships. Results provided strong empirical support for the "cognition-first" hypothesis, with declines in several cognitive domains-notably temporal orientation, calculation, and immediate recall-acting as significant upstream predictors of subsequent depressive symptoms. A modest but significant protective feedback effect from positive affect to cognitive maintenance was also identified, while negative affect showed no significant predictive role sample of older adults who were cognitively and emotionally healthy at baseline. These findings offer preliminary empirical support for a strategic shift in population health management from reactive treatment toward proactive prevention. Based on these results, the current study discusses a conceptual framework for integrating cognitive screening into primary care to identify at-risk older adults, an approach that warrants further investigation and validation. This proactive approach could enable timely, low-cost interventions aimed at promoting positive affect and cognitive resilience, offering a potentially cost-effective strategy to mitigate the long-term burden of mental illness and advance the goals of healthy aging.

Screening for social determinants of health (SDOH) has been mandated by health systems nationwide. However, a gap exists in closed-loop referral for care coordination between health care and social services. This article presents the framework of a technology-based project to facilitate closed-loop referral between health care and social service agencies in Greater Houston by leveraging and connecting the existing care coordination technology infrastructure. Ten health care and social service organizations in Greater Houston participated in the demonstration project initiated in January 2023. The authors leveraged and linked regional health information exchange (HIE) technology with a master patient index of >18 million, and sector-specific care coordination platforms to build closed-loop referral capacity between HIE-participating health care organizations and social service organizations to meet patient SDOH needs. Evaluation efforts will assess the reach, adoption, implementation, and the effectiveness of the closed-loop framework in improving social and health outcomes. The framework comprised the following 4 components: (1) establishment of collaborative governance for shared decision-making processes, fostering trust, alignment, and transparency among organizations; (2) development of technology linkages between existing platforms to facilitate seamless referrals between organizations and ensure visibility of referral outcomes; (3) integration of regional resource directories into technology infrastructure to ensure resource accessibility/quality; and (4) evaluation of the system's impact on health equity, efficiency, and cost reduction. This project aimed to close the loop for care coordination between health care and social service agencies, enable data evaluation to determine care coordination effectiveness, and lay the foundation for SDOH-related research/practice equitably.

Despite focusing on drivers of health, or social determinants of health, for more than a decade, health care organizations have made minimal progress in improving these factors and associated health outcomes. This data- and theory-driven analysis looks at (1) why that is the case and (2) how organizational leaders and operators can go about correcting it. The authors' research finds that lack of progress is often due to ill-fit, entrenched business models that were optimized for a fee-for-service environment and cannot easily pivot to focus on drivers of health. Additionally, leaders are often unclear about what to change and overwhelmed by how to do it. The authors propose a 5-step strategy and execution process to address these challenges, laying out an end-to-end road map that enables health care leaders to meaningfully improve drivers of health and associated health outcomes for their patients and communities.

There is a global mental health crisis: mental illness is underrecognized, underdiagnosed, and undertreated with adverse effects on mental, physical, and social health. In the United States, there is an insufficient number of traditional psychiatric and psychological resources to provide the mental health care needed to solve this crisis. Community-based interventions could be an important adjunct to traditional mental health care. An evaluation of peer-reviewed articles was performed describing community-based interventions and identified 3 approaches with some evidence of effectiveness: (1) interventions that enhance community mental health literacy to improve recognition of early signs of mental illness for early engagement and provide community, family, and peer support; (2) community clinics providing social, medical, and mental health care and support to transition-age youth (15-25 years); and (3) social networking activities to enhance interactions among elders suffering from social isolation and loneliness. Multisector, multidisciplinary, and multicomponent interventions involving health care providers and community-based organizations had the best evidence of effectiveness and should target transition-age youth and elders.

The authors describe a rapid implementation of medication treatment for substance use disorders in a value-based organization, delivered in the community-based, interdisciplinary primary care of Medicaid and dual-eligible members. The determinants of increased need are reviewed, as well as the growing opportunity to improve access to treatments, and a template for implementation is shared.