Neighborhood deprivation and morbid obesity: Insights from the Houston Methodist Cardiovascular Disease Health System Learning Registry

State Healthcare Regulations and Total Knee Arthroplasty Prices Across the United States

Background

The cost of healthcare services in the United States is subject to various regulatory influences, yet the impact of state-level healthcare policies and political affiliations on total knee arthroplasty (TKA) prices remains underexplored.

Methods

Using data from the Turquoise Health Database and publicly available sources, TKA prices were analyzed across states to examine the influence of Medicaid expansion, Certificate of Need (CON) laws, and state partisan lean. Multivariable regression models controlled for Gross Domestic Product per capita, Area Deprivation Index, and urbanization.

Results

Among 64,402 TKAs from 2455 hospitals ($18,164 median, interquartile range: $10,806), states with Medicaid expansion and CON laws demonstrated lower TKA prices. Republican-leaning states had significantly reduced TKA prices compared to Democrat-leaning states, even after adjusting for economic factors (P < .0001).

Conclusions

Medicaid expansion and CON laws were associated with lower TKA prices across the United States. Additionally, states with Republican political leanings tended to have lower listed prices for TKA compared to Democrat-leaning states. These findings underscore the substantial influence of state healthcare policies and political factors on healthcare costs, highlighting the complexities of pricing dynamics in the US healthcare system.

Level of evidence

IV.

Socio-Ecologic Influences on Weight Trajectories Among Children with Obesity Living in Rural and Urban Settings

Background: Childhood obesity is a risk factor for poor cardiovascular, metabolic, and respiratory health. The studies examining influences of socio-ecologic factors on weight trajectories using longitudinal data are limited, often examine single measures (e.g., proximity to parks), and have not examined the specific trajectories of children with obesity. Methods: We examined influences on weight among 1518 children, 6-12 years of age, who had obesity using body mass index (BMI) criteria. BMI slope trajectories were categorized as decreasing, flat, or increasing, with a median of 2.1 years of follow-up. We examined socio-ecologic exposures, stratified by rural and urban settings, using census tracts to map indices, including food access, proximity to parks, normalized difference vegetation index, and area deprivation index (ADI). We used ordinal logistic regression to examine the associations between the socio-ecologic factors and BMI trajectories. Results: Among the 1518 children, 360 (24%) had a decreasing BMI trajectory with the remainder having flat (23%) or increasing (53%) trajectories. Children in rural areas were more likely to live in high disadvantage areas, 85%, compared with urban children, 46%. In the multivariable ordinal model, living in a lower ADI census tract had a 0.78 (95% CI 0.61-0.99) lower odds of being in an increasing BMI slope group, and no other socio-ecologic factor was associated. Conclusions: The area deprivation index captures a range of resources and social context compared with the built environment indicators, which had no association with BMI trajectory. Further work examining how to develop effective interventions in high deprivation areas is warranted.

Health care disparities and chronic rhinosinusitis: Does neighborhood disadvantage impact outcomes in sinonasal disease?

OBJECTIVE

Socioeconomic status (SES) is linked to health outcomes but has not been well studied in patients with chronic rhinosinusitis (CRS). The area deprivation index (ADI) is a comprehensive measure of geographic SES that ranks neighborhood disadvantage. This investigation used ADI to understand the impact of neighborhood disadvantage on CRS treatment outcomes.

METHODS

A total of 642 study participants with CRS were prospectively enrolled and self-selected endoscopic sinus surgery (ESS) or continued appropriate medical therapy as treatment. The 22-item SinoNasal Outcome Test (SNOT-22) and Medical Outcomes Study Questionnaire Short-Form 6-D (SF-6D) health utility value scores were recorded pre- and post-treatment. Using residence zip codes, national ADI scores were retrospectively assigned to patients. Spearman's correlation coefficients (Rs) and Cramer's V effect size (φc) with 95% confidence interval (CI) were calculated.

RESULTS

A history of ESS was associated with significantly worse ADI scores compared to no history of ESS (φc = 0.18; 95% CI: 0.10, 0.25; p < 0.001). Baseline total SNOT-22 (Rs = 0.14; 95% CI: 0.06, 0.22; p < 0.001) and SF-6D values (Rs = -0.20; 95% CI: -0.27, -0.12; p < 0.001) were significantly negatively correlated with national ADI rank. No significant correlations between ADI and within-subject improvement, or achievement of >1 minimal clinically important difference, in SNOT-22 or SF-6D scores after treatment were found.

CONCLUSIONS

Geographic socioeconomic deprivation was associated with worse baseline disease severity and history of prior surgical intervention. However, ADI did not correlate with improvement in disease-specific outcomes. The impact of socioeconomic deprivation on outcomes in CRS requires further investigation.

Polysocial Risk Scores: Implications for Cardiovascular Disease Risk Assessment and Management

PURPOSE OF REVIEW

To review current evidence, discuss key knowledge gaps and identify opportunities for development, validation and application of polysocial risk scores (pSRS) for cardiovascular disease (CVD) risk prediction and population cardiovascular health management.

RECENT FINDINGS

Limited existing evidence suggests that pSRS are promising tools to capture cumulative social determinants of health (SDOH) burden and improve CVD risk prediction beyond traditional risk factors. However, available tools lack generalizability, are cross-sectional in nature or do not assess social risk holistically across SDOH domains. Available SDOH and clinical risk factor data in large population-based databases are under-utilized for pSRS development. Recent advances in machine learning and artificial intelligence present unprecedented opportunities for SDOH integration and assessment in real-world data, with implications for pSRS development and validation for both clinical and healthcare utilization outcomes. pSRS presents unique opportunities to potentially improve traditional "clinical" models of CVD risk prediction. Future efforts should focus on fully utilizing available SDOH data in large epidemiological databases, testing pSRS efficacy in diverse population subgroups, and integrating pSRS into real-world clinical decision support systems to inform clinical care and advance cardiovascular health equity.