BACKGROUND: The Affordable Care Act facilitated improved insurance coverage for states that expanded Medicaid coverage, but the impact on cancer outcomes is unclear. This study compared changes in the diagnosis and management of colon cancer in states that did and did not participate in Medicaid expansion. STUDY DESIGN: Using a quasi-experimental difference-indifferences (DID) approach, we analyzed Medicaid and uninsured patients in the National Cancer Data Base during 2 time periods: pre (2011-2012) and post expansion (2015-2016). Patients in non-expansion states were compared with those in January 2014 expansion states with regard to changes in patient and facility characteristics, cancer staging, treatment decisions, and surgical outcomes. RESULTS: Along with increased Medicaid coverage (DID ¼ 20.27; p < 0.001), patients in expansion states had an increase in stage I diagnoses (DID ¼ 2.97; p ¼ 0.035), distance traveled (miles, DID ¼ 6.67; p ¼ 0.005), and treatment at integrated network programs (DID ¼ 2.67; p ¼ 0.045). More early-stage patients were treated within 30 days (DID ¼ 7.24; p ¼ 0.035) and more stage IV patients received palliative care (DID ¼ 5.01; p ¼ 0.048). Among surgical patients, Medicaid expansion correlated with fewer urgent cases (< 7 days, DID ¼ e5.88; p ¼ 0.008) and more minimally invasive surgery (DID ¼ 5.00; p ¼ 0.022). There were no observed differences in postoperative outcomes or adjuvant chemotherapy. CONCLUSIONS: Medicaid expansion correlated with earlier diagnosis, enhanced access, and improved surgical care for colon cancer patients. These findings highlight the importance of improving health insurance coverage and can help guide future policy efforts.

BACKGROUND There are well-known disparities for patients injured in rural setting versus urban setting. Many cite access to care; however, the mechanisms are not defined. One potential factor is differences in field triage. Our objective was to evaluate differences in prehospital undertriage (UT) in rural setting versus urban settings. METHODS Adult patients in the Pennsylvania Trauma Outcomes Study (PTOS) registry 2000 to 2017 were included. Rural/urban setting was defined by county according to the Pennsylvania Trauma Systems Foundation. Rural/urban classification was performed for patients and centers. Undertriage was defined as patients meeting physiologic or anatomic triage criteria from the National Field Triage Guidelines who were not initially transported to a Level I or Level II trauma center. Logistic regression determined the association between UT and rural/urban setting, adjusting for transport distance and prehospital time. Models were expanded to evaluate the effect of individual triage criteria, trauma center setting, and transport mode on UT. RESULTS There were 453,112 patients included (26% rural). Undertriage was higher in rural patients (8.6% vs. 3.4%, p < 0.01). Rural setting was associated with UT after adjusting for distance and prehospital time (odds ratio [OR], 3.52; 95% confidence interval [CI], 1.82–6.78; p < 0.01). Different triage criteria were associated with UT in rural/urban settings. Rural setting was associated with UT for patients transferred to an urban center (OR, 3.32; 95% CI, 1.75–6.25; p < 0.01), but not a rural center (OR, 0.68; 95% CI, 0.08–5.53; p = 0.72). Rural setting was associated with UT for ground (OR, 5.01; 95% CI, 2.65–9.46; p < 0.01) but not air transport (OR, 1.18; 95% CI, 0.54–2.55; p = 0.68). CONCLUSION Undertriage is more common in rural settings. Specific triage criteria are associated with UT in rural settings. Lack of a rural trauma center requiring transfer to an urban center is a risk factor for UT of rural patients. Air medical transport mitigated the risk of UT in rural patients. Provider and system interventions may help reduce UT in rural settings. LEVEL OF EVIDENCE Care Management, Level IV.

BACKGROUND:Social vulnerability indices were created to measure resiliency to environmental disasters based on socioeconomic and population characteristics of discrete geographic regions. They are composed of multiple validated constructs that can also potentially identify geographically vulnerable populations after injury. Our objective was to determine if these indices correlate with injury fatality rates in the US. METHODS:We evaluated three social vulnerability indices: The Hazards & Vulnerability Research Institute's Social Vulnerability Index (SoVI), the Center for Disease Control's Social Vulnerability Index (SVI), and the Economic Innovation Group's Distressed Community Index (DCI). We analyzed SVI subindices and common individual census variables as indicators of socioeconomic status.Outcomes included age-adjusted county-level overall, firearm, and motor vehicle collision deaths per 100,000 population. Linear regression determined the association of injury fatality rates with the SoVI, SVI, and DCI. Bivariate choropleth mapping identified geographic variation and spatial autocorrelation of overall fatality, SoVI, and DCI. RESULTS:A total of 3,137 US counties were included. Only 24.6% of counties fell into the same vulnerability quintile for all three indices. Despite this, all indices were associated with increasing fatality rates for overall, firearm, and motor vehicle collision fatality. The DCI performed best by model fit, explanation of variance, and diagnostic performance on overall injury fatality. There is significant geographic variation in SoVI, DCI, and injury fatality rates at the county level across the United States, with moderate spatial autocorrelation of SoVI (Moran's I, 0.35; p < 0.01) and high autocorrelation of injury fatality rates (Moran's I, 0.77; p < 0.01) and DCI (Moran's I, 0.53; p < 0.01). CONCLUSION:While the indices contribute unique information, higher social vulnerability is associated with higher injury fatality across all indices. These indices may be useful in the epidemiologic and geographic assessment of injury-related fatality rates. Further study is warranted to determine if these indices outperform traditional measures of socioeconomic status and related constructs used in trauma research.

BACKGROUND:Social determinants of health (SDOH) impact patient outcomes in trauma. Census data are often used to account for SDOH; however, there is no consensus on which variables are most important. Social vulnerability indices offer the advantage of combining multiple constructs into a single variable. Our objective was to determine if incorporation of SDOH in patient-level risk-adjusted outcome modeling improved predictive performance. METHODS:We evaluated two social vulnerability indices at the zip code level: Distressed Community Index (DCI) and National Risk Index (NRI). Individual variable combinations from Agency for Healthcare Research and Quality's SDOH data set were used for comparison. Patients were obtained from the Pennsylvania Trauma Outcomes Study 2000 to 2020. These measures were added to a validated base mortality prediction model with comparison of area under the curve and Bayesian information criterion. We performed center benchmarking using risk-standardized mortality ratios to evaluate change in rank and outlier status based on SDOH. Geospatial analysis identified geographic variation and autocorrelation. RESULTS:There were 449,541 patients included. The DCI and NRI were associated with an increase in mortality (adjusted odds ratio, 1.02; 95% confidence interval, 1.01-1.03 per 10% percentile rank increase; p < 0.01, respectively). The DCI, NRI, and seven Agency for Healthcare Research and Quality variables also improved base model fit but discrimination was similar. Two thirds of centers changed mortality ranking when accounting for SDOH compared with the base model alone. Outlier status changed in 7% of centers, most representing an improvement from worse-than-expected to nonoutlier or nonoutlier to better-than-expected. There was significant geographic variation and autocorrelation of the DCI and NRI (DCI; Moran's I 0.62, p = 0.01; NRI; Moran's I 0.34, p = 0.01). CONCLUSION:Social determinants of health are associated with an individual patient's risk of mortality after injury. Accounting for SDOH may be important in risk adjustment for trauma center benchmarking.