Search

BACKGROUND AND OBJECTIVES: Place-based social disadvantage indices are increasingly used to pro- mote health equity, but vary in design. We compared associations between 3 commonly used indices (Social Vulnerability Index [SVI], Area Deprivation Index [ADI], and Child Opportunity Index [COI]) and infant well-child check (WCC) attendance and adolescent obesity. We hypothesized that the COI would have the strongest association with child health outcomes. METHODS: We conducted a cross-sectional analysis of 2014-2019 Duke University Health System electronic health record data. Eligible participants were ≤ 18 years old, had outpatient encounters during the study period, and resided in Durham County, North Carolina. We aggregated indices into deciles; higher deciles represented greater disadvantage. Multivariable logistic regression models quantified the association between each index and infant WCC attendance (ages 0-15 months) and adolescent obesity (11-17 years). RESULTS: There were 10 175 and 14 961 children in the WCC and obesity cohorts, respectively. All 3 indices were similarly associated with WCCs (SVI odds ratio [OR] 1.10, 95% confidence interval [CI] 1.08-1.12; ADI OR 1.10, 95% CI 1.08-1.12; COI OR 1.12, 95% CI 1.10-1.14) and obesity (SVI OR 1.06, 95% CI 1.04-1.07; ADI OR 1.08, 95% CI 1.06-1.10; COI OR 1.07, 95% CI 1.05-1.08). ORs indicate the increase in the outcome odds for every 1-decile index score increase. CONCLUSIONS: Higher disadvantage as defined by all 3 indices was similarly associated with adolescent obesity and decreased infant WCC attendance. The SVI, ADI, and COI may be equally suitable for pediatric research, but population and outcome characteristics should be considered when selecting an index. [ABSTRACT FROM AUTHOR]

Importance: Data from electronic health records (EHRs) are increasingly used for risk prediction. However, EHRs do not reliably collect sociodemographic and neighborhood information, which has been shown to be associated with health. The added contribution of neighborhood socioeconomic status (nSES) in predicting health events is unknown and may help inform population-level risk reduction strategies., Objective: To quantify the association of nSES with adverse outcomes and the value of nSES in predicting the risk of adverse outcomes in EHR-based risk models., Design, Setting, and Participants: Cohort study in which data from 90 097 patients 18 years or older in the Duke University Health System and Lincoln Community Health Center EHR from January 1, 2009, to December 31, 2015, with at least 1 health care encounter and residence in Durham County, North Carolina, in the year prior to the index date were linked with census tract data to quantify the association between nSES and the risk of adverse outcomes. Machine learning methods were used to develop risk models and determine how adding nSES to EHR data affects risk prediction. Neighborhood socioeconomic status was defined using the Agency for Healthcare Research and Quality SES index, a weighted measure of multiple indicators of neighborhood deprivation., Main Outcomes and Measures: Outcomes included use of health care services (emergency department and inpatient and outpatient encounters) and hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke., Results: Among the 90 097 patients in the training set of the study (57 507 women and 32 590 men; mean [SD] age, 47.2 [17.7] years) and the 122 812 patients in the testing set of the study (75 517 women and 47 295 men; mean [SD] age, 46.2 [17.9] years), those living in neighborhoods with lower nSES had a shorter time to use of emergency department services and inpatient encounters, as well as a shorter time to hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke. The predictive value of nSES varied by outcome of interest (C statistic ranged from 0.50 to 0.63). When added to EHR variables, nSES did not improve predictive performance for any health outcome., Conclusions and Relevance: Social determinants of health, including nSES, are associated with the health of a patient. However, the results of this study suggest that information on nSES may not contribute much more to risk prediction above and beyond what is already provided by EHR data. Although this result does not mean that integrating social determinants of health into the EHR has no benefit, researchers may be able to use EHR data alone for population risk assessment.

Background: Use of the Medicare hospice benefit has been associated with high-quality care at the end of life, and hospice length of use in particular has been used as a proxy for appropriate timing of hospice enrollment. Quantile regression has been underutilized as an alternative tool to model distributional changes in hospice length of use and hospice payments outside of the mean., Objective: To test for heterogeneity in the relationship between patient characteristics and hospice outcomes across the distribution of hospice days., Setting: Medicare Beneficiary Summary File and survey data (2014) for hospice beneficiaries in North and South Carolina with common terminal diagnoses., Measurements: Distributional shifts associated with patient characteristics were evaluated at the 25th and 75th percentiles of hospice days and hospice payments using quantile regressions and compared to the mean shift estimated by ordinary least squares (OLS) regression., Principal Findings: Significant (p < 0.001) heterogeneity in the marginal effects on hospice days and costs was observed, with patient characteristics associated with generally larger shifts in the 75th percentile than the 25th percentile. Mean effects estimated by OLS regression overestimate the magnitude of the median marginal effects for all patient characteristics except for race. Results for hospice payments in 2014 were similar., Conclusions: Methodological decisions can have a meaningful impact in the evaluation of factors influencing hospice length of use or cost.

Background: On January 1, 2016, Medicare implemented a new "two-tiered" model for hospice services, with per diem rates increased for days 1 through 60, decreased for days 61 and greater, and service intensity add-on payments made retrospectively for the last seven days of life., Objective: To estimate whether the Medicare hospice benefit's potential for cost savings will change as a result of the January 2016 change in payment structure., Design: Analysis of decedents' claims records using propensity score matching, logistic regression, and sensitivity analysis., Setting/subjects: All age-eligible Medicare decedents who received care and died in North Carolina in calendar years 2009 and 2010., Measurements: Costs to Medicare for hospice and other healthcare services., Results: Medicare costs were reduced from hospice election until death using both 2009-2010 and new 2016 payment structures and rates. Mean cost savings were $1,527 with actual payment rates, and would have been $2,105 with the new payment rates (p < 0.001). Cost savings were confirmed by reducing the number of days used for cost comparison by three days for those with hospice stays of at least four days ($4,318 using 2009-2010 rates, $3,138 for 2016 rates: p < 0.001). Cost savings were greater for males ($3,393) versus females ($1,051) and greatest in cancer ($6,706) followed by debility and failure to thrive ($5,636) and congestive heart failure ($1,309); dementia patients had higher costs (+$1,880) (p < 0.001). When adding 3 days to the comparison period, hospice increased costs to Medicare., Conclusions: Medicare savings could continue with the 2016 payment rate change. Cost savings were found for all primary diagnoses analyzed except dementia.