Repeated exposure to broad-spectrum antibiotics at ages 0 to 23 months is associated with early childhood obesity. Because common childhood infections were the most frequent diagnoses co-occurring with broad-spectrum antibiotic prescription, narrowing antibiotic selection is potentially a modifiable risk factor for childhood obesity.

Though rates of foreclosure are at a historic high, relatively little is known about the link between foreclosure and health. We performed a case-control study to examine health conditions and health care utilization in the time period prior to foreclosure. Homeowners who received a home foreclosure notice from 2005 to 2008 were matched (by name and address) to a university hospital system in Philadelphia and compared with controls who received care from the hospital system and who lived in the same zip code as cases. Outcome measures included prevalent health conditions and visit history in the 2 years prior to foreclosure. We found that people undergoing foreclosure were similar to controls with regard to age, gender, and insurance status but significantly more likely to be African American. Rates of hypertension and renal disease were significantly higher among cases after adjustment for sociodemographic characteristics. In the 2 years prior to foreclosure, cases were more likely to visit the emergency department, have an outpatient visit, and have a no-show appointment. Cases were less likely to have a primary care physicians (PCP) visit in the 6 months immediately prior to the receipt of a foreclosure notice. The results suggest changes in health care utilization in the time period prior to foreclosure. Policies designed to decrease the incidence of home foreclosure and support people during the process should consider its association with poor health and changes in health care utilization.

No abstract

In patients with diabetes, current models for predicting the risk of readmission within 30 days of hospital discharge vary in performance. We previously published the Diabetes Early Readmission Risk Indicator (DERRI TM), a logistic regression (LR) model based on 10 simple features with modest predictive performance (C-statistic 0.69). The current study aims to develop a more accurate model using deep learning on electronic health record (EHR) data. We electronically abstracted data from 36,563 patients with diabetes and at least 1 hospitalization at an urban, academic medical center between 7/1/2010 and 12/31/2020. One hospitalization per patient (index hospitalization) was randomly selected for analysis. A deep learning long short-term memory Recurrent Neural Network (RNN) was developed and compared to traditional linear and non-linear models: LR, AdaBoost, and Random Forest (RF). Models to predict unplanned, all-cause readmission were developed using demographics, vital signs, diagnostic and procedure codes, medications, laboratory tests, and administrative data as defined by the National Patient-Centered Clinical Research Network (PCORnet) Common Data Model. Unplanned readmissions were identified according to the Centers for Medicare and Medicaid (CMS) definition. A look-back time of 1 year before the index hospitalization and up to 60 previous ambulatory and hospital visits were used for learning and inference. Data dimensionality was reduced to 3,000 features by Singular Value Decomposition. The RNN model C-statistic is significantly greater than those of the traditional models (RNN 0.78, AdaBoost 0.71, RF 0.73, and LR 0.71, p<0. 0001). Likewise, the F1-score is numerically greater for the RNN model (RNN 0.76, AdaBoost 0.75, RF 0.75, LR 0.72). Direct comparison to the DERRI TM is limited by lack of EHR data on two of the component variables (employment status and zip code). The deep learning RNN model outperforms the DERRI TM and is based on more generalizable EHR data. This new model may provide the basis for a more useful readmission risk prediction tool for patients with diabetes. Deep learning models may outperform traditional models at predicting readmission risk in this population. Presentation: No date and time listed