WP 2019-397

Nursing home care is arguably the largest financial risk for the elderly without private or social insurance coverage. The annual out-of-pocket expenditure can easily exceed $70,000. Despite the substantial financial burdens on the elderly, the understanding of nursing home self-pay prices is rather sparse due to data limitation. To bridge the gap in the literature, we collected a unique and longitudinal price dataset from eight states, spanning from 2005 to 2010, to advance the understanding of the determinants and geographical variations of nursing home price and price growth. Overall, nursing home prices have consistently outpaced the inflation of consumer prices, particularly in California and Oregon. We also see faster price growth in markets where they face stricter capacity constraints and have higher for-profit market shares. Organizational structures are also significantly associated with price variations. We find that nonprofit nursing homes have higher prices than for-profit nursing homes and that chain-affiliated nursing homes charge higher prices than nonchains counterparts.U.S. Social Security Administration through the University of Michigan Retirement Research Center, award RRC08098401-09https://deepblue.lib.umich.edu/bitstream/2027.42/151935/1/wp397.pdfDescription of wp397.pdf : Working pape

Evaluation of Structured, Semi-Structured, and Free-Text Electronic Health Record Data to Classify Hepatitis C Virus (HCV) Infection

Evaluation of the United States Centers for Disease Control and Prevention (CDC)-defined HCV-related risk factors are not consistently performed as part of routine care, rendering risk-based testing susceptible to clinician bias and missed diagnoses. This work uses natural language processing (NLP) and machine learning to identify patients who are at high risk for HCV infection. Models were developed and validated to predict patients with newly identified HCV infection (detectable RNA or reported HCV diagnosis). We evaluated models with three types of variables: structured (structured-based model), semi-structured and free-text notes (text-based model), and all variables (full-set model). We applied each model to three stratifications of data: patients with no history of HCV prior to 2020, patients with a history of HCV prior to 2020, and all patients. We used XGBoost and ten-fold C-statistic cross-validation to evaluate the generalizability of the models. There were 3564 unique patients, 487 with HCV infection. The average C-statistics on the structured-based, text-based, and full-set models for all the patients were 0.777 (95% CI: 0.744–0.810), 0.677 (95% CI: 0.631–0.723), and 0.774 (95% CI: 0.735–0.813), respectively. The full-set model performed slightly better than the structured-based model and similar to text-based models for patients with no history of HCV prior to 2020; average C-statistics of 0.780, 0.774, and 0.759, respectively. NLP was able to identify six more risk factors inconsistently coded in structured elements: incarceration, needlestick, substance use or abuse, sexually transmitted infections, piercings, and tattoos. The availability of model options (structured-based or text-based models) with a similar performance can provide deployment flexibility in situations where data is limited