To evaluate 3 single-item screening measures for limited health literacy in a community-based population of English and Spanish speakers.We recruited 324 English and 314 Spanish speakers from a community research registry in Dallas, Texas, enrolled between 2009 and 2012. We used 3 screening measures: (1) How would you rate your ability to read?; (2) How confident are you filling out medical forms by yourself?; and (3) How often do you have someone help you read hospital materials? In analyses stratified by language, we used area under the receiver operating characteristic (AUROC) curves to compare each item with the validated 40-item Short Test of Functional Health Literacy in Adults.For English speakers, no difference was seen among the items. For Spanish speakers, "ability to read" identified inadequate literacy better than "help reading hospital materials" (AUROC curve = 0.76 vs 0.65; P = .019).The "ability to read" item performed the best, supporting use as a screening tool in safety-net systems caring for diverse populations. Future studies should investigate how to implement brief measures in safety-net settings and whether highlighting health literacy level influences providers' communication practices and patient outcomes.
Prediction models that identify populations at risk for high health expenditures can guide the management and allocation of financial resources.To compare the ability for identifying individuals at risk for high health expenditures between the single-item assessment of general self-rated health (GSRH), "In general, would you say your health is Excellent, Very Good, Good, Fair, or Poor?," and 3 more complex measures.We used data from a prospective cohort, representative of the US civilian noninstitutionalized population, to compare the predictive ability of GSRH to: (1) the Short Form-12, (2) the Seattle Index of Comorbidity, and (3) the Diagnostic Cost-Related Groups/Hierarchal Condition Categories Relative-Risk Score. The outcomes were total, pharmacy, and office-based annualized expenditures in the top quintile, decile, and fifth percentile and any inpatient expenditures.Medical Expenditure Panel Survey panels 8 (2003-2004, n = 7948) and 9 (2004-2005, n = 7921).The GSRH model predicted the top quintile of expenditures, as well as the SF-12, Seattle Index of Comorbidity, though not as well as the Diagnostic Cost-Related Groups/Hierarchal Condition Categories Relative-Risk Score: total expenditures [area under the curve (AUC): 0.79, 0.80, 0.74, and 0.84, respectively], pharmacy expenditures (AUC: 0.83, 0.83, 0.76, and 0.87, respectively), and office-based expenditures (AUC: 0.73, 0.74, 0.68, and 0.78, respectively), as well as any hospital inpatient expenditures (AUC: 0.74, 0.76, 0.72, and 0.78, respectively). Results were similar for the decile and fifth percentile expenditure cut-points.A simple model of GSRH and age robustly stratifies populations and predicts future health expenditures generally as well as more complex models.
Youth violence is a preventable public health issue. Few hospital-based programs intentionally focus on youth violence prevention. This project aimed to describe the Systematic Screening and Assessment (SSA) methodology used to identify existing hospital-based youth violence prevention (HBYVP) programs ready for future rigorous evaluation. To identify promising HBYVP programs currently in use and assess readiness for evaluation, data from the 2017 American Hospital Association (AHA) Annual Survey of Hospitals was used to identify hospitals with Level I-III trauma centers with reported HBYVP programs. Information for each program was gathered via environmental scan and key informant interviews. A total of 383 hospital-based violence prevention programs were identified. Two review panels were conducted with violence prevention experts to identify characteristics of programs suitable for an evaluability assessment (EA). Fifteen programs focused on youth (10–24 years old) and were identified to be promising and evaluable. Three of the 15 programs were determined to have the infrastructure and readiness necessary for rigorous evaluation. Lessons learned and best practices for SSA project success included use of streamlined outreach efforts that provide program staff with informative and culturally tailored project materials outlining information about the problem, project goals, proposed SSA activities, and altruistic benefit to the community at the initial point of contact. In addition, success of review panels was attributed to use of software to streamline panelist review processes and use of evaluation and data analysis subject matter experts to serve as panel facilitators. Communities experiencing high youth violence burden and hospitals serving these communities can improve health outcomes among youth by implementing and evaluating tailored HBYVP programs.
28 Background: Historically, hospital costs are based on a cost-to-charge ratio. The current cost system determines when a charge is filed and a bill is created, which can be days following the patient visit. This time lag between the patient visit and the billed charges can be problematic. In preparation for episode-based payments, it is essential to know the true cost of care at the time of delivery. To accomplish this goal, the University of Texas MD Anderson Cancer Center (MDACC) leveraged existing time-driven activity-based costing (TDABC) process maps to track the true costs of the patient care cycle. Methods: The first steps were to understand the patient care cycle through process mapping. Next, data sources were identified to capture patient volumes. Process maps were adjusted to capture the data sources and provide a more accurate cost. Trigger logic models were created to link data sources and the TDABC process maps to the true cost for each patient appointment. Lastly, we developed a SAS software program to compute the real-time TDABC costs for 50 patients in the Head & Neck Center. Results: Our existing data sources capture information relevant to TDABC on a regular basis. Patient appointment data provided the patient visit, and billing and time data provided approximations of the amount of time spent in the encounter and the number of resources involved in the patient visit. Out of 219 process maps, 148 (70%) were matched to existing patient appointment and charge data using the trigger logic. This allowed us to track 4,980 patient appointments for 50 patients in fifteen minutes. Conclusions: As data are collected throughout the institution, it is realized that multiple data sources are needed to reconcile the patient’s experience and to match the TDABC process maps to existing data sources. Since our data sources are updated daily and are based on a patient’s date of service, we can capture our costs of delivering care close to real-time. This process is continually refined as additional data sources are made available and as process maps are developed in other parts of MDACC.
Higher blood lead levels, a risk factor for cardiovascular disease, have been reported among patients with end-stage renal disease. We sought to determine whether higher lead levels in end-stage renal disease patients are due to lead being released from the skeleton. Fifty-one African American patients with end-stage renal disease were recruited from three Tulane University-affiliated dialysis clinics between January and July 2005. An interviewer-administered questionnaire, blood specimen collection, and 109Cd x-ray fluorescence measurement of tibia lead occurred during a single study visit. In addition, levels of serum parathyroid hormone (PTH), calcium, phosphorus, and albumin were abstracted from patients9 charts. The distributions of tibia and blood lead were similar across levels of serum PTH. Specifically, the median tibia lead was 21 μg/g and 17 μg/g for participants with PTH levels .40). The high blood lead levels observed among end-stage renal disease patients do not appear to be the result of increased bone turnover. The causes of higher blood lead levels for these patients need to be identified and attenuated.
