Prevalence of lifestyle-related behavioral information in claims data in the U.S.

OBJECTIVE: Given their association with varying health risks, lifestyle-related behaviors are essential to consider in population-level disease prevention. Health insurance claims are a key source of information for population health analytics, but the availability of lifestyle information within claims data is unknown. Our goal was to assess the availability and prevalence of data items that describe lifestyle behaviors across several domains within a large U.S. claims database. METHODS: We conducted a retrospective, descriptive analysis to determine the availability of the following claims-derived lifestyle domains: nutrition, eating habits, physical activity, weight status, emotional wellness, sleep, tobacco use, and substance use. To define these domains, we applied a serial review process with three physicians to identify relevant diagnosis and procedure codes within claims for each domain. We used enrollment files and medical claims from a large national U.S. health plan to identify lifestyle relevant codes filed between 2016 and 2020. We calculated the annual prevalence of each claims-derived lifestyle domain and the proportion of patients by count within each domain. RESULTS: Approximately half of all members within the sample had claims information that identified at least one lifestyle domain (2016 = 41.9%; 2017 = 46.1%; 2018 = 49.6%; 2019 = 52.5%; 2020 = 50.6% of patients). Most commonly identified domains were weight status (19.9-30.7% across years), nutrition (13.3-17.8%), and tobacco use (7.9-9.8%). CONCLUSION: Our study demonstrates the feasibility of using claims data to identify key lifestyle behaviors. Additional research is needed to confirm the accuracy and validity of our approach and determine its use in population-level disease prevention.

Comparing and validating medication complexity from insurance claims against electronic health records.

BACKGROUND: Patient effort to comply with complex medication instructions is known to be related to nonadherence and subsequent medical complications or health care costs. A widely used Medication Regimen Complexity Index (MRCI) has been used with electronic health records (EHRs) to identify patients who could benefit from pharmacist intervention. A similar claims-derived measure may be better suited for clinical decision support, since claims offer a more complete view of patient care and health utilization. OBJECTIVE: To define and validate a novel insurance claims-based medication complexity score (MCS) patterned after the widely used MRCI, derived from EHRs. METHODS: Insurance claims and EHR data were provided by HealthPartners (N = 54,988) (Bloomington, Minnesota) and The Johns Hopkins Health System (N = 28,589) (Baltimore, Maryland) for years 2013 and 2017, respectively. Yearly measures of medication complexity were developed for each patient and evaluated with one another using rank correlation within different clinical subgroupings. Indicators for the presence of individually complex prescriptions were also developed and assessed using exact agreement. Complexity measures were then correlated with select covariates to further validate the concordance between MCS and MRCI with respect to clinical metrics. These included demographic, comorbidity, and health care utilization markers. Prescribed medications in each system's EHR were coded using the previously validated MRCI weighting rules. Insurance claims for retail pharmacy medications were coded using our novel MCS, which closely followed MRCI scoring rules. RESULTS: EHR-based MRCI and claims-based MCS were significantly correlated with one another for most clinical subgroupings. Likewise, both measures were correlated with several covariates, including count of active medications and chronic conditions. The MCS was, in most cases, more associated with key health covariates than was MRCI, although both were consistently significant. We found that the highest correlation between MCS and MRCI is obtained with patients who have similar counts of pharmacy records between EHRs and claims (HealthPartners: P = 0.796; Johns Hopkins Health System: P = 0.779). CONCLUSIONS: The findings suggest good correspondence between MCS and MRCI and that claims data represent a useful resource for assessing medication complexity. Claims data also have major practical advantages, such as interoperability across health care systems, although they lack the detailed clinical context of EHRs. DISCLOSURES: The Johns Hopkins University holds the copyright to the Adjusted Clinical Groups (ACG) system and receives royalties from the global distribution of the ACG system. This revenue supports a portion of the authors' salary. No additional or external funding supported this work. The authors have no conflict of interest to disclose.

Comparing the predictive value of screening to the use of electronic health record data for detecting future suicidal thoughts and behavior in an urban pediatric emergency department: A preliminary analysis.

AIM: Brief screening and predictive modeling have garnered attention for utility at identifying individuals at risk of suicide. Although previous research has investigated these methods, little is known about how these methods compare against each other or work in combination in the pediatric population. METHODS: Patients were aged 8-18 years old who presented from January 1, 2017, to June 30, 2019, to a Pediatric Emergency Department (PED). All patients were screened with the Ask Suicide Questionnaire (ASQ) as part of a universal screening approach. For all models, we used 5-fold cross-validation. We compared four models: Model 1 only included the ASQ; Model 2 included the ASQ and EHR data gathered at the time of ED visit (EHR data); Model 3 only included EHR data; and Model 4 included EHR data and a single item from the ASQ that asked about a lifetime history of suicide attempt. The main outcome was subsequent PED visit with suicide-related presenting problem within a 3-month follow-up period. RESULTS: Of the N = 13,420 individuals, n = 141 had a subsequent suicide-related PED visit. Approximately 63% identified as Black. Results showed that a model based only on EHR data (Model 3) had an area under the curve (AUC) of 0.775 compared to the ASQ alone (Model 1), which had an AUC of 0.754. Combining screening and EHR data (Model 4) resulted in a 17.4% (absolute difference = 3.6%) improvement in sensitivity and 13.4% increase in AUC (absolute difference = 6.6%) compared to screening alone (Model 1). CONCLUSION: Our findings show that predictive modeling based on EHR data is helpful either in the absence or as an addition to brief suicide screening. This is the first study to compare brief suicide screening to EHR-based predictive modeling and adds to our understanding of how best to identify youth at risk of suicidal thoughts and behaviors in clinical care settings.

Validation of oxidative stress assay for schizophrenia.

Accumulating evidence implicates oxidative stress in a range of diseases, yet no objective measurement has emerged that characterizes the global nature of oxidative stress. Previously, we reported a measurement that employs the moderately strong oxidant iridium (Ir) to probe the oxidative damage in a serum sample and reported that in a small study (N = 15) the Ir-reducing capacity assay could distinguish schizophrenia from healthy control groups based on their levels of oxidative stress. Here, we used a larger sample size to evaluate the Ir-reducing capacity assay to assess its ability to discriminate the schizophrenia (N = 73) and healthy control groups (N = 45). Each serum sample was measured (in triplicate) at three different times that were separated by several weeks. The Intraclass Correlation Coefficient (ICC = 0.69) for these repeated measurements indicates the assay detects stable components in the sample (i.e., it is not detecting transient reactive species or air-oxidizable serum components). Correlations between the Ir-reducing capacity assay and independently-measured total serum protein levels (r = +0.74, p < 2.2 × 10-16) suggest the assay is detecting information in the protein pool. For cross-validation of the discrimination ability, we used machine learning and receiver operating characteristic (ROC) analysis. After adjusting for potential confounders (age and smoking status), an area under the curve (AUC) of ROC curve was calculated to be 0.89 (p = 9.3 × 10-5). In conclusion, this validation indicates the Ir-reducing capacity assay provides a simple global measure of oxidative stress, and further supports the hypothesis that oxidative stress is linked with schizophrenia.

Redox Probing for Chemical Information of Oxidative Stress.

Oxidative stress is implicated in many diseases yet no simple, rapid, and robust measurement is available at the point-of-care to assist clinicians in detecting oxidative stress. Here, we report results from a discovery-based research approach in which a redox mediator is used to probe serum samples for chemical information relevant to oxidative stress. Specifically, we use an iridium salt (K2IrCl6) to probe serum for reducing activities that can transfer electrons to iridium and thus generate detectable optical and electrochemical signals. We show that this Ir-reducing assay can detect various biological reductants and is especially sensitive to glutathione (GSH) compared to alternative assays. We performed an initial clinical evaluation using serum from 10 people diagnosed with schizophrenia, a mental health disorder that is increasingly linked to oxidative stress. The measured Ir-reducing capacity was able to discriminate people with schizophrenia from healthy controls (p < 0.005), and correlations were observed between Ir-reducing capacity and independent measures of symptom severity.