Use Of Claims Data Research Articles

An Experience Report on the Evaluation of New Forms of Care for People with Mental Illness: Pros and Cons of the Use of SHI Claims Data

The present article describes the special features of an evaluation of research in mental health services in Germany. The experiences of the evaluation of flexible and integrated treatment options with a global treatment budget in psychiatric hospitals based on routine data of more than 70 statutory health insurance (SHI) funds (EVA64 study) are systematically presented. Using the EVA64 study as an example, recommendations for the use of claims data in the field of mental health services research and in general are derived. (1) First, the study and its use of claims data is described and classified. (2) The individual outcomes of the study are presented and evaluated in order to (3) derive criteria, identify strengths and suggest potential uses of claims data. (4) Finally, recommendations for the further development of claims data from SHI funds as a basis for evaluation are described.

Accuracy of administrative claims prescription fill data to estimate glucocorticoid use and dose in patients with rheumatoid arthritis.

To assess accuracy of administrative claims prescription fill-based estimates of glucocorticoid use and dose, and approximate bias from glucocorticoid exposure misclassification. We identified adults with rheumatoid arthritis with linked Medicare and CorEvitas registry data. An algorithm identifying glucocorticoid use and average dose over 90 days from Medicare prescription fills was compared to physician-reported measures from a CorEvitas visit during the same period, using weighted kappa to compare doses (none, ≤5 mg, 5-10 mg, >10 mg/day). A deterministic sensitivity analysis examined the effect of exposure misclassification on estimated glucocorticoid-associated infection risk from a prior study. We identified 621 observations among 494 patients. Prescription fills identified glucocorticoid use in 41.9% of observations versus 31.1% identified by CorEvitas physician-report. For glucocorticoid use (yes/no), prescription fills had sensitivity 88.1% (95% CI 82.7-92.3), specificity 79.0% (74.8-82.7), PPV 65.4% (59.3-71.2), NPV 93.6% (90.6-95.9), and 81.8% agreement with CorEvitas, with kappa 0.61 (moderate to substantial agreement). There was 89.5% agreement between prescription fills and physician-reported doses, with weighted kappa 0.56 (moderate agreement). Applying these results to a prior Medicare study evaluating glucocorticoid-associated infection risk [risk ratio 1.44 (95% CI 1.41-1.48)] led to an externally adjusted risk ratio of 1.74 when accounting for exposure misclassification, representing -17% bias in infection risk estimate. This study supports the use of claims data to estimate glucocorticoid use and dose, but investigators should account for exposure misclassification, which may lead to underestimates of glucocorticoid risks. Our results could be applied to adjust risk estimates in other studies that use prescription fills to estimate glucocorticoid use.

RWD35 Coding Trends of Single Gene and Next Generation Sequencing Tests for Patients with Advanced Non-Small Cell Lung Cancer Using a EHR-Linked Claims Database

Coding, billing and reimbursement complexities limit use of claims data in assessing real-world biomarker testing, both single gene test (SGT) and next-generation sequencing (NGS). Using an electronic health record (EHR) database linked to claims, and EHR as the gold standard, we assessed the impact of different reimbursement coding algorithms on identifying biomarker tests in claims over time.

Survival benefit after radiotherapy for patients with malignant pleural mesothelioma: A propensity score-matched study.

Survival benefit after radiotherapy for patients with malignant pleural mesothelioma: A propensity score-matched study.

Validated Models Using EHRs or Claims Data to Distinguish Diabetes Type among Adults

Purpose: Clinical data provides the opportunity for efficient and timely disease surveillance. We developed and validated advanced phenotyping models to classify adult patients with diabetes to type 1, type 2, or other/indeterminate using structured fields from EHR data. To simulate the use of claims data supplemented with medication information, we compared model performance before and after the removal of body mass index (BMI) and laboratory results.

Use of claims data to predict the inpatient length of stay among U.S. stroke patients

BackgroundThe prediction of stroke inpatient length of stay (LOS) attracts much attention from researchers worldwide as it facilitates resource management and improves care. However, studies largely underutilized the claims data for predicting stroke LOS. The predictive models for the U.S. stroke population are understudied. PurposeTo evaluate the feasibility of using claims data for stroke LOS prediction and to identify new important predictors of LOS in U.S. stroke patients. MethodsData preparation, analyses, and predictive modeling processes were conducted on a retrospective dataset, including claims and EHR data about acute care stroke admissions during 2010–2018. Two tree-based models (i.e., the eXtreme Gradient Boosting (XGBoost) model and Categorical Boosting (CatBoost) model) were trained through 10-fold cross-validation and compared with a baseline model that did not include any predictors. The predictive performance was evaluated on the holdout set using mean absolute error (MAE) and root mean squared error (RMSE). Importance plots and SHAP (SHapley Additive exPlanations) plots were used to identify the important predictors. ResultsA total of 6102 stroke patients were included, with an average LOS of 6.4 days. The predictive models built using claims data (RMSE: 1.627; MAE: 0.530) performed similarly well as those built on the entire dataset, including additional variables from EHR (RMSE: 1.622; MAE: 0.533). Important predictors were admission channel and type, comorbidities (e.g., acute respiratory failure), medical services used (e.g., critical care, ambulance), facility characteristics (e.g., type, size), patient demographics, and patient socioeconomic status. Among these important predictors, admission channel and type, medical services including critical care and ambulance, facility type, and patient socioeconomic status were newly identified predictors not studied before. ConclusionClaims data are suitable for stroke LOS prediction. The newly identified important predictors from this study could be integrated with other existing key predictors identified in previous research to improve the prediction, thereby aiding in better stroke care management.

Changes in Medication Use During Pregnancy for Women with Chronic Conditions: An Analysis of Claims Data.

Evaluation of drug safety during pregnancy is dependent on the number of exposed women during routine clinical practice with data available for analysis. We examined medication fills in pregnant and nonpregnant women within select disease cohorts: general population, migraine, diabetes, and hyperlipidemia to explore the potential use of claims data to assess medication use and safety during pregnancy. This cohort study, using IBM MarketScan® Research Databases claims data, included women 10-54years of age with pregnancy resulting in a liveborn infant between January 2010 and September 2015 and matched nonpregnant women. Medication use (antidepressants, antihypertensives, sedatives, glucose-lowering medications, antiepileptics, antipsychotics, lipid-lowering medications) was abstracted from pharmacy claims 180days before last menstrual period through 180days postdelivery. Among 753,760 women in the general pregnancy population (including 73,268 migraine, 50,155 hyperlipidemia, and 8361 diabetes; non-exclusive cohorts), antidepressants, antihypertensives, and sedatives were the most commonly used medications during pregnancy. Medications of interest were less commonly used in the pregnancy cohort than in the matched nonpregnant cohort within each time period (e.g., 3.7% vs 13.1% antidepressant use in 1st trimester). Most prescription fills were less common during pregnancy then pre-pregnancy. Post-pregnancy, prescription fills increased to or exceeded pre-pregnancy levels, except antihypertensive and glucose-lowering medications, which increased during pregnancy. Medication use among pregnant women was low and different from that among matched nonpregnant women. The underlying size of large commercial claims databases offer opportunities for efficient evaluation of potential safety concerns, particularly for rare drug exposures, compared to traditional pregnancy registries.

Health care utilization and expenditures of parents of children with and without hemophilia A.

BACKGROUND: Caring for children with hemophilia A (HA) impacts many aspects of parents' lives. How this translates to caregivers' utilization of health services is unknown, and its elicitation can inform future evaluations of interventions that address caregiver burden for HA. OBJECTIVE: To understand the impact of caring for children with HA on parents' utilization of nonmental and mental health services by comparing 1-year costs and number of claims with parents of children without HA. METHODS: Retrospective matched cohort study using MarketScan commercial medical and pharmacy claims from 2011 to 2019. Children with HA were male sex, aged younger than 18 years, dependent policyholders, and had at least 1 HA-related medical claim from 2011 to 2018 and either an HA-related procedure or drug claim. Parents of children with HA (PCH) were primary or secondary policyholders, shared the same family ID as children with HA, and were continuously enrolled for 1-year post-index. PCH were matched (1:2) with parents of children without HA on age, sex, beneficiary type, child's age, number of children, index month and year, health plan type, employment status, and region. Primary outcomes were nonmental and mental health care costs (2020 US dollars). Secondary outcomes were number of nonmental health outpatient claims and utilization of mental health outpatient or drug claim. Subgroup analyses excluding parents with HA were also conducted. Productivity loss was also explored. Outcomes were compared using 2-sided, paired t-tests, and McNemar test. RESULTS: 1,068 PCH met inclusion criteria and were matched to 2,122 control parents. Mean 1-year cost for PCH was higher for nonmental health (mean difference $1,826 [95% CI = -1,000 to 4,652; P = 0.20]) and similar for mental health services (mean difference $14 [95% CI = -77 to 105; P = 0.76]). When parents with HA were excluded in the subgroup analyses, mental health cost was significantly higher for PCH (mean difference $676 [95% CI = 399 to 953; P < 0.001]). PCH had more nonmental health outpatient claims compared with parents of children without HA (mean difference 1.9 [95% CI = -1.1 to 4.9; P = 0.21]) and were 1.2 times (95% CI = 0.99 to 1.45; P = 0.07) more likely to have a mental health outpatient or drug claim. CONCLUSIONS: PCH had moderately higher health care costs and utilization compared with parents of children without HA; however, these results were not statistically significant. Future studies to better characterize HA disease severity and assess its impact on caregiver burden or to expand caregivers to spouses of adult patients with HA may be warranted. Limitations include inability to ascertain severity of HA in children and the use of claims data to capture complex effects on health care utilization. DISCLOSURES: Dr. Kim's postdoctoral fellowship is supported by Genentech, Inc. Dr. Raimundo is an employee of Genentech, Inc.

Effects of State Opioid Prescribing Laws on Use of Opioid and Other Pain Treatments Among Commercially Insured U.S. Adults.

There is concern that state laws to curb opioid prescribing may adversely affect patients with chronic noncancer pain, but the laws' effects are unclear because of challenges in disentangling multiple laws implemented around the same time. To study the association between state opioid prescribing cap laws, pill mill laws, and mandatory prescription drug monitoring program query or enrollment laws and trends in opioid and guideline-concordant nonopioid pain treatment among commercially insured adults, including a subgroup with chronic noncancer pain conditions. Thirteen treatment states that implemented a single law of interest in a 4-year period and unique groups of control states for each treatment state were identified. Augmented synthetic control analyses were used to estimate the association between each state law and outcomes. United States, 2008 to 2019. 7694514 commercially insured adults aged 18 years or older, including 1976355 diagnosed with arthritis, low back pain, headache, fibromyalgia, and/or neuropathic pain. Proportion of patients receiving any opioid prescription or guideline-concordant nonopioid pain treatment per month, and mean days' supply and morphine milligram equivalents (MME) of prescribed opioids per day, per patient, per month. Laws were associated with small-in-magnitude and non-statistically significant changes in outcomes, although CIs around some estimates were wide. For adults overall and those with chronic noncancer pain, the 13 state laws were each associated with a change of less than 1 percentage point in the proportion of patients receiving any opioid prescription and a change of less than 2 percentage points in the proportion receiving any guideline-concordant nonopioid treatment, per month. The laws were associated with a change of less than 1 in days' supply of opioid prescriptions and a change of less than 4 in average monthly MME per day per patient prescribed opioids. Results may not be generalizable to non-commercially insured populations and were imprecise for some estimates. Use of claims data precluded assessment of the clinical appropriateness of pain treatments. This study did not identify changes in opioid prescribing or nonopioid pain treatment attributable to state laws. National Institute on Drug Abuse.

A narrative review of using prescription drug databases for comorbidity adjustment: A less effective remedy or a prescription for improved model fit?

The use of claims data for identifying comorbid conditions in patients for research purposes has been widely explored. Traditional measures of comorbid adjustment included diagnostic data (e.g., ICD-9-CM or ICD-10-CM codes), with the Charlson and Elixhauser methodology being the two most common approaches. Prescription data has also been explored for use in comorbidity adjustment, however early methodologies were disappointing when compared to diagnostic measures. The objective of this methodological review is to compare results from newer studies using prescription-based data with more traditional diagnostic measures. A review of studies found on PubMed, Medline, Embase or CINAHL published between January 1990 and December 2020 using prescription data for comorbidity adjustment. A total of 50 studies using prescription drug measures for comorbidity adjustment were found. Newer prescription-based measures show promise fitting models, as measured by predictive ability, for research, especially when the primary outcomes are utilization or drug expenditure rather than diagnostic measures. More traditional diagnostic-based measures still appear most appropriate if the primary outcome is mortality or inpatient readmissions.

Benefits of a Disease Management Program for SCD in Germany 2011 - 2019: The Increased Use of Hydroxyurea Correlates with a Reduced Frequency of Acute Chest Syndrome

BackgroundWorldwide, Sickle Cell Disease (SCD) is the most common single gene disorder affecting >250,000 newborns annually. In Germany, SCD qualifies as a rare disease and almost exclusively affects immigrants from endemic countries and their descendants. The recent surge of immigration from high-prevalence countries increased the numbers of patients with SCD in Germany and raised awareness for the need of specialized care.In 2012, the German Society for Pediatric Oncology and Hematology (GPOH) mandated a consortium of five university hospitals to develop a disease management program for patients with SCD. This consortium issued treatment guidelines for SCD that strongly favor the use of hydroxyurea, initiated patient and physician education events, prepared for a universal newborn screening program that will start 09/2021, moderated a consensus on the indication of allogeneic stem cell transplantation for patients with SCD, and established a national patient registry.MethodsIn order to quantify the effect of these activities, we made use of claims data that were collected by the research institute (WIdO) of the major German insurance company, the Allgemeine Ortskrankenkasse (AOK), and of publicly accessible data collected by the Federal Statistical Office (Statistisches Bundesamt, Destatis). ICD10 codes were used to identify patients with SCD and their comorbidities. Pharmacologic treatments were quantified using the German Anatomical Therapeutic Chemical (ATC)-Classification with defined daily doses.ResultsWe estimate that the number of patients with SCD in Germany increased from approximately 2,200 in 2011 to approximately 3,200 in 2019. Analyses of administered treatments illustrate that important components of recently issued national treatment guidelines have been largely implemented. Specifically, the use of hydroxyurea has more than doubled, resulting in a proportion of approximately 45% of all patients with SCD being treated with hydroxyurea in 2019 (Figure 1A). In strong negative correlation with the use of hydroxyurea, the frequency of acute chest syndromes decreased (Figure 1B). While before the widespread use of hydroxyurea (2011-2013) 8.1% of patients with SCD were admitted at least once per year for ACS, this proportion dropped to 6.6% in the period 2017 to 2019. Similarly, the proportion of patients who required analgesics, red blood cell transfusions and hospitals admissions declined from 2011 to 2019, indicating a reduced burden of SCD with the increased use of hydroxyurea.ConclusionIn sum, these data demonstrate an association between the dissemination of nationwide treatment guidelines and changes in clinical practice in particular relating to the use of hydroxyurea. These changes translate into a remarkable improvement of key measures of disease activity in a representative population based analysis. [Display omitted] DisclosuresLobitz: Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; AddMedica: Honoraria, Membership on an entity's Board of Directors or advisory committees; Vertex: Honoraria, Membership on an entity's Board of Directors or advisory committees. Kulozik: BioMedX: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy, Honoraria; bluebird bio, Inc.: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

Benefits of a Disease Management Program for Sickle Cell Disease in Germany 2011-2019: The Increased Use of Hydroxyurea Correlates with a Reduced Frequency of Acute Chest Syndrome.

Sickle Cell Disease (SCD) is the most common monogenic disorder globally but qualifies as a rare disease in Germany. In 2012, the German Society for Paediatric Oncology and Haematology (GPOH) mandated a consortium of five university hospitals to develop a disease management program for patients with SCD. Besides other activities, this consortium issued treatment guidelines for SCD that strongly favour the use of hydroxyurea and propagated these guidelines in physician and patient education events. In order to quantify the effect of these recommendations, we made use of claims data that were collected by the research institute (WIdO) of the major German insurance company, the Allgemeine Ortskrankenkasse (AOK), and of publicly accessible data collected by the Federal Statistical Office (Statistisches Bundesamt, Destatis). While the number of patients with SCD in Germany increased from approximately 2200 in 2011 to approximately 3200 in 2019, important components of the recently issued treatment guidelines have been largely implemented. Specifically, the use of hydroxyurea has more than doubled, resulting in a proportion of approximately 44% of all patients with SCD being treated with hydroxyurea in 2019. In strong negative correlation with the use of hydroxyurea, the frequency of acute chest syndromes decreased. Similarly, the proportion of patients who required analgesics and hospitals admissions declined. In sum, these data demonstrate an association between the dissemination of treatment guidelines and changes in clinical practice. The close temporal relationship between the increased use of hydroxyurea and the reduction in the incidence of acute chest syndrome in a representative population-based analysis implies that these changes in clinical practice contributed to an improvement in key measures of disease activity.

Routine Cancer Screening Rates Rebound After Deep Drop From Pandemic Fear.

Routine Cancer Screening Rates Rebound After Deep Drop From Pandemic Fear.

Machine learning imputation of metastatic status from open claims in melanoma patients.

e21540 Background: Metastatic status is a crucial variable in most oncology studies but is not available in claims data. The objective of this study is to develop a machine learning model for Imputation of metastatic status from claims data with ground. Truth is derived from highly curated electronic medical record data. Methods: We used a set of 11389 melanoma patients from the ConcertAI real world database of intersecting claims and EMR data that includes data from CancerLinQ Discovery. Using features from claims and our gold standard labels from EMR we built an ML model using (XGBoost) extreme gradient boosting, an algorithm that iteratively combines a set of decision trees into a single model. We used 60% of the data for training, 20% for hyper-parameter tuning, and 20% for holdout testing. The model was built using 55 features. Results: The table below summarizes results. Metrics are on the final hold out set which was unseen by the model and entirely composed of highly curated EMR data. Conclusions: We are able to build a high precision model for the imputation of metastatic melanoma status using claims data. This could enable significantly better use of claims data stemming from the ability to find a metastatic cohort with very few false positives. Providing more precise cohort identification for comparative effectiveness studies. We found features such as secondary neoplasm diagnosis, anti-neoplastic meds, and radiation ranking highly in our analysis of model feature importances. Using techniques to analyze non-linear feature interactions in our AI model we found an interaction relationship between long term anti-neoplastic therapy, reported pain and metastatic status which we plan to further study. This work is preliminary and we are working to further improve model performance.[Table: see text]

CLINICAL AND ECONOMIC BURDEN OF OBSTRUCTIVE HYPERTROPHIC CARDIOMYOPATHY IN THE UNITED STATES

AIMS Obstructive hypertrophic cardiomyopathy (oHCM) is a disease of the cardiomyocyte in which dynamic left ventricular outflow track obstruction may lead to heart failure, valvular disease, and sudden death. Little is known about healthcare resource utilization (HRU) and costs associated with oHCM. This study investigated the clinical and economic burden of oHCM in patients with or without symptoms associated with oHCM. METHODS We used the US IBM MarketScan Commercial and Medicare Supplemental database to identify patients with oHCM (January 2009-March 2019). Control patients without cardiomyopathy were matched to each patient with oHCM based on age, sex, region, and index year (3:1 ratio). One-year HRU and cost data were compared between all oHCM, symptomatic oHCM, and asymptomatic oHCM subgroups, and their respective controls. RESULTS Among 11,401 eligible patients with oHCM (mean age 57 years, 42% female), 5,667 (50%) were symptomatic (23% chest pain, 57% dyspnea, 29% fatigue, 17% syncope). oHCM was associated with significant increases in all-cause hospitalizations, hospital days, outpatient visits, and total healthcare costs (mean ± standard deviation: $26,929 ± 77,720 vs. $6,808 ± 25,712, p<.001) compared with matched controls. These differences were driven mainly by the clinical and economic burden of symptomatic oHCM, which was associated with significant increases in 1-year hospitalization rates (38.0 vs. 6.9%), hospital days (3.7 ± 9.9 vs. 0.4 ± 3.0), and total healthcare costs ($43,586 ± 103,756 vs. $6,768 ± 27,618; all p<.001). Adjustment for comorbidities had minimal impact on these differences. LIMITATIONS The use of claims data relies on International Classification of Diseases (ICD-9 and ICD-10) diagnosis codes, which might be inaccurate. Only commercially insured patients were included. CONCLUSION In a real-world population, oHCM was associated with substantial increases in HRU and incremental costs of ∼$20,000/year when compared with matched controls-a difference that increased to ∼$35,000/year among symptomatic patients. Further studies are warranted to understand the potential impact of specific therapies on HRU and the economic burden of oHCM.

Clinical and economic burden of obstructive hypertrophic cardiomyopathy in the United States

ABSTRACT Aims Obstructive hypertrophic cardiomyopathy (oHCM) is a disease of the cardiomyocyte in which dynamic left ventricular outflow track obstruction may lead to heart failure, valvular disease, and sudden death. Little is known about healthcare resource utilization (HRU) and costs associated with oHCM. This study investigated the clinical and economic burden of oHCM in patients with or without symptoms associated with oHCM. Methods: We used the US IBM® MarketScan® Commercial and Medicare Supplemental database to identify patients with oHCM (January 2009–March 2019). Control patients without cardiomyopathy were matched to each patient with oHCM based on age, sex, region, and index year (3:1 ratio). One-year HRU and cost data were compared between all oHCM, symptomatic oHCM, and asymptomatic oHCM subgroups, and their respective controls. Results: Among 11,401 eligible patients with oHCM (mean age 57 years, 42% female), 5,667 (50%) were symptomatic (23% chest pain, 57% dyspnea, 29% fatigue, 17% syncope). oHCM was associated with significant increases in all-cause hospitalizations, hospital days, outpatient visits, and total healthcare costs (mean ± standard deviation: $26,929 ± 77,720 vs. $6,808 ± 25,712, p<.001) compared with matched controls. These differences were driven mainly by the clinical and economic burden of symptomatic oHCM, which was associated with significant increases in 1-year hospitalization rates (38.0% vs. 6.9%), hospital days (3.7 ± 9.9 vs. 0.4 ± 3.0), and total healthcare costs ($43,586 ± 103,756 vs. $6,768 ± 27,618; all p<.001). Adjustment for comorbidities had minimal impact on these differences. Limitations: The use of claims data relies on International Classification of Diseases (ICD-9 and ICD-10) diagnosis codes, which might be inaccurate. Only commercially insured patients were included. Conclusion: In a real-world population, oHCM was associated with substantial increases in HRU and incremental costs of ∼$20,000/year when compared with matched controls—a difference that increased to ∼$35,000/year among symptomatic patients. Further studies are warranted to understand the potential impact of specific therapies on HRU and the economic burden of oHCM. Plain Language Summary Obstructive hypertrophic cardiomyopathy (oHCM) is a medical condition in which the heart muscle becomes abnormally thick and can cause partial blockage of blood flow out of the heart. Some patients experience symptoms (such as shortness of breath, chest pain, and fatigue) from this condition while others do not. Little is known about the healthcare resource utilization (HRU) and costs associated with oHCM, and if there are any differences between patients with oHCM who experience symptoms versus those who are asymptomatic. Therefore, we performed a study to investigate the clinical and economic burden of oHCM in patients with or without symptoms associated with oHCM. Based on insurance claims data, ∼50% of all patients with diagnosed oHCM are symptomatic. Symptomatic patients experience nearly 8 times as many hospitalizations and cost the healthcare system more than $35,000 per year compared with matched controls. In contrast, asymptomatic patients with oHCM have a much smaller difference in HRU and costs (∼$3,600/year) compared with matched controls. The results of this study suggest that effective therapies for oHCM may provide economic value, even if the impact of therapy is limited solely to the relief of symptoms.

DMD – BIOMARKERS & OUTCOME MEASURES: P.119 Identification of disease progression stages in patients with Duchenne muscular dystrophy using administrative claims data in the United States

Duchenne muscular dystrophy (DMD) is a rare genetic disorder characterized by fast progressing muscular degeneration primarily in males. The rarity of DMD has limited real-world study of disease progression in these patients. This study aims to classify DMD patients by stage of progression using administrative claims data fields. Data were drawn from an administrative claims and electronic medical record (EMR) dataset from Decision Resources Group covering more than 300 million U.S. patients during 2011-2019. DMD patients were identified using EMR codes specific to DMD (SNOMED-CT). Clinical diagnosis and procedure markers reflecting disease progression were used as classification criteria. DMD progression was classified into 4 stages based on indicators of ambulatory and pulmonary ability: early ambulatory, late ambulatory, early non-ambulatory, and late non-ambulatory. Stage was assessed monthly for each patient based on observed stage classification markers and evaluated beginning at 12 months of data availability to ensure accurate classification. Identified stage was carried forward until markers of a later stage were observed. Patients were assumed not to revert to an earlier stage following observation of a given stage. 77% (N=758) of EMR-identified DMD patients had claims and markers allowing for stage classification. Classified patients had claims spanning a median period of 81.8 months. Among these patients, 27% were identified at the early ambulatory stage (median age 7 years old), 34% at the late ambulatory stage (median age 13 years old), and 23% and 16% at the early and late non-ambulatory stages with median ages of 17 and 22 years old, respectively. Classification of DMD disease progression within claims data provides insight into the real-world distribution of patients by stage and can enable future use of claims data to study additional topics such as disease burden by stage and rates of disease progression.

Interpreting big-data analysis of retrospective observational data.

Current data technology offers fantastic new opportunities to generate data that can inform us about the safety of drugs. These data will affect the way we use drugs by balancing benefits of specific agents with better and more information on their associated risks. Nowadays, possibilities to use big data to deal with safety concerns are enormous, and it is difficult not to get enthusiastic reading papers that take this approach. An outstanding example is the use of claims data of 160 000 patients with rheumatoid arthritis to assess the risk for lower-tract gastrointestinal perforation associated with tocilizumab and tofacitinib in comparison with other biological drugs.

Two Decades of Research Using Taiwan's National Health Insurance Claims Data: Bibliometric and Text Mining Analysis on PubMed.

BackgroundStudies using Taiwan’s National Health Insurance (NHI) claims data have expanded rapidly both in quantity and quality during the first decade following the first study published in 2000. However, some of these studies were criticized for being merely data-dredging studies rather than hypothesis-driven. In addition, the use of claims data without the explicit authorization from individual patients has incurred litigation.ObjectiveThis study aimed to investigate whether the research output during the second decade after the release of the NHI claims database continues growing, to explore how the emergence of open access mega journals (OAMJs) and lawsuit against the use of this database affect the research topics and publication volume and to discuss the underlying reasons.MethodsPubMed was used to locate publications based on NHI claims data between 1996 and 2017. Concept extraction using MetaMap was employed to mine research topics from article titles. Research trends were analyzed from various aspects, including publication amount, journals, research topics and types, and cooperation between authors.ResultsA total of 4473 articles were identified. A rapid growth in publications was witnessed from 2000 to 2015, followed by a plateau. Diabetes, stroke, and dementia were the top 3 most popular research topics whereas statin therapy, metformin, and Chinese herbal medicine were the most investigated interventions. Approximately one-third of the articles were published in open access journals. Studies with two or more medical conditions, but without any intervention, were the most common study type. Studies of this type tended to be contributed by prolific authors and published in OAMJs.ConclusionsThe growth in publication volume during the second decade after the release of the NHI claims database was different from that during the first decade. OAMJs appeared to provide fertile soil for the rapid growth of research based on NHI claims data, in particular for those studies with two or medical conditions in the article title. A halt in the growth of publication volume was observed after the use of NHI claims data for research purposes had been restricted in response to legal controversy. More efforts are needed to improve the impact of knowledge gained from NHI claims data on medical decisions and policy making.

Methodological Challenges when Using Claims Data of more than 70 Statutory Health Insurances - A Progress Report from the EVA64 Study

The adequate and need-based medical care of mentally ill patients places special demands on psychiatric care. The §64b Social Code Book (SGB) V enables mentoring mentally ill people through multiprofessional, cross-sectoral model projects across the treatment phase and implementing new forms of financing. These model projects have been evaluated in a prospective and retrospective claims data-based controlled cohort study (EVA64) since 2015. In September 2016 and since then annually, the data transfer of all statutory health insurance funds (SHI) involved in this evaluation took place for the first time on the basis of a consented data set description. For later analysis, the clear identification of the index hospital admission and the assignment to the model or control group are important. The methodological challenges of data provision by the data owner, the formal and content-related data preparation as well as the subsequent establishing of an evaluation data set are discussed in detail. So far, data from 71 SHI has been taken into account. In each case 20 tables with claims data from outpatient and inpatient care (including psychiatric institute outpatient departments [PIA]), drug and medical supplies as well as data from incapacity to work and personal data of the insurees. Not all tables could be filled completely by the SHIs. In addition, updates of the study designs require the adaptation of the data selection process. Even though data sets have been delievered regularly the data preparation process is still not routine. The scientific use of claims data of numerous SHIs in the context of an evaluation study represents a great challenge. In the absence of reference values for abnormalities and implausibilities, an a priori determination of test algorithms was limited; instead they had to be updated every year. The individual examination of the data of all health insurance companies remains very complex. The detailed documentation of these algorithms provides support for future comparable studies.