Development of a machine learning model to predict mild cognitive impairment using natural language processing in the absence of screening.

BACKGROUND: Patients and their loved ones often report symptoms or complaints of cognitive decline that clinicians note in free clinical text, but no structured screening or diagnostic data are recorded. These symptoms/complaints may be signals that predict who will go on to be diagnosed with mild cognitive impairment (MCI) and ultimately develop Alzheimer's Disease or related dementias. Our objective was to develop a natural language processing system and prediction model for identification of MCI from clinical text in the absence of screening or other structured diagnostic information. METHODS: There were two populations of patients: 1794 participants in the Adult Changes in Thought (ACT) study and 2391 patients in the general population of Kaiser Permanente Washington. All individuals had standardized cognitive assessment scores. We excluded patients with a diagnosis of Alzheimer's Disease, Dementia or use of donepezil. We manually annotated 10,391 clinic notes to train the NLP model. Standard Python code was used to extract phrases from notes and map each phrase to a cognitive functioning concept. Concepts derived from the NLP system were used to predict future MCI. The prediction model was trained on the ACT cohort and 60% of the general population cohort with 40% withheld for validation. We used a least absolute shrinkage and selection operator logistic regression approach (LASSO) to fit a prediction model with MCI as the prediction target. Using the predicted case status from the LASSO model and known MCI from standardized scores, we constructed receiver operating curves to measure model performance. RESULTS: Chart abstraction identified 42 MCI concepts. Prediction model performance in the validation data set was modest with an area under the curve of 0.67. Setting the cutoff for correct classification at 0.60, the classifier yielded sensitivity of 1.7%, specificity of 99.7%, PPV of 70% and NPV of 70.5% in the validation cohort. DISCUSSION AND CONCLUSION: Although the sensitivity of the machine learning model was poor, negative predictive value was high, an important characteristic of models used for population-based screening. While an AUC of 0.67 is generally considered moderate performance, it is also comparable to several tests that are widely used in clinical practice.

Comparative effectiveness of canagliflozin, SGLT2 inhibitors and non-SGLT2 inhibitors on the risk of hospitalization for heart failure and amputation in patients with type 2 diabetes mellitus: A real-world meta-analysis of 4 observational databases (OBSERVE-4D).

AIMS: Sodium glucose co-transporter 2 inhibitors (SGLT2i) are indicated for treatment of type 2 diabetes mellitus (T2DM); some SGLT2i have reported cardiovascular benefit, and some have reported risk of below-knee lower extremity (BKLE) amputation. This study examined the real-world comparative effectiveness within the SGLT2i class and compared with non-SGLT2i antihyperglycaemic agents. MATERIALS AND METHODS: Data from 4 large US administrative claims databases were used to characterize risk and provide population-level estimates of canagliflozin's effects on hospitalization for heart failure (HHF) and BKLE amputation vs other SGLT2i and non-SGLT2i in T2DM patients. Comparative analyses using a propensity score-adjusted new-user cohort design examined relative hazards of outcomes across all new users and a subpopulation with established cardiovascular disease. RESULTS: Across the 4 databases (142 800 new users of canagliflozin, 110 897 new users of other SGLT2i, 460 885 new users of non-SGLT2i), the meta-analytic hazard ratio estimate for HHF with canagliflozin vs non-SGLT2i was 0.39 (95% CI, 0.26-0.60) in the on-treatment analysis. The estimate for BKLE amputation with canagliflozin vs non-SGLT2i was 0.75 (95% CI, 0.40-1.41) in the on-treatment analysis and 1.01 (95% CI, 0.93-1.10) in the intent-to-treat analysis. Effects in the subpopulation with established cardiovascular disease were similar for both outcomes. No consistent differences were observed between canagliflozin and other SGLT2i. CONCLUSIONS: In this large comprehensive analysis, canagliflozin and other SGLT2i demonstrated HHF benefits consistent with clinical trial data, but showed no increased risk of BKLE amputation vs non-SGLT2i. HHF and BKLE amputation results were similar in the subpopulation with established cardiovascular disease. This study helps further characterize the potential benefits and harms of SGLT2i in routine clinical practice to complement evidence from clinical trials and prior observational studies.

Risk of lower extremity amputations in people with type 2 diabetes mellitus treated with sodium-glucose co-transporter-2 inhibitors in the USA: A retrospective cohort study.

AIMS: To examine the incidence of amputation in patients with type 2 diabetes mellitus (T2DM) treated with sodium glucose co-transporter 2 (SGLT2) inhibitors overall, and canagliflozin specifically, compared with non-SGLT2 inhibitor antihyperglycaemic agents (AHAs). MATERIALS AND METHODS: Patients with T2DM newly exposed to SGLT2 inhibitors or non-SGLT2 inhibitor AHAs were identified using the Truven MarketScan database. The incidence of below-knee lower extremity (BKLE) amputation was calculated for patients treated with SGLT2 inhibitors, canagliflozin, or non-SGLT2 inhibitor AHAs. Patients newly exposed to canagliflozin and non-SGLT2 inhibitor AHAs were matched 1:1 on propensity scores, and a Cox proportional hazards model was used for comparative analysis. Negative controls (outcomes not believed to be associated with any AHA) were used to calibrate P values. RESULTS: Between April 1, 2013 and October 31, 2016, 118 018 new users of SGLT2 inhibitors, including 73 024 of canagliflozin, and 226 623 new users of non-SGLT2 inhibitor AHAs were identified. The crude incidence rates of BKLE amputation were 1.22, 1.26 and 1.87 events per 1000 person-years with SGLT2 inhibitors, canagliflozin and non-SGLT2 inhibitor AHAs, respectively. For the comparative analysis, 63 845 new users of canagliflozin were matched with 63 845 new users of non-SGLT2 inhibitor AHAs, resulting in well-balanced baseline covariates. The incidence rates of BKLE amputation were 1.18 and 1.12 events per 1000 person-years with canagliflozin and non-SGLT2 inhibitor AHAs, respectively; the hazard ratio was 0.98 (95% confidence interval 0.68-1.41; P = .92, calibrated P = .95). CONCLUSIONS: This real-world study observed no evidence of increased risk of BKLE amputation for new users of canagliflozin compared with non-SGLT2 inhibitor AHAs in a broad population of patients with T2DM.

Finding treatment-resistant depression in real-world data: How a data-driven approach compares with expert-based heuristics.

BACKGROUND: Depression that does not respond to antidepressants is treatment-resistant depression (TRD). TRD definitions include assessments of treatment response, dose and duration, and implementing these definitions in claims databases can be challenging. We built a data-driven TRD definition and evaluated its performance. METHODS: We included adults with depression, ≥1 antidepressant, and no diagnosis of mania, dementia, or psychosis. Subjects were stratified into those with and without proxy for TRD. Proxies for TRD were electroconvulsive therapy, deep brain, or vagus nerve stimulation. The index date for subjects with proxy for TRD was the procedure date, and for subjects without, the date of a randomly selected visit. We used three databases. We fit decision tree predictive models. We included number of distinct antidepressants, with and without adequate doses and duration, number of antipsychotics and psychotherapies, and expert-based definitions, 3, 6, and 12 months before index date. To assess performance, we calculated area under the curve (AUC) and transportability. RESULTS: We analyzed 33,336 subjects with no proxy for TRD, and 3,566 with the proxy. Number of antidepressants and antipsychotics were selected in all periods. The best model was at 12 months with an AUC = 0.81. The rule transported well and states that a subject with ≥1 antipsychotic or ≥3 antidepressants in the last year has TRD. Applying this rule, 15.8% of subjects treated for depression had TRD. CONCLUSION: The definition that best discriminates between subjects with and without TRD considers number of distinct antidepressants (≥3) or antipsychotics (≥1) in the last year.

Methods for time-varying exposure related problems in pharmacoepidemiology: An overview.

PURPOSE: Lack of control for time-varying exposures can lead to substantial bias in estimates of treatment effects. The aim of this study is to provide an overview and guidance on some of the available methodologies used to address problems related to time-varying exposure and confounding in pharmacoepidemiology and other observational studies. The methods are explored from a conceptual rather than an analytical perspective. METHODS: The methods described in this study have been identified exploring the literature concerning to the time-varying exposure concept and basing the search on four fundamental pharmacoepidemiological problems, construction of treatment episodes, time-varying confounders, cumulative exposure and latency, and treatment switching. RESULTS: A correct treatment episodes construction is fundamental to avoid bias in treatment effect estimates. Several methods exist to address time-varying covariates, but the complexity of the most advanced approaches-eg, marginal structural models or structural nested failure time models-and the lack of user-friendly statistical packages have prevented broader adoption of these methods. Consequently, simpler methods are most commonly used, including, for example, methods without any adjustment strategy and models with time-varying covariates. The magnitude of exposure needs to be considered and properly modelled. CONCLUSIONS: Further research on the application and implementation of the most complex methods is needed. Because different methods can lead to substantial differences in the treatment effect estimates, the application of several methods and comparison of the results is recommended. Treatment episodes estimation and exposure quantification are key parts in the estimation of treatment effects or associations of interest.

Depression among people with type 2 diabetes mellitus, US National Health and Nutrition Examination Survey (NHANES), 2005-2012.

BACKGROUND: Depression in people with diabetes can result in increased risk for diabetes-related complications. The prevalence of depression has been estimated to be 17.6 % in people with type 2 diabetes mellitus (T2DM), based on studies published between 1980 and 2005. There is a lack of more recent estimates of depression prevalence among the US general T2DM population. METHODS: The present study used the US National Health and Nutrition Examination Survey (NHANES) 2005-2012 data to provide an updated, population-based estimate for the prevalence of depression in people with T2DM. NHANES is a cross-sectional survey of a nationally representative sample of the civilian, non-institutionalized US population. Starting from 2005, the Patient Health Questionnaire (PHQ-9) was included to measure signs and symptoms of depression. We defined PHQ-9 total scores ≥ 10 as clinically relevant depression (CRD), and ≥ 15 as clinically significant depression (CSD). Self-reported current antidepressant use was also combined to estimate overall burden of depression. Predictors of CRD and CSD were investigated using survey logistic regression models. RESULTS: A total of 2182 participants with T2DM were identified. The overall prevalence of CRD and CSD among people with T2DM is 10.6 % (95 % confidence interval (CI) 8.9-12.2 %), and 4.2 % (95 % CI 3.4-5.1 %), respectively. The combined burden of depressive symptoms and antidepressants may be as high as 25.4 % (95 % CI 23.0-27.9 %). Significant predictors of CRD include age (younger than 65), sex (women), income (lower than 130 % of poverty level), education (below college), smoking (current or former smoker), body mass index (≥30 kg/m(2)), sleep problems, hospitalization in the past year, and total cholesterol (≥200 mg/dl). Significant predictors of CSD also include physical activity (below guideline) and cardiovascular diseases. CONCLUSIONS: The prevalence of CRD and CSD among people with T2DM in the US may be lower than in earlier studies, however, the burden of depression remains high. Further research with longitudinal follow-up for depression in people with T2DM is needed to understand real world effectiveness of depression management.

Short QT in a cohort of 1.7 million persons: prevalence, correlates, and prognosis.

BACKGROUND: Short QT syndrome (QTc ≤ 300 ms) is a novel hereditary channelopathy linked to syncope, paroxysmal atrial fibrillation, and sudden cardiac death. However, its epidemiological features remain unsettled. OBJECTIVES: (1) To assess the prevalence of short QT in a large population-based sample; (2) to evaluate its demographic and clinical correlates and; (3) to determine its prognosis. METHODS: A database of 6.4 million electrocardiograms (ECGs) obtained between 1995 and 2008 among 1.7 million persons was used. An internal, population-based method for heart rate correction (QTcreg ) was used and all ECGs with QTcreg ≤300 ms were manually validated. Linked health plan databases were used for covariate and survival ascertainment. RESULTS: Of 6,387,070 ECGs, 1086 had an ECG with machine-read QTcreg ≤300 ms. Only 4% (45/1086) were validated yielding a prevalence of 0.7 per 100,000 or 1 of 141,935 ECGs. At the person level, the overall prevalence of QTcreg ≤300 ms was 2.7 per 100,000 or 1 of 37,335. The factors independently and significantly associated with validated QTcreg ≤300 ms were age over 65 years, Black race, prior history of ventricular dysrhythmias, chronic obstructive pulmonary disease, ST-T abnormalities, ischemia, bigeminy pattern, and digitalis effect. After 8.3 years of median follow-up and relative to normal QTcreg , validated QTcreg ≤300 ms was associated after multivariate adjustment with a 2.6-fold (95% confidence interval [CI] = 1.9-3.7) increased risk of death. CONCLUSION: QTcreg ≤300 ms was extraordinarily rare and was associated with significant ECG abnormalities and reduced survival.

Association between inflammatory joint disease and severe or treatment-resistant depression: population-based cohort and case-control studies in Sweden.

OBJECTIVE: To investigate whether the association between depression and inflammatory joint disease (IJD; rheumatoid arthritis [RA], psoriatic arthritis [PsA], ankylosing spondylitis/spondyloarthropathies [AS], and juvenile idiopathic arthritis [JIA]) is affected by the severity or treatment-resistance of depression. METHOD: Parallel cohort studies and case-control studies among 600,404 patients with a depressive episode identified in Swedish nationwide administrative registers. Prospective and retrospective risk for IJD in patients with depression was compared to matched population comparators, and the same associations were investigated in severe or treatment-resistant depression. Analyses were adjusted for comorbidities and sociodemographic covariates. RESULTS: Patients with depression had an increased risk for later IJD compared to population comparators (adjusted hazard ratio (aHR) for any IJD 1.34 [95% CI 1.30-1.39]; for RA 1.27 [1.15-1.41]; PsA 1.45 [1.29-1.63]; AS 1.32 [1.15-1.52]). In case-control studies, patients with depression more frequently had a history of IJD compared to population controls (adjusted odds ratio (aOR) for any IJD 1.43 [1.37-1.50]; RA 1.39 [1.29-1.49]; PsA 1.59 [1.46-1.73]; AS 1.49 [1.36-1.64]; JIA 1.52 [1.35-1.71]). These associations were not significantly different for severe depression or TRD. CONCLUSION: IJD and depression are bidirectionally associated, but this association does not seem to be influenced by the severity or treatment resistance of depression.

Evaluating the impact of database heterogeneity on observational study results.

Clinical studies that use observational databases to evaluate the effects of medical products have become commonplace. Such studies begin by selecting a particular database, a decision that published papers invariably report but do not discuss. Studies of the same issue in different databases, however, can and do generate different results, sometimes with strikingly different clinical implications. In this paper, we systematically study heterogeneity among databases, holding other study methods constant, by exploring relative risk estimates for 53 drug-outcome pairs and 2 widely used study designs (cohort studies and self-controlled case series) across 10 observational databases. When holding the study design constant, our analysis shows that estimated relative risks range from a statistically significant decreased risk to a statistically significant increased risk in 11 of 53 (21%) of drug-outcome pairs that use a cohort design and 19 of 53 (36%) of drug-outcome pairs that use a self-controlled case series design. This exceeds the proportion of pairs that were consistent across databases in both direction and statistical significance, which was 9 of 53 (17%) for cohort studies and 5 of 53 (9%) for self-controlled case series. Our findings show that clinical studies that use observational databases can be sensitive to the choice of database. More attention is needed to consider how the choice of data source may be affecting results.

Analysis of patient claims data to determine the prevalence of hidradenitis suppurativa in the United States.

BACKGROUND: Recent prevalence estimates for hidradenitis suppurativa (HS), a chronic inflammatory skin condition, are limited by timeliness, population size, and generalizability. OBJECTIVE: We sought to develop prevalence estimates for HS in the United States using large health care claims databases. METHODS: A retrospective analysis used PharMetrics Integrated Database to gather health care claims information for HS among patients with 12 or more months of continuous enrollment in a commercial health care plan throughout 2007. Included patients had: 1 or more diagnoses with International Classification of Diseases, Ninth Revision, Clinical Modification code 705.83 for HS during 2007 without a Current Procedural Terminology code for HS; 1 or more Current Procedural Terminology codes of 11450, 11451, 11462, 11463, 11470, or 11471 during 2007 without International Classification of Diseases, Ninth Revision, Clinical Modification code 705.83; or both. Age- and gender-specific prevalence projections were calculated. RESULTS: Among included patients (n = 7927), mean age (SD) was 38.2 (14.73) years, and 5834 (74%) were women. Most patients (n = 5205; 66%) were aged 30 to 64 years. The overall prevalence estimate was 0.053% (95% confidence interval 0.051-0.054). When adjusted for gender and age, prevalence rates were 0.052% and 0.051%, respectively. The most common procedures for HS were excision of skin and subcutaneous tissue axillary/inguinal simple or intermediate repair. LIMITATIONS: Limitations were a health insured-only population; 12-month enrollment period for 2007; HS-specific procedural codes; and possible HS misclassifications. CONCLUSION: We found a low rate of clinically detected HS (0.053%; approximately 146,000-162,000 patients in the United States in 2007), with affected persons almost 3 times as likely to be female and the highest prevalence in those aged 18 to 44 years.

Validation of a population-based method to assess drug-induced alterations in the QT interval: a self-controlled crossover study.

PURPOSE: The purpose of this study was to ascertain, in the context of an integrated health care delivery system, the association between a comprehensive list of drugs known to have potential QT liability and QT prolongation or shortening. METHODS: By using a self-controlled crossover study with 59 467 subjects, we ascertained intra-individual change in log-linear regression-corrected QT (QTcreg ) during the period between 1995 and mid-2008 for 90 drugs while adjusting for age, gender, race/ethnicity, comorbid conditions, number of electrocardiograms (ECGs), and time between pre-ECG and post-ECG. The proportion of users of each drug-developing incident long QT was also estimated. RESULTS: Two drugs (nicardipine and levalbuterol) had no statistically significant intra-individual QTcreg shortening effects, 10 drugs had no statistically significant prolonging effect, and 78 (87%) of the drugs had statistically significant intra-individual mean QTcreg lengthening effects, ranging from 7.6 ms for aripiprazole to 25.2 ms for amiodarone. Three drugs were associated with mean QTcreg prolongation of 20 ms or greater: amiodarone (antiarrhythmic), terfenadine (antihistaminic), and quinidine (antiarrhythmic); whereas 11 drugs were associated with mean QTcreg prolongation of 15 ms or greater but less than 20 ms: trimipramine (tricyclic antidepressant), clomipramine (tricyclic antidepressant), disopyramide (antiarrhythmic), chlorpromazine (antipsychotic), sotalol (beta blocker), itraconazole (antifungal), phenylpropanolamine (decongestant/anorectic), fenfluramine (appetite suppressant), midodrine (antihypotensive), digoxin (cardiac glycoside/antiarrhythmic), and procainamide (antiarrhythmic). CONCLUSIONS: QT prolonging effects were common and varied in strength. Our results lend support to past Food and Drug Administration regulatory actions and support the role for ongoing surveillance of drug-induced QT prolongation.

A survey of adult awareness and use of medicine containing acetaminophen.

PURPOSE: To compare awareness, use of acetaminophen, and preferences for receiving information aimed at reducing acetaminophen overdose. METHODS: A survey of health plan enrollees identified from automated pharmacy data who were long-term and acute users of opioids with acetaminophen (n = 720 each cohort), and a general population cohort (n = 360) during the 2010-2011 cold/flu season. A 74% response rate was achieved. Differences were tested across the three cohorts, and by level of education, using age-adjusted regression models. RESULTS: Use of over-the-counter or prescription medicine containing acetaminophen in the prior 2 weeks was reported by 84% in the long-term opioid cohort, 76% in the acute opioid cohort, and 36% in the general population, but use of over-the-counter medicine with acetaminophen did not differ across the cohorts (30-34%). All three cohorts were unlikely to correctly identify drugs containing acetaminophen, but the opioid cohorts performed slightly better than the general population. Those with higher education performed slightly better when asked to identify acetaminophen products than those with no college education. The average usual daily acetaminophen dose (mg/day) reported was highest in the long-term opioid cohort (1185), followed by the acute opioid cohort (1010), and the general population (891)-p < 0.001. Estimated supratherapeutic exposure (>4000 mg/day) was rare but three to five times more common in the opioid cohorts than in the general population. CONCLUSIONS: Acetaminophen use is common, and supratherapeutic exposure may be of concern in users of opioids. Knowledge of which drugs contain acetaminophen appears inadequate; better labeling and proactive education from professionals may be impactful.

Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership.

BACKGROUND: Expanded availability of observational healthcare data (both administrative claims and electronic health records) has prompted the development of statistical methods for identifying adverse events associated with medical products, but the operating characteristics of these methods when applied to the real-world data are unknown. METHODS: We studied the performance of eight analytic methods for estimating of the strength of association-relative risk (RR) and associated standard error of 53 drug-adverse event outcome pairs, both positive and negative controls. The methods were applied to a network of ten observational healthcare databases, comprising over 130 million lives. Performance measures included sensitivity, specificity, and positive predictive value of methods at RR thresholds achieving statistical significance of p < 0.05 or p < 0.001 and with absolute threshold RR > 1.5, as well as threshold-free measures such as area under receiver operating characteristic curve (AUC). RESULTS: Although no specific method demonstrated superior performance, the aggregate results provide a benchmark and baseline expectation for risk identification method performance. At traditional levels of statistical significance (RR > 1, p < 0.05), all methods have a false positive rate >18%, with positive predictive value <38%. The best predictive model, high-dimensional propensity score, achieved an AUC = 0.77. At 50% sensitivity, false positive rate ranged from 16% to 30%. At 10% false positive rate, sensitivity of the methods ranged from 9% to 33%. CONCLUSIONS: Systematic processes for risk identification can provide useful information to supplement an overall safety assessment, but assessment of methods performance suggests a substantial chance of identifying false positive associations.

Evaluation of alternative standardized terminologies for medical conditions within a network of observational healthcare databases.

Large electronic databases of health care information, such as administrative claims and electronic health records, are available and are being used in a number of public health settings, including drug safety surveillance. However, because of a lack of standardization, clinical terminologies may differ across databases. With the aid of existing resources and expert coders, we have developed mapping tables to convert ICD-9-CM diagnosis codes used in some existing databases to SNOMED-CT and MedDRA. In addition, previously developed definitions for specific health outcomes of interest were mapped to the same standardized vocabularies. We evaluated how vocabulary mapping affected (1) the retention of clinical data from two test databases, (2) the semantic space of outcome definitions, (3) the prevalence of each outcome in the test databases, and (4) the reliability of analytic methods designed to detect drug-outcome associations in the test databases. Although vocabulary mapping affected the semantic space of some outcome definitions, as well as the prevalence of some outcomes in the test databases, it had only minor effects on the analysis of drug-outcome associations. Furthermore, both SNOMED-CT and MedDRA were viable for use as standardized vocabularies in systems designed to perform active medical product surveillance using disparate sources of observational data.

Registration of Observational Studies: perspectives from an industry-based epidemiology group.

Registration of randomized clinical trials has become standard practice and is enforced through publication policies and governmental regulations. However, the registration of observational studies remains controversial. In this commentary, we propose that a compromise can be reached on which observation should be registered based on study design and study intent.

Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership.

The U.S. Food and Drug Administration (FDA) Amendments Act of 2007 mandated that the FDA develop a system for using automated health care data to identify risks of marketed drugs and other medical products. The Observational Medical Outcomes Partnership is a public-private partnership among the FDA, academia, data owners, and the pharmaceutical industry that is responding to the need to advance the science of active medical product safety surveillance by using existing observational databases. The Observational Medical Outcomes Partnership's transparent, open innovation approach is designed to systematically and empirically study critical governance, data resource, and methodological issues and their interrelationships in establishing a viable national program of active drug safety surveillance by using observational data. This article describes the governance structure, data-access model, methods-testing approach, and technology development of this effort, as well as the work that has been initiated.

Medications for attention-deficit/hyperactivity disorder in Japan: A retrospective cohort study of label compliance.

AIM: To assess label compliance in prescription of medications approved for treatment of attention-deficit/hyperactivity disorder (ADHD) in Japan at the time of this study: methylphenidate (MPH), atomoxetine, and guanfacine. METHODS: Retrospective descriptive study was conducted in prevalent-user cohorts from the Japan Medical Data Center database. Patients who were prescribed a study drug between January 1, 2013 and September 30, 2018 and were in the database for ≥30 days were included. A prescription was considered compliant if all 4 criteria were satisfied: appropriate age, daily dose not exceeding the approved maximum, no contraindicated concurrent medications, and no contraindicated conditions. RESULTS: Among 17 418 patients who were prescribed a study drug during 2013-2018, 73% were male and 53% were children (aged <18 years). Fewer than 2% of prescriptions were for patients outside the approved age, 10%-13% of patients in the atomoxetine and MPH cohorts received ≥1 prescription exceeding maximum approved dose, no patients were co-prescribed a contraindicated medication, and 16%-18% of patients in the MPH cohorts had ≥1 contraindicated condition. During their first 500 days of use, for approximately 73%-86% of patients, all prescriptions were compliant with all label requirements. CONCLUSIONS: Among patients exposed to ADHD medications in Japan during 2013-2018, nearly all prescriptions for these medications were label-compliant for age. For >85% of patients, all prescriptions were label-compliant for dose, and for approximately 80%, all prescriptions were label-compliant for contraindicated conditions. We did not find evidence of widespread abuse or noncompliant use of prescribed ADHD medications.

Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study.

BACKGROUND: Identifying the medical conditions that are associated with poor health is crucial to prioritize decisions for future research and organizing care. However, assessing the burden of disease in the general population is complex, lengthy, and expensive. Claims databases that include self-reported health status can be used to assess the impact of medical conditions on the health in a population. OBJECTIVE: This study aimed to identify medical conditions that are highly predictive of poor health status using claims databases. METHODS: To determine the medical conditions most highly predictive of poor health status, we used a retrospective cohort study using 2 US claims databases. Subjects were commercially insured patients. Health status was measured using a self-report health status response. All medical conditions were included in a least absolute shrinkage and selection operator regression model to assess which conditions were associated with poor versus excellent health. RESULTS: A total of 1,186,871 subjects were included; 61.64% (731,587/1,186,871) reported having excellent or very good health. The leading medical conditions associated with poor health were cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), sleep disorders, seizures, male reproductive tract infections, chronic obstructive pulmonary disease, cardiomyopathy, dementia, and headaches. CONCLUSIONS: Understanding the impact of disease in a commercially insured population is critical to identify subjects who may be at risk for reduced productivity and job loss. Claims database studies can measure the impact of medical conditions on the health status in a population and to assess changes overtime and could limit the need to collect prospective collection of information, which is slow and expensive, to assess disease burden. Leading medical conditions associated with poor health in a commercially insured population were the ones associated with high burden of disease such as cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), infections, chronic obstructive pulmonary disease, cardiomyopathy, and dementia. However, sleep disorders, seizures, male reproductive tract infections, and headaches were also part of the leading medical conditions associated with poor health that had not been identified before as being associated with poor health and deserve more attention.

Incidence, prevalence and prescription patterns of antipsychotic medications use in Asia and US: A cross-nation comparison with common data model.

The use of antipsychotic medications (APMs) could be different among countries due to availability, approved indications, characteristics and clinical practice. However, there is limited literature providing comparisons of APMs use among countries. To examine trends in antipsychotic prescribing in Taiwan, Hong Kong, Japan, and the United States, we conducted a cross-national study from 2002 to 2014 b y using the distributed network approach with common data model. We included all patients who had at least a record of antipsychotic prescription in this study, and defined patients without previous exposure of antipsychotics for 6 months before the index date as new users for incidence estimation. We calculated the incidence, prevalence, and prescription rate of each medication by calendar year. Among older patients, sulpiride was the most incident [incidence rate (IR) 11.0-23.3) and prevalent [prevalence rate (PR) 11.9-14.3) APM in Taiwan, and most prevalent (PR 2.5-3.9) in Japan. Quetiapine and haloperidol were most common in the United States (IR 8.1-9.5; PR 18.0-18.4) and Hong Kong (PR 8.8-13.7; PR 10.6-12.7), respectively. The trend of quetiapine use was increasing in Taiwan, Hong Kong and the United States. As compared to older patients, the younger patients had more propensity to be prescribed second-generation APM for treatment in four countries. Trends in antipsychotic prescribing varied among countries. Quetiapine use was most prevalent in the United States and increasing in Taiwan and Hong Kong. The increasing use of quetiapine in the elderly patients might be due to its safety profile compared to other APMs.

Resource use and cost of diagnostic workup of women with suspected breast cancer.

We estimated resource use and costs associated with a diagnostic workup for suspected breast cancer among Medicare beneficiaries. Using Medicare claims data, we found that the average cost of a diagnostic workup for suspected breast cancer--whether it eventuated in a breast cancer diagnosis or not--was $361, and did not vary by presentation (signs/symptoms or screening mammography). In the aggregate, we estimate that Medicare spends approximately $679 million annually on diagnostic workups for women with suspected breast cancer, and that false positive mammograms result in diagnostic costs of approximately $250 million.