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.

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.

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.

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.

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.

Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases.

BACKGROUND: Confounding by disease severity is an issue in pharmacoepidemiology studies of rheumatoid arthritis (RA), due to channeling of sicker patients to certain therapies. To address the issue of limited clinical data for confounder adjustment, a patient-level prediction model to differentiate between patients prescribed and not prescribed advanced therapies was developed as a surrogate for disease severity, using all available data from a US claims database. METHODS: Data from adult RA patients were used to build regularized logistic regression models to predict current and future disease severity using a biologic or tofacitinib prescription claim as a surrogate for moderate-to-severe disease. Model discrimination was assessed using the area under the receiver (AUC) operating characteristic curve, tested and trained in Optum Clinformatics® Extended DataMart (Optum) and additionally validated in three external IBM MarketScan® databases. The model was further validated in the Optum database across a range of patient cohorts. RESULTS: In the Optum database (n = 68,608), the AUC for discriminating RA patients with a prescription claim for a biologic or tofacitinib versus those without in the 90 days following index diagnosis was 0.80. Model AUCs were 0.77 in IBM CCAE (n = 75,579) and IBM MDCD (n = 7,537) and 0.75 in IBM MDCR (n = 36,090). There was little change in the prediction model assessing discrimination 730 days following index diagnosis (prediction model AUC in Optum was 0.79). CONCLUSIONS: A prediction model demonstrated good discrimination across multiple claims databases to identify RA patients with a prescription claim for advanced therapies during different time-at-risk periods as proxy for current and future moderate-to-severe disease. This work provides a robust model-derived risk score that can be used as a potential covariate and proxy measure to adjust for confounding by severity in multivariable models in the RA population. An R package to develop the prediction model and risk score are available in an open source platform for researchers.

Corrigendum to "Treatment resistant depression incidence estimates from studies of health insurance databases depend strongly on the details of the operating definition" [Heliyon 4 (7) (July 2018) e00707].

[This corrects the article DOI: 10.1016/j.heliyon.2018.e00707.].

Penetration of new antidiabetic medications in East Asian countries and the United States: A cross-national comparative study.

BACKGROUND: The number of patients with diabetes is increasing particularly in Asia-Pacific region. Many of them are treated with antidiabetics. As the basis of the studies on the benefit and harm of antidiabetic drugs in the region, the information on patterns of market penetration of new classes of antidiabetic medications is important in providing context for subsequent research and analyzing and interpreting results. METHODS: We compared penetration patterns of dipeptidyl peptidase-4 (DPP-4) inhibitors in Taiwan, Hong Kong, Japan, and the United States. We used the Taiwan National Health Insurance Research Database, a random sample of the Hong Kong Clinical Data Analysis and Reporting System, the Japan Medical Data Center database, and a 5% random sample of the US Medicare database converted to the Observational Medical Outcomes Partnership's Common Data Model to identify new users of oral antidiabetic medications. We standardized prevalence and incidence rates of medication use by age and sex to those in the 2010 Taiwanese population. We compared age, sex, comorbid conditions, and concurrent medications between new users of DPP-4 inhibitors and biguanides. RESULTS: Use of DPP-4 inhibitors 1 year after market entry was highest in Japan and lowest in Hong Kong. New users had more heart failure, hyperlipidemia, and renal failure than biguanide users in Taiwan, Hong Kong, and the United States while the proportions were similar in Japan. In a country with low penetration of DPP-4 inhibitors (eg, Hong Kong), users had diabetes with multiple comorbid conditions compared with biguanidine users. In a country with high penetration (eg, Japan), the proportion of users with comorbid conditions was similar to that of biguanide users. CONCLUSIONS: We observed a marked difference of the penetration patterns of newly marketed antidiabetics in different countries in Asia. Those results will provide the basic information useful in the future studies.

Treatment resistant depression incidence estimates from studies of health insurance databases depend strongly on the details of the operating definition.

Background: Health services databases provide population-based data that have been used to describe the epidemiology and costs of treatment resistant depression (TRD). This retrospective cohort study estimated TRD incidence and, via sensitivity analyses, assessed the variation of TRD incidence within the range of implementation choices. Methods: In three US databases widely used for observational studies, we defined TRD as failure of two medications as evidenced by their replacement or supplementation by other medications, and set maximum durations (caps) for how long a medication regimen could remain in use and still be eligible to fail. Results: TRD incidence estimates varied approximately 2-fold between the two databases (CCAE, Medicaid) that described socioeconomically different non-elderly populations; for a given cap varied 2-fold to 4-fold within each database across the other implementation choices; and if the cap was also allowed to vary, varied 6-fold or 7-fold within each database. Limitations: The main limitations were typical of studies from health services databases and included the lack of complete -rather than recent - medical histories, the limited amount of clinical information, and the assumption that medication dispensed was consumed as directed. Conclusion: In retrospective cohort studies from health services databases, TRD incidence estimates vary widely depending on the implementation choices. Unless a firm basis for narrowing the range of these choices can be found, or a different analytic approach not dependent on such choices is adopted, TRD incidence and prevalence estimates from such databases will be difficult to compare or interpret.

Applying a common data model to Asian databases for multinational pharmacoepidemiologic studies: opportunities and challenges.

OBJECTIVE: The goal of the Asian Pharmacoepidemiology Network is to study the effectiveness and safety of medications commonly used in Asia using databases from individual Asian countries. An efficient infrastructure to support multinational pharmacoepidemiologic studies is critical to this effort. STUDY DESIGN AND SETTING: We converted data from the Japan Medical Data Center database, Taiwan's National Health Insurance Research Database, Hong Kong's Clinical Data Analysis and Reporting System, South Korea's Ajou University School of Medicine database, and the US Medicare 5% sample to the Observational Medical Outcome Partnership common data model (CDM). RESULTS: We completed and documented the process for the CDM conversion. The coordinating center and participating sites reviewed the documents and refined the conversions based on the comments. The time required to convert data to the CDM varied widely across sites and included conversion to standard terminology codes and refinements of the conversion based on reviews. We mapped 97.2%, 86.7%, 92.6%, and 80.1% of domestic drug codes from the USA, Taiwan, Hong Kong, and Korea to RxNorm, respectively. The mapping rate from Japanese domestic drug codes to RxNorm (70.7%) was lower than from other countries, and we mapped remaining unmapped drugs to Anatomical Therapeutic Chemical Classification System codes. Because the native databases used international procedure coding systems for which mapping tables have been established, we were able to map >90% of diagnosis and procedure codes to standard terminology codes. CONCLUSION: The CDM established the foundation and reinforced collaboration for multinational pharmacoepidemiologic studies in Asia. Mapping of terminology codes was the greatest challenge, because of differences in health systems, cultures, and coding systems.

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.

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.

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.

Incidence of diabetic ketoacidosis among patients with type 2 diabetes mellitus treated with SGLT2 inhibitors and other antihyperglycemic agents.

AIMS: To estimate and compare incidence of diabetes ketoacidosis (DKA) among patients with type 2 diabetes who are newly treated with SGLT2 inhibitors (SGLT2i) versus non-SGLT2i antihyperglycemic agents (AHAs) in actual clinical practice. METHODS: A new-user cohort study design using a large insurance claims database in the US. DKA incidence was compared between new users of SGLT2i and new users of non-SGLT2i AHAs pair-matched on exposure propensity scores (EPS) using Cox regression models. RESULTS: Overall, crude incidence rates (95% CI) per 1000 patient-years for DKA were 1.69 (1.22-2.30) and 1.83 (1.58-2.10) among new users of SGLT2i (n=34,442) and non-SGLT2i AHAs (n=126,703). These rates more than doubled among patients with prior insulin prescriptions but decreased by more than half in analyses that excluded potential autoimmune diabetes (PAD). The hazard ratio (95% CI) for DKA comparing new users of SGLT2i to new users of non-SGLT2i AHAs was 1.91 (0.94-4.11) (p=0.09) among the 30,196 EPS-matched pairs overall, and 1.13 (0.43-3.00) (p=0.81) among the 27,515 EPS-matched pairs that excluded PAD. CONCLUSIONS: This was the first observational study that compared DKA risk between new users of SGLT2i and non-SGLT2i AHAs among patients with type 2 diabetes, and overall no statistically significant difference was detected.

Atypical Antipsychotics and the Risks of Acute Kidney Injury and Related Outcomes Among Older Adults: A Replication Analysis and an Evaluation of Adapted Confounding Control Strategies.

OBJECTIVE: A recently published analysis of population-based claims data from Ontario, Canada reported higher risks of acute kidney injury (AKI) and related outcomes among older adults who were new users of atypical antipsychotics (AAPs) compared with unexposed patients. In light of these findings, the objective of the current study was to further investigate the risks of AKI and related outcomes among older adults receiving AAPs. METHODS: A replication of the previously published analysis was performed using the US Truven MarketScan Medicare Supplemental database (MDCR) among patients aged 65 years and older. Compared with non-users of AAPs, the study compared the risk of AKI and related outcomes with users of AAPs (quetiapine, risperidone, olanzapine, aripiprazole, or paliperidone) using a 1-to-1 propensity score matched analysis. In addition, we performed adapted analyses that: (1) included all covariates used to fit propensity score models in outcome models; and (2) required patients to have a diagnosis of schizophrenia, bipolar disorder, or major depression and a healthcare visit within 90 days prior to the index date. RESULTS: AKI effect estimates [as odds ratios (ORs) with 95% confidence intervals (CIs)] were significantly elevated in our MDCR replication analyses (OR 1.45, 95% CI 1.32-1.60); however, in adapted analyses, associations were not significant (OR 0.91, 95% CI 0.78-1.07)). In analyses of AKI and related outcomes, results were mostly consistent between the previously published and the MDCR replication analyses. The primary change that attenuated associations in adapted analyses was the requirement for patients to have a mental health condition and a healthcare visit prior to the index date. CONCLUSIONS: The MDCR analysis yielded similar results when the methodology of the previously published analysis was replicated, but, in adapted analyses, we did not find significantly higher risks of AKI and related outcomes. The contrast of results between our replication and adapted analyses may be due to the analytic approach used to compare patients (and potential confounding by indication). Further research is warranted to evaluate these associations, while also examining methods to account for differences in older adults who do and do not use these medications.

Depression Is Associated With High Levels of C-Reactive Protein and Low Levels of Fractional Exhaled Nitric Oxide: Results From the 2007-2012 National Health and Nutrition Examination Surveys.

OBJECTIVE: Major depressive disorder may be due to psychoneuroimmunological dysfunction, as studies have documented increased levels of a variety of inflammatory mediators in depressed subjects. Nitric oxide (NO) is marker of inflammation, and fractional exhaled NO (FeNO) is a marker of airway inflammation. Plasma NO and FeNO levels have been shown to be lower in subjects with depression in small studies. We sought to assess the association of depression with C-reactive protein (CRP) and FeNO levels in a large and representative sample of the US population. METHODS: Population-based cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES). NHANES collects health information about the US population through interviews, medical examinations, and laboratory tests. We included subjects ≥ 20 years old who participated in NHANES in 2007 to 2012, responded to the depression questions, and had CRP values or ≥ 2 reproducible FeNO measures. Depression was measured using the 9-item Patient Health Questionnaire (PHQ-9). Subjects were classified as depressed if PHQ-9 scores were ≥ 10. FeNO and CRP levels were log transformed. Unadjusted and adjusted regression analyses were conducted. RESULTS: A total of 14,276 subjects responded to the PHQ-9, and 7.73% had depressive symptoms. Of these subjects, 10,036 had CRP values and 12,513 had FeNO measurements. Subjects with depressive symptoms had, after adjustment, CRP levels that were 31% higher (95% confidence interval [CI], 14% to 50%) and FeNO levels that were 10.7% lower (95% CI, -2.5% to -17.1%) than in subjects with no depressive symptoms. CONCLUSIONS: Depression is associated with high CRP levels and low FeNO levels. Of importance, this study (1) assesses the association of depression with CRP and exhaled NO levels in a large and representative sample of the US population, (2) contributes to the neuroimmunological dimension of depression, (3) confirms the association of depression with high levels of CRP, and (4) assesses, for the first time, the association of depression with peripheral NO in more than 10,000 subjects from the general population.

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.

Seasonality in acute liver injury? Findings in two health care claims databases.

BACKGROUND: Presumed seasonal use of acetaminophen-containing products for relief of cold/influenza ("flu") symptoms suggests that there might also be a corresponding seasonal pattern for acute liver injury (ALI), a known clinical consequence of acetaminophen overdose. OBJECTIVE: The objective of this study was to determine whether there were any temporal patterns in hospitalizations for ALI that would correspond to assumed acetaminophen use in cold/flu season. METHODS: In the period 2002-2010, monthly hospitalization rates for ALI using a variety of case definitions were calculated. Data sources included Truven MarketScan(®) Commercial Claims and Encounters (CCAE) and Medicare Supplemental and Coordination of Benefits (MDCR) databases. We performed a statistical test for seasonality of diagnoses using the periodic generalized linear model. To validate that the test can distinguish seasonal from nonseasonal patterns, we included two positive controls (ie, diagnoses of the common cold [acute nasopharyngitis] and influenza), believed to change with seasons, and two negative controls (female breast cancer and diabetes), believed to be insensitive to season. RESULTS: A seasonal pattern was observed in monthly rates for common cold and influenza diagnoses, but this pattern was not observed for monthly rates of ALI, with or without comorbidities (cirrhosis or hepatitis), breast cancer, or diabetes. The statistical test for seasonality was significant for positive controls (P<0.001 for each diagnosis in both databases) and nonsignificant for ALI and negative controls. CONCLUSION: No seasonal pattern was observed in the diagnosis of ALI. The positive and negative controls showed the expected patterns, strengthening the validity of the statistical and visual tests used for detecting seasonality.

Changes in Body Weight Among People With Type 2 Diabetes Mellitus in the United States, NHANES 2005-2012.

PURPOSE: To understand weight loss strategies, weight changes, goals, and behaviors in people with type 2 diabetes mellitus (T2DM) and whether these differ by ethnicity. METHODS: T2DM was identified by self-reported diagnosis using the NHANES 2005-2012 data, which also included measured and self-reported current body weight and height, self-reported weight the prior year, and self-reported aspired weight. Nineteen weight loss strategies were evaluated for association with ≥5% weight loss or weight gain versus <5% weight change. RESULTS: Among people with T2DM, 88.0% were overweight/obese (body mass index [BMI] ≥25 kg/m(2)) in the prior year and 86.1% the current year. About 60% of the overweight/obese took weight loss actions, mostly using diet-related methods with average weight lost <5%. Two most "effective" methods reported (smoking, taking laxatives/vomiting) are also potentially most harmful. Similar BMI distributions but different goals and behaviors about weight and weight loss were observed across ethnicity. Only physical activity meeting the recommended level and changing eating habits were consistently associated with favorable and statistically significant weight change. CONCLUSIONS: Weight management in T2DM is an ongoing challenge, regardless of ethnicity/race. Among overweight/obese T2DM subjects, recommended level of physical activity and changing eating habits were associated with statistically significant favorable weight change.