Comparing the Centers for Medicare and Medicaid Services (CMS) enrollment data death dates to the National Death Index (NDI).

PURPOSE: In the United States, the National Death Index (NDI) is the most complete source of death information, while epidemiologic studies with mortality outcomes often rely on U.S. Medicare data for outcome ascertainment. The purpose of this study was to assess the agreement of death information between the Centers for Medicare & Medicaid Services (CMS) Medicare enrolment data and NDI. METHODS: Using Medicare and NDI data from 1999 through 2016, we identified Medicare beneficiaries who were reported dead in the CMS Medicare enrolment database (EDB) and Common Medicare Environment (CME), linked these beneficiaries to the NDI using CMS Health Insurance Claim number, and compared death dates between the two data sources. To assess agreement between our data sources, we calculated kappa scores; where a kappa of 1 indicates perfect agreement and a kappa of 0 indicates agreement equivalent to chance. We also examined CMS to NDI linkage and death date matching for stability over time. RESULTS: Of the 36 785 640, Medicare beneficiaries reported dead in CMS enrollment data from 1999 to 2016, 97.5% were linked to the NDI. A kappa score of 0.98 showed a near perfect agreement between NDI and CMS reported deaths. The percentage of linked cases exactly matching on death dates increased from 94.8% in 1999 to 99.4% in 2016. CONCLUSIONS: Our findings suggest strong concordance between death dates as recorded by CMS enrollment data and the NDI in the entire Medicare population.

Stability of Suicide Risk Prediction Models During Changes in Health Care Delivery.

OBJECTIVE: The authors aimed to use health records data to examine how the accuracy of statistical models predicting self-harm or suicide changed between 2015 and 2019, as health systems implemented suicide prevention programs. METHODS: Data from four large health systems were used to identify specialty mental health visits by patients ages ≥11 years, assess 311 potential predictors of self-harm (including demographic characteristics, historical risk factors, and index visit characteristics), and ascertain fatal or nonfatal self-harm events over 90 days after each visit. New prediction models were developed with logistic regression with LASSO (least absolute shrinkage and selection operator) in random samples of visits (65%) from each calendar year and were validated in the remaining portion of the sample (35%). RESULTS: A model developed for visits from 2009 to mid-2015 showed similar classification performance and calibration accuracy in a new sample of about 13.1 million visits from late 2015 to 2019. Area under the receiver operating characteristic curve (AUC) ranged from 0.840 to 0.849 in the new sample, compared with 0.851 in the original sample. New models developed for each year for 2015-2019 had classification performance (AUC range 0.790-0.853), sensitivity, and positive predictive value similar to those of the previously developed model. Models selected similar predictors from 2015 to 2019, except for more frequent selection of depression questionnaire data in later years, when questionnaires were more frequently recorded. CONCLUSIONS: A self-harm prediction model developed with 2009-2015 visit data performed similarly when applied to 2015-2019 visits. New models did not yield superior performance or identify different predictors.

Suicide poisoning mortality: a comparison of the national poison data system and centers for disease control national dataset.

The America's Poison Centres National Poison Data System (NPDS) is set up for the active surveillance of voluntarily reported poisoning cases in near real-time. The Centres for Disease Control and Prevention (CDC)'s Wide-ranging Online Data for Epidemiologic Research (WONDER) database is final national mortality data from state registries. We compared suicide poisoning deaths in both datasets from 2000 to 2020 and tested their relationship using a simple linear regression model. Mean annual suicide poisoning deaths during the review period were 699 (SD 145) in NPDS, and 6150 (SD 577) in WONDER. NPDS annual cases averaged 11% of cases recorded in WONDER (SD 2%; Range 8%-16%). The regression coefficient for the linear relationship between annual deaths recorded in both datasets was 0.18 (p-value<0.001, R2=0.51). The rapidly available NPDS data on fatal self-poisoning may provide sentinel surveillance regarding self-poisonings, but do not reliably predict final national data on suicide poisoning.

Predicting risk of suicidal behavior from insurance claims data vs. linked data from insurance claims and electronic health records.

PURPOSE: Observational studies assessing effects of medical products on suicidal behavior often rely on health record data to account for pre-existing risk. We assess whether high-dimensional models predicting suicide risk using data derived from insurance claims and electronic health records (EHRs) are superior to models using data from insurance claims alone. METHODS: Data were from seven large health systems identified outpatient mental health visits by patients aged 11 or older between 1/1/2009 and 9/30/2017. Data for the 5 years prior to each visit identified potential predictors of suicidal behavior typically available from insurance claims (e.g., mental health diagnoses, procedure codes, medication dispensings) and additional potential predictors available from EHRs (self-reported race and ethnicity, responses to Patient Health Questionnaire or PHQ-9 depression questionnaires). Nonfatal self-harm events following each visit were identified from insurance claims data and fatal self-harm events were identified by linkage to state mortality records. Random forest models predicting nonfatal or fatal self-harm over 90 days following each visit were developed in a 70% random sample of visits and validated in a held-out sample of 30%. Performance of models using linked claims and EHR data was compared to models using claims data only. RESULTS: Among 15 845 047 encounters by 1 574 612 patients, 99 098 (0.6%) were followed by a self-harm event within 90 days. Overall classification performance did not differ between the best-fitting model using all data (area under the receiver operating curve or AUC = 0.846, 95% CI 0.839-0.854) and the best-fitting model limited to data available from insurance claims (AUC = 0.846, 95% CI 0.838-0.853). Competing models showed similar classification performance across a range of cut-points and similar calibration performance across a range of risk strata. Results were similar when the sample was limited to health systems and time periods where PHQ-9 depression questionnaires were recorded more frequently. CONCLUSION: Investigators using health record data to account for pre-existing risk in observational studies of suicidal behavior need not limit that research to databases including linked EHR data.

Scalable Incident Detection via Natural Language Processing and Probabilistic Language Models.

Post marketing safety surveillance depends in part on the ability to detect concerning clinical events at scale. Spontaneous reporting might be an effective component of safety surveillance, but it requires awareness and understanding among healthcare professionals to achieve its potential. Reliance on readily available structured data such as diagnostic codes risk under-coding and imprecision. Clinical textual data might bridge these gaps, and natural language processing (NLP) has been shown to aid in scalable phenotyping across healthcare records in multiple clinical domains. In this study, we developed and validated a novel incident phenotyping approach using unstructured clinical textual data agnostic to Electronic Health Record (EHR) and note type. It's based on a published, validated approach (PheRe) used to ascertain social determinants of health and suicidality across entire healthcare records. To demonstrate generalizability, we validated this approach on two separate phenotypes that share common challenges with respect to accurate ascertainment: 1) suicide attempt; 2) sleep-related behaviors. With samples of 89,428 records and 35,863 records for suicide attempt and sleep-related behaviors, respectively, we conducted silver standard (diagnostic coding) and gold standard (manual chart review) validation. We showed Area Under the Precision-Recall Curve of ∼ 0.77 (95% CI 0.75-0.78) for suicide attempt and AUPR ∼ 0.31 (95% CI 0.28-0.34) for sleep-related behaviors. We also evaluated performance by coded race and demonstrated differences in performance by race were dissimilar across phenotypes and require algorithmovigilance and debiasing prior to implementation.

Assessing the risk of intentional self-harm in montelukast users: an updated Sentinel System analysis using ICD-10 coding.

BACKGROUND: Montelukast prescribing information includes a Boxed Warning issued in March 2020 regarding neuropsychiatric adverse events. A previous Sentinel System study of asthma patients from 2000 to 2015 did not demonstrate an increased risk of intentional self-harm measured using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, with montelukast compared to inhaled corticosteroids (ICS). METHODS: Using a new user cohort study design, we examined intentional self-harm events in patients aged 10 years and older who were incident users of either montelukast or ICS as monotherapy, with a diagnosis of asthma, between October 1, 2015, to June 30, 2022, in the Sentinel System. We measured intentional self-harm using ICD-10-CM codes, which may have better accuracy for capturing suicide attempts than ICD-9-CM codes. We used inverse probability of treatment weighting to balance baseline covariates. We performed subgroup analyses by age group, sex, psychiatric history, and pre/post Boxed Warning era and conducted sensitivity analyses varying type of care setting of the outcome and exposure episode gaps. RESULTS: Among 752,230 and 724,855 patients in the montelukast and ICS exposure groups respectively, we found no association between montelukast use and self-harm compared to ICS use [Hazard Ratio (95% Confidence Interval): 0.96 (0.85, 1.08)]. This finding was consistent across all subgroups, and sensitivity analyses. CONCLUSION: Our results cannot exclude other neuropsychiatric idiosyncratic reactions to montelukast. Compared to the previous Sentinel study, this study identified about double the rate of self-harm events, suggesting a greater sensitivity of ICD-10 codes for measuring self-harm than ICD-9.

Risk of Severe COVID-19 in Prevalent Users of Alpha-1 Adrenergic Receptor Antagonists: A National Case-Control Study of Medicare Beneficiaries.

BACKGROUND: Alpha-1 adrenergic receptor antagonists prevent cytokine storm in mouse sepsis models. This led to the hypothesis that alpha-1 blockers may prevent severe coronavirus disease 2019 (COVID-19), which is characterized by hypercytokinemia and progressive respiratory failure. METHODS: We performed an observational case-control study in male Medicare beneficiaries aged 65 years or older, with or without benign prostatic hyperplasia (BPH), and treated with alpha-1 receptor blockers or 5-alpha reductase inhibitors. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were estimated for outcomes of uncomplicated and severe COVID-19 hospitalization (intensive care unit admission, invasive mechanical ventilation, or death). RESULTS: There were 20,963 cases of hospitalized COVID-19 matched to 101,161 controls on calendar date and neighborhood of residence. In the primary analysis (males with BPH), there was no difference in risk of uncomplicated COVID-19 hospitalization (aOR 1.08, 95% CI 0.996-1.17) or hospitalization with severe complications (aOR 0.97, 95% CI 0.88-1.08). In the secondary analysis (males with or without BPH), the corresponding aORs were 1.02 (95% CI, 0.96-1.09) (uncomplicated) and 0.99 (95% CI, 0.91-1.07) (complicated), respectively. Subgroup and sensitivity analyses yielded similar results. Of note, there was no difference in risk of severe COVID-19 hospitalization when comparing non-selective vs selective alpha-1 blocker use (aOR 0.98, 95% CI 0.86-1.10), higher- vs lower-dose alpha-1 blocker use (aOR 0.96, 95% CI 0.86-1.08), or current vs remote alpha-1 blocker use (aOR 1.04, 95% CI 0.91-1.18). CONCLUSIONS: Prevalent use of alpha-1 receptor blockers was not associated with a protective or harmful effect on risk of uncomplicated or severe hospitalized COVID-19.

The use of analgesics for intentional self-poisoning: Trends in U.S. poison center data.

In the U.S., intentional self-poisonings with analgesics that are available without a prescription increased from 2000 to 2018. Given concerns regarding mental health outcomes during the COVID-19 pandemic, we examined and compared trends in pediatric and adult intentional self-poisoning with acetaminophen, aspirin, ibuprofen, and naproxen from 2016 to 2021 using the National Poison Data System (NPDS) to see if these trends have continued. We extracted annual case counts of all suspected suicide attempts from intentional poisoning, and of suspected suicide attempts resulting in major effects or death, from the NPDS for non-prescription single ingredient adult formulation acetaminophen, non-prescription single ingredient adult formulation aspirin, single ingredient formulation ibuprofen, and single ingredient formulation naproxen. We enumerated the cases by year, age, and gender. Most cases of intentional self-poisoning within the review period involved acetaminophen and ibuprofen and the 13-19-year-olds constituted the highest proportion of intentional self-poisoning cases across age groups for all four analgesics. Cases involving females predominated cases involving males by 3:1 or greater. The 13-19-year-old age group also represented the largest proportion of cases that resulted in major clinical effects or deaths. An increasing trend in suicide poisoning cases with acetaminophen and ibuprofen was observed in the 6-19-years age group and this trend appeared to exacerbate from 2020 to 2021 corresponding with the start of the COVID-19 pandemic period.

Accuracy of ICD-10-CM encounter diagnoses from health records for identifying self-harm events.

OBJECTIVE: Assess the accuracy of ICD-10-CM coding of self-harm injuries and poisonings to identify self-harm events. MATERIALS AND METHODS: In 7 integrated health systems, records data identified patients reporting frequent suicidal ideation. Records then identified subsequent ICD-10-CM injury and poisoning codes indicating self-harm as well as selected codes in 3 categories where uncoded self-harm events might be found: injuries and poisonings coded as undetermined intent, those coded accidental, and injuries with no coding of intent. For injury and poisoning encounters with diagnoses in those 4 groups, relevant clinical text was extracted from records and assessed by a blinded panel regarding documentation of self-harm intent. RESULTS: Diagnostic codes selected for review include all codes for self-harm, 43 codes for undetermined intent, 26 codes for accidental intent, and 46 codes for injuries without coding of intent. Clinical text was available for review for 285 events originally coded as self-harm, 85 coded as undetermined intent, 302 coded as accidents, and 438 injury events with no coding of intent. Blinded review of full-text clinical records found documentation of self-harm intent in 254 (89.1%) of those originally coded as self-harm, 24 (28.2%) of those coded as undetermined, 24 (7.9%) of those coded as accidental, and 48 (11.0%) of those without coding of intent. CONCLUSIONS: Among patients at high risk, nearly 90% of injuries and poisonings with ICD-10-CM coding of self-harm have documentation of self-harm intent. Reliance on ICD-10-CM coding of intent to identify self-harm would fail to include a small proportion of true self-harm events.

Mortality Among Parkinson's Disease Patients Treated With Pimavanserin or Atypical Antipsychotics: An Observational Study in Medicare Beneficiaries.

OBJECTIVE: Pimavanserin, a serotonin 5-HT2 antagonist, is indicated for treatment of hallucinations and delusions associated with Parkinson's disease psychosis. In premarketing trials in patients with Parkinson's disease psychosis, 11% of patients died during open-label pimavanserin treatment. Antipsychotics, which are used off-label in Parkinson's disease psychosis, increase mortality in dementia patients. The authors compared mortality with pimavanserin and atypical antipsychotics in a large database. METHODS: This was a retrospective new-user cohort study of Medicare beneficiaries with Parkinson's disease initiating pimavanserin (N=3,227) or atypical antipsychotics (N=18,442) from April 2016 to March 2019. All-cause mortality hazard ratios and 95% confidence intervals were estimated for pimavanserin compared with atypical antipsychotics, using segmented proportional hazards regression over 1-180 and 181+ days of treatment. Potential confounding was addressed through inverse probability of treatment weighting (IPTW). RESULTS: Pimavanserin users had a mean age of approximately 78 years, and 45% were female. Before IPTW, some comorbidities were more prevalent in atypical antipsychotic users; after IPTW, comorbidities were well balanced between groups. In the first 180 days of treatment, mortality was approximately 35% lower with pimavanserin than with atypical antipsychotics (hazard ratio=0.65, 95% CI=0.53, 0.79), with approximately one excess death per 30 atypical antipsychotic-treated patients; however, during treatment beyond 180 days, there was no additional mortality advantage with pimavanserin (hazard ratio=1.05, 95% CI=0.82, 1.33). Pimavanserin showed no mortality advantage in nursing home patients. CONCLUSIONS: Pimavanserin use was associated with lower mortality than atypical antipsychotic use during the first 180 days of treatment, but only in community-dwelling patients, not nursing home residents.

Systematic review of risk of SARS-CoV-2 infection and severity of COVID-19 with therapies approved to treat multiple sclerosis.

There is growing concern that multiple sclerosis (MS) patients on certain therapies may be at higher risk for severe coronavirus disease 2019 (COVID-19). We conducted a systematic literature review to examine the available data on U.S. therapies approved to treat MS and the risk of SARS-CoV-2 infection or severe COVID-19 outcomes. We conducted searches in PubMed, Embase, and the WHO COVID-19 database through May 2, 2021, and retrieved articles describing clinical data on therapies approved to treat MS and the risk of infection with SARS-CoV-2 or the effects of such therapies on clinical outcomes of COVID-19. The literature search identified a total of 411 articles: 97 in PubMed, 227 in Embase, and 87 in the WHO database. After excluding duplicates and screening, we identified 15 articles of interest. We identified an additional article through a broader secondary weekly search in PubMed. Thus, ultimately, we reviewed 16 observational studies. Available data, which suggest that MS patients treated with anti-CD20 monoclonal antibodies may be at increased risk for severe COVID-19, are subject to relevant limitations. Generally, studies did not identify increased risk for COVID-19 worsening with other therapies approved to treat MS. Based on observational data, biological plausibility, novelty of the drug-event association, and public health implications in a subpopulation with potential impaired response to the COVID-19 vaccines, this safety signal merits further monitoring.

Risk of hospitalized depression and intentional self-harm with brand and authorized generic sertraline.

BACKGROUND: Recent suggestions of therapeutic inequivalence of brand and generic sertraline have raised concerns about disproportionately higher adverse events among generic users. OBJECTIVE: To assess the impact of confounding in a comparison of the risks of worsening depression and intentional self-harm (ISH) between users of brand name sertraline and its pharmaceutically equivalent authorized generic (AG). METHODS: Using a retrospective new-user cohort design, we identified patients with a diagnosis code for depression aged ≥12 years who were continuously enrolled in a Sentinel Data Partner health plan for ≥180 days before their first sertraline dispensing between June 30, 2006 and September 30, 2015. New use was defined as no evidence of sertraline dispensing in the 180 days before index date. We matched each brand name user to up to 10 AG users using propensity scores (PS) and conducted case-centered logistic regression to assess the risks of hospitalized depression and ISH. RESULTS: Before PS matching, brand name users were significantly less likely to be hospitalized for depression [Hazard Ratio (HR) = 0.70 (95% confidence interval (CI): 0.53-0.94)]. However, in the matched analysis, we observed no statistical difference between brand and AG users [HR = 0.84 (95% CI: 0.59-1.21)]. The risk of ISH did not significantly differ between the exposure groups in unmatched (HR = 0.99 (95% CI: 0.60-1.62) and matched analyses [HR = 0.91 (95% CI: 0.49-1.70). CONCLUSION: In depressed patients receiving brand versus AG sertraline, patient characteristics confounded the association with hospitalization. Baseline differences were ameliorated by PS matching resulting in no statistical difference between brand and AG sertraline users.

Risk of Psychiatric Adverse Events Among Montelukast Users.

BACKGROUND: There have been conflicting results from observational studies regarding the risk of psychiatric adverse events (PAEs) with montelukast use. OBJECTIVE: To determine whether there are associations of depressive disorders, self-harm, and suicide with use of montelukast compared with inhaled corticosteroid (ICS) use. METHODS: Using data from the Sentinel Distributed Database from January 1, 2000, to September 30, 2015, patients (n = 457,377) exposed to montelukast or ICS, aged 6 years and older with a diagnosis of asthma, were matched 1:1 on propensity scores. Hazard ratios (HRs) and 95% CIs were estimated for each study outcome overall and by age, sex, psychiatric history, and pre-/post-2008 labeling updates using Cox proportional hazards regression models. RESULTS: Exposure to montelukast was associated with a lower risk of treated outpatient depressive disorder (HR, 0.91; 95% CI, 0.89-0.93). No increased risks of inpatient depressive disorder (HR, 1.06; 95% CI, 0.90-1.24), self-harm (HR, 0.92; 95% CI, 0.69-1.21), or self-harm using a modified algorithm (HR, 0.81; 95% CI, 0.63-1.05) were observed with montelukast use compared with ICS use. Most PAEs occurred in the roughly one-third of patients having a past psychiatric history. CONCLUSIONS: When compared with use of ICS, we did not find associations between montelukast use and hospitalizations for depression or self-harm events. Our findings should be interpreted considering the study's limitations. Psychiatric comorbidity was common, and most PAEs occurred in patients with a past psychiatric history.

Developing a Standardized and Reusable Method to Link Distributed Health Plan Databases to the National Death Index: Methods Development Study Protocol.

BACKGROUND: Certain medications may increase the risk of death or death from specific causes (eg, sudden cardiac death), but these risks may not be identified in premarket randomized trials. Having the capacity to examine death in postmarket safety surveillance activities is important to the US Food and Drug Administration's (FDA) mission to protect public health. Distributed networks of electronic health plan databases used by the FDA to conduct multicenter research or medical product safety surveillance studies often do not systematically include death or cause-of-death information. OBJECTIVE: This study aims to develop reusable, generalizable methods for linking multiple health plan databases with the Centers for Disease Control and Prevention's National Death Index Plus (NDI+) data. METHODS: We will develop efficient administrative workflows to facilitate multicenter institutional review board (IRB) review and approval within a distributed network of 6 health plans. The study will create a distributed NDI+ linkage process that avoids sharing of identifiable patient information between health plans or with a central coordinating center. We will develop standardized criteria for selecting and retaining NDI+ matches and methods for harmonizing linked information across multiple health plans. We will test our processes within a use case comprising users and nonusers of antiarrhythmic medications. RESULTS: We will use the linked health plan and NDI+ data sets to estimate the incidences and incidence rates of mortality and specific causes of death within the study use case and compare the results with reported estimates. These comparisons provide an opportunity to assess the performance of the developed NDI+ linkage approach and lessons for future studies requiring NDI+ linkage in distributed database settings. This study is approved by the IRB at Harvard Pilgrim Health Care in Boston, MA. Results will be presented to the FDA at academic conferences and published in peer-reviewed journals. CONCLUSIONS: This study will develop and test a reusable distributed NDI+ linkage approach with the goal of providing tested NDI+ linkage methods for use in future studies within distributed data networks. Having standardized and reusable methods for systematically obtaining death and cause-of-death information from NDI+ would enhance the FDA's ability to assess mortality-related safety questions in the postmarket, real-world setting. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21811.

Antipsychotic Use and Stroke: A Retrospective Comparative Study in a Non-Elderly Population.

OBJECTIVE: To evaluate stroke risk among users of typical antipsychotics compared to users of atypical antipsychotics in a non-elderly and non-demented US population. METHODS: New users of antipsychotics aged 18-64 years without dementia were identified via electronic health care data from 13 health plans participating in the Sentinel System from January 2001 to September 2015. The risk of hospitalized stroke events, identified via ICD-9-CM diagnostic criteria, was compared between typical and atypical antipsychotic users using 1:1 matching on propensity score. Adjusted hazard ratios (HRs) and 95% CIs during the entire follow-up period and during 1- to 15-day and 16- to 90-day risk windows were estimated. The risk associated with haloperidol use was estimated separately. RESULTS: A total of 45,495 typical antipsychotic users were matched 1:1 to atypical antipsychotic users. While unmatched HRs suggest an increased stroke risk among typical antipsychotic users compared to atypical antipsychotic users, no increased risk was observed after matching during the entire follow-up period (HR = 0.87; 95% CI, 0.54-1.41), the 1- to 15-day risk window (HR = 1.16; 95% CI, 0.41-3.32), or the 16- to 90-day risk window (HR = 0.52; 95% CI, 0.20-1.36). The adjusted HR for haloperidol was 1.31 (95% CI, 0.54-3.21). CONCLUSION: These findings were not suggestive of an increased stroke risk in typical antipsychotic users compared to atypical antipsychotic users in a non-elderly and non-demented population.

A systematic review of validated suicide outcome classification in observational studies.

BACKGROUND: Suicidal outcomes, including ideation, attempt, and completed suicide, are an important drug safety issue, though few epidemiological studies address the accuracy of suicidal outcome ascertainment. Our primary objective was to evaluate validated methods for suicidal outcome classification in electronic health care database studies. METHODS: We performed a systematic review of PubMed and EMBASE to identify studies that validated methods for suicidal outcome classification published 1 January 1990 to 15 March 2016. Abstracts and full texts were screened by two reviewers using prespecified criteria. Sensitivity, specificity, and predictive value for suicidal outcomes were extracted by two reviewers. Methods followed PRISMA-P guidelines, PROSPERO Protocol: 2016: CRD42016042794. RESULTS: We identified 2202 citations, of which 34 validated the accuracy of measuring suicidal outcomes using International Classification of Diseases (ICD) codes or algorithms, chart review or vital records. ICD E-codes (E950-9) for suicide attempt had 2-19% sensitivity, and 83-100% positive predictive value (PPV). ICD algorithms that included events with 'uncertain' intent had 4-70% PPV. The three best-performing algorithms had 74-92% PPV, with improved sensitivity compared with E-codes. Read code algorithms had 14-68% sensitivity and 0-56% PPV. Studies estimated 19-80% sensitivity for chart review, and 41-97% sensitivity and 100% PPV for vital records. CONCLUSIONS: Pharmacoepidemiological studies measuring suicidal outcomes often use methodologies with poor sensitivity or predictive value or both, which may result in underestimation of associations between drugs and suicidal behaviour. Studies should validate outcomes or use a previously validated algorithm with high PPV and acceptable sensitivity in an appropriate population and data source.

Overall and cause-specific mortality in the Sentinel system: A power analysis.

PURPOSE: Mortality data within the Sentinel Death Tables remain generally uncharacterized. Assessment of mortality data within Sentinel will help inform its utility for medical product safety studies. METHODS: To determine if Sentinel contains sufficient all-cause and cause-specific mortality events to power postmarketing safety studies. We calculated crude rates of all-cause mortality and suicide and proportional mortality from suicide from 2004 to 2012 in seven Sentinel data partners. Results were stratified by data partner, sex, age group, and calendar year and compared with national estimates from Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research. We performed sample size estimations for all-cause mortality and 10 leading causes of death. RESULTS: We observed 479 694 deaths, including 5811 suicides, during 68 million person-years of follow-up. Pooled mean death and suicide rates in the data partners were 710 and 8.6 per 100 000 person-years, respectively (vs 810 and 11.8 nationally). The mean proportional mortality from suicide among the data partners was 1.2%, compared with 1.5% nationally. National trends of decreasing overall mortality and increasing proportional mortality for suicide were reflected within Sentinel. We estimated that detecting hazard ratios of 1.25 and 3 would require 16 442 and 460 exposed patients, respectively, for overall mortality, and 1.3 million and 37 411, respectively, for suicide. CONCLUSIONS: This was the first study to investigate mortality data in the Sentinel death tables. We found that all-cause mortality appeared well powered for use as a safety outcome and cause-specific mortality outcomes may be adequately powered in certain circumstances. Further investigation into the quality of the Sentinel death data is needed.