Pharmacoepidemiological Database Research Articles

Establishment and representativeness of the stockholm sodium cohort: a laboratorial and pharmacoepidemiologic database covering 1.6 million individuals in the stockholm county

Establishment and representativeness of the stockholm sodium cohort: a laboratorial and pharmacoepidemiologic database covering 1.6 million individuals in the stockholm county

Establishment and representativeness of the Stockholm Sodium Cohort: A laboratorial and pharmacoepidemiologic database covering 1.6 million individuals in the Stockholm County

ObjectiveHyponatremia is associated with considerable morbidity and mortality, but causal links have been difficult to establish. Here, we describe the establishment and representativeness of the Stockholm Sodium Cohort (SSC), designed to study etiologies and outcomes of hyponatremia. Study Design and SettingAll residents of Stockholm County undertaking at least one serum sodium test between 2005–2018 were included in the SSC. Individual-level test results from over 100 laboratory parameters relevant to hyponatremia were collected and linked to data on demographics, socioeconomic status, healthcare contacts, diagnoses and dispensed prescription medications using national registers. ResultsA total of 1,632,249 individuals, corresponding to 64% of the population of Stockholm County, were included in the SSC. Coverage increased with advancing age, ranging from 32% in children and adolescents (≤18 years) to 97% among the oldest (≥80 years). The coverage of SSC included the vast majority of patients in Stockholm County diagnosed with diabetes mellitus (93%), myocardial infarction (98%), ischemic stroke (97%), cancer (85%), pneumonias requiring inpatient care (95%) and deaths (88%). ConclusionSSC is the first cohort specifically designed to investigate sodium levels in a large, population-based setting. It includes a wide range of administrative health data and laboratory analyses. The coverage is high, particularly among elderly and individuals with comorbidities. Consequently, the cohort has a large potential for exploration of various aspects of hyponatremia.

Recording type 2 diabetes mellitus in a standardised central Saudi database: a retrospective validation study

ObjectivesThis study was conducted to assess the validity of recording (and the original diagnostic practice) of type 2 diabetes mellitus at a hospital whose records were integrated to a centralised...

Association Between Folic Acid Prescription Fills and Suicide Attempts and Intentional Self-harm Among Privately Insured US Adults

Suicide is a leading cause of death in the United States, having increased more than 30% from 2000 to 2018. An inexpensive, safe, widely available treatment for preventing suicidal behavior could reverse this trend. To confirm a previous signal for decreased risk of suicide attempt following prescription fills for folic acid in a national pharmacoepidemiologic study of patients treated with folic acid. A within-person exposure-only cohort design was used to study the dynamic association between folic acid (vitamin B9) prescription fills over a 24-month period and suicide attempts and intentional self-harm. Data were collected from a pharmacoepidemiologic database of US medical claims (MarketScan) for patients with private health insurance who filled a folic acid prescription between 2012 and 2017. The same analysis was repeated with a control supplement (cyanocobalamin, vitamin B12). Data were analyzed from August 2021 to June 2022. Folic acid prescription fills. Suicide attempt or intentional self-harm resulting in an outpatient visit or inpatient admission as identified by codes from the International Statistical Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification. Data on 866 586 patients were collected; 704 514 (81.30%) were female, and 90 296 (10.42%) were 60 years and older. Overall, there were 261 suicidal events during months covered by a folic acid prescription (5 521 597 person-months) for a rate of 4.73 per 100 000 person-months, compared with 895 suicidal events during months without folic acid (8 432 340) for a rate of 10.61 per 100 000 person-months. Adjusting for age and sex, diagnoses related to suicidal behavior, diagnoses related to folic acid deficiency, folate-reducing medications, history of folate-reducing medications, and history of suicidal events, the hazard ratio (HR) for folic acid for suicide events was 0.56 (95% CI, 0.48-0.65), with similar results for the modal dosage of 1 mg of folic acid per day (HR, 0.57; 95% CI, 0.48-0.69) and women of childbearing age (HR, 0.60; 95% CI, 0.50-0.73). A duration-response analysis (1-mg dosage) revealed a 5% decrease in suicidal events per month of additional treatment (HR, 0.95; 95% CI, 0.93-0.97). The same analysis for the negative control, cyanocobalamin, found no association with suicide attempt (HR, 1.01; 95% CI, 0.80-1.27). This large-scale pharmacoepidemiologic study of folic acid found a beneficial association in terms of lower rates of suicide attempts. The results warrant the conduct of a randomized clinical trial with suicidal ideation and behavior as outcomes of interest. If confirmed, folic acid may be a safe, inexpensive, and widely available treatment for suicidal ideation and behavior.

Collection of Data on Adverse Events Related to Medicinal Products: A Survey Among Registries in the ENCePP Resources Database.

IntroductionAs patient registries are not subject to regulatory requirements on the collection of adverse events (AEs) related to medicinal products, they may not have foreseen the collection of such information on a routine basis or as part of specific data collection schemes.ObjectiveThe European Medicines Agency conducted a survey among registries to better understand their approach towards the collection, management and reporting of AEs related to medicines.MethodAn online survey composed of 15 questions was distributed in May 2020 to registries listed in the European Network of Centres in Pharmacoepidemiology and Pharmacovigilance (ENCePP) resources database for completion by August 2020. Aggregated results are presented in this paper.ResultsOne third of the registries completed the survey (31/85; 36.5%). Most of the respondents routinely collect information on medicines (29/31; 93.5%), out of which 65.5% (19/29) also collect data on AEs and adverse drug reactions (ADRs). Frequencies and timelines for collecting and reporting AEs/ADRs vary widely across registries, as does their level of experience in providing data to third parties for regulatory purposes.ConclusionsThe low response rate may indicate little interest in this topic or that registries were not originally developed for routine data collection on AEs/ADRs and, ultimately, monitoring of the safety of medicines. Results indicate that clear guidance on the collection and use of real-world data in regulatory frameworks and strengthened collaboration between registry holders, academia, regulators and medicines developers are needed to achieve comprehensive and high levels of quality of safety data captured by registries to support regulatory decision making. These will hopefully be enabled by the European Medicines Regulatory Network strategy to 2025.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40264-022-01188-x.

Arterial blood gas analysis: as safe as we think? A multicentre historical cohort study.

PurposeArterial punctures (APs) for arterial blood gas (ABG) analyses are much-used medical procedures. To date, no large studies have been conducted on the major complication rate of APs. We aimed to describe the risk of major complications within 7 days after puncture and investigate whether using antithrombotic medication affected this.MethodsWe included all APs performed for ABG analysis at three Danish hospitals from January 1, 1993 to February 25, 2013. We excluded APs ordered by the anaesthesiology department, intensive care unit (ICU) or in patients <18 years old. Data on the patient level were extracted from the Danish National Patient Registry, Danish Civil Registration System and Odense Pharmaco-Epidemiologic Database (OPED), the latter providing us with information on antithrombotic medication. Initially, two clinicians compiled a list with all procedures and diagnoses that could possibly be a consequence of APs. The selected procedures and diagnoses were further categorised independently by three surgeons and used to indicate the complication rate.ResultsWe analysed 473 327 APs and found 669 (0.14%, 95% CI 0.13–0.15) APs led to major complications: embolisms or thrombosis (49.0%), aneurysms (15.4%), nerve damage (1.5%), arteriovenous fistulas (0.6%) or of another kind (33.5%). The identified major complication rates in patients on antithrombotic medication were increased (OR 1.31, 95% CI 1.07–1.61).ConclusionAPs for ABG analyses are safe procedures. The major complication rate within 7 days was 0.14% (95% CI 0.13–0.15). Patients on antithrombotic medication carry an increased risk of developing major complications.

A new likelihood model for analyses of pharmacoepidemiologic case\u2013control studies which avoids decision rules for determining latent exposure status

BackgroundCase–control studies based on pharmaco-epidemiological databases typically use decision rules to determine exposure status from information on dates of prescription redemptions, although this induces misclassification. The reverse Waiting Time Distribution has been suggested as a likelihood based model to estimate the latent exposure status, and we therefore suggest to extend this into a joint likelihood based model, which incorporates both the latent exposure status and the exposure-outcome association. This will achieve consistency and efficiency of the estimates, i.e. they can be expected to be asymptotically unbiased and have optimal precision.MethodsWe established a joint likelihood for the observed case–control status and last prescription redemption before the index date. The likelihood combines the ordinary logistic regression likelihood and the reverse Waiting Time Distribution, and allows inclusion of covariates in both parts to adjust for observed confounders. We conducted a simulation study of the new model and standard models based on decision rules for exposure and the probability of being exposed, respectively, to assess the relative bias and variability of estimates. Lastly, we applied the models to a case–control study on use of nonsteroidal anti-inflammatory drugs and risk of upper-gastrointestinal bleeding.ResultsIn simulation studies the new model had low relative bias (< 1.4%) and largely retained nominal coverage probabilities (90.2% to 95.1% of nominal 95% confidence intervals), also when moderate misspecification was introduced. All standard methods generally had substantial bias (-21.1% to 17.0%) and low coverage probabilities (0.0% to 68.9%). When analyzing the empirical case–control study, the new method estimated the effect of nonsteroidal anti-inflammatory drugs on risk of with upper-gastrointestinal bleeding hospitalization to 2.52 (1.59 – 3.45), whereas the other methods had estimates ranging from 3.52 (2.19 – 5.65) to 5.17 (2.40 – 11.11).ConclusionsUnlike standard methods, the proposed model gave nearly unbiased estimates with adequate coverage probabilities in simulation studies. The developed model demonstrates the potential for the reverse Waiting Time Distribution to be integrated with existing likelihood based analyses in pharmacoepidemiological studies.

Heterogeneity of the pharmacologic treatment of allergic rhinitis in Europe based on MIDAS and OTCims platforms.

The practice of allergology varies widely between countries, and the costs and sales for the treatment of rhinitis differ depending on practices and health systems. To understand these differences and their implications, the rhinitis market was studied in some of the EU countries. We conducted a pharmaco-epidemiological database analysis to assess the medications that were being prescribed for allergic rhinitis in the years 2016, 2017 and 2018. We used the IQVIA platforms for prescribed medicines (MIDAS®- Meaningful Integration of Data, Analytics and Services) and for OTC medicines (OTC International Market Tracking-OTCims). We selected the five most important markets in the EU (France, Germany, Italy, Poland and Spain). Intranasal decongestants were excluded from the analyses because they are rarely prescribed for allergic rhinitis. For both Standard Units (SU) and costs, France is leading the other countries. In terms of SU, the four other countries are similar. For costs, Poland is lower than the three others. However, medication use differs largely. For 2018, in SU, intranasal corticosteroid is the first treatment in Poland (70.0%), France (51.3%), Spain (51.1%) and Germany (50.3%), whereas the Italian market is dominated by systemic antihistamines (41.4%) followed by intranasal corticosteroids (30.1%). Results of other years were similar. There are major differences between countries in terms of rhinoconjunctivitis medication usage.

Outcome misclassification: Impact, usual practice in pharmacoepidemiology database studies and an online aid to correct biased estimates of risk ratio or cumulative incidence.

It is well documented that outcome misclassification can bias a point estimate. We aimed to understand current practice in addressing this bias in pharmacoepidemiology database studies and to develop an open source application (app) from existing methodology to demonstrate the impact and mechanism of this bias on results. Studies of an exposure and a clinical outcome were selected from all Pharmacoepidemiology and Drug Safety publications during 2017 and any reference to outcome misclassification described. An app to correct risk ratio (RR) and cumulative incidence for outcome misclassification was developed from a published methodology and used to demonstrate the impact of correction on point estimates. Eight (19%) of 43 papers selected reported estimates of outcome ascertainment accuracy with positive predictive value (PPV) the most commonly reported measure (7 of 8 studies). Three studies (7%) corrected for the bias, 1 by exposure strata, and 5 (12%) restricted analyses to confirmed cases. The app (app http://apps.p-95.com/ISPE/) uses values of PPV and sensitivity (or a range of possible values) in each exposure strata and returns corrected point estimates and confidence intervals. The app demonstrates that small differences between comparison groups in PPV or sensitivity can introduce bias even when accuracy estimates are high. Outcome misclassification is not usually corrected in pharmacoepidemiology database studies although correction methods using routinely measured indices are available. Error indices are needed for each comparison group to correct RR estimates for these errors. The app should encourage understanding of this bias and increase adjustment.

Real-world data in Saudi Arabia: Current situation and challenges for regulatory decision-making.

To present the process of establishing a pharmacoepidemiological database in Saudi Arabia, challenges and models used. The database establishment has started in 2017 by piloting the conversion of electronic health records of one hospital to the Observational Health Data Sciences and Informatics (OHDSI), Observational Medical Outcomes Partnership's Common Data Model (OMOP). During the pilot phase we have faced several challenges such as limited contribution in providing data by local medical institution due to uncertainty about data governance, diversity of systems used by hospitals, inconsistent coding of medical information, and limited awareness about data structure from participating hospital. The pilot phase was completed in 2019 containing information about patient attributes, medical care, therapies, and other additional services for around 130 000 patients in Saudi Arabia. The majority of patients were below the age of 50 years (89%), and acute respiratory infections were the most frequent diagnosis. The data quality was acceptable and no major anomalies were detected during the conversion. We demonstrated a successful creation of a pilot database using OHDSI Common Data Model. Our experience with the pilot database could be extended to other institutions to create a national dataset that could be used to generate real-world evidence.

Electronic healthcare databases in Europe: descriptive analysis of characteristics and potential for use in medicines regulation

ObjectiveElectronic healthcare databases (EHDs) are useful tools for drug development and safety evaluation but their heterogeneity of structure, validity and access across Europe complicates the conduct of multidatabase studies. In...

Introduction of national guidelines for restrictive blood transfusion threshold for hip fracture patients\u2013\u2013a consecutive cohort study based on complete follow-up in national databases

BackgroundRandomized controlled trials have demonstrated that a restrictive red blood cell (RBC) transfusion strategy lowers transfusion frequency without affecting mortality. However, the external validity of these trials has not been tested in a large cohort. The purpose was to estimate the effect of introducing a National Clinical Guideline (NCG) for a restrictive hemoglobin transfusion threshold on transfusion frequency and mortality in hip fracture patients > 65 years old.MethodsA consecutive cohort study of hip fracture patients > 65 years old residing in the southern region of Denmark was conducted using prospectively gathered data from registers during two separate 1-year time periods. The first period from October 1, 2012, to September 30, 2013, included 1494 patients and used a liberal transfusion threshold, whereas the second period from October 1, 2015, to September 30, 2016, including 1414 participants used a restrictive threshold from the NCG. Participant data for age, sex, body mass index, Charlson Comorbidity Index, time to surgery, and death were retrieved from the Danish Interdisciplinary Registry of Hip Fractures and were merged with RBC transfusion and medication data extracted from the Danish Transfusion and Odense Pharmacoepidemiological Databases, respectively. Cox proportional hazards models were used to test relative mortality risk for the restrictive group compared with the liberal group at 30 and 90 days.ResultsOverall RBC transfusions decreased from 42 to 30% (p < 0.001). The 30-day mortality rate (95% CI) was 9% (8;11) in the restrictive group and 13% (11;14) in the liberal group (p < 0.008), whereas the adjusted relative mortality risk was 0.72 (0.57;0.91). The 90-day mortality rate was 15% (13;17) in the restrictive group and 19% (17;21) in the liberal group, whereas the adjusted relative mortality risk was 0.78 (0.65;0.94).ConclusionThese data suggest that the introduction of an NCG on restrictive blood transfusion leads to lower transfusion frequency in hip fracture patients > 65 years old. Even though this reduction is associated with decreased mortality at both 30 and 90 days, it may be explained by other issues than restrictive transfusion strategy. There has been an improvement in the mortality of hip fracture patients in Denmark, and we suggest that a restrictive transfusion strategy does not lead to increased mortality.

A Systematic Review of Coding Systems Used in Pharmacoepidemiology and Database Research

Clinical coding systems have been developed to translate real-world healthcare information such as prescriptions, diagnoses and procedures into standardized codes appropriate for use in large healthcare datasets. Due to the lack of information on coding system characteristics and insufficient uniformity in coding practices, there is a growing need for better understanding of coding systems and their use in pharmacoepidemiology and observational real world data research. To determine: 1) the number of available coding systems and their characteristics, 2) which pharmacoepidemiology databases are they adopted in, 3) what outcomes and exposures can be identified from each coding system, and 4) how robust they are with respect to consistency and validity in pharmacoepidemiology and observational database studies. Electronic literature database and unpublished literature searches, as well as hand searching of relevant journals were conducted to identify eligible articles discussing characteristics and applications of coding systems in use and published in the English language between 1986 and 2016. Characteristics considered included type of information captured by codes, clinical setting(s) of use, adoption by a pharmacoepidemiology database, region, and available mappings. Applications articles describing the use and validity of specific codes, code lists, or algorithms were also included. Data extraction was performed independently by two reviewers and a narrative synthesis was performed. A total of 897 unique articles and 57 coding systems were identified, 17% of which included country-specific modifications or multiple versions. Procedures (55%), diagnoses (36%), drugs (38%), and site of disease (39%) were most commonly and directly captured by these coding systems. The systems were used to capture information from the following clinical settings: inpatient (63%), ambulatory (55%), emergency department (ED, 34%), and pharmacy (13%). More than half of all coding systems were used in Europe (59%) and North America (57%). 34% of the reviewed coding systems were utilized in at least 1 of the 16 pharmacoepidemiology databases of interest evaluated. 21% of coding systems had studies evaluating the validity and consistency of their use in research within pharmacoepidemiology databases of interest. The most prevalent validation method was comparison with a review of patient charts, case notes or medical records (64% of reviewed validation studies). The reported performance measures in the reviewed studies varied across a large range of values (PPV 0-100%, NPV 6-100%, sensitivity 0-100%, specificity 23-100% and accuracy 16-100%) and were dependent on many factors including coding system(s), therapeutic area, pharmacoepidemiology database, and outcome. Coding systems vary by type of information captured, clinical setting, and pharmacoepidemiology database and region of use. Of the 57 reviewed coding systems, few are routinely and widely applied in pharmacoepidemiology database research. Indication and outcome dependent heterogeneity in coding system performance suggest that accurate definitions and algorithms for capturing specific exposures and outcomes within large healthcare datasets should be developed on a case-by-case basis and in consultation with clinical experts.

Enrichment of Claims Data with Official Causes of Death Using a Record Linkage with the Epidemiological Cancer Registry of North Rhine-Westphalia: Feasibility Study and Comparison of Procedures

Claims data of the statutory health insurance (SHI) are an important data source for the evaluation of cancer prevention programs. However, this source does not contain relevant information on cause of death. This study examined whether individual claims data can be enriched with data on the required cause of death using record linkage procedures with suitable external data sources. In the German pharmacoepidemiologic research database (GePaRD) we identified a sample of 25,528 deceased female residents of North Rhine Westphalia (NRW) who, according to GePaRD information, died between 2006 and 2013. Date and cause of all deaths among inhabitants of NRW since 2005 were available in the epidemiological cancer registry of NRW. In cooperation with 2 SHI companies, we tried to match each individual of the sample with a case of death in NRW and the corresponding cause of death using a probabilistic and, alternatively, a deterministic linkage procedure. Of the study sample, 94.72% were successfully matched by the probabilistic and 93.36% by the deterministic method. The probabilistic and the deterministic record linkage approach produced comparably high matching rates. Cases without matches are probably due to errors occurring at the stage of personal data entry. Given the lower technical efforts, the deterministic approach appears to be the method of choice for the enrichment of claims data with cause of death information from suitable external data sources in Germany.

A Pharmacoepidemiology Database System for Monitoring Risk Due to the Use of Medicines by New Zealand Primary Care Patients.

The use of large record-linked healthcare databases for drug safety research and surveillance is now accepted practice. New Zealand's standardized national healthcare datasets provide the potential to automate the conduct of pharmacoepidemiological studies to provide rapid validation of medicine safety signals. Our objectives were to describe the methodology undertaken by a semi-automated computer system developed to rapidly assess risk due to drug exposure in New Zealand's population of primary care patients and to compare results from three studies with previously published findings. Data from three national databases were linked at the patient level in the automated studies. A retrospective nested case-control design was used to evaluate risk for upper gastrointestinal bleeding (UGIB), acute kidney failure (AKF), and serious arrhythmia associated with individual medicines, therapeutic classes of medicines, and concurrent use of medicines from multiple therapeutic classes. The patient cohort available for each study included 5,194,256 patients registered between 2007 and 2014, with a total of 34,630,673 patient-years at risk. An increased risk for UGIB was associated with non-steroidal anti-inflammatory drugs (NSAIDs) (adjusted odds ratio [AOR] 4.16, 95% confidence interval [CI] 3.90-4.43, p<0.001) and selective serotonin reuptake inhibitors (AOR 1.39, 95% CI 1.20-1.62, p<0.001); an increased risk for AKF was associated with NSAIDs (AOR 1.78, 95% CI 1.73-1.83, p<0.001) and proton pump inhibitors (AOR 1.78, 95% CI 1.72-1.83, p<0.001); and an increased risk for serious arrhythmia was associated with fluoroquinolones (AOR 1.38, 95% CI 1.26-151, p<0.001) and penicillins (AOR 1.69, 95% CI 1.61-1.77, p<0.001). Automated case-control studies using New Zealand's healthcare datasets can replicate associations of risk with drug exposure consistent with previous findings. Their speed of conduct enables systematic monitoring of risk for adverse events associated with a wide range of medicines.

Estimating medication stopping fraction and real-time prevalence of drug use in pharmaco-epidemiologic databases. An application of the reverse waiting time distribution.

To introduce the reverse waiting time distribution (WTD) and show how it can be used to estimate stopping fractions and real-time prevalence of treatment in pharmacoepidemiological studies. The reverse WTD is the distribution of time from the last dispensed prescription of each patient within a time window to the end of it. It is a mirrored version of the ordinary WTD, which considers the first dispensed prescription of patients within a time window. Based on renewal process theory, the reverse WTD can be analyzed as an ordinary WTD with maximum likelihood estimation. Based on Danish prescription data for NSAIDs, warfarin, bendroflumethiazide and levothyroxine in the years 2013 and 2014, we compared estimates from the reverse WTD to those of the ordinary WTD regarding prevalence, stopping fractions and the 80th percentiles of the inter-arrival distributions. The fraction of all users in 2013 stopping treatment varied from 73.1% (NSAID) to 9.3% (levothyroxine). Comparing prevalence estimates of the reverse WTD at the end of 2013 with those of the ordinary WTD at the start of 2014, relative differences did not exceed 4.8%. For the estimated 80th percentiles of the inter-arrival distribution, differences did not exceed 3.3%. The reverse WTD allows estimation of the aggregated fraction of users stopping treatment and prevalence, especially when the WTD reliably separates current users from users who have stopped treatment. It may replace ad-hoc decision rules for automated implementations, and it yields estimates of real-time prevalence. Copyright © 2017 John Wiley & Sons, Ltd.

Refining estimates of prescription durations by using observed covariates in pharmacoepidemiological databases: an application of the reverse waiting time distribution.

The study aimed to develop an automated method to estimate prescription durations in pharmacoepidemiological studies that may depend on patient and redemption characteristics. We developed an estimation algorithm based on maximum likelihood estimation for the reverse waiting time distribution (WTD), which is the distribution of time from the last prescription of each patient within a time window to the end of the time window. The reverse WTD consists of two distinctly different components: one component for prevalent users and one for patients stopping treatment. We extended the model to allow parameters of the reverse WTD to depend on linear combinations of covariates to obtain estimates and confidence intervals for percentiles of the inter-arrival density (time from one prescription to the subsequent). We applied the method to redemptions of warfarin, using the amount of drug filled, patient sex and patient age as covariates. The estimated prescription durations increased with redeemed amount and age. Women generally had longer prescription durations, which increased more with age than men. For 70-year-old women redeeming 300+ pills, we predicted a 95th percentile of the inter-arrival density of 225 (95%CI: 201, 249)days. For 50-year-old men redeeming 100 pills, the corresponding prediction was 97 (88, 106)days. The algorithm allows estimation of prescription durations based on the reverse WTD, which can depend upon observed covariates. Statistical uncertainty intervals and tests allow statistical inference on the influence of observed patient and prescription characteristics. The method may replace ad hoc decision rules. Copyright © 2017 John Wiley & Sons, Ltd.

Extent, Time Course, and Moderators of Antipsychotic Treatment in Youth With Mood Disorders: Results of a Meta-Analysis and Meta-Regression Analyses.

To meta-analytically examine the trends and correlates of antipsychotic use in youth with mood disorders. Systematic literature search without language restriction in PubMed/MEDLINE/PsycINFO from database inception through March 2015 using the following search terms: (antipsychotic* OR neuroleptic* OR "dopamine blocker*" OR antidopaminergic) AND (child* OR adolescen* OR pediatric OR youth) AND (prescription* OR prescrib* OR use OR utilization OR database OR pharmacoepidemiolog* OR frequency OR rate OR rates). Random effects meta-analysis and meta-regression analyses were conducted. Included were studies reporting on the frequency of (1) mood disorders in antipsychotic-treated youth (≤ 19 years) and (2) antipsychotic use in youth with mood disorders. Two independent investigators abstracted data on study, patient, and treatment characteristics. Forty-one studies were meta-analyzed (N = 518,919, mean ± SD age = 12.8 ± 1.8 years, males = 65.7%). Altogether, 24.2% of antipsychotic-treated youth had a mood disorder diagnosis (studies = 34, depression spectrum disorder = 10.9%, bipolar spectrum disorder = 13.6%). In longitudinal studies, the overall proportion increased significantly from 17.3% in 2000 (range, 1996-2009) to 24.5% in 2006 (range, 2004-2011) (odds ratio [OR] = 1.50; 95% confidence interval [CI], 1.26-1.79; P < .0001). This increase was driven entirely by bipolar spectrum diagnoses (2001 = 11.1%, 2006 = 16.3%, P < .0001), rather than depression spectrum diagnoses (2001 = 9.1%, 2007 = 9.2%, P = .77). Among youth with mood disorders (8 studies), 24.0% received antipsychotics (depression spectrum disorder = 4.6%; bipolar spectrum disorder = 44.0%). The proportion of youth with mood disorder diagnoses increased significantly among antipsychotic-treated youth, driven entirely by an increase in youth with bipolar spectrum disorders. Progress in understanding the reasons for these trends and for an evaluation of the appropriateness of the observed antipsychotic prescribing requires more detailed information than is available in traditional pharmacoepidemiologic databases.

Odense Pharmacoepidemiological Database: A Review of Use and Content.

The Odense University Pharmacoepidemiological Database (OPED) is a prescription database established in 1990 by the University of Southern Denmark, covering reimbursed prescriptions from the county of Funen in Denmark and the region of Southern Denmark (1.2 million inhabitants). It is still active and thereby has more than 25 years of continuous coverage. In this MiniReview, we review its history, content, quality, coverage, governance and some of its uses. OPED's data include the Danish Civil Registration Number (CPR), which enables unambiguous linkage with virtually all other health-related registers in Denmark. Among its research uses, we review record linkage studies of drug effects, advanced drug utilization studies, some examples of method development and use of OPED as sampling frame to recruit patients for field studies or clinical trials. With the advent of other, more comprehensive sources of prescription data in Denmark, OPED may still play a role as in certain data-intensive regional studies.

Conducting Privacy-Preserving Multivariable Propensity Score Analysis When Patient Covariate Information Is Stored in Separate Locations.

Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemiologic database studies. Such networks may contain data that are not physically pooled but instead are distributed horizontally (separate patients within each data source) or vertically (separate measures within each data source) in order to preserve patient privacy. While multivariable methods for the analysis of horizontally distributed data are frequently employed, few practical approaches have been put forth to deal with vertically distributed health-care databases. In this paper, we propose 2 propensity score-based approaches to vertically distributed data analysis and test their performance using 5 example studies. We found that these approaches produced point estimates close to what could be achieved without partitioning. We further found a performance benefit (i.e., lower mean squared error) for sequentially passing a propensity score through each data domain (called the "sequential approach") as compared with fitting separate domain-specific propensity scores (called the "parallel approach"). These results were validated in a small simulation study. This proof-of-concept study suggests a new multivariable analysis approach to vertically distributed health-care databases that is practical, preserves patient privacy, and warrants further investigation for use in clinical research applications that rely on health-care databases.