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1.  The Prevalence and Trends of Antiviral Medication Use during Pregnancy in the U.S. A population-based study of 664,297 deliveries in 2001-2007 
Maternal and child health journal  2014;18(1):10.1007/s10995-013-1234-9.
To evaluate the prevalence, trends, timing and duration of exposure to antiviral medications during pregnancy within a US cohort of pregnant women and to evaluate the proportion of deliveries with a viral infection diagnosis among women given antiviral medication during pregnancy.
Live-born deliveries between 2001 and 2007, to women aged 15 to 45 years, were included from the Medication Exposure in Pregnancy Risk Evaluation Program (MEPREP), a collaborative research program between the U.S. Food and Drug Administration and eleven health plans. They were evaluated for prevalence, timing, duration, and temporal trends of exposure to antiviral medications during pregnancy. We also calculated the proportion of deliveries with a viral infection diagnosis among those exposed to antiviral medications.
Among 664,297 live births, the overall prevalence of antiviral exposure during pregnancy was 4% (n=25,155). Between 2001 and 2007, antiviral medication exposure during pregnancy doubled from 2.5% to 5%. The most commonly used antiviral medication was acyclovir, with 3% of the deliveries being exposed and most of the exposure occurring after the 1st trimester. Most deliveries exposed to antiviral medications were exposed for less than 30 days (2% of all live births). Forty percent of the women delivering an infant exposed to antiviral medications had a herpes diagnosis.
Our findings highlight the increased prevalence of women delivering an infant exposed to antiviral medications over time. These findings support the need for large, well-designed studies to assess the safety and effectiveness of these medications during pregnancy.
PMCID: PMC3776000  PMID: 23420306
2.  Prescription Medication Burden in Patients with Newly-Diagnosed Diabetes: A SUrveillance, PREvention, and ManagEment of Diabetes Mellitus (SUPREME-DM) Study 
To understand the burden of medication use for newly-diagnosed diabetes patients both before and after diabetes diagnosis, and to identify subpopulations of newly-diagnosed diabetes patients who face a relatively high drug burden.
Retrospective cohort.
Eleven U.S. integrated health systems.
196,654 insured adults aged ≥20 diagnosed with newly-diagnosed diabetes from 1/1/2005 – 12/31/2009.
Main Outcome Measure
Number of unique therapeutic classes of drugs dispensed in the 12 months prior to, and 12 months post, the diagnosis of diabetes in 5 categories: overall, antihypertensive, antihyperlipidemic, mental health, and antihyperglycemic (post-period only).
The mean number of drug classes used by newly-diagnosed diabetes patients is high before diagnosis (5.0), and increases significantly afterwards (6.6, p<.001). Eighty-one percent of this increase is due to antihyperglycemic initiation and increased use of medications to control hypertension and lipid levels. Multivariate analyses showed that overall drug burden after diabetes diagnosis was higher in female, older, white, and obese patients, as well as among those with higher A1cs and comorbidity levels (p<.001 for all comparisons). The overall number of drug classes used by newly-diagnosed diabetes patients after diagnosis decreased slightly between 2005 and 2009 (p<.001).
Diabetes patients face significant drug burden to control diabetes and other comorbidities, and our data indicate an increased focus on cardiovascular disease risk factor control after diabetes diagnosis. However, total drug burden may be slightly decreasing over time. This information can be valuable to pharmacists working with newly-diagnosed diabetes patients to address their increasing drug regimen complexity.
PMCID: PMC4161641  PMID: 24860866
diabetes; medication burden; surveillance
3.  Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling 
Journal of clinical epidemiology  2013;66(8 0):S99-S109.
Clinical trials are unlikely to ever be launched for many Comparative Effectiveness Research (CER) questions. Inferences from hypothetical randomized trials may however be emulated with marginal structural modeling (MSM) using observational data but success in adjusting for time-dependent confounding and selection bias typically relies on parametric modeling assumptions. If these assumptions are violated, inferences from MSM may be inaccurate. In this article, we motivate the application of a data-adaptive estimation approach called Super Learning to avoid reliance on arbitrary parametric assumptions in CER.
Study Design and Setting
Using the electronic health records data from adults with new onset type 2 diabetes, we implemented MSM with inverse probability weighting estimation to evaluate the effect of three oral anti-diabetic therapies on the worsening of glomerular filtration rate.
Inferences from IPW estimation were noticeably sensitive to the parametric assumptions about the associations between both the exposure and censoring processes and the main suspected source of confounding, i.e., time-dependent measurements of hemoglobin A1c. Super Learning was successfully implemented to harness flexible confounding and selection bias adjustment from existing machine learning algorithms.
Erroneous IPW inference about clinical effectiveness due to arbitrary and incorrect modeling decisions may be avoided with Super Learning.
PMCID: PMC3713501  PMID: 23849160
super learning; marginal structural model; inverse probability weighting; comparative effectiveness research; time-dependent confounding; selection bias
4.  Standardizing Terminology and Definitions of Medication Adherence and Persistence in Research employing Electronic Databases 
Medical care  2013;51(8 0 3):S11-S21.
To propose a unifying set of definitions for prescription adherence research utilizing electronic health record prescribing databases, prescription dispensing databases, and pharmacy claims databases and to provide a conceptual framework to operationalize these definitions consistently across studies.
We reviewed recent literature to identify definitions in electronic database studies of prescription-filling patterns for chronic oral medications. We then develop a conceptual model and propose standardized terminology and definitions to describe prescription-filling behavior from electronic databases.
The conceptual model we propose defines two separate constructs: medication adherence and persistence. We define primary and secondary adherence as distinct sub-types of adherence. Metrics for estimating secondary adherence are discussed and critiqued, including a newer metric (New Prescription Medication Gap measure) that enables estimation of both primary and secondary adherence.
Terminology currently used in prescription adherence research employing electronic databases lacks consistency. We propose a clear, consistent, broadly applicable conceptual model and terminology for such studies. The model and definitions facilitate research utilizing electronic medication prescribing, dispensing, and/or claims databases and encompasses the entire continuum of prescription-filling behavior.
Employing conceptually clear and consistent terminology to define medication adherence and persistence will facilitate future comparative effectiveness research and meta-analytic studies that utilize electronic prescription and dispensing records.
PMCID: PMC3727405  PMID: 23774515
medication adherence; medication persistence; medication discontinuation; refill compliance; refill persistence; administrative; database; electronic health record; computerized medical record systems
5.  Methods of linking mothers and infants using health plan data for studies of pregnancy outcomes 
Research on medication safety in pregnancy often utilizes health plan and birth certificate records. This study discusses methods used to link mothers with infants, a crucial step in such research.
We describe how 8 sites participating in the Medication Exposure in Pregnancy Risk Evaluation Program created linkages between deliveries, infants and birth certificates for the 2001–2007 birth cohorts. We describe linkage rates across sites and, for two sites, we compare the characteristics of populations linked using different methods.
Of 299,260 deliveries, 256,563 (86%; range by site, 74–99%) could be linked to infants using a deterministic algorithm. At two sites, using birth certificate data to augment mother-infant linkage increased the representation of mothers who were Hispanic or non-white, younger, Medicaid recipients, or had low educational level. A total of 236,460 (92%; range by site, 82–100%) deliveries could be linked to a birth certificate.
Tailored approaches enabled linking most deliveries to infants and to birth certificates, even when data systems differed. The methods used may affect the composition of the population identified. Linkages established with such methods can support sound pharmacoepidemiology studies of maternal drug exposure outside the context of a formal registry.
PMCID: PMC3707923  PMID: 23596095
Birth Certificates; Medicaid; Pregnancy Outcome/epidemiology; Medical Record Linkage
6.  Validation of an Algorithm to Estimate Gestational Age in Electronic Health Plan Databases 
To validate an algorithm that uses delivery date and diagnosis codes to define gestational age at birth in electronic health plan databases.
Using data from 225,384 live born deliveries among women aged 15–45 years in 2001–2007 within 8 of the 11 health plans participating in the Medication Exposure in Pregnancy Risk Evaluation Program, we compared 1) the algorithm-derived gestational age versus the “gold-standard” gestational age obtained from the infant birth certificate files; and 2) the prenatal exposure status of two antidepressants (fluoxetine and sertraline) and two antibiotics (amoxicillin and azithromycin) as determined by the algorithm-derived versus the gold-standard gestational age.
The mean algorithm-derived gestational age at birth was lower than the mean obtained from the birth certificate files among singleton deliveries (267.9 versus 273.5 days) but not among multiple-gestation deliveries (253.9 versus 252.6 days). The algorithm-derived prenatal exposure to the antidepressants had a sensitivity and a positive predictive value (PPV) of ≥95%, and a specificity and a negative predictive value (NPV) of almost 100%. Sensitivity and PPV were both ≥90%, and specificity and NPV were both >99% for the antibiotics.
A gestational age algorithm based upon electronic health plan data correctly classified medication exposure status in most live born deliveries, but misclassification may be higher for drugs typically used for short durations.
PMCID: PMC3644383  PMID: 23335117
algorithm; database; gestational age; maternal exposure; pregnancy; validation studies
7.  Association Between Anti-TNF-α Therapy and Interstitial Lung Disease 
Anti-TNF-α agents have been hypothesized to increase the risk of interstitial lung disease (ILD), including its most severe manifestation, pulmonary fibrosis.
We conducted a cohort study among autoimmune disease patients who were members of Kaiser Permanente Northern California, 1998–2007. We obtained therapies from pharmacy data and diagnoses of ILD from review of X-ray and computed tomography reports. We compared new users of anti-TNF-α agents to new users of non-biologic therapies using Cox proportional hazards analysis to adjust for baseline propensity scores and time-varying use of glucocorticoids. We also made head-to-head comparisons between anti-TNF-α agents.
Among the 8,417 persons included in the analysis, 38 (0.4%) received a diagnostic code for ILD by the end of follow-up, including 23 of 4,200 (0.5%) who used anti-TNF-α during study follow-up, and 15 of 5,423 (0.3%) who used only non-biologic therapies. The age- and gender-standardized incidence rate of ILD, per 100 person-years, was 0.21 (95% CI 0–0.43) for rheumatoid arthritis and appreciably lower for other autoimmune diseases. Compared to use of non-biologic therapies, use of anti-TNF-α therapy was not associated with a diagnosis of ILD among RA patients (adjusted hazard ratio, 1.03; 95% CI 0.51–2.07). Nor did head-to-head comparisons across anti-TNF-α agents suggest important differences in risk, although the number of cases available for analysis was limited.
The study provides evidence that compared to non-biologic therapies anti-TNF-α therapy does not increase the occurrence of ILD among patients with autoimmune diseases, and informs research design of future safety studies of ILD.
PMCID: PMC3618622  PMID: 23359391
Rheumatoid arthritis; psoriatic arthritis; psoriasis; Crohn’s Disease; ulcerative colitis; inflammatory bowel disease; pharmacoepidemiology; drug safety; drug toxicity; adverse events; cohort studies; propensity scores; automated healthcare data; interstitial lung disease; pulmonary fibrosis
8.  Prevalence and trends in the use of antipsychotic medications during pregnancy in the U.S., 2001–2007: A population-based study of 585,615 deliveries 
Archives of women's mental health  2013;16(2):149-157.
To estimate the prevalence of and temporal trends in prenatal antipsychotic medication use within a cohort of pregnant women in the U.S.
We identified live born deliveries to women aged 15–45 years in 2001–2007 from 11 U.S. health plans participating in the Medication Exposure in Pregnancy Risk Evaluation Program (MEPREP). We ascertained prenatal exposure to antipsychotics from health plan pharmacy dispensing files, gestational age from linked infant birth certificate files, and ICD-9-CM diagnosis codes from health plan claims files. We calculated the prevalence of prenatal use of atypical and typical antipsychotics according to year of delivery, trimester of pregnancy, and mental health diagnosis.
Among 585,615 qualifying deliveries, 4,223 (0.72%) were to women who received an atypical antipsychotic and 548 (0.09%) were to women receiving a typical antipsychotic any time from 60 days before pregnancy through delivery. There was a 2.5-fold increase in atypical antipsychotic use during the study period, from 0.33% (95% confidence interval: 0.29%, 0.37%) in 2001 to 0.82% (0.76%, 0.88%) in 2007, while the use of typical antipsychotics remained stable. Depression was the most common mental health diagnosis among deliveries to women with atypical antipsychotic use (63%), followed by bipolar disorder (43%) and schizophrenia (13%).
The number and proportion of pregnancies exposed to atypical antipsychotics has increased dramatically in recent years. Studies are needed to examine the comparative safety and effectiveness of these medications relative to other therapeutic options in pregnancy.
PMCID: PMC3715880  PMID: 23389622
Antipsychotics; database; pregnancy; prevalence
9.  Validity of Health Plan and Birth Certificate Data for Pregnancy Research 
To evaluate the validity of health plan and birth certificate data for pregnancy research.
A retrospective study was conducted using administrative and claims data from 11 U.S. health plans, and corresponding birth certificate data from state health departments. Diagnoses, drug dispensings, and procedure codes were used to identify infant outcomes (cardiac defects, anencephaly, preterm birth, and neonatal intensive care unit [NICU] admission) and maternal diagnoses (asthma and systemic lupus erythematosus [SLE]) recorded in the health plan data for live born deliveries between January 2001 and December 2007. A random sample of medical charts (n = 802) was abstracted for infants and mothers identified with the specified outcomes. Information on newborn, maternal, and paternal characteristics (gestational age at birth, birth weight, previous pregnancies and live births, race/ethnicity) was also abstracted and compared to birth certificate data. Positive predictive values (PPVs) were calculated with documentation in the medical chart serving as the gold standard.
PPVs were 71% for cardiac defects, 37% for anencephaly, 87% for preterm birth, and 92% for NICU admission. PPVs for algorithms to identify maternal diagnoses of asthma and SLE were ≥ 93%. Our findings indicated considerable agreement (PPVs > 90%) between birth certificate and medical record data for measures related to birth weight, gestational age, prior obstetrical history, and race/ethnicity.
Health plan and birth certificate data can be useful to accurately identify some infant outcomes, maternal diagnoses, and newborn, maternal, and paternal characteristics. Other outcomes and variables may require medical record review for validation.
PMCID: PMC3492503  PMID: 22753079
administrative databases; birth certificate; positive predictive value; pregnancy; validation
10.  Prevalence, Trends, and Patterns of Use of Antidiabetic Medications Among Pregnant Women, 2001–2007 
Obstetrics and gynecology  2013;121(1):http://10.1097/AOG.0b013e318278ce86.
To describe the prevalence, trends, and patterns in use of antidiabetic medications to treat hyperglycemia and insulin resistance prior to and during pregnancy in a large U.S. cohort of insured pregnant women.
Pregnancies resulting in livebirths were identified (N=437,950) from 2001–2007 among 372,543 women 12–50 years of age at delivery from 10 health maintenance organizations participating in the Medication Exposure in Pregnancy Risk Evaluation Program. Information for these descriptive analyses, including all antidiabetic medications dispensed during this period, was extracted from electronic health records and infant birth certificates.
Just over one percent (1.21%) of deliveries were to women dispensed antidiabetic medication(s) in the 120 days before pregnancy. Use of antidiabetic medications before pregnancy increased from 0.66% of deliveries in 2001 to 1.66% of deliveries in 2007 (p<0.001) due to a rise in metformin use. Most women using metformin before pregnancy had a diagnosis code for polycystic ovaries or female infertility (67.2%) while only 13.6% had a diagnosis code for diabetes. The use of antidiabetic medications during the second or third trimester of pregnancy increased from 2.8% of deliveries in 2001 to 3.6% in 2007 (p <0.001). Approximately two-thirds (68%) of women using metformin before pregnancy did not use any antidiabetic medications during pregnancy.
Antidiabetic medication use prior to and during pregnancy rose from 2001–2007, possibly due to increasing prevalence of gestational diabetes mellitus, type 1 and type 2 diabetes, and other conditions associated with insulin resistance.
PMCID: PMC3811068  PMID: 23262934
11.  Association Between Anti-TNF-α Therapy and All-Cause Mortality 
Pharmacoepidemiology and drug safety  2012;21(12):1311-1320.
To compare mortality among patients with selected autoimmune diseases treated with anti-tumor necrosis factor alpha (TNF-α) agents with similar patients treated with non-biologic therapies.
Cohort study set within several large health care programs, 1998–2007. Autoimmune disease patients were identified using diagnoses from computerized healthcare data. Use of anti-TNF-α agents and comparison non-biologic therapies were identified from pharmacy data and mortality was identified from vital records and other sources. We compared new users of anti-TNF-α agents to new users of non-biologic therapies using propensity scores and Cox proportional hazards analysis to adjust for baseline differences. We also made head-to-head comparisons among anti-TNF-α agents.
Among the 46,424 persons included in the analysis, 2,924 (6.3%) had died by the end of follow-up, including 1,754 (6.1%) of the 28,941 with a dispensing of anti-TNF-α agent and 1,170 (6.7%) of the 17,483 who used non-biologic treatment alone. Compared to use of non-biologic therapies, use of anti-TNF-α therapy was not associated with an increased mortality in patients with rheumatoid arthritis (adjusted hazard ratio [aHR] 0.93 with 95% CI 0.85–1.03); psoriasis, psoriatic arthritis, or ankylosing spondylitis (combined aHR 0.81 with CI 0.61–1.06; or inflammatory bowel disease (aHR 1.12 with CI 0.85–1.46). Mortality rates did not differ to an important degree between patients treated with etanercept, adalimumab, or infliximab.
Anti-TNF-α therapy was not associated with increased mortality among patients with autoimmune diseases.
PMCID: PMC3565563  PMID: 23065964
Rheumatoid arthritis; psoriatic arthritis; psoriasis; Crohn’s Disease; ulcerative colitis; inflammatory bowel disease; pharmacoepidemiology; drug safety; drug toxicity; adverse events; cohort studies; propensity scores; automated healthcare data; mortality
12.  A Pragmatic Framework for Single-site and Multisite Data Quality Assessment in Electronic Health Record-based Clinical Research 
Medical care  2012;50(0):10.1097/MLR.0b013e318257dd67.
Answers to clinical and public health research questions increasingly require aggregated data from multiple sites. Data from electronic health records and other clinical sources are useful for such studies, but require stringent quality assessment. Data quality assessment is particularly important in multisite studies to distinguish true variations in care from data quality problems.
We propose a “fit-for-use” conceptual model for data quality assessment and a process model for planning and conducting single-site and multisite data quality assessments. These approaches are illustrated using examples from prior multisite studies.
Critical components of multisite data quality assessment include: thoughtful prioritization of variables and data quality dimensions for assessment; development and use of standardized approaches to data quality assessment that can improve data utility over time; iterative cycles of assessment within and between sites; targeting assessment toward data domains known to be vulnerable to quality problems; and detailed documentation of the rationale and outcomes of data quality assessments to inform data users. The assessment process requires constant communication between site-level data providers, data coordinating centers, and principal investigators.
A conceptually based and systematically executed approach to data quality assessment is essential to achieve the potential of the electronic revolution in health care. High-quality data allow “learning health care organizations” to analyze and act on their own information, to compare their outcomes to peers, and to address critical scientific questions from the population perspective.
PMCID: PMC3833692  PMID: 22692254
data quality; data quality assessment; single-site studies; multisite studies
13.  Medication Exposure in Pregnancy Risk Evaluation Program 
Maternal and child health journal  2012;16(7):1349-1354.
To describe a program to study medication safety in pregnancy, the Medication Exposure in Pregnancy Risk Evaluation Program (MEPREP). MEPREP is a multi-site collaborative research program developed to enable the conduct of studies of medication use and outcomes in pregnancy. Collaborators include the U.S. Food and Drug Administration and researchers at the HMO Research Network, Kaiser Permanente Northern and Southern California, and Vanderbilt University. Datasets have been created at each site linking healthcare data for women delivering an infant between January 1, 2001 and December 31, 2008 and infants born to these women. Standardized data files include maternal and infant characteristics, medication use, and medical care at 11 health plans within 9 states; birth certificate data were obtained from the state departments of public health. MEPREP currently involves more than 20 medication safety researchers and includes data for 1,221,156 children delivered to 933,917 mothers. Current studies include evaluations of the prevalence and patterns of use of specific medications and a validation study of data elements in the administrative and birth certificate data files. MEPREP can support multiple studies by providing information on a large, ethnically and geographically diverse population. This partnership combines clinical and research expertise and data resources to enable the evaluation of outcomes associated with medication use during pregnancy.
PMCID: PMC3361624  PMID: 22002179
Pregnancy; Birth outcomes; Distributed data network
14.  Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic 
Pharmaceutics  2013;5(1):179-200.
Background: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method—the tree-based scan statistic (TreeScan). Results: We identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold. Conclusions: The GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds.
PMCID: PMC3834945  PMID: 24300404
pharmacovigilance; drug safety surveillance; adverse events data mining; gamma Poisson shrinkage; tree-based scan statistic
15.  Characteristics of Patients with Primary Non-adherence to Medications for Hypertension, Diabetes, and Lipid Disorders 
Information comparing characteristics of patients who do and do not pick up their prescriptions is sparse, in part because adherence measured using pharmacy claims databases does not include information on patients who never pick up their first prescription, that is, patients with primary non-adherence. Electronic health record medication order entry enhances the potential to identify patients with primary non-adherence, and in organizations with medication order entry and pharmacy information systems, orders can be linked to dispensings to identify primarily non-adherent patients.
This study aims to use database information from an integrated system to compare patient, prescriber, and payment characteristics of patients with primary non-adherence and patients with ongoing dispensings of newly initiated medications for hypertension, diabetes, and/or hyperlipidemia.
This is a retrospective observational cohort study.
Participants of this study include patients with a newly initiated order for an antihypertensive, antidiabetic, and/or antihyperlipidemic within an 18-month period.
Proportion of patients with primary non-adherence overall and by therapeutic class subgroup. Multivariable logistic regression modeling was used to investigate characteristics associated with primary non-adherence relative to ongoing dispensings.
The proportion of primarily non-adherent patients varied by therapeutic class, including 7% of patients ordered an antihypertensive, 11% ordered an antidiabetic, 13% ordered an antihyperlipidemic, and 5% ordered medications from more than one of these therapeutic classes within the study period. Characteristics of patients with primary non-adherence varied across therapeutic classes, but these characteristics had poor ability to explain or predict primary non-adherence (models c-statistics = 0.61–0.63).
Primary non-adherence varies by therapeutic class. Healthcare delivery systems should pursue linking medication orders with dispensings to identify primarily non-adherent patients. We encourage conduct of research to determine interventions successful at decreasing primary non-adherence, as characteristics available from databases provide little assistance in predicting primary non-adherence.
PMCID: PMC3250550  PMID: 21879374
medication adherence; primary non-adherence; antihypertensive adherence; antidiabetic adherence; antihyperlipidemic adherence
16.  Extension of Kaplan-Meier methods in observational studies with time-varying treatment 
Value in Health  2011;15(1):167-174.
Inverse probability of treatment weighted (IPTW) Kaplan-Meier estimates have been developed to compare two treatments in the presence of confounders in observational studies. Recently, stabilized weights were developed to reduce the influence of extreme IPTW weights in estimating treatment effects. The objective of this paper was to use adjusted Kaplan-Meier estimates and modified log-rank and Wilcoxon tests to examine the effect of a treatment which varies over time in an observational study.
In this paper, we propose stabilized weight (SW) adjusted Kaplan-Meier estimates and modified log-rank and Wilcoxon tests when the treatment is time-varying over the follow-up period. We applied these new methods in examining the effect of an anti-platelet agent, clopidogrel, on subsequent events, including bleeding, myocardial infarction, and death after a Drug-Eluting Stent was implanted into a coronary artery. In this population, clopidogrel use may change over time based on patients' behavior (e.g., non-adherence) and physicians' recommendations (e.g., end of duration of therapy). Consequently, clopidogrel use was treated as a time-varying variable.
We demonstrate that 1) the sample sizes at three chosen time points are almost identical in the original and weighted datasets, and 2) the covariates between patients on and off clopidogrel were well balanced after SWs were applied to the original samples.
The SW-adjusted Kaplan-Meier estimates and modified log-rank and Wilcoxon tests are useful in presenting and comparing survival functions for time-varying treatments in observational studies while adjusting for known confounders.
PMCID: PMC3267428  PMID: 22264985
Observational study; Kaplan Meier estimates; Stabilized weights; Time-varying treatment; Stents
17.  Improving the validity of determining medication adherence from electronic health record medications orders 
We developed an accurate and valid medication order algorithm to identify from electronic health records the definitive medication order intended for dispensing and applied this process to identify a cohort of patients and to stratify them into one of three medication adherence groups: early non-persistence, primary non-adherence, or ongoing adherence. We identified medication order data from electronic health record tables, obtained the orders, and linked the orders to dispensings. These steps were then used to identify patients newly prescribed antihypertensive, antidiabetic, or antihyperlipidemic medications and to determine the adherence group of each patient. Record review validated each process step, thus increasing the accuracy of group assignment as well as the criteria used to select patients. This work is an important first step to accurately identify study-specific patient adherence cohorts and allow more comprehensive estimates of population medication adherence.
PMCID: PMC3168310  PMID: 21613641
18.  ADHD Medications and Risk of Serious Cardiovascular Events In Young and Middle-Aged Adults 
Jama  2011;306(24):2673-2683.
More than 1.5 million US adults use stimulants and other medications labeled for treatment of attention deficit hyperactivity disorder (ADHD). These agents can increase heart rate and blood pressure, raising concerns about their cardiovascular safety.
Examine whether current use of medications used primarily to treat ADHD is associated with increased risk of serious cardiovascular events in young and middle-aged adults.
Retrospective, population-based cohort study
Computerized health records from 4 study sites (OptumInsight Epidemiology, Tennessee Medicaid, Kaiser Permanente California, and the HMO Research Network), starting in 1986 at one site and ending in 2005 at all sites, with additional covariate assessment using 2007 survey data.
Adults aged 25–64 years with dispensed prescriptions for methylphenidate, amphetamine, or atomoxetine at baseline. Each medication user (n=150,359) was matched to two non-users on study site, birth year, sex, and calendar year (total users and non-users=443,198).
Main Outcome
Serious cardiovascular events, including myocardial infarction (MI), sudden cardiac death (SCD), or stroke. Comparison between current or new users and remote users to account for potential healthy user bias.
During 806,182 person-years of follow-up (median 1.3 years per person), 1357 cases of MI, 296 cases of SCD, and 575 cases of stroke occurred. There were 107,322 person-years of current use (median 0.33 years), with a crude incidence per 1000 person-years of 1.34 (95% CI, 1.14–1.57) for MI, 0.30 (95% CI, 0.20–0.42) for SCD, and 0.56 (95% CI, 0.43–0.72) for stroke. The multivariable adjusted rate ratio (RR) of serious cardiovascular events for current use vs non-use of ADHD medications was 0.83 (95% CI 0.72–0.96). Among new users of ADHD medications, the adjusted RR was 0.77 (95% CI 0.63–0.94). The adjusted RR was 1.03 (95% CI, 0.86–1.24) for current use vs remote use, and was 1.02 (95% CI, 0.82–1.28) for new use vs remote use.
Among young and middle-aged adults, current or new use of ADHD medications, compared with non-use or remote use, was not associated with an increased risk of serious cardiovascular events. Apparent protective associations likely represent healthy user bias.
PMCID: PMC3350308  PMID: 22161946
19.  Clostridium difficile Infection, Colorado and the Northwestern United States, 20071 
Emerging Infectious Diseases  2012;18(6):960-962.
To determine the incidence of Clostridium difficile infection during 2007, we examined infection in adult inpatient and outpatient members of a managed-care organization. Incidence was 14.9 C. difficile infections per 10,000 patient-years. Extrapolating this rate to US adults, we estimate that 284,875 C. difficile infections occurred during 2007.
PMCID: PMC3358157  PMID: 22608207
Clostridium difficile; infection; incidence; bacteria; Colorado; United States
20.  Risks of congenital malformations and perinatal events among infants exposed to calcium channel and beta-blockers during pregnancy 
Calcium channel blockers and beta-blockers are widely used during pregnancy, but data on their safety for the developing infant is scarce. We used population-based data from 5 HMOs to study risks for perinatal complications and congenital defects among infants exposed in-utero.
We studied women older than 15 years delivering an infant between 1/1/96 to 12/31/00, who had been continuously enrolled with prescription drug coverage for >= one year prior to delivery. Information on prescription drug dispensings, inpatient and outpatient diagnoses and procedures was obtained from automated databases at each HMO.
There were 584 full-term infants exposed during pregnancy to beta-blockers and 804 full-term infants exposed to calcium-channel blockers, and over 75,000 unexposed mother-infant pairs with >= 30 days follow-up. Infants exposed to beta-blockers in the third trimester of pregnancy had over three-fold increased risk for hypoglycemia (RR 3.1; 95% CI 2.2, 4.2) and an approximately two-fold increased risk for feeding problems (RR 1.8; 95% CI 1.3, 2.5). Infants exposed to calcium-channel blockers in the third trimester had an increased risk for seizures (RR 3.6 95% CI 1.3, 10.4). Chart review confirmed the majority of the exposed seizure and hypoglycemia cases. There were no increased risks for congenital anomalies among either group of infants, except for the category of upper alimentary tract anomalies; this increased risk was based on only two exposed cases.
Infants whose mothers receive beta-blockers are at increased risk for neonatal hypoglycemia, while those whose mothers take calcium-channel blockers are at increased risk for neonatal seizures.
PMCID: PMC3232183  PMID: 21254284
calcium channel blockers; beta-blockers; pregnancy; perinatal; malformation; anomalies; prescription drug; drug safety
21.  Construction of a Multisite DataLink Using Electronic Health Records for the Identification, Surveillance, Prevention, and Management of Diabetes Mellitus: The SUPREME-DM Project 
Electronic health record (EHR) data enhance opportunities for conducting surveillance of diabetes. The objective of this study was to identify the number of people with diabetes from a diabetes DataLink developed as part of the SUPREME-DM (SUrveillance, PREvention, and ManagEment of Diabetes Mellitus) project, a consortium of 11 integrated health systems that use comprehensive EHR data for research.
We identified all members of 11 health care systems who had any enrollment from January 2005 through December 2009. For these members, we searched inpatient and outpatient diagnosis codes, laboratory test results, and pharmaceutical dispensings from January 2000 through December 2009 to create indicator variables that could potentially identify a person with diabetes. Using this information, we estimated the number of people with diabetes and among them, the number of incident cases, defined as indication of diabetes after at least 2 years of continuous health system enrollment.
The 11 health systems contributed 15,765,529 unique members, of whom 1,085,947 (6.9%) met 1 or more study criteria for diabetes. The nonstandardized proportion meeting study criteria for diabetes ranged from 4.2% to 12.4% across sites. Most members with diabetes (88%) met multiple criteria. Of the members with diabetes, 428,349 (39.4%) were incident cases.
The SUPREME-DM DataLink is a unique resource that provides an opportunity to conduct comparative effectiveness research, epidemiologic surveillance including longitudinal analyses, and population-based care management studies of people with diabetes. It also provides a useful data source for pragmatic clinical trials of prevention or treatment interventions.
PMCID: PMC3457753  PMID: 22677160
22.  Detection of Pelvic Inflammatory Disease: Development of an Automated Case-Finding Algorithm Using Administrative Data 
ICD-9 codes are conventionally used to identify pelvic inflammatory disease (PID) from administrative data for surveillance purposes. This approach may include non-PID cases. To refine PID case identification among women with ICD-9 codes suggestive of PID, a case-finding algorithm was developed using additional variables. Potential PID cases were identified among women aged 15–44 years at Group Health (GH) and Kaiser Permanente Colorado (KPCO) and verified by medical record review. A classification and regression tree analysis was used to develop the algorithm at GH; validation occurred at KPCO. The positive predictive value (PPV) for using ICD-9 codes alone to identify clinical PID cases was 79%. The algorithm identified PID appropriate treatment and age 15–25 years as predictors. Algorithm sensitivity (GH = 96.4%; KPCO = 90.3%) and PPV (GH = 86.9%; KPCO = 84.5%) were high, but specificity was poor (GH = 45.9%; KPCO = 37.0%). In GH, the algorithm offered a practical alternative to medical record review to further improve PID case identification.
PMCID: PMC3226320  PMID: 22144849
23.  The Positive Predictive Value of a Hyperkalemia Diagnosis in Automated Health Care Data 
Pharmacoepidemiology and drug safety  2010;19(11):1204-1208.
Our objectives were to determine performance of coded hyperkalemia diagnosis at identifying 1) clinically-evident hyperkalemia and 2) serum potassium ≥ 6 mmol/liter.
This retrospective observational study included 8,722 patients with diabetes within an integrated healthcare system who newly-initiated an angiotensin converting enzyme inhibitor, angiotensin receptor blocker, or spironolactone. The primary outcome was first hyperkalemia-associated event (hospitalization, emergency department visit or death within 24 hours of coded diagnosis and/or potassium ≥ 6 mmol/liter) during the first year of therapy. Medical records were reviewed.
Among a random sample of 99 patients not coded as having hyperkalemia, none had hyperkalemia upon record review. Among all 64 patients identified as having hyperkalemia, all had hospitalization or emergency department visit associated with coded diagnosis or elevated potassium. Of 55 with coded diagnosis, 42 (PPV 76%) had clinically-evident hyperkalemia; 32 (PPV 58%) had potassium ≥ 6. Of 9 identified using only potassium ≥ 6, 7 (PPV 78%) had clinically-evident hyperkalemia.
Nearly one-fourth of patients with coded diagnosis do not have clinically-evident hyperkalemia and nearly one-half do not have potassium ≥ 6. Because both false positives and negatives occur with coded diagnoses, medical record validation of hyperkalemia-associated outcomes is necessary.
PMCID: PMC2996391  PMID: 20878650
Hyperkalemia; positive predictive value; sensitivity; specificity; ACEi; ARB
24.  Nontuberculous Mycobacterial Lung Disease Prevalence at Four Integrated Health Care Delivery Systems 
Rationale: Single-site clinic-based studies suggest an increasing prevalence of pulmonary nontuberculous mycobacteria (NTM) disease, but systematic data are lacking.
Objectives: To describe prevalence and trends for NTM lung disease at four geographically diverse integrated heath care delivery systems in the United States.
Methods: We abstracted mycobacterial culture results from electronic laboratory databases and linked to other datasets containing clinical and demographic information. Possible cases were defined as a single positive NTM pulmonary isolate, and definite cases were defined as two positive sputum cultures, or one positive culture from a bronchoalveolar lavage or lung biopsy. Annual prevalence was calculated using United States census data; average annual prevalence is presented for 2004–2006. Poisson regression models were used to estimate the annual percent change in prevalence.
Measurements and Main Results: A total of 28,697 samples from 7,940 patients were included in the analysis. Of these, 3,988 (50%) were defined as possible cases, and 1,865 (47%) of these were defined as definite cases. Average annual (2004–2006) site-specific prevalence ranged from 1.4 to 6.6 per 100,000. Prevalence was 1.l- to 1.6-fold higher among women relative to men across sites. The prevalence of NTM lung disease was increasing significantly at the two sites where trends were studied, by 2.6% per year among women and 2.9% per year among men. Among persons aged greater than or equal to 60 years, annual prevalence increased from 19.6 per 100,000 during 1994–1996 to 26.7 per 100,000 during 2004–2006.
Conclusions: The epidemiology of nontuberculous mycobacterial lung disease is changing, with a predominance of women and increasing prevalence at the sites studied.
PMCID: PMC2970866  PMID: 20538958
epidemiology; prevalence; nontuberculous mycobacteria; atypical mycobacteria
25.  Diabetes and Drug-Associated Hyperkalemia: Effect of Potassium Monitoring 
Renin-angiotensin-aldosterone system (RAAS) inhibitors are associated with hyperkalemia, but there is little evidence demonstrating patients who receive potassium monitoring have a lower rate of hyperkalemia.
To evaluate the association between potassium monitoring and serious hyperkalemia-associated adverse outcomes among patients with diabetes newly initiating RAAS inhibitor therapy.
Retrospective observational study.
Patients with diabetes without end-stage renal disease initiating RAAS inhibitor therapy between 2001 and 2006 at three integrated health care systems.
Potassium monitoring and first hyperkalemia-associated adverse event during the initial year of therapy. Hyperkalemia-associated adverse events included hospitalizations, emergency department visits or deaths within 24 h of hyperkalemia diagnosis and/or diagnostic potassium ≥6 mmol/l. Incidence rates were calculated in person-years (p-y). We used inverse probability propensity score weighting to adjust for differences between patients with and without monitoring; Poisson regression was used to obtain adjusted relative risks.
A total of 19,391 of 27,355 patients (71%) received potassium monitoring. Serious hyperkalemia-associated events occurred at an incidence rate of 10.2 per 1,000 p-y. Compared to patients without monitoring, adjusted relative risk of hyperkalemia-associated adverse events among all patients with monitoring was 0.50 (0.37, 0.66); in the subset of patients who also had chronic kidney disease (n = 2,176), adjusted relative risk was 0.29 (0.18, 0.46).
Patients prescribed RAAS inhibitors who have both diabetes and chronic kidney disease and receive potassium monitoring are less likely to experience a serious hyperkalemia-associated adverse event compared to similar patients who did not receive potassium monitoring. This evidence supports existing consensus-based guidelines.
PMCID: PMC2842549  PMID: 20087674
hyperkalemia; hyperpotassemia; angiotensin-converting enzyme inhibitor; ACEi; angiotensin receptor blocker; ARB; spironolactone; RAAS inhibitor

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