Developing a PRogram to Educate and Sensitize Caregivers to Reduce the Inappropriate Prescription Burden in the Elderly with Alzheimer's Disease (D-PRESCRIBE-AD): Trial protocol and rationale of an open-label pragmatic, prospective randomized controlled trial.

CONTEXT: Potentially inappropriate prescribing of medications in older adults, particular those with dementia, can lead to adverse drug events including falls and fractures, worsening cognitive impairment, emergency department visits, and hospitalizations. Educational mailings from health plans to patients and their providers to encourage deprescribing conversations may represent an effective, low-cost, "light touch", approach to reducing the burden of potentially inappropriate prescription use in older adults with dementia. OBJECTIVES: The objective of the Developing a PRogram to Educate and Sensitize Caregivers to Reduce the Inappropriate Prescription Burden in Elderly with Alzheimer's Disease (D-PRESCRIBE-AD) trial is to evaluate the effect of a health plan based multi-faceted educational outreach intervention to community dwelling patients with dementia who are currently prescribed sedative/hypnotics, antipsychotics, or strong anticholinergics. METHODS: The D-PRESCRIBE-AD is an open-label pragmatic, prospective randomized controlled trial (RCT) comparing three arms: 1) educational mailing to both the health plan patient and their prescribing physician (patient plus physician arm, n = 4814); 2) educational mailing to prescribing physician only (physician only arm, n = 4814); and 3) usual care (n = 4814) among patients with dementia enrolled in two large United States based health plans. The primary outcome is the absence of any dispensing of the targeted potentially inappropriate prescription during the 6-month study observation period after a 3-month black out period following the mailing. Secondary outcomes include dose-reduction, polypharmacy, healthcare utilization, mortality and therapeutic switching within targeted drug classes. CONCLUSION: This large pragmatic RCT will contribute to the evidence base on promoting deprescribing of potentially inappropriate medications among older adults with dementia. If successful, such light touch, inexpensive and highly scalable interventions have the potential to reduce the burden of potentially inappropriate prescribing for patients with dementia. ClinicalTrials.gov Identifier: NCT05147428.

Sequential Data-Mining for Adverse Events After Recombinant Herpes Zoster Vaccination Using the Tree-Based Scan Statistic.

Tree-based scan statistics have been successfully used to study the safety of several vaccines without prespecifying health outcomes of concern. In this study, the binomial tree-based scan statistic was applied sequentially to detect adverse events in days 1-28 compared with days 29-56 after recombinant herpes zoster (RZV) vaccination, with 5 looks at the data and formal adjustment for the repeated analyses over time. IBM MarketScan data on commercially insured persons ≥50 years of age receiving RZV during January 1, 2018, to May 5, 2020, were used. With 999,876 doses of RZV included, statistically significant signals were detected only for unspecified adverse effects/complications following immunization, with attributable risks as low as 2 excess cases per 100,000 vaccinations. Ninety percent of cases in the signals occurred in the week after vaccination and, based on previous studies, likely represent nonserious events like fever, fatigue, and headache. Strengths of our study include its untargeted nature, self-controlled design, and formal adjustment for repeated testing. Although the method requires prespecification of the risk window of interest and may miss some true signals detectable using the tree-temporal variant of the method, it allows for early detection of potential safety problems through early initiation of ongoing monitoring.

Monitoring Drug Safety in Pregnancy with Scan Statistics: A Comparison of Two Study Designs.

BACKGROUND: Traditional surveillance of adverse infant outcomes following maternal medication exposures relies on pregnancy exposure registries, which are often underpowered. We characterize the statistical power of TreeScan, a data mining tool, to identify potential signals in the setting of perinatal medication exposures and infant outcomes. METHODS: We used empirical data to inform background incidence of major congenital malformations and other birth conditions. Statistical power was calculated using two probability models compatible with TreeScan, Bernoulli and Poisson, while varying the sample size, magnitude of the risk increase, and incidence of a specified outcome. We also simulated larger referent to exposure matching ratios when using the Bernoulli model in the setting of fixed N:1 propensity score matching. Finally, we assessed the impact of outcome misclassification on power. RESULTS: The Poisson model demonstrated greater power to detect signals than the Bernoulli model across all scenarios and suggested a sample size of 4,000 exposed pregnancies is needed to detect a twofold increase in risk of a common outcome (approximately 8 per 1,000) with 85% power. Increasing the fixed matching ratio with the Bernoulli model did not reliably increase power. An outcome definition with high sensitivity is expected to have somewhat greater power to detect signals than an outcome definition with high positive predictive value. CONCLUSIONS: Use of the Poisson model with an outcome definition that prioritizes sensitivity may be optimal for signal detection. TreeScan is a viable method for surveillance of adverse infant outcomes following maternal medication use.

Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System.

PURPOSE: It is a priority of the US Food and Drug Administration (FDA) to monitor the safety of medications used during pregnancy. Pregnancy exposure registries and cohort studies utilizing electronic health record data are primary sources of information but are limited by small sample sizes and limited outcome assessment. TreeScan™, a statistical data mining tool, can be applied within the FDA Sentinel System to simultaneously identify multiple potential adverse neonatal and infant outcomes after maternal medication exposure. METHODS: We implemented TreeScan using the Sentinel analytic tools in a cohort of linked live birth deliveries and infants nested in the IBM MarketScan® Research Database. As a case study, we compared first trimester fluoroquinolone use and cephalosporin use. We used the Bernoulli and Poisson TreeScan statistics with compatible propensity score-based study designs for confounding control (matching and stratification) and used multiple propensity score models with various strategies for confounding control to inform best practices. We developed a hierarchical outcome tree including major congenital malformations and outcomes of gestational length and birth weight. RESULTS: A total of 1791 fluoroquinolone-exposed and 8739 cephalosporin-exposed mother-infant pairs were eligible for analysis. Both TreeScan analysis methods resulted in single alerts that were deemed to be due to uncontrolled confounding or otherwise not warranting follow-up. CONCLUSIONS: In this implementation of TreeScan using Sentinel analytic tools, we did not observe any new safety signals for fluoroquinolone use in the first trimester. TreeScan, with tailored or high-dimensional propensity scores for confounding control, is a valuable tool in addition to current safety surveillance methods for medications used during pregnancy.

A Broad Safety Assessment of the Recombinant Herpes Zoster Vaccine.

The recombinant herpes zoster vaccine (RZV), approved as a 2-dose series in the United States in October 2017, has proven highly effective and generally safe. However, a small risk of Guillain-Barré syndrome after vaccination was identified after approval, and questions remain about other possible adverse events. This data-mining study assessed RZV safety in the United States using the self-controlled tree-temporal scan statistic, scanning data on thousands of diagnoses recorded during follow-up to detect any statistically unusual temporal clustering of cases within a large hierarchy of diagnoses. IBM MarketScan data on commercially insured persons at least 50 years of age receiving RZV between January 1, 2018, and May 5, 2020, were used, including 56 days of follow-up; 1,014,329 doses were included. Statistically significant clustering was found within a few days of vaccination for unspecified adverse effects, complications, or reactions to immunization or other medical substances/care; fever; unspecified allergy; syncope/collapse; cellulitis; myalgia; and dizziness/giddiness. These findings are consistent with the known safety profile of this and other injected vaccines. No cluster of Guillain-Barré syndrome was detected, possibly due to insufficient sample size. This signal-detection method has now been applied to 5 vaccines, with consistently plausible results, and seems a promising addition to vaccine-safety evaluation methods.

Identifying prescribing cascades in Alzheimer's disease and related dementias: The calcium channel blocker-diuretic prescribing cascade.

PURPOSE: Prescribing cascades occur when a physician prescribes a new drug to address the side-effect of another drug. Persons with Alzheimer's disease and related dementias (ADRD) are at increased risk for prescribing cascades. Our objective was to develop an approach to estimating the proportion of calcium channel blocker-diuretic (CCB-diuretic) prescribing cascades among persons with ADRD in two U.S. health plans. METHODS: We identified patients aged ≥50 on January 1, 2017, dispensed a drug to treat ADRD in the 365-days prior to/on cohort entry date. Patients had medical/pharmacy coverage for 1 year before and through cohort entry. We excluded individuals with an institutional stay encounter in the 45 days prior to cohort entry and censored patients based on: disenrollment from coverage, death, or end of data. We identified incident and prevalent CCB use in the 183-days following cohort entry, and identified subsequent incident diuretic use among incident and prevalent CCB-users within 365-days from cohort entry. RESULTS: There were 121 538 eligible patients. Approximately 62% were female, with a mean age of 79.5 (SD ±8.6). Overall 2.1% of the cohort experienced a prevalent CCB-diuretic prescribing cascade with 1586 incident diuretic-users among 36 462 prevalent CCB-users (4.3%, 95% CI 4.1-4.6%]); and there were161 incident diuretic-users among 3304 incident CCB-users (4.9%, 95% CI 4.2-5.7%) (incident CCB-diuretic cascade). CONCLUSIONS: We describe an approach to identify prescribing cascades in persons with ADRD, which can be used to assess the proportion of prescribing cascades in large cohorts. We determined the proportion of CCB-diuretic prescribing cascades was low.

A General Propensity Score for Signal Identification Using Tree-Based Scan Statistics.

The tree-based scan statistic (TreeScan; Martin Kulldorff, Harvard Medical School, Boston, Massachusetts) is a data-mining method that adjusts for multiple testing of correlated hypotheses when screening thousands of potential adverse events for signal identification. Simulation has demonstrated the promise of TreeScan with a propensity score (PS)-matched cohort design. However, it is unclear which variables to include in a PS for applied signal identification studies to simultaneously adjust for confounding across potential outcomes. We selected 4 pairs of medications with well-understood safety profiles. For each pair, we evaluated 5 candidate PSs with different combinations of 1) predefined general covariates (comorbidity, frailty, utilization), 2) empirically selected (data-driven) covariates, and 3) covariates tailored to the drug pair. For each pair, statistical alerting patterns were similar with alternative PSs (≤11 alerts in 7,996 outcomes scanned). Inclusion of covariates tailored to exposure did not appreciably affect screening results. Inclusion of empirically selected covariates can provide better proxy coverage for confounders but can also decrease statistical power. Unlike tailored covariates, empirical and predefined general covariates can be applied "out of the box" for signal identification. The choice of PS depends on the level of concern about residual confounding versus loss of power. Potential signals should be followed by pharmacoepidemiologic assessment where confounding control is tailored to the specific outcome(s) under investigation.

A Broad Safety Assessment of the 9-Valent Human Papillomavirus Vaccine.

Parents indicate that safety is their top concern about human papillomavirus (HPV) vaccination. A data-mining method not requiring prespecification of health outcome(s) or postexposure period(s) of potentially increased risk can be used to identify possible associations between an exposure and any of thousands of medically attended health outcomes; this method was applied to data on the 9-valent HPV vaccine (HPV9) to detect potential safety problems. Data on 9- to 26-year-olds who had received HPV9 vaccine between November 4, 2016, and August 5, 2018, inclusive, were extracted from the MarketScan database and analyzed for statistically significant clustering of incident diagnoses within the hierarchy of diagnoses coded using the International Classification of Diseases and temporally within the 1 year after vaccination, using the self-controlled tree-temporal scan statistic and TreeScan software. Only 56 days of postvaccination enrollment was required; subsequent follow-up was censored at disenrollment. Multiple testing was adjusted for. The analysis included 493,089 doses of HPV9. Almost all signals resulted from temporal confounding, not unexpected with a 1-year follow-up period. The only plausible signals were for nonspecific adverse events (e.g., injection-site reactions, headache) on days 1-2 after vaccination, with attributable risks as low as 1 per 100,000 vaccinees. Considering the broad scope of the evaluation and the high statistical power, the findings of no specific serious adverse events should provide reassurance about this vaccine's safety.

Antidopaminergic-Antiparkinsonian Medication Prescribing Cascade in Persons with Alzheimer's Disease.

OBJECTIVES: Persons living with Alzheimer's disease (AD) may be at increased risk for prescribing cascades due to greater multimorbidity, polypharmacy, and the need for more complex care. Our objective was to assess the proportion of the antidopaminergic-antiparkinsonian medication prescribing cascades among persons living with Alzheimer's disease. SETTING: Two large administrative claims databases in the United States. PARTICIPANTS: We identified patients aged ≥50 on January 1, 2017, who were dispensed a drug used to treat Alzheimer's disease for at least 1 day in the 365 days prior to or on cohort entry date and who had medical and pharmacy coverage in the 365 days prior to the cohort entry date. We excluded individuals with a recent institutional stay. We identified incident antidopaminergic (antipsychotic/metoclopramide) use in the 183 days following cohort entry and identified subsequent incident antiparkinsonian drug use within 8 to 365 days. RESULTS: There were 121,538 patients with Alzheimer's disease eligible for inclusion. Approximately 62% were women with a mean age of 79.5 (SD ± 8.6). The mean number of drugs dispensed was 9.2 (SD ± 4.9). There were 36 incident antiparkinsonian users among 4,534 incident antipsychotic/metoclopramide users (0.8%). CONCLUSION: We determined that the proportion of antidopaminergic-antiparkinsonian medication prescribing cascades, widely considered as high-priority, was low. Our approach can be used to assess the proportion of prescribing cascades in populations considered to be at high risk and to prioritize system-level interventional efforts to improve medication safety in these patients.

Using the Self-Controlled Tree-Temporal Scan Statistic to Assess the Safety of Live Attenuated Herpes Zoster Vaccine.

The self-controlled tree-temporal scan statistic allows detection of potential vaccine- or drug-associated adverse events without prespecifying the specific events or postexposure risk intervals of concern. It thus opens a promising new avenue for safety studies. The method has been successfully used to evaluate the safety of 2 vaccines for adolescents and young adults, but its suitability to study vaccines for older adults had not been established. The present study applied the method to assess the safety of live attenuated herpes zoster vaccination during 2011-2017 in US adults aged ≥60 years, using claims data from Truven Health MarketScan Research Databases. Counts of International Classification of Diseases diagnosis codes recorded in emergency department or hospital settings were scanned for any statistically unusual clustering within a hierarchical tree structure of diagnoses and within 42 days after vaccination. Among 1.24 million vaccinations, 4 clusters were found: cellulitis on days 1-3, nonspecific erythematous condition on days 2-4, "other complications . . ." on days 1-3, and nonspecific allergy on days 1-6. These results are consistent with local injection-site reactions and other known, generally mild, vaccine-associated adverse events and a favorable safety profile. This method might be useful for assessing the safety of other vaccines for older adults.

Pregnancy-Associated Cancer: A U.S. Population-Based Study.

BACKGROUND: The incidence of pregnancy-associated cancer (PAC) is expected to increase as more women delay childbearing until later ages. However, information on frequency and incidence of PAC is scarce in the United States. METHODS: We identified pregnancies among women aged 10-54 years during 2001-2013 from five U.S. health plans participating in the Cancer Research Network (CRN) and the Medication Exposure in Pregnancy Risk Evaluation Program (MEPREP). We extracted information from the health plans' administrative claims and electronic health record databases, tumor registries, and infants' birth certificate files to estimate the frequency and incidence of PAC, defined as cancer diagnosed during pregnancy and up to 1 year postpartum. RESULTS: We identified 846 PAC events among 775,709 pregnancies from 2001 to 2013. The overall incidence estimate was 109.1 (95% confidence interval [CI] = 101.8-116.7) per 100,000 pregnancies. There was an increase in the incidence between 2002 and 2012 (the period during which complete data were available), from 75.0 (95% CI = 54.9-100.0) per 100,000 pregnancies in 2002 to 138.5 (95% CI = 109.1-173.3) per 100,000 pregnancies in 2012. The most common invasive cancers diagnosed were breast (n = 208, 24.6%), thyroid (n = 168, 19.9%), melanoma (n = 93, 11.0%), hematologic (n = 87, 10.3%), and cervix/uterus (n = 74, 8.7%). CONCLUSIONS: Our study provides contemporary incidence estimates of PAC from a population-based cohort of U.S. women. These estimates provide the data needed to help develop clinical and public health policies aimed at diagnosing PAC at an early stage and initiating appropriate therapeutic interventions in a timely manner.

Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.

The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-controlled analysis. We propose a method that combines tree-based scan statistics with propensity score-matched analysis of new initiator cohorts, a robust design for investigations of drug safety. We conducted plasmode simulations to evaluate performance. In multiple realistic scenarios, tree-based scan statistics in cohorts that were propensity score matched to adjust for confounding outperformed tree-based scan statistics in unmatched cohorts. In scenarios where confounding moved point estimates away from the null, adjusted analyses recovered the prespecified type 1 error while unadjusted analyses inflated type 1 error. In scenarios where confounding moved point estimates toward the null, adjusted analyses preserved power, whereas unadjusted analyses greatly reduced power. Although complete adjustment of true confounders had the best performance, matching on a moderately mis-specified propensity score substantially improved type 1 error and power compared with no adjustment. When there was true elevation in risk of an adverse event, there were often co-occurring signals for clinically related concepts. TreeScan with propensity score matching shows promise as a method for screening and prioritization of potential adverse events. It should be followed by clinical review and safety studies specifically designed to quantify the magnitude of effect, with confounding control targeted to the outcome of interest.

Assessment of Quadrivalent Human Papillomavirus Vaccine Safety Using the Self-Controlled Tree-Temporal Scan Statistic Signal-Detection Method in the Sentinel System.

The self-controlled tree-temporal scan statistic-a new signal-detection method-can evaluate whether any of a wide variety of health outcomes are temporally associated with receipt of a specific vaccine, while adjusting for multiple testing. Neither health outcomes nor postvaccination potential periods of increased risk need be prespecified. Using US medical claims data in the Food and Drug Administration's Sentinel system, we employed the method to evaluate adverse events occurring after receipt of quadrivalent human papillomavirus vaccine (4vHPV). Incident outcomes recorded in emergency department or inpatient settings within 56 days after first doses of 4vHPV received by 9- through 26.9-year-olds in 2006-2014 were identified using International Classification of Diseases, Ninth Revision, diagnosis codes and analyzed by pairing the new method with a standard hierarchical classification of diagnoses. On scanning diagnoses of 1.9 million 4vHPV recipients, 2 statistically significant categories of adverse events were found: cellulitis on days 2-3 after vaccination and "other complications of surgical and medical procedures" on days 1-3 after vaccination. Cellulitis is a known adverse event. Clinically informed investigation of electronic claims records of the patients with "other complications" did not suggest any previously unknown vaccine safety problem. Considering that thousands of potential short-term adverse events and hundreds of potential risk intervals were evaluated, these findings add significantly to the growing safety record of 4vHPV.

Statistical Power for Postlicensure Medical Product Safety Data Mining.

OBJECTIVE: To perform sample size calculations when using tree-based scan statistics in longitudinal observational databases. METHODS: Tree-based scan statistics enable data mining on epidemiologic datasets where thousands of disease outcomes are organized into hierarchical tree structures with automatic adjustment for multiple testing. We show how to evaluate the statistical power of the unconditional and conditional Poisson versions. The null hypothesis is that there is no increase in the risk for any of the outcomes. The alternative is that one or more outcomes have an excess risk. We varied the excess risk, total sample size, frequency of the underlying event rate, and the level of across-the-board health care utilization. We also quantified the reduction in statistical power resulting from specifying a risk window that was too long or too short. RESULTS: For 500,000 exposed people, we had at least 98 percent power to detect an excess risk of 1 event per 10,000 exposed for all outcomes. In the presence of potential temporal confounding due to across-the-board elevations of health care utilization in the risk window, the conditional tree-based scan statistic controlled type I error well, while the unconditional version did not. DISCUSSION: Data mining analyses using tree-based scan statistics expand the pharmacovigilance toolbox, ensuring adequate monitoring of thousands of outcomes of interest while controlling for multiple hypothesis testing. These power evaluations enable investigators to design and optimize implementation of retrospective data mining analyses.

Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic.

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.

Methods of linking mothers and infants using health plan data for studies of pregnancy outcomes.

PURPOSE: 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. METHODS: We describe how eight 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. RESULTS: 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. CONCLUSIONS: 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.

Drug safety data mining with a tree-based scan statistic.

PURPOSE: In post-marketing drug safety surveillance, data mining can potentially detect rare but serious adverse events. Assessing an entire collection of drug-event pairs is traditionally performed on a predefined level of granularity. It is unknown a priori whether a drug causes a very specific or a set of related adverse events, such as mitral valve disorders, all valve disorders, or different types of heart disease. This methodological paper evaluates the tree-based scan statistic data mining method to enhance drug safety surveillance. METHODS: We use a three-million-member electronic health records database from the HMO Research Network. Using the tree-based scan statistic, we assess the safety of selected antifungal and diabetes drugs, simultaneously evaluating overlapping diagnosis groups at different granularity levels, adjusting for multiple testing. Expected and observed adverse event counts were adjusted for age, sex, and health plan, producing a log likelihood ratio test statistic. RESULTS: Out of 732 evaluated disease groupings, 24 were statistically significant, divided among 10 non-overlapping disease categories. Five of the 10 signals are known adverse effects, four are likely due to confounding by indication, while one may warrant further investigation. CONCLUSION: The tree-based scan statistic can be successfully applied as a data mining tool in drug safety surveillance using observational data. The total number of statistical signals was modest and does not imply a causal relationship. Rather, data mining results should be used to generate candidate drug-event pairs for rigorous epidemiological studies to evaluate the individual and comparative safety profiles of drugs.

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.

This study aims 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. 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.

Validation of an algorithm to estimate gestational age in electronic health plan databases.

PURPOSE: To validate an algorithm that uses delivery date and diagnosis codes to define gestational age at birth in electronic health plan databases. METHODS: Using data from 225,384 live born deliveries to women aged 15-45 years in 2001-2007 within eight 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 file 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. RESULTS: The mean algorithm-derived gestational age at birth was lower than the mean obtained from the birth certificate file among singleton deliveries (267.9 vs 273.5 days) but not among multiple-gestation deliveries (253.9 vs 252.6 days). The algorithm-derived prenatal exposure to the antidepressants had a sensitivity and a positive predictive value of ≥95%, and a specificity and a negative predictive value of almost 100%. Sensitivity and positive predictive value were both ≥90%, and specificity and negative predictive value were both >99% for the antibiotics. CONCLUSIONS: A gestational age algorithm based upon electronic health plan data correctly classified medication exposure status in most live born deliveries, but trimester-specific misclassification may be higher for drugs typically used for short durations.

Validity of health plan and birth certificate data for pregnancy research.

PURPOSE: To evaluate the validity of health plan and birth certificate data for pregnancy research. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.