Using automated medical records for rapid identification of illness syndromes (syndromic surveillance): the example of lower respiratory infection

BACKGROUND

Gaps in disease surveillance capacity, particularly for emerging infections and bioterrorist attack, highlight a need for efficient, real time identification of diseases.

METHODS

We studied automated records from 1996 through 1999 of approximately 250,000 health plan members in greater Boston.

RESULTS

We identified 152,435 lower respiratory infection illness visits, comprising 106,670 episodes during 1,143,208 person-years. Three diagnoses, cough (ICD9CM 786.2), pneumonia not otherwise specified (ICD9CM 486) and acute bronchitis (ICD9CM 466.0) accounted for 91% of these visits, with expected age and sex distributions. Variation of weekly occurrences corresponded closely to national pneumonia and influenza mortality data. There was substantial variation in geographic location of the cases.

CONCLUSION

This information complements existing surveillance programs by assessing the large majority of episodes of illness for which no etiologic agents are identified. Additional advantages include: a) sensitivity, uniformity and efficiency, since detection of events does not depend on clinicians' to actively report diagnoses, b) timeliness, the data are available within a day of the clinical event; and c) ease of integration into automated surveillance systems. These features facilitate early detection of conditions of public health importance, including regularly occurring events like seasonal respiratory illness, as well as unusual occurrences, such as a bioterrorist attack that first manifests as respiratory symptoms. These methods should also be applicable to other infectious and non-infectious conditions. Knowledge of disease patterns in real time may also help clinicians to manage patients, and assist health plan administrators in allocating resources efficiently.

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.

Use of automated ambulatory-care encounter records for detection of acute illness clusters, including potential bioterrorism events

The advent of domestic bioterrorism has emphasized the need for enhanced detection of clusters of acute illness. We describe a monitoring system operational in eastern Massachusetts, based on diagnoses obtained from electronic records of ambulatory-care encounters. Within 24 hours, ambulatory and telephone encounters recording patients with diagnoses of interest are identified and merged into major syndrome groups. Counts of new episodes of illness, rates calculated from health insurance records, and estimates of the probability of observing at least this number of new episodes are reported for syndrome surveillance. Census tracts with unusually large counts are identified by comparing observed with expected syndrome frequencies. During 1996-1999, weekly counts of new cases of lower respiratory syndrome were highly correlated with weekly hospital admissions. This system complements emergency room- and hospital-based surveillance by adding the capacity to rapidly identify clusters of illness, including potential bioterrorism events.

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.

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.

FDA drug prescribing warnings: is the black box half empty or half full?

PURPOSE

Black box warnings (BBWs) are the Food and Drug Administration's (FDA) strongest labeling requirements for high-risk medicines. It is unknown how frequently physicians prescribe BBW drugs and whether they do so in compliance with the warnings. The purpose of the present study was to assess the frequency of use of BBW medications in ambulatory care and prescribing compliance with BBW recommendations.

METHODS

This retrospective study used automated claims data of 929 958 enrollees in 10 geographically diverse health plans in the United States to estimate frequency of use in ambulatory care of 216 BBW drugs/drug groups between 1/1/99 and 31/6/01. We assessed dispensing compliance with the BBW requirements for selected drugs.

RESULTS

During a 30-month period, more than 40% of enrollees received at least one medication that carried a BBW that could potentially apply to them. We found few instances of prescribing during pregnancy of BBW drugs absolutely contra-indicated in pregnancy. There was almost no co-prescribing of contra-indicated drugs with the two QT-interval-prolonging BBW drugs evaluated. Most non-compliance occurred with recommendations for baseline laboratory monitoring (49.6% of all therapy initiations that should have been accompanied by baseline laboratory monitoring were not).

CONCLUSIONS

Many individuals receive drugs considered to carry the potential for serious risk. For some of these drugs, use is largely consistent with their BBW, while for others it is not. Since it will not be possible to avoid certain drug- associated risks, it will be important to develop effective methods to use BBWs and other methods to minimize risks.

Medication Exposure in Pregnancy Risk Evaluation Program

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Triple challenges-small sample size in both exposure and control groups to scan rare maternal outcomes in a signal identification approach: a simulation study

There is a dearth of safety data on maternal outcomes after perinatal medication exposure. Data-mining for unexpected adverse event occurrence in existing datasets is a potentially useful approach. One method, the Poisson tree-based scan statistic (TBSS), assumes that the expected outcome counts, based on incidence of outcomes in the control group, are estimated without error. This assumption may be difficult to satisfy with a small control group. Our simulation study evaluated the effect of imprecise incidence proportions from the control group on TBSS' ability to identify maternal outcomes in pregnancy research. We simulated base case analyses with "true" expected incidence proportions and compared these with imprecise incidence proportions derived from sparse control samples. We varied parameters that have an impact on type I error and statistical power (exposure group size, outcome's incidence proportion, and effect size). We found that imprecise incidence proportions generated by a small control group resulted in inaccurate alerting, inflation of type I error, and removal of very rare outcomes for TBSS analysis due to "zero" background counts. Ideally, the control size should be at least several times larger than the exposure size to limit the number of false positive alerts and retain statistical power for true alerts. This article is part of a Special Collection on Pharmacoepidemiology.

High-Risk Medications in Persons Living With Dementia: A Randomized Clinical Trial

Importance

Individuals with Alzheimer disease (AD) and Alzheimer disease-related dementias (ADRD) may be at increased risk for adverse outcomes relating to inappropriate prescribing of certain high-risk medications, including antipsychotics, sedative-hypnotics, and strong anticholinergic agents.

Objective

To evaluate the effect of a patient/caregiver and prescriber-mailed educational intervention on potentially inappropriate prescribing to patients with AD or ADRD.

Design, Setting, and Participants

This prospective, open-label, pragmatic randomized clinical trial, embedded in 2 large national health plans, was conducted from April 2022 to June 2023. The trial included patients with AD or ADRD and use of any of 3 drug classes targeted for deprescribing (antipsychotics, sedative-hypnotics, or strong anticholinergics).

Interventions

Patients were randomized to 1 of 3 arms: (1) a mailing of educational materials specific to the medication targeted for deprescribing to both the patient and their prescribing clinician; (2) a mailing to the prescribing clinician only; or (3) a usual care arm.

Main Outcomes and Measures

Analysis was performed using a modified intention-to-treat approach. The primary study outcome was the dispensing of the medication targeted for deprescribing during a 6-month study observation period. Secondary outcomes included changes in medication-specific mean daily dose and health service utilization.

Results

Among 12 787 patients included in the modified intention-to-treat analysis, 8742 (68.4%) were female, and the mean (SD) age was 77.3 (9.4) years. The cumulative incidence of being dispensed a medication targeted for deprescribing was 76.7% (95% CI, 75.4-78.0) in the patient and prescriber mailing group, 77.9% (95% CI, 76.5-79.1) in the prescriber mailing only group, and 77.5% (95% CI, 76.2-78.8) in the usual care group. Hazard ratios were 0.99 (95% CI, 0.94-1.04) for the patient and prescriber group and 1.00 (95% CI, 0.96-1.06) for the prescriber only group compared with the usual care group. There were no differences between the groups for secondary outcomes.

Conclusions and Relevance

These findings suggest medication-specific educational mailings targeting patients with AD or ADRD and their clinicians are not effective in reducing the use of high-risk medications.

Trial Registration

ClinicalTrials.gov Identifier: NCT05147428.