Identification of seizures among adults and children following influenza vaccination using health insurance claims data

Healthcare utilization in children across the care continuum during the COVID-19 pandemic

Objectives

Healthcare utilization decreased during the COVID-19 pandemic, likely due to reduced transmission of infections and healthcare avoidance. Though various investigations have described these changing patterns in children, most have analyzed specific care settings. We compared healthcare utilization, prescriptions, and diagnosis patterns in children across the care continuum during the first year of the pandemic with preceding years.

Study design

Using national claims data, we compared enrollees under 18 years during the pre-pandemic (January 2016 –mid-March 2020) and pandemic (mid-March 2020 through March 2021) periods. The pandemic was further divided into early (mid-March through mid-June 2020) and middle (mid-June 2020 through March 2021) periods. Utilization was compared using interrupted time series.

Results

The mean number of pediatric enrollees/month was 2,519,755 in the pre-pandemic and 2,428,912 in the pandemic period. Utilization decreased across all settings in the early pandemic, with the greatest decrease (76.9%, 95% confidence interval [CI] 72.6–80.5%) seen for urgent care visits. Only well visits returned to pre-pandemic rates during the mid-pandemic. Hospitalizations decreased by 43% (95% CI 37.4–48.1) during the early pandemic and were still 26.6% (17.7–34.6) lower mid-pandemic. However, hospitalizations in non-psychiatric facilities for various mental health disorders increased substantially mid-pandemic.

Conclusion

Healthcare utilization in children dropped substantially during the first year of the pandemic, with a shift away from infectious diseases and a spike in mental health hospitalizations. These findings are important to characterize as we monitor the health of children, can be used to inform healthcare strategies during subsequent COVID-19 surges and/or future pandemics, and may help identify training gaps for pediatric trainees. Subsequent investigations should examine how changes in healthcare utilization impacted the incidence and outcomes of specific diseases.

Safety of Gabapentin Prescribed for Any Indication in a Large Clinical Cohort of 571,718 US Veterans with and without Alcohol Use Disorder

BACKGROUND

Gabapentin is prescribed for seizures and pain and has efficacy for treating alcohol use disorder (AUD) starting at doses of 900 milligrams per day (mg/d). Recent evidence suggests safety concerns associated with gabapentin including adverse neurologic effects. Individuals with hepatitis C (HCV), HIV, or AUD may be at increased risk due to comorbidities and potential medication interactions.

METHODS

We identified patients prescribed gabapentin for ≥ 60 days for any indication between 2002 and 2015. We propensity-score matched each gabapentin-exposed patient with up to 5 gabapentin-unexposed patients. We followed patients for 2 years or until diagnosed with (i) falls or fractures, or (ii) altered mental status using validated ICD-9 diagnostic codes. We used Poisson regression to estimate incidence rates and relative risk (RR) of these adverse events in association with gabapentin exposure overall and stratified by age, race/ethnicity, sex, HCV, HIV, AUD, and dose.

RESULTS

Incidence of falls or fractures was 1.81 per 100 person-years (PY) among 140,310 gabapentin-exposed and 1.34/100 PY among 431,408 gabapentin-unexposed patients (RR 1.35, 95% confidence interval [CI] 1.28 to 1.44). Incidence of altered mental status was 1.08/100 PY among exposed and 0.97/100 PY among unexposed patients, RR of 1.12 (95% CI 1.04 to 1.20). Excess risk of falls or fractures associated with gabapentin exposure was observed in all subgroups except patients with HCV, HIV, or AUD; however, these groups had elevated incidence regardless of exposure. There was a clear dose-response relationship for falls or fractures with highest risk observed among those prescribed ≥ 2,400 mg/d (RR 1.90, 95% CI 1.50 to 2.40). Patients were at increased risk for altered mental status at doses 600 to 2,399 mg/d; however, low number of events in the highest dose category limited power to detect a statistically significant association ≥ 2,400 mg/d.

CONCLUSIONS

Gabapentin is associated with falls or fractures and altered mental status. Clinicians should be monitoring gabapentin safety, especially at doses ≥ 600 mg/d, in patients with and without AUD.

Use of FDA's Sentinel System to Quantify Seizure Risk Immediately Following New Ranolazine Exposure

INTRODUCTION

Neurological complications including seizures have been reported with ranolazine. We sought to quantify the risk of seizure-related hospitalizations or emergency department events following ranolazine exposure in the Sentinel System (2006-2015).

STUDY DESIGN AND SETTING

Eligibility criteria were new use of ranolazine after 183 days washout period and absence of seizure diagnoses, anti-epileptic drugs, or seizure-related disorders during the baseline period.

RESULTS

Among 52,155 ranolazine users, we identified 28 seizures in the 1-32 days after new ranolazine dispensing: 12 occurring in days 1-10 (high-risk window), 11 in days 11-20 (moderate-risk window) and 5 in the control window (days 21-32). Assuming an equal likelihood of seizure events across the 32-day observation window, we estimate an attributable risk of 0.9 excess cases per 10,000 exposed users. Using a self-controlled risk interval design with exact logistic regression, seizures were elevated in the high-risk window (relative risk [RR] 2.88 (95% confidence interval [CI] 1.01-8.33) compared with the control window. No significant increased risk was observed in the moderate window. Half of the seizure cases had a diagnosis of renal disease, although seizure risk was not significant (RR 3.20 [CI 0.82-14.01]). A majority of patients in both risk windows were 75 years or older.

CONCLUSION

Our study suggests risk among younger ranolazine patients is rare. Given the imprecision of the risk estimates, we interpret the elevated seizure risk following ranolazine exposure with caution. Further analysis in a larger elderly population is warranted.

Determining the Time of Cancer Recurrence Using Claims or Electronic Medical Record Data

PURPOSE

Data from claims and electronic medical records (EMRs) are frequently used to identify clinical events (eg, cancer diagnosis, stroke). However, accurately determining the time of clinical events can be challenging, and the methods used to generate time estimates are underdeveloped. We sought to develop an approach to determine the time of a clinical event-cancer recurrence-using high-dimensional longitudinal structured data.

METHODS

Manual chart abstraction provided information regarding the actual time of cancer recurrence. These data were linked to claims from Medicare or structured EMR data from the Cancer Research Network, which were used to determine time of recurrence for patients with lung or colorectal cancer. We analyzed the longitudinal profile of codes that could help determine the time of recurrence, adjusted for systematic differences between code dates and recurrence dates, and integrated time estimates from different codes to empirically derive an optimal algorithm.

RESULTS

We identified twelve code groups that could help determine the time of recurrence. Using claims data for patients with lung cancer, the optimal algorithm consisted of three code groups and provided an average prediction error of 4.8 months. Using EMR data or applying this approach to patients with colorectal cancer yielded similar results.

CONCLUSION

Time estimates were improved by selecting codes not necessarily the same as those used to identify recurrence, combining time estimates from multiple code groups, and adjusting for systematic bias between code dates and recurrence dates. Improving the accuracy of time estimates for clinical events can facilitate research, quality measurement, and process improvement.

Tramadol and the risk of seizure: nested case-control study of US patients with employer-sponsored health benefits

OBJECTIVES

Tramadol is a widely prescribed analgesic that influences both opioid and monoamine neurotransmission. While seizures have been reported with its use, the risk in clinical practice has not been well characterised. We examined risk of seizure with tramadol relative to codeine, a comparable opioid analgesic.

DESIGN

Retrospective nested case-control study. For each case, we identified up to 10 controls matched on age, sex, US state of residence and date of cohort entry (±365 days). We calculated ORs to determine the association between seizure and exposure to tramadol, codeine (≥15 mg), both or neither, in the preceding 30 days.

SETTING

Cohort of patients, who had continuous health coverage and resided in the same state for≥3 years, identified from linked administrative health data in US MarketScan databases from 2009 to 2012.

PARTICIPANTS

We identified 96 753 patients with seizure and 888 540 matched controls.

PRIMARY AND SECONDARY OUTCOME MEASURES

In the primary analysis, we defined cases using a broad definition of seizure (based on either an outpatient physician claim for seizure disorder or a seizure-related emergency department visit or hospitalisation). In a secondary analysis, we used a more specific definition of seizure restricted to a hospital visit with a principal diagnosis of seizure.

RESULTS

In the primary analysis, we found no association between risk of seizure and exposure to tramadol compared with codeine (OR 1.03, 95% CI 0.93 to 1.15). However, in the secondary analysis (using a more specific definition of seizure), this association was statistically significant (OR 1.41, 95% CI 1.11 to 1.79).

CONCLUSIONS

Tramadol was not associated with an increased risk of seizure defined by inpatient and outpatient diagnoses. However, this finding was sensitive to the outcome definition used and requires further study.