A scan statistic for identifying optimal risk windows in vaccine safety studies using self-controlled case series design

An individual segmented trajectory approach for identifying opioid use patterns using longitudinal dispensing data

PURPOSE

The aim of this study is to use electronic opioid dispensing data to develop an individual segmented trajectory approach for identifying opioid use patterns. The resulting opioid use patterns can be used for examining the association between opioid use and drug overdose.

METHODS

We retrospectively assembled a cohort of members on long-term opioid therapy (LTOT) between January 1, 2006 and June 30, 2019 who were 18 years and older and enrolled in one of three health care systems in the US. We have developed an individual segmented trajectory analysis for identifying various opioid use patterns by scanning over the follow-up and finding distinct opioid use patterns based on variability measured with coefficient of variation and trends of milligram morphine equivalents levels.

RESULTS

Among 31, 865 members who were on LTOT between January 1, 2006 and June 30, 2019, 58.3% were female, and the average age was 55.4 years (STD = 15.4). The study population had 152 557 person-years of follow-up, with an average follow-up of 4.4 years per enrollment per person (STD = 3.4). This novel approach identified up to 13 distinct patterns including 88 756 episodes of "stable" pattern (42.1%) with an average follow-up of 11.2 months, 29 140 episodes of "increasing" pattern (13.8%) with an average follow-up of 6.0 months, 13 201 episodes of ≤10% dose reduction (6.3%) with an average follow-up of 10.4 months, 7286 episodes of 11%-20% dose reduction (3.5%) with an average follow-up of 5.3 months, 4457 episodes of 21%-30% dose reduction (2.1%) with an average follow-up of 4.0 months, and 9903 episodes of >30% dose reduction (4.7%) with an average follow-up of 2.6 months.

CONCLUSIONS

A novel approach was developed to identify 13 distinct opioid use patterns using each individual's longitudinal dispensing data and these patterns can be used in examining overdose risk during the time that these patterns are ongoing.

Association Between Opioid Dose Reduction Rates and Overdose Among Patients Prescribed Long-Term Opioid Therapy

BACKGROUND

Tapering long-term opioid therapy is an increasingly common practice, yet rapid opioid dose reductions may increase the risk of overdose. The objective of this study was to compare overdose risk following opioid dose reduction rates of ≤10%, 11% to 20%, 21% to 30%, and >30% per month to stable dosing.

METHODS

We conducted a retrospective cohort study in three health systems in Colorado and Wisconsin. Participants were patients ≥18 years of age prescribed long-term opioid therapy between January 1, 2006, and June 30, 2019. Five opioid dosing patterns and drug overdoses (fatal and nonfatal) were identified using electronic health records, pharmacy records, and the National Death Index. Cox proportional hazard regression was conducted on a propensity score-weighted cohort to estimate adjusted hazard ratios (aHRs) for follow-up periods of 1, 3, 6, 9, and 12 months after a dose reduction.

RESULTS

In a cohort of 17 540 patients receiving long-term opioid therapy, 42.7% of patients experienced a dose reduction. Relative to stable dosing, a dose reduction rate of >30% was associated with an increased risk of overdose and the aHR estimates decreased as the follow-up increased; the aHRs for the 1-, 6- and 12-month follow-ups were 5.33 (95% CI, 1.98-14.34), 1.81 (95% CI,1.08-3.03), and 1.49 (95% CI, 0.97-2.27), respectively. The slower tapering rates were not associated with overdose risk.

CONCLUSIONS

Patients receiving long-term opioid therapy exposed to dose reduction rates of >30% per month had increased overdose risk relative to patients exposed to stable dosing. Results support the use of slow dose reductions to minimize the risk of overdose.

Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample

Vaccine safety studies are often electronic health record (EHR)-based observational studies. These studies often face significant methodological challenges, including confounding and misclassification of adverse event. Vaccine safety researchers use self-controlled case series (SCCS) study design to handle confounding effect and employ medical chart review to ascertain cases that are identified using EHR data. However, for common adverse events, limited resources often make it impossible to adjudicate all adverse events observed in electronic data. In this paper, we considered four approaches for analyzing SCCS data with confirmation rates estimated from an internal validation sample: (1) observed cases, (2) confirmed cases only, (3) known confirmation rate, and (4) multiple imputation (MI). We conducted a simulation study to evaluate these four approaches using type I error rates, percent bias, and empirical power. Our simulation results suggest that when misclassification of adverse events is present, approaches such as observed cases, confirmed case only, and known confirmation rate may inflate the type I error, yield biased point estimates, and affect statistical power. The multiple imputation approach considers the uncertainty of estimated confirmation rates from an internal validation sample, yields a proper type I error rate, largely unbiased point estimate, proper variance estimate, and statistical power.

Bias and estimation under misspecification of the risk period in self-controlled case series studies

The self-controlled case series (SCCS) method is useful for estimating the relative incidence (RI) of acute events, such as adverse events (AEs) during a specified risk period following an exposure (e.g., 6-week period after vaccinations or 30-day period after infection-related hospitalizations). In practice, the "optimal" risk period is unknown and must be specified. To date, two approaches are available to guide the specification of the risk period. Both methods do not fully utilize the nature of the bias due to misspecification, which to date has not been characterized. Thus, we elucidate the bias of SCCS estimate of the RI when the risk period is misspecified. We then propose a novel method that more effectively estimates the optimal risk period and the associated RI of AEs. The new method incorporates information on the functional form of the bias. Efficacy of the proposed approach is illustrated with substantial reduction in bias and variance in simulation studies. The proposed method is illustrated with two SCCS studies to determine the (1) risk of idiopathic thrombocytopenic purpura after measles-mumps-rubella vaccination in children and (2) risk of cardiovascular events after infection-related hospitalizations in older patients on dialysis.

Safety of diphtheria, tetanus, acellular pertussis and inactivated poliovirus (DTaP-IPV) vaccine

BACKGROUND

In 2008, a diphtheria, tetanus, acellular pertussis, and inactivated poliovirus combined vaccine (DTaP-IPV) was licensed for use in children 4 through 6 years of age. While pre-licensure studies did not demonstrate significant safety concerns, the number vaccinated in these studies was not sufficient to examine the risk of uncommon but serious adverse events.

OBJECTIVE

To assess the risk of serious adverse events following DTaP-IPV vaccination.

METHODS

The study was conducted from January 2009 through September 2012 in the Vaccine Safety Datalink (VSD) project. In the VSD, electronic vaccination and encounter data are updated and aggregated weekly as part of ongoing surveillance activities. Based on previous reports and biologic plausibility, eight potential adverse events were monitored: meningitis/encephalitis; seizures; stroke; Guillain-Barré syndrome; Stevens-Johnson syndrome; anaphylaxis; serious allergic reactions other than anaphylaxis; and serious local reactions. Adverse event rates in DTaP-IPV recipients were compared to historical incidence rates in the VSD population prior to 2009. Sequential probability ratio testing was used to analyze the data on a weekly basis.

RESULTS

During the study period, 201,116 children received DTaP-IPV vaccine. Ninety-seven percent of DTaP-IPV recipients also received other vaccines on the same day, typically measles-mumps-rubella and varicella vaccines. There was no statistically significant increased risk of any of the eight pre-specified adverse events among DTaP-IPV recipients when compared to historical incidence rates.

CONCLUSIONS

In this safety surveillance study of more than 200,000 DTaP-IPV vaccine recipients, there was no evidence of increased risk for any of the pre-specified adverse events monitored. Continued surveillance of DTaP-IPV vaccine safety may be warranted to monitor for rare adverse events, such as Guillain-Barré syndrome.