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.

Opioid Overdose Risk Following Hospital Discharge Among Individuals Prescribed Long-Term Opioid Therapy: a Risk Interval Analysis.

BACKGROUND: Individuals prescribed long-term opioid therapy (LTOT) have increased risk of readmission and death after hospital discharge. The risk of opioid overdose during the immediate post-discharge time period is unknown. OBJECTIVE: To examine the association between time since hospital discharge and opioid overdose among individuals prescribed LTOT. DESIGN: Self-controlled risk interval analysis. PARTICIPANTS: Adults prescribed LTOT with at least one hospital discharge at a safety-net health system and a non-profit healthcare organization in Colorado. MAIN MEASURES: We identified individuals prescribed LTOT who were discharged from January 2006 through June 2019. The outcome was a composite of fatal and non-fatal opioid overdoses during a 90-day post-discharge observation period, identified using electronic health record (EHR) and vital statistics data. Risk intervals included days 0-6 after index and subsequent hospital discharges. Control intervals ranged from days 7 to 89 after index discharge and included all other time during the observation period that did not fall within a risk interval or time readmitted during a subsequent hospitalization, which was excluded. Poisson regression was used to estimate incidence rate ratios (IRR) and 95% confidence intervals (CI) for overdose events during risk in comparison to control intervals. KEY RESULTS: We identified 7695 adults (63.3% over 55 years, 59.4% female, 20.3% Hispanic) who experienced 9499 total discharges during the study period. Twenty-one overdoses occurred during their observation periods (1174 per 100,000 person-years [9 in risk, 12 in control]). Overdose risk was significantly higher during the risk interval in comparison to the control interval (IRR 6.92; 95% CI 2.92-16.43). CONCLUSION: During the first 7 days after hospital discharge, individuals prescribed LTOT appear to be at elevated risk for opioid overdose. Clarifying mechanisms of overdose risk may help inform in-hospital and post-discharge prevention strategies.

Update of a Multivariable Opioid Overdose Risk Prediction Model to Enhance Clinical Care for Long-term Opioid Therapy Patients.

BACKGROUND: Clinical opioid overdose risk prediction models can be useful tools to reduce the risk of overdose in patients prescribed long-term opioid therapy (LTOT). However, evolving overdose risk environments and clinical practices in addition to potential harmful model misapplications require careful assessment prior to widespread implementation into clinical care. Models may need to be tailored to meet local clinical operational needs and intended applications in practice. OBJECTIVE: To update and validate an existing opioid overdose risk model, the Kaiser Permanente Colorado Opioid Overdose (KPCOOR) Model, in patients prescribed LTOT for implementation in clinical care. DESIGN, SETTING, AND PARTICIPANTS: The retrospective cohort study consisted of 33, 625 patients prescribed LTOT between January 2015 and June 2019 at Kaiser Permanente Colorado, with follow-up through June 2021. MAIN MEASURES: The outcome consisted of fatal opioid overdoses identified from vital records and non-fatal opioid overdoses from emergency department and inpatient settings. Predictors included demographics, medication dispensings, substance use disorder history, mental health history, and medical diagnoses. Cox proportional hazards regressions were used to model 2-year overdose risk. KEY RESULTS: During follow-up, 65 incident opioid overdoses were observed (111.4 overdoses per 100,000 person-years) in the study cohort, of which 11 were fatal. The optimal risk model needed to risk-stratify patients and to be easily interpreted by clinicians. The original 5-variable model re-validated on the new study cohort had a bootstrap-corrected C-statistic of 0.73 (95% CI, 0.64-0.85) compared to a C-statistic of 0.80 (95% CI, 0.70-0.88) in the updated model and 0.77 (95% CI, 0.66-0.87) in the final adapted 7-variable model, which was also well-calibrated. CONCLUSIONS: Updating and adapting predictors for opioid overdose in the KPCOOR Model with input from clinical partners resulted in a parsimonious and clinically relevant model that was poised for integration in clinical care.

Racial and ethnic disparities in influenza vaccination coverage among pregnant women in the United States: The contribution of vaccine-related attitudes.

OBJECTIVE: Racial and ethnic disparities in influenza vaccination coverage among pregnant women in the United States have been documented. This study assessed the contribution of vaccine-related attitudes to coverage disparities. METHODS: Surveys were conducted following the 2019-2020 and 2020-2021 influenza seasons in a US research network. Using electronic health record data to identify pregnant women, random samples were selected for surveying; non-Hispanic Black women and influenza-unvaccinated women were oversampled. Regression-based decomposition analyses were used to assess the contribution of vaccine-related attitudes to racial and ethnic differences in influenza vaccination. Data were combined across survey years, and analyses were weighted and accounted for survey design. RESULTS: Survey response rate was 41.2% (721 of 1748) for 2019-2020 and 39.3% (706 of 1798) for 2020-2021. Self-reported influenza vaccination was higher among non-Hispanic White respondents (79.4% coverage, 95% CI 73.1%-85.7%) than Hispanic (66.2% coverage, 95% CI 52.5%-79.9%) and non-Hispanic Black (55.8% coverage, 95% CI 50.2%-61.4%) respondents. For all racial and ethnic groups, a high proportion (generally >80%) reported being seen for care, recommended for influenza vaccination, and offered vaccination. In decomposition analyses, vaccine-related attitudes (e.g., worry about vaccination causing influenza; concern about vaccine safety and effectiveness) explained a statistically significant portion of the observed racial and ethnic disparities in vaccination. Maternal age, education, and health status were not significant contributors after controlling for vaccine-related attitudes. CONCLUSIONS: In a setting with relatively high influenza vaccination coverage among pregnant women, racial and ethnic disparities in coverage were identified. Vaccine-related attitudes were associated with the disparities observed.

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.

Naloxone Co-Dispensing with Opioids: a Cluster Randomized Pragmatic Trial.

BACKGROUND: Although naloxone prevents opioid overdose deaths, few patients prescribed opioids receive naloxone, limiting its effectiveness in real-world settings. Barriers to naloxone prescribing include concerns that naloxone could increase risk behavior and limited time to provide necessary patient education. OBJECTIVE: To determine whether pharmacy-based naloxone co-dispensing affected opioid risk behavior. Secondary objectives were to assess if co-dispensing increased naloxone acquisition, increased patient knowledge about naloxone administration, and affected opioid dose and other substance use. DESIGN: Cluster randomized pragmatic trial of naloxone co-dispensing. SETTING: Safety-net health system in Denver, Colorado, between 2017 and 2020. PARTICIPANTS: Seven pharmacies were randomized. Pharmacy patients (N=768) receiving opioids were followed using automated data for 10 months. Pharmacy patients were also invited to complete surveys at baseline, 4 months, and 8 months; 325 survey participants were enrolled from November 15, 2017, to January 8, 2019. INTERVENTION: Intervention pharmacies implemented workflows to co-dispense naloxone while usual care pharmacies provided usual services. MAIN MEASURES: Survey instruments assessed opioid risk behavior; hazardous drinking; tobacco, cannabis, and other drug use; and knowledge. Naloxone dispensings and opioid dose were evaluated using pharmacy data among pharmacy patients and survey participants. Intention-to-treat analyses were conducted using generalized linear mixed models accounting for clustering at the pharmacy level. KEY RESULTS: Opioid risk behavior did not differ by trial group (P=0.52; 8-month vs. baseline adjusted risk ratio [ARR] 1.07; 95% CI 0.78, 1.47). Compared with usual care pharmacies, naloxone dispensings were higher in intervention pharmacies (ARR 3.38; 95% CI 2.21, 5.15) and participant knowledge increased (P=0.02; 8-month vs. baseline adjusted mean difference 1.05; 95% CI 0.06, 2.04). There was no difference in other substance use by the trial group. CONCLUSION: Co-dispensing naloxone with opioids effectively increased naloxone receipt and knowledge but did not increase self-reported risk behavior. TRIAL REGISTRATION: Registered at ClinicalTrials.gov ; Identifier: NCT03337100.

Safety Monitoring of JYNNEOS Vaccine During the 2022 Mpox Outbreak - United States, May 22-October 21, 2022.

JYNNEOS (Modified Vaccinia Ankara vaccine, Bavarian Nordic) is recommended in the United States for persons exposed to or at high risk for exposure to Monkeypox virus during the 2022 monkeypox (mpox) outbreak (1). JYNNEOS is a live, nonreplicating viral vaccine licensed for the prevention of smallpox and mpox in adults aged ≥18 years, administered as a 0.5-mL 2-dose series given 28 days apart by subcutaneous injection (2). On August 9, 2022, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for administration of 0.1 mL doses by intradermal injection for adults aged ≥18 years as a strategy to increase vaccine supply, and administration of 0.5 mL doses subcutaneously for persons aged <18 years (3). During May 22-October 21, 2022, a total of 987,294 JYNNEOS vaccine doses were administered in the United States. CDC has monitored JYNNEOS vaccine safety using the Vaccine Adverse Event Reporting System (VAERS) and the Vaccine Safety Datalink (VSD) for vaccine recipients of all ages, and through single-patient emergency Investigational New Drug (EIND) procedures for persons aged <18 years vaccinated before August 9, 2022. The most common adverse health events reported to VAERS for adults were nonserious and included injection site reactions, which was consistent with the prelicensure studies. Adverse health events were reported at similar rates for doses received by intradermal and subcutaneous administration. Serious adverse events were rare in adults, and no serious adverse events have been identified among persons aged <18 years. Overall, postlicensure and postauthorization surveillance to date support JYNNEOS vaccine safety.

COVID-19 Vaccination and Non-COVID-19 Mortality Risk - Seven Integrated Health Care Organizations, United States, December 14, 2020-July 31, 2021.

By September 21, 2021, an estimated 182 million persons in the United States were fully vaccinated against COVID-19.* Clinical trials indicate that Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), and Janssen (Johnson & Johnson; Ad.26.COV2.S) vaccines are effective and generally well tolerated (1-3). However, daily vaccination rates have declined approximately 78% since April 13, 2021†; vaccine safety concerns have contributed to vaccine hesitancy (4). A cohort study of 19,625 nursing home residents found that those who received an mRNA vaccine (Pfizer-BioNTech or Moderna) had lower all-cause mortality than did unvaccinated residents (5), but no studies comparing mortality rates within the general population of vaccinated and unvaccinated persons have been conducted. To assess mortality not associated with COVID-19 (non-COVID-19 mortality) after COVID-19 vaccination in a general population setting, a cohort study was conducted during December 2020-July 2021 among approximately 11 million persons enrolled in seven Vaccine Safety Datalink (VSD) sites.§ After standardizing mortality rates by age and sex, this study found that COVID-19 vaccine recipients had lower non-COVID-19 mortality than did unvaccinated persons. After adjusting for demographic characteristics and VSD site, this study found that adjusted relative risk (aRR) of non-COVID-19 mortality for the Pfizer-BioNTech vaccine was 0.41 (95% confidence interval [CI] = 0.38-0.44) after dose 1 and 0.34 (95% CI = 0.33-0.36) after dose 2. The aRRs of non-COVID-19 mortality for the Moderna vaccine were 0.34 (95% CI = 0.32-0.37) after dose 1 and 0.31 (95% CI = 0.30-0.33) after dose 2. The aRR after receipt of the Janssen vaccine was 0.54 (95% CI = 0.49-0.59). There is no increased risk for mortality among COVID-19 vaccine recipients. This finding reinforces the safety profile of currently approved COVID-19 vaccines in the United States.

COVID-19 Vaccination Coverage Among Insured Persons Aged ≥16 Years, by Race/Ethnicity and Other Selected Characteristics - Eight Integrated Health Care Organizations, United States, December 14, 2020-May 15, 2021.

COVID-19 vaccination is critical to ending the COVID-19 pandemic. Members of minority racial and ethnic groups have experienced disproportionate COVID-19-associated morbidity and mortality (1); however, COVID-19 vaccination coverage is lower in these groups (2). CDC used data from CDC's Vaccine Safety Datalink (VSD)* to assess disparities in vaccination coverage among persons aged ≥16 years by race and ethnicity during December 14, 2020-May 15, 2021. Measures of coverage included receipt of ≥1 COVID-19 vaccine dose (i.e., receipt of the first dose of the Pfizer-BioNTech or Moderna COVID-19 vaccines or 1 dose of the Janssen COVID-19 vaccine [Johnson & Johnson]) and full vaccination (receipt of 2 doses of the Pfizer-BioNTech or Moderna COVID-19 vaccines or 1 dose of Janssen COVID-19 vaccine). Among 9.6 million persons aged ≥16 years enrolled in VSD during December 14, 2020-May 15, 2021, ≥1-dose coverage was 48.3%, and 38.3% were fully vaccinated. As of May 15, 2021, coverage with ≥1 dose was lower among non-Hispanic Black (Black) and Hispanic persons (40.7% and 41.1%, respectively) than it was among non-Hispanic White (White) persons (54.6%). Coverage was highest among non-Hispanic Asian (Asian) persons (57.4%). Coverage with ≥1 dose was higher among persons with certain medical conditions that place them at higher risk for severe COVID-19 (high-risk conditions) (63.8%) than it was among persons without such conditions (41.5%) and was higher among persons who had not had COVID-19 (48.8%) than it was among those who had (42.4%). Persons aged 18-24 years had the lowest ≥1-dose coverage (28.7%) among all age groups. Continued monitoring of vaccination coverage and efforts to improve equity in coverage are critical, especially among populations disproportionately affected by COVID-19.

Sensitivity analyses of unmeasured and partially-measured confounders using multiple imputation in a vaccine safety study.

PURPOSE: Sensitivity analyses have played an important role in pharmacoepidemiology studies using electronic health records data. Despite the existence of quantitative bias analysis in pharmacoepidemiologic studies, simultaneously adjusting for unmeasured and partially measured confounders is challenging in vaccine safety studies. Our objective was to develop a flexible approach for conducting sensitivity analyses of unmeasured and partially-measured confounders concurrently for a vaccine safety study. METHODS: We derived conditional probabilities for an unmeasured confounder based on bias parameters, used these conditional probabilities and Monte Carlo simulations to impute the unmeasured confounder, and re-constructed the analytic datasets as if the unmeasured confounder had been observed. We simultaneously imputed a partially measured confounder using a prediction model. We considered unmeasured breastfeeding and partially measured family history of Type 1 diabetes (T1DM) in a study examining the association between exposure to rotavirus vaccination and T1DM. RESULTS: Before sensitivity analyses, the hazard ratios (HR) were 1.50 (95% CI, 0.81-2.77) for those partially exposed and 1.03 (95% CI, 0.62-1.72) for those fully exposed with unexposed children as the referent group. When breastfeeding and family history of T1DM were adjusted, the HR was 1.55 (95% CI, 0.84-2.87) for the partially exposed group; the HR was 0.98 (95% CI, 0.58-1.63) for the fully exposed group. CONCLUSIONS: We conclude that adjusting for unmeasured breastfeeding and partially measured family history of T1DM did not alter the conclusion that there was no evidence of association between rotavirus vaccination and developing T1DM. This novel approach allows for simultaneous adjustment for multiple unmeasured and partially-measured confounders.

Identifying naloxone administrations in electronic health record data using a text-mining tool.

Background: Effective and efficient methods are needed to identify naloxone administrations within electronic health record (EHR) data to conduct overdose surveillance and research. The objective of this study was to develop and validate a text-mining tool to identify naloxone administrations in EHR data. Methods: Clinical notes stored in databases between January 2017 and March 2018 were used to iteratively develop a text-mining tool to identify naloxone administrations. The first iteration of the tool used broad search terms. Then, after reviewing clinical notes of overdose encounters, we developed a list of phrases that described naloxone administrations to inform iteration two. While validating iteration two, additional phrases were found, which were then added to inform the final iteration. The comparator was an administrative code query extracted from the EHR. Medical record review was used to identify true positives. The primary outcome was the positive predictive values (PPV) of the second iteration, final iteration, and administrative code query. Results: Iteration two, the final iteration, and the administrative code had PPVs of 84.3% (95% confidence interval [CI] 78.6-89.0%), 83.8% (95% CI 78.6-88.2%), and 57.1% (95% CI 47.1-66.8%), respectively. Both iterations of the tool had a significantly higher PPV than the administrative code (p < 0.001). Conclusions: A text-mining tool improved the identification of naloxone administrations in EHR data from less than 60% with the administrative code to greater than 80% with both versions of the tool. Text-mining tools can inform the use of more sophisticated informatics methods, which often require significant time, resource, and expertise investment.

Surveillance for Adverse Events After COVID-19 mRNA Vaccination.

Importance: Safety surveillance of vaccines against COVID-19 is critical to ensure safety, maintain trust, and inform policy. Objectives: To monitor 23 serious outcomes weekly, using comprehensive health records on a diverse population. Design, Setting, and Participants: This study represents an interim analysis of safety surveillance data from Vaccine Safety Datalink. The 10 162 227 vaccine-eligible members of 8 participating US health plans were monitored with administrative data updated weekly and supplemented with medical record review for selected outcomes from December 14, 2020, through June 26, 2021. Exposures: Receipt of BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) COVID-19 vaccination, with a risk interval of 21 days for individuals after vaccine dose 1 or 2 compared with an interval of 22 to 42 days for similar individuals after vaccine dose 1 or 2. Main Outcomes and Measures: Incidence of serious outcomes, including acute myocardial infarction, Bell palsy, cerebral venous sinus thrombosis, Guillain-Barré syndrome, myocarditis/pericarditis, pulmonary embolism, stroke, and thrombosis with thrombocytopenia syndrome. Incidence of events that occurred among vaccine recipients 1 to 21 days after either dose 1 or 2 of a messenger RNA (mRNA) vaccine was compared with that of vaccinated concurrent comparators who, on the same calendar day, had received their most recent dose 22 to 42 days earlier. Rate ratios (RRs) were estimated by Poisson regression, adjusted for age, sex, race and ethnicity, health plan, and calendar day. For a signal, a 1-sided P < .0048 was required to keep type I error below .05 during 2 years of weekly analyses. For 4 additional outcomes, including anaphylaxis, only descriptive analyses were conducted. Results: A total of 11 845 128 doses of mRNA vaccines (57% BNT162b2; 6 175 813 first doses and 5 669 315 second doses) were administered to 6.2 million individuals (mean age, 49 years; 54% female individuals). The incidence of events per 1 000 000 person-years during the risk vs comparison intervals for ischemic stroke was 1612 vs 1781 (RR, 0.97; 95% CI, 0.87-1.08); for appendicitis, 1179 vs 1345 (RR, 0.82; 95% CI, 0.73-0.93); and for acute myocardial infarction, 935 vs 1030 (RR, 1.02; 95% CI, 0.89-1.18). No vaccine-outcome association met the prespecified requirement for a signal. Incidence of confirmed anaphylaxis was 4.8 (95% CI, 3.2-6.9) per million doses of BNT162b2 and 5.1 (95% CI, 3.3-7.6) per million doses of mRNA-1273. Conclusions and Relevance: In interim analyses of surveillance of mRNA COVID-19 vaccines, incidence of selected serious outcomes was not significantly higher 1 to 21 days postvaccination compared with 22 to 42 days postvaccination. While CIs were wide for many outcomes, surveillance is ongoing.

SARS-CoV-2 Infection Among Hospitalized Pregnant Women: Reasons for Admission and Pregnancy Characteristics - Eight U.S. Health Care Centers, March 1-May 30, 2020.

Pregnant women might be at increased risk for severe coronavirus disease 2019 (COVID-19), possibly related to changes in their immune system and respiratory physiology* (1). Further, adverse birth outcomes, such as preterm delivery and stillbirth, might be more common among pregnant women infected with SARS-CoV-2, the virus that causes COVID-19 (2,3). Information about SARS-CoV-2 infection during pregnancy is rapidly growing; however, data on reasons for hospital admission, pregnancy-specific characteristics, and birth outcomes among pregnant women hospitalized with SARS-CoV-2 infections are limited. During March 1-May 30, 2020, as part of Vaccine Safety Datalink (VSD)† surveillance of COVID-19 hospitalizations, 105 hospitalized pregnant women with SARS-CoV-2 infection were identified, including 62 (59%) hospitalized for obstetric reasons (i.e., labor and delivery or another pregnancy-related indication) and 43 (41%) hospitalized for COVID-19 illness without an obstetric reason. Overall, 50 (81%) of 62 pregnant women with SARS-CoV-2 infection who were admitted for obstetric reasons were asymptomatic. Among 43 pregnant women hospitalized for COVID-19, 13 (30%) required intensive care unit (ICU) admission, six (14%) required mechanical ventilation, and one died from COVID-19. Prepregnancy obesity was more common (44%) among pregnant women hospitalized for COVID-19 than that among asymptomatic pregnant women hospitalized for obstetric reasons (31%). Likewise, the rate of gestational diabetes (26%) among pregnant women hospitalized for COVID-19 was higher than it was among women hospitalized for obstetric reasons (8%). Preterm delivery occurred in 15% of pregnancies among 93 women who delivered, and stillbirths (fetal death at ≥20 weeks' gestation) occurred in 3%. Antenatal counseling emphasizing preventive measures (e.g., use of masks, frequent hand washing, and social distancing) might help prevent COVID-19 among pregnant women,§ especially those with prepregnancy obesity and gestational diabetes, which might reduce adverse pregnancy outcomes.

Modifying and Evaluating the Opioid Overdose Knowledge Scale for Prescription Opioids: A Pilot Study of the Rx-OOKS.

OBJECTIVE: To develop a validated instrument that measures knowledge about prescription opioid overdose. METHODS: Within an integrated health care system, we adapted, piloted, and tested the reliability and predictive validity of a modified Opioid Overdose Knowledge Scale (OOKS) instrument specific to prescription opioids (Rx-OOKS) with a patient population prescribed long-term opioid therapy and potentially at risk of opioid overdose. We used an interdisciplinary team approach and patient interviews to adapt the instrument. We then piloted the survey on a patient sample and assessed it using Cronbach's alpha and logistic regression. RESULTS: Rx-OOKS (N = 56) resulted in a three-construct, 25-item instrument. Internal consistency was acceptable for the following constructs: "signs of an overdose" (10 items) at α = 0.851, "action to take with opioid overdose" (seven items) at α = 0.692, and "naloxone use knowledge" (eight items) at α = 0.729. One construct, "risks of an overdose" (three items), had an α of 0.365 and was subsequently eliminated from analysis due to poor performance. We conducted logistic regression to determine if any of the constructs was strongly associated with future naloxone receipt. Higher scores on "actions to take in an overdose" had nine times the odds of receiving naloxone (odds ratio [OR] = 9.00, 95% confidence interval [CI] = 1.42-57.12); higher "naloxone use knowledge" scores were 15.8 times more likely to receive naloxone than those with lower scores (OR = 15.83, 95% CI = 1.68-149.17). CONCLUSIONS: The Rx-OOKS survey instrument can reliably measure knowledge about prescription opioid overdose recognition and naloxone use. Further, knowledge about actions to take during an opioid overdose and naloxone use were associated with future receipt of naloxone.

Development and evaluation of a standardized research definition for opioid overdose outcomes.

Background: Increasing epidemiologic and intervention research is being conducted on opioid overdose, a serious and potentially fatal outcome. However, there is little consensus on how to verify opioid overdose outcomes for research purposes. To ensure reproducibility, minimize misclassification, and permit data harmonization across studies, standardized and consistent overdose definitions are needed. The aims were to develop a case criteria classification scheme based on information commonly available in medical records and to compare it with reviewing physician clinical impression and simple encounter documentation. Methods: In 2 large health systems, we developed a case criteria classification scheme for opioid overdose based on prior literature, expert opinion, and pilot testing with sample medical records. We then identified emergency department and hospital encounters (n = 259) with at least 1 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code representing a broad range of opioid and non-opioid related poisonings. Physicians conducted structured medical record reviews to identify the proposed case criteria and generate a clinical impression, and trained abstractors verified documentation. We then compared the case criteria classification scheme with clinical impression and encounter documentation. Results: We developed a quantitative opioid overdose case criteria classification scheme that included 3 sets of major criteria and 9 minor criteria (supporting documentation). For the encounters identified using poisoning codes, the proportion verified as opioid overdoses using the case criteria classification scheme, clinical impression, and encounter documentation ranged from 50.4% to 52.7% at one site and 55.5% to 67.2% at the second site. Discrepancies across approaches and sites related to differences in available records and documentation of clinical signs of overdose. Conclusions: We propose a novel case criteria classification scheme for opioid overdose that could be used to rigorously and consistently define overdose across multiple research settings. However, prior to widespread use, further refinement and validation are needed.

Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy.

BACKGROUND: Naloxone is a life-saving opioid antagonist. Chronic pain guidelines recommend that physicians co-prescribe naloxone to patients at high risk for opioid overdose. However, clinical tools to efficiently identify patients who could benefit from naloxone are lacking. OBJECTIVE: To develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone. DESIGN: Retrospective cohort. SETTING: Derivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado. PARTICIPANTS: We developed a predictive model in a cohort of 42,828 patients taking chronic opioid therapy and externally validated the model in 10,708 patients. MAIN MEASURES: Potential predictors and outcomes (nonfatal pharmaceutical and heroin overdoses) were extracted from electronic health records. Fatal overdose outcomes were identified from state vital records. To match the approximate shelf-life of naloxone, we used Cox proportional hazards regression to model the 2-year risk of overdose. Calibration and discrimination were assessed. KEY RESULTS: A five-variable predictive model showed good calibration and discrimination (bootstrap-corrected c-statistic = 0.73, 95% confidence interval [CI] 0.69-0.78) in the derivation site, with sensitivity of 66.1% and specificity of 66.6%. In the validation site, the model showed good discrimination (c-statistic = 0.75, 95% CI 0.70-0.80) and less than ideal calibration, with sensitivity and specificity of 82.2% and 49.5%, respectively. CONCLUSIONS: Among patients on chronic opioid therapy, the predictive model identified 66-82% of all subsequent opioid overdoses. This model is an efficient screening tool to identify patients who could benefit from naloxone to prevent overdose deaths. Population differences across the two sites limited calibration in the validation site.

Addressing Parental Vaccine Concerns: Engagement, Balance, and Timing.

The recent United States measles epidemic has sparked another contentious national discussion about childhood vaccination. A growing number of parents are expressing concerns about the safety of vaccines, often fueled by misinformation from the internet, books, and other nonmedical sources. Many of these concerned parents are choosing to refuse or delay childhood vaccines, placing their children and surrounding communities at risk for serious diseases that are nearly 100% preventable with vaccination. Between 10% and 15% of parents are asking physicians to space out the timing of vaccines, which often poses an ethical dilemma for physicians. This trend reflects a tension between personal liberty and public health, as parents fight to control the decisions that affect the health of their children and public health officials strive to maintain high immunization rates to prevent outbreaks of vaccine-preventable diseases. Interventions to address this emerging public health issue are needed. We describe a framework by which web-based interventions can be used to help parents make evidence-based decisions about childhood vaccinations.

Bias from outcome misclassification in immunization schedule safety research.

PURPOSE: The Institute of Medicine recommended conducting observational studies of childhood immunization schedule safety. Such studies could be biased by outcome misclassification, leading to incorrect inferences. Using simulations, we evaluated (1) outcome positive predictive values (PPVs) as indicators of bias of an exposure-outcome association, and (2) quantitative bias analyses (QBA) for bias correction. METHODS: Simulations were conducted based on proposed or ongoing Vaccine Safety Datalink studies. We simulated 4 studies of 2 exposure groups (children with no vaccines or on alternative schedules) and 2 baseline outcome levels (100 and 1000/100 000 person-years), with 3 relative risk (RR) levels (RR = 0.50, 1.00, and 2.00), across 1000 replications using probabilistic modeling. We quantified bias from non-differential and differential outcome misclassification, based on levels previously measured in database research (sensitivity > 95%; specificity > 99%). We calculated median outcome PPVs, median observed RRs, Type 1 error, and bias-corrected RRs following QBA. RESULTS: We observed PPVs from 34% to 98%. With non-differential misclassification and true RR = 2.00, median bias was toward the null, with severe bias (median observed RR = 1.33) with PPV = 34% and modest bias (median observed RR = 1.83) with PPV = 83%. With differential misclassification, PPVs did not reflect median bias, and there was Type 1 error of 100% with PPV = 90%. QBA was generally effective in correcting misclassification bias. CONCLUSIONS: In immunization schedule studies, outcome misclassification may be non-differential or differential to exposure. Overall outcome PPVs do not reflect the distribution of false positives by exposure and are poor indicators of bias in individual studies. Our results support QBA for immunization schedule safety research.

Methods for addressing "innocent bystanders" when evaluating safety of concomitant vaccines.

PURPOSE: The need to develop methods for studying the safety of childhood immunization schedules has been recognized by the Institute of Medicine and Department of Health and Human Services. The recommended childhood immunization schedule includes multiple vaccines in a visit. A key concern is safety of concomitant (same day) versus separate day vaccination. This paper addresses a methodological challenge for observational studies using a self-controlled design to investigate the safety of concomitant vaccination. METHODS: We propose a process for distinguishing which of several concomitantly administered vaccines is responsible for increased risk of an adverse event while adjusting for confounding due to relationships between effect modifying risk factors and concomitant vaccine combinations. We illustrate the approach by re-examining the known increase in risk of seizure 7 to 10 days after measles-mumps-rubella (MMR) vaccination and evaluating potential independent or modifying effects of other vaccines. RESULTS: Initial analyses suggested that DTaP had both an independent and potentiating effect on seizure. After accounting for the relationship between age at vaccination and vaccine combination, there was little evidence for increased risk of seizure with same day administration of DTaP and MMR; incidence rate ratio, 95% confidence interval 1.2 (0.9-1.6), P value = Î¸.226. CONCLUSION: We have shown that when using a self-controlled design to investigate safety of concomitant vaccination, it can be critically important to adjust for time-invariant effect modifying risk factors, such as age at time of vaccination, which are structurally related to vaccination patterns due to recommended immunization schedules.

The safety of live attenuated influenza vaccine in children and adolescents 2 through 17 years of age: A Vaccine Safety Datalink study.

PURPOSE: To evaluate the safety of live attenuated influenza vaccine (LAIV) in children 2 through 17 years of age. METHODS: The study was conducted in 6 large integrated health care organizations participating in the Vaccine Safety Datalink (VSD). Trivalent LAIV safety was assessed in children who received LAIV between September 1, 2003 and March 31, 2013. Eighteen pre-specified adverse event groups were studied, including allergic, autoimmune, neurologic, respiratory, and infectious conditions. Incident rate ratios (IRRs) were calculated for each adverse event, using self-controlled case series analyses. For adverse events with a statistically significant increase in risk, or an IRR > 2.0 regardless of statistical significance, manual medical record review was performed to confirm case status. RESULTS: During the study period, 396 173 children received 590 018 doses of LAIV. For 13 adverse event groups, there was no significant increased risk of adverse events following LAIV. Five adverse event groups (anaphylaxis, syncope, Stevens-Johnson syndrome, adverse effect of drug, and respiratory failure) met criteria for manual medical record review. After review to confirm cases, 2 adverse event groups remained significantly associated with LAIV: anaphylaxis and syncope. One confirmed case of anaphylaxis was observed following LAIV, a rate of 1.7 per million LAIV doses. Five confirmed cases of syncope were observed, a rate of 8.5 per million doses. CONCLUSIONS: In a study of trivalent LAIV safety in a large cohort of children, few serious adverse events were detected. Anaphylaxis and syncope occurred following LAIV, although rarely. These data provide reassurance regarding continued LAIV use.