Incidence of outcomes relevant to vaccine safety monitoring in a US commercially-insured population

BACKGROUND

Background incidence rates (IRs) of potential safety outcomes among vaccine eligible individuals can inform assessment of vaccine safety. Vaccine safety surveillance often uses claims databases, but the impact of outcome definitions on background IR estimates is largely unexplored. Using two definitions for each outcome, we estimated background IRs of 32 cardiac, metabolic, allergic, autoimmune, neurologic, hematologic and nephrologic outcomes among individuals eligible to receive pneumococcal vaccination.

METHODS

We defined a cohort of individuals aged 6-100 years in US commercial health plans who had ≥12 months of health plan enrollment between January 2007 and August 2014 and no previous record of conjugate or simple polysaccharide pneumococcal vaccination. We developed a sensitive and a specific definition for each outcome, with the specific definition requiring evidence of additional care consistent with the outcome. IRs per 100,000 person-years for each outcome were presented overall and stratified by age, gender, and invasive pneumococcal disease (IPD) risk category.

RESULTS

We followed 19.9 million individuals for a median of 2.5 years. Wide variation was seen in IRs across different definitions of the 32 outcomes, with 19 (59%) outcomes having a specific definition IR less than half of the sensitive definition IR. IRs were particularly variable by definition for outcomes categorized as either hematologic/nephrologic or neurologic (mean ratio of specific IR to sensitive IR = 0.26 and 0.30, respectively). Across definitions, the IRs of the 32 outcomes were often highest in females, adults ≥65, and those at higher IPD risk.

CONCLUSIONS

Background IRs of safety outcomes relevant to populations indicated for pneumococcal vaccine varied by outcome definitions and population subgroups in this large US commercially-insured population. Given large differences in estimated IRs using sensitive versus specific case definitions, neurologic, and hematologic/nephrologic safety outcomes as compared to allergic and autoimmune outcomes may warrant more refined definitions and medical record validation.

Predictive model algorithms identifying early and advanced stage ER+/HER2- breast cancer in claims data

PURPOSE

Claims databases offer large populations for research, but lack clinical details. We aimed to develop predictive models to identify estrogen receptor positive (ER+) and human epidermal growth factor negative (HER2-) early breast cancer (ESBC) and advanced stage breast cancer (ASBC) in a claims database.

METHODS

Female breast cancer cases in Anthem's Cancer Care Quality Program served as the gold standard validation sample. Predictive models were developed from clinical knowledge and empirically from claims data using logistic and lasso regression. Model performance was assessed by classification rates and c-statistics. Models were applied to the HealthCore Integrated Research Database (claims) to identify cohorts of women with ER+/HER2- ESBC and ASBC.

RESULTS

The validation sample included 3184 women with ER+/HER2- ESBC and 1436 with ER+/HER2- ASBC. Predictive models for ER+/HER2- ESBC and ASBC included 25 and 20 factors, respectively. Models had robust discrimination in identifying cases (c-stat = 0.92 for ESBC and 0.95 for ASBC). Compared with a traditional a priori algorithm developed with clinical insight alone, the ER+/HER2- ASBC-predictive model had better positive predictive value (PPV) (0.91, 95% CI, 0.90-0.93, vs 0.69, 95% CI, 0.66-0.73) and sensitivity (0.54 vs 0.35). Models were applied to the claims database to identify cohorts of 33 001 and 3198 women with ER+/HER2- ESBC and ASBC.

CONCLUSION

We conducted a validation study and developed predictive models to identify in a claims database cohorts of women with ER+/HER2- ESBC and ASBC. The models identified large cohorts in the claims data that can be used to characterize indications in the evaluation of targeted therapies.

Youth Speak Out on School Food Environments

In Philadelphia, over 40% of youth are overweight or obese. The objective in this assessment was to learn about urban residents' perspectives regarding the local food environment and its impact on eating behaviors. Using photo-elicitation, 20 adolescents reflected on their food environments through photographs and corresponding interviews. Without specific prompting from interviewers, every participant raised concerns about their school food environments, which they commonly found to be unhealthy and unappealing. Participants' responses reflected four themes: (1) mixed reviews regarding the healthfulness of school vending machines, (2) lunch from home versus lunch from school, (3) factors that influenced food choice at school, and (4) critiques of school food environments. Students embraced the photo-elicitation approach as a way to convey their concerns and to suggest opportunities for improvements. School nurses, who are trusted by students and school personnel, are well-positioned to solicit student input and advocate for healthier school food environments.

Identifying patients according to breast cancer stage and ER/HER2 receptor status using claims data

e13096

Background: Safety analyses of targeted oncology therapies often require cancer stage and receptor status. Automated claims databases offer the largest populations to study rare outcomes, but lack these characteristics. We conducted a validation study of claims data using predictive models to identify two cohorts of women with early-stage and advanced-stage ER+/HER2- breast cancer (ESBC and ASBC, respectively). Methods: Retrospective cohort and validation study using electronic data linkage of a nationwide claims database (HealthCore Integrated Research Database – HIRD) and Anthem’s Cancer Care Quality program (CCQP). The CCQP served as a validation sample. We used claims data to develop two predictive models to estimate breast cancer stage and receptor status. We applied these models to the HIRD to identify ER+/HER2- ESBC and ASBC cohorts. In each cohort we assessed adverse event (AE) rates. Results:: In addition to breast cancer diagnoses, predictive models for ER+/HER2- ESBC and ASBC included 21 and 15 factors, respectively (Table).When compared to an a priori ASBC algorithm developed from clinical experience, the claims-based predictive model for ASBC had better PPV (0.78 v. 0.62) with similar sensitivity (0.39 v. 0.38). For the ASBC cohort, selected AE rates per 100 person-years included: anemia, 26.4; neutropenia, 14.3; pulmonary embolism, 5.7; and leukopenia, 4.0. Conclusions: Identification ofcancer stage and biomarkers using claims data can be improved through predictive modeling. ER+/HER2- ESBC and ASBC cohorts are being utilized for characterizing indications and conducting safety evaluations of targeted therapies. [Table: see text]

A Randomized Controlled Trial of Opt-In Versus Opt-Out Enrollment Into a Diabetes Behavioral Intervention

PURPOSE

To examine the effect of an opt-out default recruitment strategy compared to a conventional opt-in strategy on enrollment and adherence to a behavioral intervention for poorly controlled diabetic patients.

DESIGN

Randomized controlled trial.

SETTING

University of Pennsylvania primary care practices.

PARTICIPANTS

Participants of this trial included those with (1) age 18 to 80 years; (2) diabetes diagnosis; and (3) a measured hemoglobin A1c (HbA1c) greater than 8% in the past 12 months.

INTERVENTION

We randomized eligible patients into opt-in and opt-out arms prior to enrollment. Those in the opt-out arm received a letter stating that they were enrolled into a diabetes research study with the option to opt out, and those in the opt-in arm received a standard recruitment letter.

MEASURES

Main end points include enrollment rate, defined as the proportion of participants who attended the baseline visit, and adherence to daily glycemic monitoring.

ANALYSIS

We powered our study to detect a 20% difference in adherence to device usage between arms and account for a 10% attrition rate.

RESULTS

Of the 569 eligible participants who received a recruitment letter, 496 were randomized to the opt-in arm and 73 to the opt-out arm. Enrollment rates were 38% in the opt-out arm and 13% in the opt-in arm ( P < .001).

CONCLUSIONS

Opt-out defaults, where clinically appropriate, could be a useful approach for increasing the generalizability of low-risk trials testing behavioral interventions in clinical settings.

Effect of Financial Incentives to Physicians, Patients, or Both on Lipid Levels: A Randomized Clinical Trial

IMPORTANCE

Financial incentives to physicians or patients are increasingly used, but their effectiveness is not well established.

OBJECTIVE

To determine whether physician financial incentives, patient incentives, or shared physician and patient incentives are more effective than control in reducing levels of low-density lipoprotein cholesterol (LDL-C) among patients with high cardiovascular risk.

DESIGN, SETTING, AND PARTICIPANTS

Four-group, multicenter, cluster randomized clinical trial with a 12-month intervention conducted from 2011 to 2014 in 3 primary care practices in the northeastern United States. Three hundred forty eligible primary care physicians (PCPs) were enrolled from a pool of 421. Of 25,627 potentially eligible patients of those PCPs, 1503 enrolled. Patients aged 18 to 80 years were eligible if they had a 10-year Framingham Risk Score (FRS) of 20% or greater, had coronary artery disease equivalents with LDL-C levels of 120 mg/dL or greater, or had an FRS of 10% to 20% with LDL-C levels of 140 mg/dL or greater. Investigators were blinded to study group, but participants were not.

INTERVENTIONS

Primary care physicians were randomly assigned to control, physician incentives, patient incentives, or shared physician-patient incentives. Physicians in the physician incentives group were eligible to receive up to $1024 per enrolled patient meeting LDL-C goals. Patients in the patient incentives group were eligible for the same amount, distributed through daily lotteries tied to medication adherence. Physicians and patients in the shared incentives group shared these incentives. Physicians and patients in the control group received no incentives tied to outcomes, but all patient participants received up to $355 each for trial participation.

MAIN OUTCOMES AND MEASURES

Change in LDL-C level at 12 months.

RESULTS

Patients in the shared physician-patient incentives group achieved a mean reduction in LDL-C of 33.6 mg/dL (95% CI, 30.1-37.1; baseline, 160.1 mg/dL; 12 months, 126.4 mg/dL); those in physician incentives achieved a mean reduction of 27.9 mg/dL (95% CI, 24.9-31.0; baseline, 159.9 mg/dL; 12 months, 132.0 mg/dL); those in patient incentives achieved a mean reduction of 25.1 mg/dL (95% CI, 21.6-28.5; baseline, 160.6 mg/dL; 12 months, 135.5 mg/dL); and those in the control group achieved a mean reduction of 25.1 mg/dL (95% CI, 21.7-28.5; baseline, 161.5 mg/dL; 12 months, 136.4 mg/dL; P < .001 for comparison of all 4 groups). Only patients in the shared physician-patient incentives group achieved reductions in LDL-C levels statistically different from those in the control group (8.5 mg/dL; 95% CI, 3.8-13.3; P = .002).

CONCLUSIONS AND RELEVANCE

In primary care practices, shared financial incentives for physicians and patients, but not incentives to physicians or patients alone, resulted in a statistically significant difference in reduction of LDL-C levels at 12 months. This reduction was modest, however, and further information is needed to understand whether this approach represents good value.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT01346189.