Monica Fujii

Glad you asked: participants' opinions of re-consent for dbGap data submission.

No consensus exists about when researchers need additional participant consent (re-consent) to submit existing data to the federal database of Genotypes and Phenotypes (dbGaP). Re-consent for submission of their data to dbGaP was sought from 1,340 study participants, 1,159 (86%) of whom agreed. We invited the first 400 of those who agreed to complete a telephone survey about their reasoning for their consent decision and their satisfaction with the re-consent process; 365 participants completed the survey. Respondents reported that it was very (69%) or somewhat (21%) important that they were asked for their permission. Many respondents considered alternatives to consent, such as notification-only or opt-out, to be unacceptable (67% and 40%, respectively). These results suggest that re-consent for dbGaP deposition may be advisable in certain cases to anticipate and honor participant preferences.

Frequent Antibiotic Use and Second Breast Cancer Events

Background: Antibiotic use may be associated with higher breast cancer risk and breast cancer mortality, but no study has evaluated the relation between antibiotic use and second breast cancer events (SBCE). Methods: We conducted a retrospective cohort study among women ≥18 years, diagnosed with incident stage I/II breast cancer during 1990–2008. Antibiotic use and covariates were obtained from health plan administrative databases and medical record review. Frequent antibiotic use was defined as ≥4 antibiotic dispensings in any moving 12-month period after diagnosis. Our outcome was SBCE defined as recurrence or second primary breast cancer. We used multivariable Cox proportional hazards models to estimate HR and 95% confidence intervals (CI), accounting for competing risks. Results: A total of 4,216 women were followed for a median of 6.7 years. Forty percent were frequent antibiotic users and 558 (13%) had an SBCE. Results are suggestive of a modest increased risk of SBCE (HR, 1.15; 95% CI, 0.88–1.50) among frequent antibiotic users compared with nonusers. Any potential increased risk was not supported when we evaluated recent use and past use. We observed no dose–response trends for SBCE with increasing duration of antibiotic use nor did we find evidence for altered SBCE risk in the antibiotic classes studied. Conclusions: Frequent antibiotic use may be associated with modestly elevated risk of SBCEs, but the association was not significant. Impact: Additional investigation by antibiotic class and underlying indication are important next steps given the high prevalence of frequent antibiotic use and growing number of breast cancer survivors. Cancer Epidemiol Biomarkers Prev; 22(9); 1588–99. ©2013 AACR.

Financial difficulty, worry about affording care, and benefit finding in long-term survivors of cancer

To examine the associations of worry about affording care and reporting financial difficulties with benefit finding in long‐term cancer survivors.

Accuracy of ICD-9 codes to identify nonunion and malunion and developing algorithms to improve case-finding of nonunion and malunion

OBJECTIVE To evaluate the accuracy of using ICD-9 codes to identify nonunions (NU) and malunions (MU) among adults with a prior fracture code and to explore case-finding algorithms. STUDY DESIGN Medical chart review of potential NU (N=300) and MU (N=288) cases. True NU cases had evidence of NU and no evidence of MU in the chart (and vice versa for MUs) or were confirmed by the study clinician. Positive predictive values (PPV) were calculated for ICD-9 codes. Case-finding algorithms were developed by a classification and regression tree analysis using additional automated data, and these algorithms were compared to true case status. SETTING Group Health Cooperative. RESULTS Compared to true cases as determined from chart review, the PPV of ICD-9 codes for NU and MU were 89% (95% CI, 85-92%) and 47% (95% CI, 41-53%), respectively. A higher proportion of true cases (NU: 95%; 95% CI, 90-98%; MU: 56%; 95% CI, 47-66%) were found among subjects with 1+ additional codes occurring in the 12months following the initial code. There was no case-finding algorithm for NU developed given the high PPV of ICD-9 codes. For MU, the best case-finding algorithm classified people as an MU case if they had a fracture in the forearm, hand, or skull and had no visit with an NU diagnosis code in the 12-month post MU diagnosis. PPV for this MU case-finding algorithm increased to 84%. CONCLUSIONS Identifying NUs with its ICD-9 code is reasonable. Identifying MUs with automated data can be improved by using a case-finding algorithm that uses additional information. Further validation of the MU algorithms in different populations is needed, as well as exploration of its performance in a larger sample.