Vaccine Safety Datalink infrastructure enhancements for evaluating the safety of maternal vaccination.

BACKGROUND: Identifying pregnancy episodes and accurately estimating their beginning and end dates are imperative for observational maternal vaccine safety studies using electronic health record (EHR) data. METHODS: We modified the Vaccine Safety Datalink (VSD) Pregnancy Episode Algorithm (PEA) to include both the International Classification of Disease, ninth revision (ICD-9 system) and ICD-10 diagnosis codes, incorporated additional gestational age data, and validated this enhanced algorithm with manual medical record review. We also developed the new Dynamic Pregnancy Algorithm (DPA) to identify pregnancy episodes in real time. RESULTS: Around 75% of the pregnancy episodes identified by the enhanced VSD PEA were live births, 12% were spontaneous abortions (SABs), 10% were induced abortions (IABs), and 0.4% were stillbirths (SBs). Gestational age was identified for 99% of live births, 89% of SBs, 69% of SABs, and 42% of IABs. Agreement between the PEA-assigned and abstractor-identified pregnancy outcome and outcome date was 100% for live births, but was lower for pregnancy losses. When gestational age was available in the medical record, the agreement was higher for live births (97%), but lower for pregnancy losses (75%). The DPA demonstrated strong concordance with the PEA and identified pregnancy episodes ⩾6 months prior to the outcome date for 89% of live births. CONCLUSION: The enhanced VSD PEA is a useful tool for identifying pregnancy episodes in EHR databases. The DPA improves the timeliness of pregnancy identification and can be used for near real-time maternal vaccine safety studies. PLAIN LANGUAGE SUMMARY: Improving identification of pregnancies in the Vaccine Safety Datalink electronic medical record databases to allow for better and faster monitoring of vaccination safety during pregnancy Introduction: It is important to monitor of the safety of vaccines after they have been approved and licensed by the Food and Drug Administration, especially among women vaccinated during pregnancy. The Vaccine Safety Datalink (VSD) monitors vaccine safety through observational studies within large databases of electronic medical records. Since 2012, VSD researchers have used an algorithm called the Pregnancy Episode Algorithm (PEA) to identify the medical records of women who have been pregnant. Researchers then use these medical records to study whether receiving a particular vaccine is linked to any negative outcomes for the woman or her child.Methods: The goal of this study was to update and enhance the PEA to include the full set of medical record diagnostic codes [both from the older International Classification of Disease, ninth revision (ICD-9 system) and the newer ICD-10 system] and to incorporate additional sources of data about gestational age. To ensure the validity of the PEA following these enhancements, we manually reviewed medical records and compared the results with the algorithm. We also developed a new algorithm, the Dynamic Pregnancy Algorithm (DPA), to identify women earlier in pregnancy, allowing us to conduct more timely vaccine safety assessments.Results: The new version of the PEA identified 2,485,410 pregnancies in the VSD database. The enhanced algorithm more precisely estimated the beginning of pregnancies, especially those that did not result in live births, due to the new sources of gestational age data.Conclusion: Our new algorithm, the DPA, was successful at identifying pregnancies earlier in gestation than the PEA. The enhanced PEA and the new DPA will allow us to better evaluate the safety of current and future vaccinations administered during or around the time of pregnancy.

Data Quality Assessment and Multi-Organizational Reporting: Tools to Enhance Network Knowledge.

OBJECTIVE: Multi-organizational research requires a multi-organizational data quality assessment (DQA) process that combines and compares data across participating organizations. We demonstrate how such a DQA approach complements traditional checks of internal reliability and validity by allowing for assessments of data consistency and the evaluation of data patterns in the absence of an external "gold standard." METHODS: We describe the DQA process employed by the Data Coordinating Center (DCC) for Kaiser Permanente's (KP) Center for Effectiveness and Safety Research (CESR). We emphasize the CESR DQA reporting system that compares data summaries from the eight KP organizations in a consistent, standardized manner. RESULTS: We provide examples of multi-organization comparisons from DQA to confirm expectations about different aspects of data quality. These include: 1) comparison of direct data extraction from the electronic health records (EHR) and 2) comparison of non-EHR data from disparate sources. DISCUSSION: The CESR DCC has developed codes and procedures for efficiently implementing and reporting DQA. The CESR DCC approach is to 1) distribute DQA tools to empower data managers at each organization to assess their data quality at any time, 2) summarize and disseminate findings to address data shortfalls or document idiosyncrasies, and 3) engage data managers and end-users in an exchange of knowledge about the quality and its fitness for use. CONCLUSION: The KP CESR DQA model is applicable to networks hoping to improve data quality. The multi-organizational reporting system promotes transparency of DQA, adds to network knowledge about data quality, and informs research.

Pain management in the last six months of life among women who died of ovarian cancer.

Previous studies indicate that the symptoms of many dying cancer patients are undertreated and many suffer unnecessary pain. We obtained data retrospectively from three large health maintenance organizations, and examined the analgesic drug therapies received in the last six months of life by women who died of ovarian cancer between 1995 and 2000. Subjects were identified through cancer registries and administrative data. Outpatient medications used during the final six months of life were obtained from pharmacy databases. Pain information was obtained from medical charts. We categorized each medication based on the World Health Organization classification for pain management (mild, moderate, or intense). Of the 421 women, only 64 (15%) had no mention of pain in their charts. The use of medications typically prescribed for moderate to severe pain ("high intensity" drugs) increased as women approached death. At 5-6 months before death, 55% of women were either on no pain medication or medication generally used for mild pain; only 9% were using the highest intensity regimen. The percentage on the highest intensity regimen (drugs generally used for severe pain) increased to 22% at 3-4 months before death and 54% at 1-2 months. Older women (70 or older) were less likely to be prescribed the highest intensity medication than those under age 70 (44% vs. 70%, P<0.001). No differences were found in the use of the high intensity drugs by race, marital status, year of diagnosis, stage of disease, or comorbidity. Our finding that only 54% of women with pain were given high intensity medication near death indicates room for improvement in the care of ovarian cancer patients at the end of life.

Retention of enrollees following a cancer diagnosis within health maintenance organizations in the Cancer Research Network.

Population laboratories with complete clinical information on episodes of care are needed to support research on the quality of care delivered to cancer patients. Data resources within the Cancer Research Network (CRN) may overcome many of the limitations of existing cancer databases, but their potential clinical value depends on the stability of the enrolled population. To assess this issue, we studied the retention rates among survivors of the 132 580 patients diagnosed with cancer from January 1, 1993, through December 31, 1998, who were enrolled at five health maintenance organization sites participating in the CRN. Enrollees were followed from cancer diagnosis through death, disenrollment, or the end of follow-up (i.e., December 31, 1999). The retention rate among survivors for all cancers combined at 1 and 5 years after cancer diagnosis was 96.0% (95% confidence interval [CI] = 95.9% to 96.1%) and 83.9% (95% CI = 83.4% to 84.3%), respectively. The proportion of enrollees diagnosed with cancer who remained enrolled and available for evaluation suggests that the CRN is well-suited for studies of the quality of care for cancer patients, survivorship, and long-term outcomes.