Detection of Pelvic Inflammatory Disease: Development of an Automated Case-Finding Algorithm Using Administrative Data

ICD-9 codes are conventionally used to identify pelvic inflammatory disease (PID) from administrative data for surveillance purposes. This approach may include non-PID cases. To refine PID case identification among women with ICD-9 codes suggestive of PID, a case-finding algorithm was developed using additional variables. Potential PID cases were identified among women aged 15–44 years at Group Health (GH) and Kaiser Permanente Colorado (KPCO) and verified by medical record review. A classification and regression tree analysis was used to develop the algorithm at GH; validation occurred at KPCO. The positive predictive value (PPV) for using ICD-9 codes alone to identify clinical PID cases was 79%. The algorithm identified PID appropriate treatment and age 15–25 years as predictors. Algorithm sensitivity (GH = 96.4%; KPCO = 90.3%) and PPV (GH = 86.9%; KPCO = 84.5%) were high, but specificity was poor (GH = 45.9%; KPCO = 37.0%). In GH, the algorithm offered a practical alternative to medical record review to further improve PID case identification

A Synthesis of Current Surveillance Planning Methods for the Sequential Monitoring of Drug and Vaccine Adverse Effects Using Electronic Health Care Data

Introduction: The large-scale assembly of electronic health care data combined with the use of sequential monitoring has made proactive postmarket drug- and vaccine-safety surveillance possible. Although sequential designs have been used extensively in randomized trials, less attention has been given to methods for applying them in observational electronic health care database settings. Existing Methods: We review current sequential-surveillance planning methods from randomized trials, and the Vaccine Safety Datalink (VSD) and Mini-Sentinel Pilot projects—two national observational electronic health care database safety monitoring programs. Future Surveillance Planning: Based on this examination, we suggest three steps for future surveillance planning in health care databases: (1) prespecify the sequential design and analysis plan, using available feasibility data to reduce assumptions and minimize later changes to initial plans; (2) assess existing drug or vaccine uptake, to determine if there is adequate information to proceed with surveillance, before conducting more resource-intensive planning; and (3) statistically evaluate and clearly communicate the sequential design with all those designing and interpreting the safety-surveillance results prior to implementation. Plans should also be flexible enough to accommodate dynamic and often unpredictable changes to the database information made by the health plans for administrative purposes. Conclusions: This paper is intended to encourage dialogue about establishing a more systematic, scalable, and transparent sequential design-planning process for medical-product safety-surveillance systems utilizing observational electronic health care databases. Creating such a framework could yield improvements over existing practices, such as designs with increased power to assess serious adverse events

Baseline representativeness of patients in clinics enrolled in the PRimary care Opioid Use Disorders treatment (PROUD) trial: comparison of trial and non-trial clinics in the same health systems

BACKGROUND: Pragmatic primary care trials aim to test interventions in real world health care settings, but clinics willing and able to participate in trials may not be representative of typical clinics. This analysis compared patients in participating and non-participating clinics from the same health systems at baseline in the PRimary care Opioid Use Disorders treatment (PROUD) trial. METHODS: This observational analysis relied on secondary electronic health record and administrative claims data in 5 of 6 health systems in the PROUD trial. The sample included patients 16-90 years at an eligible primary care visit in the 3 years before randomization. Each system contributed 2 randomized PROUD trial clinics and 4 similarly sized non-trial clinics. We summarized patient characteristics in trial and non-trial clinics in the 2 years before randomization ( baseline ). Using mixed-effect regression models, we compared trial and non-trial clinics on a baseline measure of the primary trial outcome (clinic-level patient-years of opioid use disorder (OUD) treatment, scaled per 10,000 primary care patients seen) and a baseline measure of the secondary trial outcome (patient-level days of acute care utilization among patients with OUD). RESULTS: Patients were generally similar between the 10 trial clinics (n = 248,436) and 20 non-trial clinics (n = 341,130), although trial clinics\u27 patients were slightly younger, more likely to be Hispanic/Latinx, less likely to be white, more likely to have Medicaid/subsidized insurance, and lived in less wealthy neighborhoods. Baseline outcomes did not differ between trial and non-trial clinics: trial clinics had 1.0 more patient-year of OUD treatment per 10,000 patients (95% CI: - 2.9, 5.0) and a 4% higher rate of days of acute care utilization than non-trial clinics (rate ratio: 1.04; 95% CI: 0.76, 1.42). CONCLUSIONS: trial clinics and non-trial clinics were similar regarding most measured patient characteristics, and no differences were observed in baseline measures of trial primary and secondary outcomes. These findings suggest trial clinics were representative of comparably sized clinics within the same health systems. Although results do not reflect generalizability more broadly, this study illustrates an approach to assess representativeness of clinics in future pragmatic primary care trials

Frequency of medically attended adverse events following tetanus and diphtheria toxoid vaccine in adolescents and young adults: a Vaccine Safety Datalink study

<p>Abstract</p> <p>Background</p> <p>Local reactions are the most commonly reported adverse events following tetanus and diphtheria toxoid (Td) vaccine and the risk of local reactions may increase with number of prior Td vaccinations.</p> <p>Methods</p> <p>To estimate the risk of medically attended local reactions following Td vaccination in adolescents and young adults we conducted a six-year retrospective cohort study assessing 436,828 Td vaccinations given to persons 9 through 25 years of age in the Vaccine Safety Datalink population from 1999 through 2004.</p> <p>Results</p> <p>Overall, the estimated risk of a medically attended local reaction was 3.6 events per 10,000 Td vaccinations. The lowest risk (2.8 events per 10,000 vaccinations) was found in the 11 to 15 year old age group. In comparison with that group, the event risks were significantly higher in both the 9 to 10 and 21 to 25 year old age groups. The risk of a local reaction was significantly higher in persons who had received another tetanus and diphtheria toxoid containing vaccine (TDCV) in the previous five years (incidence rate ratio, 2.9; 95% confidence interval, 1.2 to 7.2). Twenty-eight percent of persons with a local reaction to Td vaccine were prescribed antibiotics.</p> <p>Conclusion</p> <p>Medically attended local reactions were uncommon following Td vaccination. The risk of those reactions varied by age and by prior receipt of TDCVs. These findings provide a point of reference for future evaluations of the safety profile of newer vaccines containing tetanus or diphtheria toxoid.</p

Mortality risk after COVID-19 vaccination: A self-controlled case series study

Background Although previous studies found no-increased mortality risk after COVID-19 vaccination, residual confounding bias might have impacted the findings. Using a modified self-controlled case series (SCCS) design, we assessed the risk of non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes after primary series COVID-19 vaccination. Methods We analyzed all deaths between December 14, 2020, and August 11, 2021, among individuals from eight Vaccine Safety Datalink sites. Demographic characteristics of deaths in recipients of COVID-19 vaccines and unvaccinated individuals were reported. We conducted SCCS analyses by vaccine type and death outcomes and reported relative incidences (RI). The observation period for death spanned from the dates of emergency use authorization to the end of the study period (August 11, 2021) without censoring the observation period upon death. We pre-specified a primary risk interval of 28-day and a secondary risk interval of 14-day after each vaccination dose. Adjusting for seasonality in mortality analyses is crucial because death rates vary over time. Deaths among unvaccinated individuals were included in SCCS analyses to account for seasonality by incorporating calendar month in the models. Results For Pfizer-BioNTech (BNT162b2), RIs of non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes were below 1 and 95 % confidence intervals (CIs) excluded 1 across both doses and both risk intervals. For Moderna (mRNA-1273), RI point estimates of all outcomes were below 1, although the 95 % CIs of two RI estimates included 1: cardiac-related (RI = 0.78, 95 % CI, 0.58-1.04) and non-COVID-19 cardiac-related mortality (RI = 0.80, 95 % CI, 0.60-1.08) 14 days after the second dose in individuals without pre-existing cancer and heart disease. For Janssen (Ad26.COV2.S), RIs of four cardiac-related death outcomes ranged from 0.94 to 0.98 for the 14-day risk interval, and 0.68 to 0.72 for the 28-day risk interval and 95 % CIs included 1. Conclusion Using a modified SCCS design and adjusting for temporal trends, no-increased risk was found for non-COVID-19 mortality, all-cause mortality, and four cardiac-related death outcomes among recipients of the three COVID-19 vaccines used in the US

Identification of Incident Uterine Fibroids Using Electronic Medical Record Data

Background: Uterine fibroids are the most common benign tumors of the uterus that are associated with considerable morbidity in women. Diagnosis codes have been used to identify symptomatic fibroid cases, but their accuracy, especially for incident cases, is uncertain. This study assessed the accuracy of diagnosis codes in identifying incident fibroids and developed algorithms to improve incident fibroid case-finding using additional electronic data. Methods: Women aged 18–65 years who received an ICD-9 diagnosis code for uterine fibroid during 2012–2014 were identified from electronic databases at Group Health Cooperative, an integrated health care system in Washington State. Women with a fibroid history or hysterectomy were excluded. Medical records were reviewed on a random sample of 617 women to confirm incident fibroid status. Additional data on demographics, symptoms, treatment, imaging, health care utilization, comorbidities and medication were collected. Classification and regression tree analysis incorporating these additional data were used to develop algorithms to identify incident fibroid. We focused on an algorithm with high sensitivity (ie, maximizing the inclusion of true incident cases) and another with high specificity (ie, avoiding incorrect inclusion of noncases as incident cases). Algorithm performance was assessed by calculating sensitivity, specificity and positive predictive value (PPV) using medical record as gold standard. Results: Among the 617 women, mean age at diagnosis was 48 years. Medical record review confirmed 583 (95%) fibroid cases and 482 incident cases, a 78% PPV for incident cases based on diagnosis codes alone. Incorporating additional electronic data, the algorithm classified 395 incident cases among women with at least 2 pelvic ultrasounds on and prior to diagnosis date. Of these, 344 were correctly classified as incident cases, yielding an 87% PPV. Sensitivity was 71% and specificity 62%. A second algorithm further classified women based on a fibroid code of 218.9 in 2 years after diagnosis and lower body mass index yielded 93% PPV, 53% sensitivity and 85% specificity. Conclusion: Identification of incident uterine fibroids through ICD-9 diagnosis codes alone was good with moderate PPV. Algorithms using additional electronic data improved incident fibroid case finding with higher PPV, and either higher sensitivity or higher specificity to meet different study aims