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Background: Insurance claims data have been used to inform an understanding of Lyme disease epidemiology and cost of care, however few such studies have incorporated post-treatment symptoms following diagnosis. Using longitudinal data from a private, employer-based health plan in an endemic US state, we compared outpatient care utilization pre- and post-Lyme disease diagnosis. We hypothesized that utilization would be higher in the post-diagnosis period, and that temporal trends would differ by age and gender. Methods: Members with Lyme disease were required to have both a corresponding ICD-9 code and a fill of an antibiotic indicated for treatment of the infection within 30 days of diagnosis. A 2-year 'pre- diagnosis' period and a 2-year 'post-diagnosis period' were centered around the diagnosis month. Lyme disease-relevant outpatient care visits were defined as specific primary care, specialty care, or urgent care visits. Descriptive statistics examined visits during these pre- and post-diagnosis periods, and the association between these periods and the number of visits was explored using generalized linear mixed effects models adjusting for age, season of the year, and gender. Results: The rate of outpatient visits increased 26% from the pre to the post-Lyme disease diagnosis periods among our 317-member sample (rate ratio = 1.26 [1.18, 1.36], p < 0.001). Descriptively, care utilization increases appeared to persist across months in the post-diagnosis period. Women's care utilization increased by 36% (1.36 [1.24, 1.50], p < 0.001), a significantly higher increase than the 14% increase found among men (1.14 [1.02, 1.27], p = 0.017). This gender difference was mainly driven by adult members. We found a borderline significant 17% increase in visits for children < 18 years, (1.17 [0.99, 1.38], p = 0.068), and a 31% increase for adults ≥ 18 years (1.31 [1.21, 1.42], p < 0.001). Conclusions: Although modest at the population level, the statistically significant increases in post-Lyme diagnosis outpatient care we observed were persistent and unevenly distributed across demographic and place of service categories. As Lyme disease cases continue to grow, so will the cumulative prevalence of persistent symptoms after treatment. Therefore, it will be important to confirm these findings and understand their significance for care utilization and cost, particularly against the backdrop of other post-acute infectious syndromes. [ABSTRACT FROM AUTHOR]

The epidemiology of Lyme disease has been examined utilizing insurance claims from privately insured individuals; however, it is unknown whether reported patterns vary among the publicly insured. We examined trends in incidence rates of first Lyme disease diagnosis among 384,652 Maryland Medicaid recipients enrolled from July 2004 to June 2011. Age-, sex-, county-, season-, and year-specific incidence rates were calculated, and mixed-effects multiple logistic regression models were used to study the relationship between Lyme disease diagnosis and these variables. The incidence rate in our sample was 97.65 cases per 100,000 person-years (95% confidence interval (CI): 91.53, 104.06), and there was a 13% average annual increase in the odds of a Lyme disease diagnosis (odds ratio = 1.13, 95% CI: 1.09, 1.17; P < 0.001). Incidence rates for males and females were not significantly different, though males were significantly more likely to be diagnosed during high-season months (relative risk (RR) = 1.24, 95% CI: 1.06, 1.44) and less likely to be diagnosed during low-season months (RR = 0.63, 95% CI: 0.46, 0.87) than females. Additionally, adults were significantly more likely than children to be diagnosed during low-season months (RR = 1.59, 95% CI: 1.19, 2.12). While relatively rare in this study sample, Lyme disease diagnoses do occur in a Medicaid population in a Lyme-endemic state. [ABSTRACT FROM AUTHOR]

BACKGROUND Measuring processes of care performance rates is an invaluable tool for quality improvement; however, collecting daily process measure data is time-consuming and burdensome. OBJECTIVE To evaluate the accuracy of sampling strategies to estimate monthly compliance rates with ventilator-associated pneumonia prevention measures. SETTING AND PARTICIPANTS A total of 37 intensive care units affiliated with 29 hospitals participating in a 2-state 35-month ventilator-associated pneumonia prevention collaborative. Analysis was limited to 325 unit-months with complete data entry rates. METHODS We calculated unit-month level actual and sample monthly compliance rates for 6 ventilator-associated pneumonia prevention measures, using 4 sampling strategies: sample 1 day per month, sample 1 day per week, sample 7 consecutive days per month, and sample 7 consecutive days per month plus additional consecutive days as necessary to obtain at least 30 ventilator-days for that month whenever possible. We compared sample versus actual rates using paired t test and χ2 test. RESULTS Mean sampling accuracy ranged 84%-97% for 1 day per month, 91%-98% for 1 day per week, 92%-98% for 7 consecutive days per month, and 96%-99% for 7 consecutive days with at least 30 days per month if possible. The most accurate sampling strategy was to sample 7 consecutive days with at least 30 ventilator-days per month if possible. With this strategy, sample rates were within 10% of actual rates in 88%-99% of unit-months and within 5% of actual rates in 74%-97% of unit-months. CONCLUSION Sampling process measures intermittently rather than continually can yield accurate estimates of process measure performance rates. Infect Control Hosp Epidemiol 2016;37:1037-1043.

In this article, the authors describe an initiative that established an infrastructure to manage quality and safety efforts throughout a complex health care system and that improved performance on core measures for acute myocardial infarction, heart failure, pneumonia, surgical care, and children's asthma. The Johns Hopkins Medicine Board of Trustees created a governance structure to establish health care system-wide oversight and hospital accountability for quality and safety efforts throughout Johns Hopkins Medicine. The Armstrong Institute for Patient Safety and Quality was formed; institute leaders used a conceptual model nested in a fractal infrastructure to implement this initiative to improve performance at two academic medical centers and three community hospitals, starting in March 2012. The initiative aimed to achieve ≥ 96% compliance on seven inpatient process-of-care core measures and meet the requirements for the Delmarva Foundation and Joint Commission awards. The primary outcome measure was the percentage of patients at each hospital who received the recommended process of care. The authors compared health system and hospital performance before (2011) and after (2012, 2013) the initiative. The health system achieved ≥ 96% compliance on six of the seven targeted measures by 2013. Of the five hospitals, four received the Delmarva Foundation award and two received The Joint Commission award in 2013. The authors argue that, to improve quality and safety, health care systems should establish a system-wide governance structure and accountability process. They also should define and communicate goals and measures and build an infrastructure to support peer learning.