Jerome I. Tokars

Background: Several observational studies have shown decreases in measured influenza vaccine effectiveness (mVE) during influenza seasons. One study found decreases of 6–11%/month during the 2011–2012 to 2014–2015 seasons. These findings could indicate waning immunity but could also occur if vaccine effectiveness is stable and vaccine provides partial protection in all vaccinees (“leaky”) rather than complete protection in a subset of vaccinees. Since it is unknown whether influenza vaccine is leaky, we simulated the 2011–2012 to 2014–2015 influenza seasons to estimate the potential contribution of leaky vaccine effect to the observed decline in mVE. Methods: We used available data to estimate daily numbers of vaccinations and infections with A/H1N1, A/H3N2, and B viruses. We assumed that vaccine effect was leaky, calculated mVE as 1 minus the Mantel-Haenszel relative risk of vaccine on incident cases, and determined the mean mVE change per 30 days since vaccination. Because change in mVE was highly dependent on infection rates, we performed simulations using low (15%) and high (31%) total (including symptomatic and asymptomatic) seasonal infection rates. Results: For the low infection rate, decreases (absolute) in mVE per 30 days after vaccination were 2% for A/H1N1 and 1% for A/H3N2and B viruses. For the high infection rate, decreases were 5% for A/H1N1, 4% for A/H3, and 3% for B viruses. Conclusions: The leaky vaccine bias could account for some, but probably not all, of the observed intraseasonal decreases in mVE. These results underscore the need for strategies to deal with intraseasonal vaccine effectiveness decline.

The seasonal incidence of influenza is often approximated as “5% to 20%”. We used two methods to estimate the seasonal incidence of symptomatic influenza in the United States. First, we made a statistical estimate extrapolated from influenza-associated hospitalization rates for 2010–11 to 2015–16, collected as part of national surveillance, covering approximately 9% of the United States, and including the existing mix of vaccinated and unvaccinated persons. Second, we performed a literature search and meta-analysis of published manuscripts that followed cohorts of subjects during 1996–2016 to detect laboratory-confirmed symptomatic influenza among unvaccinated persons; we adjusted this result to the United States median vaccination coverage and effectiveness during 2010–2016. The statistical estimate of influenza incidence among all ages ranged from 3.0–11.3% among seasons, with median values of 8.3 (95% confidence interval [CI] 7.3%, 9.7%) for all ages, 9.3% (CI 8.2%, 11.1%) for children <18 years and 8.9% (CI 8.2%, 9.9%) for adults 18–64 years. Corresponding values for the meta-analysis were 7.1% (CI 6.1, 8.1) for all ages, 8.7% (6.6, 10.5) for children, and 5.1% (3.6, 6.6) for adults. The two approaches produced comparable results for children and persons of all ages. The statistical estimates are more versatile and permit estimation of season-to-season variation. During 2010–2016, the incidence of symptomatic influenza among vaccinated and unvaccinated United States residents, including both medically attended and non-attended infections, was approximately 8% and varied from 3% to 11% among seasons.

Purpose: The Centers for Disease Control and Prevention Emerging Infections Program implemented active, population‐based surveillance for Guillain–Barré syndrome (GBS) following H1N1 vaccines in 10 states/metropolitan areas. We report additional analyses of these data using self‐controlled methods, which avoid potential confounding from person‐level factors and co‐morbidities.Methods: Surveillance officers identified GBS cases with symptom onset during October 2009–April 2010 and ascertained receipt of H1N1 vaccines. We calculated self‐controlled relative risks by comparing the number of cases with onset during a risk interval 1–42 days after vaccination with cases with onset during fixed (days 43–84) or variable (days 43–end of study period) control intervals. We calculated attributable risks by applying statistically significant relative risks to an independent estimate of GBS incidence.Results: Fifty‐nine GBS cases received H1N1 vaccine with or without seasonal vaccine. The relative risk was 2.1 (95%CI 1.2, 3.5) by the variable‐window and 3.0 (95%CI 1.4, 6.4) by the fixed‐window analyses. The corresponding attributable risks per million doses administered were 1.5 (95%CI 0.3, 3.4) and 2.8 (95%CI 0.6, 7.4).Conclusions: These attributable risks are similar to those of some previous formulations of seasonal influenza vaccine (about one to two cases per million doses administered), suggesting a low risk of GBS following the H1N1 vaccine that is not clearly higher than that of seasonal influenza vaccines. Published 2012. This article is a US Government work and is in the public domain in the USA.

BioSense is the US national automated biosurveillance system. Data regarding chief complaints and diagnoses are automatically pre-processed into 11 broader syndromes (e.g., respiratory) and 78 narrower sub-syndromes (e.g., asthma). The objectives of this report are to present the types of illness and injury that can be studied using these data and the frequency of visits for the syndromes and sub-syndromes in the various data types; this information will facilitate use of the system and comparison with other systems.

Algorithm modifications may improve sensitivity for detecting artificially added data.

The Centers for Disease Control and Prevention's (CDC's) BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Our study focused on finding useful anomalies at manageable alert rates according to available BioSense data history.

Objective.: To describe methods to assess the practical impact of risk adjustment for central line-days on the interpretation of central line–associated bloodstream infection (BSI) rates, because collecting these data is often burdensome.Methods.: We analyzed data from 247 hospitals that reported to the adult and pediatric intensive care unit component of the National Nosocomial Infections Surveillance System from 1995 through 2003. For each unit each year, we calculated the percentile error as the absolute value of the difference between the percentile based on a risk-adjusted or more-sophisticated measure (eg, the central line–day rate) and the percentile based on a crude or less-sophisticated measure (eg, the patient-day rate). Using rate per central line–day as the “gold standard,” we calculated performance characteristics (eg, sensitivity and predictive values) of rate per patient-day for finding central line–associated BSI rates higher or lower than the mean. Greater impact of risk adjustment is indicated by higher values for percentile error and lower values for performance characteristics.Results.: The median percentile error was ± 7 (ie, the percentile based on central line-days could be 7% higher or lower than the percentile based on patient-days). This error was less than 10 percentile points for 62% of the unit-years, was between 10 and 19 percentile points for 22% of the unit-years, and was 20 percentile points or more for 15% of the unit-years. Use of the rate based on patient-days had a sensitivity of 76% and a positive predictive value of 61% for detecting a significantly high or low central line–associated BSI rate.Conclusions.: We found that risk adjustment for central line–days has an important impact on the calculated central line–associated BSI percentile for some units. Similar methods can be used to evaluate the impact of other risk adjustment methods. Our results support current recommendations to use central line–days for surveillance of central line–associated BSI when comparisons are made among facilities.

Surveillance of health care—associated infections and antimicrobial resistance is an important aspect of prevention. In 2004, the Centers for Disease Control and Prevention had 3 national health care surveillance systems. During 2004–2005, these will be combined into a single Internet-based system, the National Healthcare Safety Network (NHSN). The NHSN will feature a number of enhancements, and ultimately, all US hospitals and other health care facilities will be encouraged to participate. Health care surveillance using standard methods has been very useful and is cited as a model for prevention. However, alternative approaches may improve health care surveillance by reducing complexity, decreasing the burden of data collection, and improving accuracy. These alternative approaches include adopting simpler methods and more-objective definitions, using sampling and estimation, substituting information in computer databases for manually collected data, and increasing surveillance for process measures with known prevention efficacy. Maintaining successful features of standard systems, adopting alternate surveillance approaches, and exploiting new technologies, such as the Internet, will make health care surveillance an even better tool for prevention.

Interpretation of blood cultures that are positive for coagulase-negative staphylococci (CoNS) is often difficult. Predictive values for blood cultur