Waning of Measured Influenza Vaccine Effectiveness Over Time: The Potential Contribution of Leaky Vaccine Effect.

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.

Seasonal Incidence of Symptomatic Influenza in the United States.

Background: The seasonal incidence of influenza is often approximated as 5%-20%. Methods: We used 2 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 2010-2011 to 2015-2016, 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 US median vaccination coverage and effectiveness during 2010-2016. Results: 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% (95% CI, 8.2%-11.1%) for children <18 years, and 8.9% (95% CI, 8.2%-9.9%) for adults 18-64 years. Corresponding values for the meta-analysis were 7.1% (95% CI, 6.1%-8.1%) for all ages, 8.7% (95% CI, 6.6%-10.5%) for children, and 5.1% (95% CI, 3.6%-6.6%) for adults. Conclusions: The 2 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 US residents, including both medically attended and nonattended infections, was approximately 8% and varied from 3% to 11% among seasons.

Outbreak of Middle East Respiratory Syndrome at Tertiary Care Hospital, Jeddah, Saudi Arabia, 2014.

During March-May 2014, a Middle East respiratory syndrome (MERS) outbreak occurred in Jeddah, Saudi Arabia, that included many persons who worked or received medical treatment at King Fahd General Hospital. We investigated 78 persons who had laboratory-confirmed MERS during March 2-May 10 and documented contact at this hospital. The 78 persons with MERS comprised 53 patients, 16 healthcare workers, and 9 visitors. Among the 53 patients, the most probable sites of acquisition were the emergency department (22 patients), inpatient areas (17), dialysis unit (11), and outpatient areas (3). Infection control deficiencies included limited separation of suspected MERS patients, patient crowding, and inconsistent use of infection control precautions; aggressive improvements in these deficiencies preceded a decline in cases. MERS coronavirus transmission probably was multifocal, occurring in multiple hospital settings. Continued vigilance and strict application of infection control precautions are necessary to prevent future MERS outbreaks.

Multifacility Outbreak of Middle East Respiratory Syndrome in Taif, Saudi Arabia.

Middle East respiratory syndrome (MERS) coronavirus (MERS-CoV) is a novel respiratory pathogen first reported in 2012. During September 2014-January 2015, an outbreak of 38 cases of MERS was reported from 4 healthcare facilities in Taif, Saudi Arabia; 21 of the 38 case-patients died. Clinical and public health records showed that 13 patients were healthcare personnel (HCP). Fifteen patients, including 4 HCP, were associated with 1 dialysis unit. Three additional HCP in this dialysis unit had serologic evidence of MERS-CoV infection. Viral RNA was amplified from acute-phase serum specimens of 15 patients, and full spike gene-coding sequencing was obtained from 10 patients who formed a discrete cluster; sequences from specimens of 9 patients were closely related. Similar gene sequences among patients unlinked by time or location suggest unrecognized viral transmission. Circulation persisted in multiple healthcare settings over an extended period, underscoring the importance of strengthening MERS-CoV surveillance and infection-control practices.

Health care worker contact with MERS patient, Saudi Arabia.

To investigate potential transmission of Middle East respiratory syndrome coronavirus (MERS-CoV) to health care workers in a hospital, we serologically tested hospital contacts of the index case-patient in Saudi Arabia, 4 months after his death. None of the 48 contacts showed evidence of MERS-CoV infection.

Cumulative risk of Guillain-Barré syndrome among vaccinated and unvaccinated populations during the 2009 H1N1 influenza pandemic.

OBJECTIVES: We sought to assess risk of Guillain-Barré syndrome (GBS) among influenza A (H1N1) 2009 monovalent (pH1N1) vaccinated and unvaccinated populations at the end of the 2009 pandemic. METHODS: We applied GBS surveillance data from a US population catchment area of 45 million from October 15, 2009, through May 31, 2010. GBS cases meeting Brighton Collaboration criteria were included. We calculated the incidence density ratio (IDR) among pH1N1 vaccinated and unvaccinated populations. We also estimated cumulative GBS risk using life table analysis. Additionally, we used vaccine coverage data and census population estimates to calculate denominators. RESULTS: There were 392 GBS cases; 64 (16%) occurred after pH1N1vaccination. The vaccinated population had lower average risk (IDR = 0.83, 95% confidence interval = 0.63, 1.08) and lower cumulative risk (6.6 vs 9.2 cases per million persons, P = .012) of GBS. CONCLUSIONS: Our findings suggest that at the end of the influenza season cumulative GBS risk was less among the pH1N1vaccinated than the unvaccinated population, suggesting the benefit of vaccination as it relates to GBS. The observed potential protective effect on GBS attributed to vaccination warrants further study.

Highly pathogenic avian influenza A(H7N3) virus in poultry workers, Mexico, 2012.

We identified 2 poultry workers with conjunctivitis caused by highly pathogenic avian influenza A(H7N3) viruses in Jalisco, Mexico. Genomic and antigenic analyses of 1 isolate indicated relatedness to poultry and wild bird subtype H7N3 viruses from North America. This isolate had a multibasic cleavage site that might have been derived from recombination with host rRNA.

Adverse event reports after tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis vaccines in pregnant women.

OBJECTIVE: We sought to characterize reports to the Vaccine Adverse Event Reporting System (VAERS) of pregnant women who received tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis vaccine (Tdap). STUDY DESIGN: We searched VAERS for reports of pregnant women who received Tdap from Jan. 1, 2005, through June 30, 2010. We conducted a clinical review of reports and available medical records. RESULTS: We identified 132 reports of Tdap administered to pregnant women; 55 (42%) described no adverse event (AE). No maternal or infant deaths were reported. The most frequent pregnancy-specific AE was spontaneous abortion in 22 (16.7%) reports. Injection site reactions were the most frequent non-pregnancy-specific AE found in 6 (4.5%) reports. One report with a major congenital anomaly (gastroschisis) was identified. CONCLUSION: During a time when Tdap was not routinely recommended in pregnancy, review of reports to VAERS in pregnant women after Tdap did not identify any concerning patterns in maternal, infant, or fetal outcomes.

The risk of Guillain-Barré syndrome associated with influenza A (H1N1) 2009 monovalent vaccine and 2009-2010 seasonal influenza vaccines: results from self-controlled analyses.

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.

Method selection and adaptation for distributed monitoring of infectious diseases for syndromic surveillance.

BACKGROUND: Automated surveillance systems require statistical methods to recognize increases in visit counts that might indicate an outbreak. In prior work we presented methods to enhance the sensitivity of C2, a commonly used time series method. In this study, we compared the enhanced C2 method with five regression models. METHODS: We used emergency department chief complaint data from US CDC BioSense surveillance system, aggregated by city (total of 206 hospitals, 16 cities) during 5/2008-4/2009. Data for six syndromes (asthma, gastrointestinal, nausea and vomiting, rash, respiratory, and influenza-like illness) was used and was stratified by mean count (1-19, 20-49, ≥50 per day) into 14 syndrome-count categories. We compared the sensitivity for detecting single-day artificially-added increases in syndrome counts. Four modifications of the C2 time series method, and five regression models (two linear and three Poisson), were tested. A constant alert rate of 1% was used for all methods. RESULTS: Among the regression models tested, we found that a Poisson model controlling for the logarithm of total visits (i.e., visits both meeting and not meeting a syndrome definition), day of week, and 14-day time period was best. Among 14 syndrome-count categories, time series and regression methods produced approximately the same sensitivity (<5% difference) in 6; in six categories, the regression method had higher sensitivity (range 6-14% improvement), and in two categories the time series method had higher sensitivity. DISCUSSION: When automated data are aggregated to the city level, a Poisson regression model that controls for total visits produces the best overall sensitivity for detecting artificially added visit counts. This improvement was achieved without increasing the alert rate, which was held constant at 1% for all methods. These findings will improve our ability to detect outbreaks in automated surveillance system data.

Influenza Antiviral Treatment and Length of Stay.

BACKGROUND: Antiviral treatment is recommended for hospitalized patients with suspected and confirmed influenza, but evidence is limited among children. We evaluated the effect of antiviral treatment on hospital length of stay (LOS) among children hospitalized with influenza. METHODS: We included children <18 years hospitalized with laboratory-confirmed influenza in the US Influenza Hospitalization Surveillance Network. We collected data for 2 cohorts: 1 with underlying medical conditions not admitted to the ICU (n = 309, 2012-2013) and an ICU cohort (including children with and without underlying conditions; n = 299, 2010-2011 to 2012-2013). We used a Cox model with antiviral receipt as a time-dependent variable to estimate hazard of discharge and a Kaplan-Meier survival analysis to determine LOS. RESULTS: Compared with those not receiving antiviral agents, LOS was shorter for those treated ≤2 days after illness onset in both the medical conditions (adjusted hazard ratio: 1.37, P = .02) and ICU (adjusted hazard ratio: 1.46, P = .007) cohorts, corresponding to 37% and 46% increases in daily discharge probability, respectively. Treatment ≥3 days after illness onset had no significant effect in either cohort. In the medical conditions cohort, median LOS was 3 days for those not treated versus 2 days for those treated ≤2 days after symptom onset (P = .005). CONCLUSIONS: Early antiviral treatment was associated with significantly shorter hospitalizations in children with laboratory-confirmed influenza and high-risk medical conditions or children treated in the ICU. These results support Centers for Disease Control and Prevention recommendations for prompt empiric antiviral treatment in hospitalized patients with suspected or confirmed influenza.

Automated monitoring of clusters of falls associated with severe winter weather using the BioSense system.

OBJECTIVES: To identify and characterise clusters of emergency department (ED) visits for fall injuries during the 2007-2008 winter season. METHODS: Hospital ED chief complaints and diagnoses from hospitals reporting to the Centers for Disease Control and Prevention BioSense system were analysed. The authors performed descriptive analyses, used time series charts on data aggregated by metropolitan statistical areas (MSAs), and used SaTScan to find spatial-temporal clusters of visits from falls. RESULTS: In 2007-2008, 17 clusters of falls in 13 MSAs were found; the median number of excess ED visits for falls was 71 per day. SaTScan identified 11 clusters of falls, of which seven corresponded to MSA clusters found by time series and five included more than one state/district. Most clusters coincided with known periods of snowfall or freezing rain. CONCLUSION: The results show the role that a national automated system can play in tracking widespread injuries. Such a system could be harnessed to assist with prevention strategies.

Summary of data reported to CDC's national automated biosurveillance system, 2008.

BACKGROUND: 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. METHODS: For each major data source, we summarized information on the facilities, timeliness, patient demographics, and rates of visits for each syndrome and sub-syndrome. RESULTS: In 2008, the primary data sources were the 333 US Department of Defense, 770 US Veterans Affairs, and 532 civilian hospital emergency department facilities. Median times from patient visit to record receipt at CDC were 2.2 days, 2.0 days, and 4 hours for these sources respectively. Among sub-syndromes, we summarize mean 2008 visit rates in 45 infectious disease categories, 11 injury categories, 7 chronic disease categories, and 15 other categories. CONCLUSIONS: We present a systematic summary of data that is automatically available to public health departments for monitoring and responding to emergencies.

Enhancing time-series detection algorithms for automated biosurveillance.

BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14-28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data.

Evaluation of sliding baseline methods for spatial estimation for cluster detection in the biosurveillance system.

BACKGROUND: 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. METHODS: The study dataset included more than 3 years of daily counts of military outpatient clinic visits for respiratory and rash syndrome groupings. We applied four spatial estimation methods in implementations of space-time scan statistics cross-checked in Matlab and C. We compared the utility of these methods according to the resultant background cluster rate (a false alarm surrogate) and sensitivity to injected cluster signals. The comparison runs used a spatial resolution based on the facility zip code in the patient record and a finer resolution based on the residence zip code. RESULTS: Simple estimation methods that account for day-of-week (DOW) data patterns yielded a clear advantage both in background cluster rate and in signal sensitivity. A 28-day baseline gave the most robust results for this estimation; the preferred baseline is long enough to remove daily fluctuations but short enough to reflect recent disease trends and data representation. Background cluster rates were lower for the rash syndrome counts than for the respiratory counts, likely because of seasonality and the large scale of the respiratory counts. CONCLUSION: The spatial estimation method should be chosen according to characteristics of the selected data streams. In this dataset with strong day-of-week effects, the overall best detection performance was achieved using subregion averages over a 28-day baseline stratified by weekday or weekend/holiday behavior. Changing the estimation method for particular scenarios involving different spatial resolution or other syndromes can yield further improvement.

An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States.

INTRODUCTION: To evaluate the public health benefit of yearly influenza vaccinations, CDC estimates the number of influenza cases and hospitalizations averted by vaccine. Available input data on cases and vaccinations is aggregated by month and the estimation model is intentionally simple, raising concerns about the accuracy of estimates. METHODS: We created a synthetic dataset with daily counts of influenza cases and vaccinations, calculated "true" averted cases using a reference model applied to the daily data, aggregated the data by month to simulate data that would actually be available, and evaluated the month-level data with seven test methods (including the current method). Methods with averted case estimates closest to the reference model were considered most accurate. To examine their performance under varying conditions, we re-evaluated the test methods when synthetic data parameters (timing of vaccination relative to cases, vaccination coverage, infection rate, and vaccine effectiveness) were varied over wide ranges. Finally, we analyzed real (i.e., collected by surveillance) data from 2010 to 2017 comparing the current method used by CDC with the best-performing test methods. RESULTS: In the synthetic dataset (population 1 million persons, vaccination uptake 55%, seasonal infection risk without vaccination 12%, vaccine effectiveness 48%) the reference model estimated 28,768 averted cases. The current method underestimated averted cases by 9%. The two best test methods estimated averted cases with <1% error. These two methods also worked well when synthetic data parameters were varied over wide ranges (≤6.2% error). With the real data, these two methods estimated numbers of averted cases that are a median 8% higher than the currently-used method. CONCLUSIONS: We identified two methods for estimating numbers of influenza cases averted by vaccine that are more accurate than the currently-used algorithm. These methods will help us to better assess the benefits of influenza vaccination.

Measurement of the impact of risk adjustment for central line-days on interpretation of central line-associated bloodstream infection rates.

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 (i.e., 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.

Preventing catheter-associated bloodstream infections: a survey of policies for insertion and care of central venous catheters from hospitals in the prevention epicenter program.

OBJECTIVE: To determine the extent to which evidence-based practices for the prevention of central venous catheter (CVC)-associated bloodstream infections are incorporated into the policies and practices of academic intensive care units (ICUs) in the United States and to determine variations in the policies on CVC insertion, use, and care. DESIGN: A 9-page written survey of practices and policies for nontunneled CVC insertion and care. SETTING: ICUs in 10 academic tertiary-care hospitals. PARTICIPANTS: ICU medical directors and nurse managers. RESULTS: Twenty-five ICUs were surveyed (1-6 ICUs per hospital). In 80% of the units, 5 separate groups of clinicians inserted 24%-50% of all nontunneled CVCs. In 56% of the units, placement of more than two-thirds of nontunneled CVCs was performed in a single location in the hospital. Twenty units (80%) had written policies for CVC insertion. Twenty-eight percent of units had a policy requiring maximal sterile-barrier precautions when CVCs were placed, and 52% of the units had formal educational programs with regard to CVC insertion. Eighty percent of the units had a policy requiring staff to perform hand hygiene before inserting CVCs, but only 36% and 60% of the units required hand hygiene before accessing a CVC and treating the exit site, respectively. CONCLUSION: ICU policy regarding the insertion and care of CVCs varies considerably from hospital to hospital. ICUs may be able to improve patient outcome if evidence-based guidelines for CVC insertion and care are implemented.

A multicenter intervention to prevent catheter-associated bloodstream infections.

BACKGROUND: Education-based interventions can reduce the incidence of catheter-associated bloodstream infection. The generalizability of findings from single-center studies is limited. OBJECTIVE: To assess the effect of a multicenter intervention to prevent catheter-associated bloodstream infections. DESIGN: An observational study with a planned intervention. SETTING: Twelve intensive care units and 1 bone marrow transplantation unit at 6 academic medical centers. PATIENTS: Patients admitted during the study period. INTERVENTION: Updates of written policies, distribution of a 9-page self-study module with accompanying pretest and posttest, didactic lectures, and incorporation into practice of evidence-based guidelines regarding central venous catheter (CVC) insertion and care. MEASUREMENTS: Standard data collection tools and definitions were used to measure the process of care (ie, the proportion of nontunneled catheters inserted into the femoral vein and the condition of the CVC insertion site dressing for both tunneled and nontunneled catheters) and the incidence of catheter-associated bloodstream infection. RESULTS: Between the preintervention period and the postintervention period, the percentage of CVCs inserted into the femoral vein decreased from 12.9% to 9.4% (relative ratio, 0.73; 95% confidence interval [CI], 0.61-0.88); the total proportion of catheter insertion site dressings properly dated increased from 26.6% to 34.4% (relative ratio, 1.29; 95% CI, 1.17-1.42), and the overall rate of catheter-associated bloodstream infections decreased from 11.2 to 8.9 infections per 1,000 catheter-days (relative rate, 0.79; 95% CI, 0.67-0.93). The effect of the intervention varied among individual units. CONCLUSIONS: An education-based intervention that uses evidence-based practices can be successfully implemented in a diverse group of medical and surgical units and reduce catheter-associated bloodstream infection rates.