Local spatial and temporal processes of influenza in Pennsylvania, USA: 2003-2009

Background

Influenza is a contagious respiratory disease responsible for annual seasonal epidemics in temperate climates. An understanding of how influenza spreads geographically and temporally within regions could result in improved public health prevention programs. The purpose of this study was to summarize the spatial and temporal spread of influenza using data obtained from the Pennsylvania Department of Health''s influenza surveillance system.

Methodology and Findings

We evaluated the spatial and temporal patterns of laboratory-confirmed influenza cases in Pennsylvania, United States from six influenza seasons (2003–2009). Using a test of spatial autocorrelation, local clusters of elevated risk were identified in the South Central region of the state. Multivariable logistic regression indicated that lower monthly precipitation levels during the influenza season (OR = 0.52, 95% CI: 0.28, 0.94), fewer residents over age 64 (OR = 0.27, 95% CI: 0.10, 0.73) and fewer residents with more than a high school education (OR = 0.76, 95% CI: 0.61, 0.95) were significantly associated with membership in this cluster. In addition, time series analysis revealed a temporal lag in the peak timing of the influenza B epidemic compared to the influenza A epidemic.

Conclusions

These findings illustrate a distinct spatial cluster of cases in the South Central region of Pennsylvania. Further examination of the regional transmission dynamics within these clusters may be useful in planning public health influenza prevention programs.     

Latitudinal Variations in Seasonal Activity of Influenza and Respiratory Syncytial Virus (RSV): A Global Comparative Review

Background

There is limited information on influenza and respiratory syncytial virus (RSV) seasonal patterns in tropical areas, although there is renewed interest in understanding the seasonal drivers of respiratory viruses.

Methods

We review geographic variations in seasonality of laboratory-confirmed influenza and RSV epidemics in 137 global locations based on literature review and electronic sources. We assessed peak timing and epidemic duration and explored their association with geography and study settings. We fitted time series model to weekly national data available from the WHO influenza surveillance system (FluNet) to further characterize seasonal parameters.

Results

Influenza and RSV activity consistently peaked during winter months in temperate locales, while there was greater diversity in the tropics. Several temperate locations experienced semi-annual influenza activity with peaks occurring in winter and summer. Semi-annual activity was relatively common in tropical areas of Southeast Asia for both viruses. Biennial cycles of RSV activity were identified in Northern Europe. Both viruses exhibited weak latitudinal gradients in the timing of epidemics by hemisphere, with peak timing occurring later in the calendar year with increasing latitude (P<0.03). Time series model applied to influenza data from 85 countries confirmed the presence of latitudinal gradients in timing, duration, seasonal amplitude, and between-year variability of epidemics. Overall, 80% of tropical locations experienced distinct RSV seasons lasting 6 months or less, while the percentage was 50% for influenza.

Conclusion

Our review combining literature and electronic data sources suggests that a large fraction of tropical locations experience focused seasons of respiratory virus activity in individual years. Information on seasonal patterns remains limited in large undersampled regions, included Africa and Central America. Future studies should attempt to link the observed latitudinal gradients in seasonality of viral epidemics with climatic and population factors, and explore regional differences in disease transmission dynamics and attack rates.     

Demonstrating the Use of High-Volume Electronic Medical Claims Data to Monitor Local and Regional Influenza Activity in the US

Introduction

Fine-grained influenza surveillance data are lacking in the US, hampering our ability to monitor disease spread at a local scale. Here we evaluate the performances of high-volume electronic medical claims data to assess local and regional influenza activity.

Material and Methods

We used electronic medical claims data compiled by IMS Health in 480 US locations to create weekly regional influenza-like-illness (ILI) time series during 2003–2010. IMS Health captured 62% of US outpatient visits in 2009. We studied the performances of IMS-ILI indicators against reference influenza surveillance datasets, including CDC-ILI outpatient and laboratory-confirmed influenza data. We estimated correlation in weekly incidences, peak timing and seasonal intensity across datasets, stratified by 10 regions and four age groups (<5, 5–29, 30–59, and 60+ years). To test IMS-Health performances at the city level, we compared IMS-ILI indicators to syndromic surveillance data for New York City. We also used control data on laboratory-confirmed Respiratory Syncytial Virus (RSV) activity to test the specificity of IMS-ILI for influenza surveillance.

Results

Regional IMS-ILI indicators were highly synchronous with CDC''s reference influenza surveillance data (Pearson correlation coefficients rho≥0.89; range across regions, 0.80–0.97, P<0.001). Seasonal intensity estimates were weakly correlated across datasets in all age data (rho≤0.52), moderately correlated among adults (rho≥0.64) and uncorrelated among school-age children. IMS-ILI indicators were more correlated with reference influenza data than control RSV indicators (rho = 0.93 with influenza v. rho = 0.33 with RSV, P<0.05). City-level IMS-ILI indicators were highly consistent with reference syndromic data (rho≥0.86).

Conclusion

Medical claims-based ILI indicators accurately capture weekly fluctuations in influenza activity in all US regions during inter-pandemic and pandemic seasons, and can be broken down by age groups and fine geographical areas. Medical claims data provide more reliable and fine-grained indicators of influenza activity than other high-volume electronic algorithms and should be used to augment existing influenza surveillance systems.     

The Association of Meningococcal Disease with Influenza in the United States, 1989–2009

Importance and Objective

Prior influenza infection is a risk factor for invasive meningococcal disease. Quantifying the fraction of meningococcal disease attributable to influenza could improve understanding of viral-bacterial interaction and indicate additional health benefits to influenza immunization.

Design, Setting and Participants

A time series analysis of the association of influenza and meningococcal disease using hospitalizations in 9 states from 1989–2009 included in the State Inpatient Databases from the Agency for Healthcare Research and Quality and the proportion of positive influenza tests by subtype reported to the Centers for Disease Control. The model accounts for the autocorrelation of meningococcal disease and influenza between weeks, temporal trends, co-circulating respiratory syncytial virus, and seasonality. The influenza-subtype-attributable fraction was estimated using the model coefficients. We analyzed the synchrony of seasonal peaks in hospitalizations for influenza, respiratory syncytial virus, and meningococcal disease.

Results and Conclusions

In 19 of 20 seasons, influenza peaked≤2 weeks before meningococcal disease, and peaks were highly correlated in time (ρ = 0.95; P <.001). H3N2 and H1N1 peaks were highly synchronized with meningococcal disease while pandemic H1N1, B, and respiratory syncytial virus were not. Over 20 years, 12.8% (95% CI, 9.1–15.0) of meningococcal disease can be attributable to influenza in the preceding weeks with H3N2 accounting for 5.2% (95% CI, 3.0–6.5), H1N1 4.3% (95% CI, 2.6–5.6), B 3.0% (95% CI, 0.8–4.9) and pH1N1 0.2% (95% CI, 0–0.4). During the height of influenza season, weekly attributable fractions reach 59%. While vaccination against meningococcal disease is the most important prevention strategy, influenza vaccination could provide further protection, particularly in young children where the meningococcal disease vaccine is not recommended or protective against the most common serogroup.     

Correlation between National Influenza Surveillance Data and Google Trends in South Korea

Background

In South Korea, there is currently no syndromic surveillance system using internet search data, including Google Flu Trends. The purpose of this study was to investigate the correlation between national influenza surveillance data and Google Trends in South Korea.

Methods

Our study was based on a publicly available search engine database, Google Trends, using 12 influenza-related queries, from September 9, 2007 to September 8, 2012. National surveillance data were obtained from the Korea Centers for Disease Control and Prevention (KCDC) influenza-like illness (ILI) and virologic surveillance system. Pearson''s correlation coefficients were calculated to compare the national surveillance and the Google Trends data for the overall period and for 5 influenza seasons.

Results

The correlation coefficient between the KCDC ILI and virologic surveillance data was 0.72 (p<0.05). The highest correlation was between the Google Trends query of H1N1 and the ILI data, with a correlation coefficient of 0.53 (p<0.05), for the overall study period. When compared with the KCDC virologic data, the Google Trends query of bird flu had the highest correlation with a correlation coefficient of 0.93 (p<0.05) in the 2010-11 season. The following queries showed a statistically significant correlation coefficient compared with ILI data for three consecutive seasons: Tamiflu (r = 0.59, 0.86, 0.90, p<0.05), new flu (r = 0.64, 0.43, 0.70, p<0.05) and flu (r = 0.68, 0.43, 0.77, p<0.05).

Conclusions

In our study, we found that the Google Trends for certain queries using the survey on influenza correlated with national surveillance data in South Korea. The results of this study showed that Google Trends in the Korean language can be used as complementary data for influenza surveillance but was insufficient for the use of predictive models, such as Google Flu Trends.     

Seasonal Patterns in Human A (H5N1) Virus Infection: Analysis of Global Cases

Background

Human cases of highly pathogenic avian influenza (HPAI) A (H5N1) have high mortality. Despite abundant data on seasonal patterns in influenza epidemics, it is unknown whether similar patterns exist for human HPAI H5N1 cases worldwide. Such knowledge could help decrease avian-to-human transmission through increased prevention and control activities during peak periods.

Methods

We performed a systematic search of published human HPAI H5N1 cases to date, collecting month, year, country, season, hemisphere, and climate data. We used negative binomial regression to predict changes in case incidence as a function of season. To investigate hemisphere as a potential moderator, we used AIC and the likelihood-ratio test to compare the season-only model to nested models including a main effect or interaction with hemisphere. Finally, we visually assessed replication of seasonal patterns across climate groups based on the Köppen-Geiger climate classification.

Findings

We identified 617 human cases (611 with complete seasonal data) occurring in 15 countries in Southeast Asia, Africa, and the Middle East. Case occurrence was much higher in winter (n = 285, p = 0.03) than summer (n = 64), and the winter peak occurred across diverse climate groups. There was no significant interaction between hemisphere and season.

Interpretation

Across diverse climates, HPAI H5N1 virus infection in humans increases significantly in winter. This is consistent with increased poultry outbreaks and HPAI H5N1 virus transmission during cold and dry conditions. Prioritizing prevention and control activities among poultry and focusing public health messaging to reduce poultry exposures during winter months may help to reduce zoonotic transmission of HPAI H5N1 virus in resource-limited settings.     

Health-Related Behaviors and Effectiveness of Trivalent Inactivated versus Live Attenuated Influenza Vaccine in Preventing Influenza-Like Illness among Young Adults

Background

Vaccination is the preferred preventive strategy against influenza. Though health behaviors are known to affect immunity and vaccine delivery modes utilize different immune processes, data regarding the preferred influenza vaccine type among adults endorsing specific health-related behaviors (alcohol use, tobacco use, and exercise level) are limited.

Methods

The relative effectiveness of two currently available influenza vaccines were compared for prevention of influenza-like illness during 2 well-matched influenza seasons (2006/2007, 2008/2009) among US military personnel aged 18–49 years. Relative vaccine effectiveness was compared between those self-reporting and not reporting recent smoking history and potential alcohol problem, and by exercise level using Cox proportional hazard modeling adjusted for sociodemographic and military factors, geographic area, and other health behaviors.

Results

28,929 vaccination events and 3936 influenza-like illness events over both influenza seasons were studied. Of subjects, 27.5% were smokers, 7.7% had a potential alcohol-related problem, 10.5% reported minimal exercise, and 4.4% reported high exercise levels. Overall, the risk of influenza-like illness did not significantly differ between live attenuated and trivalent inactivated influenza vaccine recipients (hazard ratio, 0.98; 95% confidence interval, 0.90–1.06). In the final adjusted model, the relative effectiveness of the 2 vaccine types did not differ by smoking status (p = 0.10), alcohol status (p = 0.21), or activity level (p = 0.11).

Conclusions

Live attenuated and trivalent inactivated influenza vaccines were similarly effective in preventing influenza-like illness among young adults and did not differ by health-related behavior status. Influenza vaccine efforts should continue to focus simply on delivering vaccine.     

Comparison of Different Risk Perception Measures in Predicting Seasonal Influenza Vaccination among Healthy Chinese Adults in Hong Kong: A Prospective Longitudinal Study

Background

Risk perception is a reported predictor of vaccination uptake, but which measures of risk perception best predict influenza vaccination uptake remain unclear.

Methodology

During the main influenza seasons (between January and March) of 2009 (Wave 1) and 2010 (Wave 2),505 Chinese students and employees from a Hong Kong university completed an online survey. Multivariate logistic regression models were conducted to assess how well different risk perceptions measures in Wave 1 predicted vaccination uptake against seasonal influenza in Wave 2.

Principal Findings

The results of the multivariate logistic regression models showed that feeling at risk (β = 0.25, p = 0.021) was the better predictor compared with probability judgment while probability judgment (β = 0.25, p = 0.029 ) was better than beliefs about risk in predicting subsequent influenza vaccination uptake. Beliefs about risk and feeling at risk seemed to predict the same aspect of subsequent vaccination uptake because their associations with vaccination uptake became insignificant when paired into the logistic regression model. Similarly, to compare the four scales for assessing probability judgment in predicting vaccination uptake, the 7-point verbal scale remained a significant and stronger predictor for vaccination uptake when paired with other three scales; the 6-point verbal scale was a significant and stronger predictor when paired with the percentage scale or the 2-point verbal scale; and the percentage scale was a significant and stronger predictor only when paired with the 2-point verbal scale.

Conclusions/Significance

Beliefs about risk and feeling at risk are not well differentiated by Hong Kong Chinese people. Feeling at risk, an affective-cognitive dimension of risk perception predicts subsequent vaccination uptake better than do probability judgments. Among the four scales for assessing risk probability judgment, the 7-point verbal scale offered the best predictive power for subsequent vaccination uptake.     

Respiratory virus infection and risk of invasive meningococcal disease in central Ontario, Canada

Background

In temperate climates, invasive meningococcal disease (IMD) incidence tends to coincide with or closely follow peak incidence of influenza virus infection; at a seasonal level, increased influenza activity frequently correlates with increased seasonal risk of IMD.

Methods

We evaluated 240 cases of IMD reported in central Ontario, Canada, from 2000 to 2006. Associations between environmental and virological (influenza A, influenza B and respiratory syncytial virus (RSV)) exposures and IMD incidence were evaluated using negative binomial regression models controlling for seasonal oscillation. Acute effects of weekly respiratory virus activity on IMD risk were evaluated using a matched-period case-crossover design with random directionality of control selection. Effects were estimated using conditional logistic regression.

Results

Multivariable negative binomial regression identified elevated IMD risk with increasing influenza A activity (per 100 case increase, incidence rate ratio = 1.18, 95% confidence interval (CI): 1.06, 1.31). In case-crossover models, increasing weekly influenza A activity was associated with an acute increase in the risk of IMD (per 100 case increase, odds ratio (OR)  = 2.03, 95% CI: 1.28 to 3.23). Increasing weekly RSV activity was associated with increased risk of IMD after adjusting for RSV activity in the previous 3 weeks (per 100 case increase, OR = 4.31, 95% CI: 1.14, 16.32). No change in disease risk was seen with increasing influenza B activity.

Conclusions

We have identified an acute effect of influenza A and RSV activity on IMD risk. If confirmed, these finding suggest that influenza vaccination may have the indirect benefit of reducing IMD risk.     

Use of Neuraminidase Inhibitors for Rapid Containment of Influenza: A Systematic Review and Meta-Analysis of Individual and Household Transmission Studies

Objectives

To assess the effectiveness of neuraminidase inhibitors for use in rapid containment of influenza.

Method

We conducted a systematic review and meta-analysis in accordance with the PRISMA statement. Healthcare databases and sources of grey literature were searched up to 2012 and records screened against protocol eligibility criteria. Data extraction and risk of bias assessments were performed using a piloted form. Results were synthesised narratively and we undertook meta-analyses to calculate pooled estimates of effect, statistical heterogeneity and assessed publication bias.

Findings

Nine randomised controlled trials (RCTs) and eight observational studies met the inclusion criteria. Neuraminidase inhibitors provided 67 to 89% protection for individuals following prophylaxis. Meta-analysis of individual protection showed a significantly lower pooled odds of laboratory confirmed seasonal or influenza A(H1N1)pdm09 infection following oseltamivir usage compared to placebo or no therapy (n = 8 studies; odds ratio (OR) = 0.11; 95% confidence interval (CI) = 0.06 to 0.20; p<0.001; I² = 58.7%). This result was comparable to the pooled odds ratio for individual protection with zanamivir (OR = 0.23; 95% CI 0.16 to 0.35). Similar point estimates were obtained with widely overlapping 95% CIs for household protection with oseltamivir or zanamivir. We found no studies of neuraminidase inhibitors to prevent population-wide community transmission of influenza.

Conclusion

Oseltamivir and zanamivir are effective for prophylaxis of individuals and households irrespective of treatment of the index case. There are no data which directly support an effect on wider community transmission.

Protocol Registry

PROSPERO registration number: CRD42013003880     

Time-Specific Ecologic Niche Models Forecast the Risk of Hemorrhagic Fever with Renal Syndrome in Dongting Lake District,China, 2005–2010

Background

Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne infectious disease, is one of the most serious public health threats in China. Increasing our understanding of the spatial and temporal patterns of HFRS infections could guide local prevention and control strategies.

Methodology/Principal Findings

We employed statistical models to analyze HFRS case data together with environmental data from the Dongting Lake district during 2005–2010. Specifically, time-specific ecologic niche models (ENMs) were used to quantify and identify risk factors associated with HFRS transmission as well as forecast seasonal variation in risk across geographic areas. Results showed that the Maximum Entropy model provided the best predictive ability (AUC = 0.755). Time-specific Maximum Entropy models showed that the potential risk areas of HFRS significantly varied across seasons. High-risk areas were mainly found in the southeastern and southwestern areas of the Dongting Lake district. Our findings based on models focused on the spring and winter seasons showed particularly good performance. The potential risk areas were smaller in March, May and August compared with those identified for June, July and October to December. Both normalized difference vegetation index (NDVI) and land use types were found to be the dominant risk factors.

Conclusions/Significance

Our findings indicate that time-specific ENMs provide a useful tool to forecast the spatial and temporal risk of HFRS.     

Local variations in spatial synchrony of influenza epidemics

Background

Understanding the mechanism of influenza spread across multiple geographic scales is not complete. While the mechanism of dissemination across regions and states of the United States has been described, understanding the determinants of dissemination between counties has not been elucidated. The paucity of high resolution spatial-temporal influenza incidence data to evaluate disease structure is often not available.

Methodology and Findings

We report on the underlying relationship between the spread of influenza and human movement between counties of one state. Significant synchrony in the timing of epidemics exists across the entire state and decay with distance (regional correlation = 62%). Synchrony as a function of population size display evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations is a stronger predictor of influenza spread than adult movement to and from workplaces suggesting that non-routine and leisure travel drive local epidemics.

Conclusions

These findings highlight the complex nature of influenza spread across multiple geographic scales.     

The geographic synchrony of seasonal influenza: a waves across Canada and the United States

Background

As observed during the 2009 pandemic, a novel influenza virus can spread globally before the epidemic peaks locally. As consistencies in the relative timing and direction of spread could form the basis for an early alert system, the objectives of this study were to use the case-based reporting system for laboratory confirmed influenza from the Canadian FluWatch surveillance program to identify the geographic scale at which spatial synchrony exists and then to describe the geographic patterns of influenza A virus across Canada and in relationship to activity in the United States (US).

Methodology/Principal Findings

Weekly laboratory confirmations for influenza A were obtained from the Canadian FluWatch and the US FluView surveillance programs from 1997/98 to 2006/07. For the six seasons where at least 80% of the specimens were antigenically similar, we identified the epidemic midpoint of the local/regional/provincial epidemics and analyzed trends in the direction of spread. In three out of the six seasons, the epidemic appeared first in Canada. Regional epidemics were more closely synchronized across the US (3–5 weeks) compared to Canada (5–13 weeks), with a slight gradient in timing from the southwest regions in the US to northeast regions of Canada and the US. Cities, as well as rural areas within provinces, usually peaked within a couple of weeks of each other. The anticipated delay in peak activity between large cities and rural areas was not observed. In some mixed influenza A seasons, lack of synchronization sub-provincially was evident.

Conclusions/Significance

As mixing between regions appears to be too weak to force a consistency in the direction and timing of spread, local laboratory-based surveillance is needed to accurately assess the level of influenza activity in the community. In comparison, mixing between urban communities and adjacent rural areas, and between some communities, may be sufficient to force synchronization.     

Immunogenicity and Acceptance of Influenza A (H1N1) Vaccine in a Cohort of Chronic Hepatitis C Patients Receiving Pegylated-Interferon Treatment

Background & Aims

Individuals at risk of (H1N1) influenza A infection are recommended to receive vaccination. Chronic hepatitis C (CHC) patients receiving treatment might be at a higher risk of respiratory bacterial infections after influenza infection. However, there are no observational studies evaluating the immunogenicity, tolerance and acceptance of 2009 influenza A vaccine in CHC patients.

Methods

We evaluated the immunogenicity of influenza A vaccine (Pandemrix®) by using the hemagglutination inhibition (HI) titers method in a well defined cohort of CHC patients receiving or not receiving pegylated-interferon and ribavirin, and compared it with healthy subjects (controls). A group of patients with inflammatory bowel disease (IBD) under immunosuppression, thought to have a lower immune response to seasonal influenza vaccine, were also included as a negative control group. In addition, tolerance to injection site reactions and acceptance was assessed by a validated questionnaire (Vaccinees'' perception of injection-VAPI-questionnaire).

Results

Of 114 subjects invited to participate, 68% accepted and, after exclusions, 72 were included. Post-vaccination geometric mean titers and seroprotection/seroconversion rates were optimal in CHC patients with ongoing treatment (n = 15; 232, CI95% 46–1166; 93%; 93%), without treatment (n = 10; 226, CI95% 69–743: 100%; 100%) and controls (n = 15;168, CI95% 42–680; 93%; 86%) with no differences between groups (P = 0.8). In contrast, IBD patients had a significantly lower immunogenic response (n = 27; 60, CI95% 42–680;66%;66%; P = 0.006). All the groups showed a satisfactory tolerance although CHC patients with ongoing treatment showed more local discomfort after vaccine injection.

Conclusion

There appeared to be no differences between CHC patients and healthy controls in serological response and acceptance of (H1N1) influenza vaccination.     

Guillain-Barré Syndrome,Influenza Vaccination,and Antecedent Respiratory and Gastrointestinal Infections: A Case-Centered Analysis in the Vaccine Safety Datalink, 2009–2011

Background

Guillain-Barré Syndrome (GBS) can be triggered by gastrointestinal or respiratory infections, including influenza. During the 2009 influenza A (H1N1) pandemic in the United States, monovalent inactivated influenza vaccine (MIV) availability coincided with high rates of wildtype influenza infections. Several prior studies suggested an elevated GBS risk following MIV, but adjustment for antecedent infection was limited.

Methods

We identified patients enrolled in health plans participating in the Vaccine Safety Datalink and diagnosed with GBS from July 2009 through June 2011. Medical records of GBS cases with 2009–10 MIV, 2010–11 trivalent inactivated influenza vaccine (TIV), and/or a medically-attended respiratory or gastrointestinal infection in the 1 through 141 days prior to GBS diagnosis were reviewed and classified according to Brighton Collaboration criteria for diagnostic certainty. Using a case-centered design, logistic regression models adjusted for patient-level time-varying sources of confounding, including seasonal vaccinations and infections in GBS cases and population-level controls.

Results

Eighteen confirmed GBS cases received vaccination in the 6 weeks preceding onset, among 1.27 million 2009–10 MIV recipients and 2.80 million 2010–11 TIV recipients. Forty-four confirmed GBS cases had infection in the 6 weeks preceding onset, among 3.77 million patients diagnosed with medically-attended infection. The observed-versus-expected odds that 2009–10 MIV/2010–11 TIV was received in the 6 weeks preceding GBS onset was odds ratio = 1.54, 95% confidence interval (CI), 0.59–3.99; risk difference = 0.93 per million doses, 95% CI, −0.71–5.16. The association between GBS and medically-attended infection was: odds ratio = 7.73, 95% CI, 3.60–16.61; risk difference = 11.62 per million infected patients, 95% CI, 4.49–26.94. These findings were consistent in sensitivity analyses using alternative infection definitions and risk intervals for prior vaccination shorter than 6 weeks.

Conclusions

After adjusting for antecedent infections, we found no evidence for an elevated GBS risk following 2009–10 MIV/2010–11 TIV influenza vaccines. However, the association between GBS and antecedent infection was strongly elevated.     

Household Transmission of Influenza A(H1N1)pdm09 in the Pandemic and Post-Pandemic Seasons

Background

The transmission of influenza viruses occurs person to person and is facilitated by contacts within enclosed environments such as households. The aim of this study was to evaluate secondary attack rates and factors associated with household transmission of laboratory-confirmed influenza A(H1N1)pdm09 in the pandemic and post-pandemic seasons.

Methods

During the 2009–2010 and 2010–2011 influenza seasons, 76 sentinel physicians in Navarra, Spain, took nasopharyngeal and pharyngeal swabs from patients diagnosed with influenza-like illness. A trained nurse telephoned households of those patients who were laboratory-confirmed for influenza A(H1N1)pdm09 to ask about the symptoms, risk factors and vaccination status of each household member.

Results

In the 405 households with a patient laboratory-confirmed for influenza A(H1N1)pdm09, 977 susceptible contacts were identified; 16% of them (95% CI 14–19%) presented influenza-like illness and were considered as secondary cases. The secondary attack rate was 14% in 2009–2010 and 19% in the 2010–2011 season (p = 0.049), an increase that mainly affected persons with major chronic conditions. In the multivariate logistic regression analysis, the risk of being a secondary case was higher in the 2010–2011 season than in the 2009–2010 season (adjusted odds ratio: 1.72; 95% CI 1.17–2.54), and in children under 5 years, with a decreasing risk in older contacts. Influenza vaccination was associated with lesser incidence of influenza-like illness near to statistical significance (adjusted odds ratio: 0.29; 95% CI 0.08–1.03).

Conclusion

The secondary attack rate in households was higher in the second season than in the first pandemic season. Children had a greater risk of infection. Preventive measures should be maintained in the second pandemic season, especially in high-risk persons.     

Timing of Antimicrobial Therapy after Identification of Ventilator-Associated Condition Is Not Associated with Mortality in Patients with Ventilator-Associated Pneumonia: A Cohort Study

Purpose

Delays in antimicrobial therapy increase mortality in ventilator-associated pneumonia (VAP). The more objective ventilator-associated complications (VAC) are increasingly used for quality reporting. It is unknown if delays in antimicrobial administration, after patients meet VAC criteria, leads to worse outcomes.

Materials and Methods

Cohort of 81 episodes of antimicrobial treatment for VAP. We compared mortality, superinfections and treatment failures conditional on the timing of identification of VAC.

Results

60% of patients with VAC had an identifiable episode at least 48 before the initiation of antimicrobials. Antimicrobial administration after the identification of VAC was not associated with intensive care unit (ICU) mortality (OR 0.71, 95% CI 0.11–4.48, p = 0.701) compared to immediate antimicrobial administration. Similarly, the risk of treatment failure or superinfection was not affected by the timing of administration of antimicrobials in VAC (HR 0.95, 95% CI 0.42–2.19, p = 0.914).

Conclusions

We observed no signal of harm associated with the timing to initiate antimicrobials after the identification of a VAC. The identification of VAC should not lead clinicians to start antimicrobials before a diagnosis of VAP can be established.