Showing papers by "Sheena G. Sullivan published in 2016"

Theoretical Basis of the Test-Negative Study Design for Assessment of Influenza Vaccine Effectiveness.

Abstract: Influenza viruses undergo frequent antigenic changes. As a result, the viruses circulating change within and between seasons, and the composition of the influenza vaccine is updated annually. Thus, estimation of the vaccine's effectiveness is not constant across seasons. In order to provide annual estimates of the influenza vaccine's effectiveness, health departments have increasingly adopted the "test-negative design," using enhanced data from routine surveillance systems. In this design, patients presenting to participating general practitioners with influenza-like illness are swabbed for laboratory testing; those testing positive for influenza virus are defined as cases, and those testing negative form the comparison group. Data on patients' vaccination histories and confounder profiles are also collected. Vaccine effectiveness is estimated from the odds ratio comparing the odds of testing positive for influenza among vaccinated patients and unvaccinated patients, adjusting for confounders. The test-negative design is purported to reduce bias associated with confounding by health-care-seeking behavior and misclassification of cases. In this paper, we use directed acyclic graphs to characterize potential biases in studies of influenza vaccine effectiveness using the test-negative design. We show how studies using this design can avoid or minimize bias and where bias may be introduced with particular study design variations.

Differential age susceptibility to influenza B/Victoria lineage viruses in the 2015 Australian influenza season

Abstract: Influenza B viruses make up an important part of the burden from seasonal influenza globally. The 2015 season in Australia saw an unusual predominance of influenza B with a distinctive switch during the season from B/Yamagata/16/88 lineage viruses to B/Victoria/2/87 lineage viruses. We also noted significant differences in the age groups infected by the different B lineages, with B/Victoria infecting a younger population than B/Yamagata, that could not be explained by potential prior exposure.

Pooled influenza vaccine effectiveness estimates for Australia, 2012-2014.

Abstract: Data were pooled from three Australian sentinel general practice influenza surveillance networks to estimate Australia-wide influenza vaccine coverage and effectiveness against community presentations for laboratory-confirmed influenza for the 2012, 2013 and 2014 seasons. Patients presenting with influenza-like illness at participating GP practices were swabbed and tested for influenza. The vaccination odds of patients testing positive were compared with patients testing negative to estimate influenza vaccine effectiveness (VE) by logistic regression, adjusting for age group, week of presentation and network. Pooling of data across Australia increased the sample size for estimation from a minimum of 684 to 3,683 in 2012, from 314 to 2,042 in 2013 and from 497 to 3,074 in 2014. Overall VE was 38% [95% confidence interval (CI) 24-49] in 2012, 60% (95% CI 45-70) in 2013 and 44% (95% CI 31-55) in 2014. For A(H1N1)pdm09 VE was 54% (95% CI-28 to 83) in 2012, 59% (95% CI 33-74) in 2013 and 55% (95% CI 39-67) in 2014. For A(H3N2), VE was 30% (95% CI 14-44) in 2012, 67% (95% CI 39-82) in 2013 and 26% (95% CI 1-45) in 2014. For influenza B, VE was stable across years at 56% (95% CI 37-70) in 2012, 57% (95% CI 30-73) in 2013 and 54% (95% CI 21-73) in 2014. Overall VE against influenza was low in 2012 and 2014 when A(H3N2) was the dominant strain and the vaccine was poorly matched. In contrast, overall VE was higher in 2013 when A(H1N1)pdm09 dominated and the vaccine was a better match. Pooling data can increase the sample available and enable more precise subtype- and age group-specific estimates, but limitations remain.

Concordance of interim and final estimates of influenza vaccine effectiveness: a systematic review.

Abstract: The World Health Organization's Global Influenza Surveillance and Response System meets twice a year to generate a recommendation for the composition of the seasonal influenza vaccine. Interim vaccine effectiveness (VE) estimates provide a preliminary indication of influenza vaccine performance during the season and may be useful for decision making. We reviewed 17 pairs of studies reporting 33 pairs of interim and final estimates using the test-negative design to evaluate whether interim estimates can reliably predict final estimates. We examined features of the study design that may be correlated with interim estimates being substantially different from their final estimates and identified differences related to change in study period and concomitant changes in sample size, proportion vaccinated and proportion of cases. An absolute difference of no more than 10% between interim and final estimates was found for 18 of 33 reported pairs of estimates, including six of 12 pairs reporting VE against any influenza, six of 10 for influenza A(H1N1)pdm09, four of seven for influenza A(H3N2) and two of four for influenza B. While we identified inconsistencies in the methods, the similarities between interim and final estimates support the utility of generating and disseminating preliminary estimates of VE while virus circulation is ongoing.

Regression approaches in the test-negative study design for assessment of influenza vaccine effectiveness.

Abstract: Influenza vaccination is the most practical means available for preventing influenza virus infection and is widely used in many countries. Because vaccine components and circulating strains frequently change, it is important to continually monitor vaccine effectiveness (VE). The test-negative design is frequently used to estimate VE. In this design, patients meeting the same clinical case definition are recruited and tested for influenza; those who test positive are the cases and those who test negative form the comparison group. When determining VE in these studies, the typical approach has been to use logistic regression, adjusting for potential confounders. Because vaccine coverage and influenza incidence change throughout the season, time is included among these confounders. While most studies use unconditional logistic regression, adjusting for time, an alternative approach is to use conditional logistic regression, matching on time. Here, we used simulation data to examine the potential for both regression approaches to permit accurate and robust estimates of VE. In situations where vaccine coverage changed during the influenza season, the conditional model and unconditional models adjusting for categorical week and using a spline function for week provided more accurate estimates. We illustrated the two approaches on data from a test-negative study of influenza VE against hospitalization in children in Hong Kong which resulted in the conditional logistic regression model providing the best fit to the data.

How severe was the 2015 influenza season in Australia

Abstract: ASPREN1⁄4Australian Sentinel Practices Research Network; ILI1⁄4 influenza-like illness; SPNWA1⁄4Sentinel Practitioners Network of Western Australia; VicSPIN1⁄4Victorian Sentinel Practice Influenza Network. * For ASPREN and VicSPIN: overall proportions of positive swabs collected during the influenza season (MayeOctober). u MAustralia depicted a severe influenza season, focusing on the record number of laboratoryconfirmed influenza cases notified to health authorities. We used sentinel surveillance data and a weekly online symptoms survey to compare the magnitude of the 2015 influenza season in Australia with recent seasons.

A brief overview of influenza surveillance systems in Australia, 2015.

Abstract: The World Health Organization (WHO) has estimated that worldwide 5% to 15% of the population is affected by influenza each year, with between three and 5 million cases of severe illness and about 250,000 to 500,000 deaths.1 In Australia, it has been estimated that the disease is associated with 366 respiratory and 1,400 all-cause deaths,2 18,000 hospitalisations and over 300,000 general practice consultations3 each year. The morbidity, mortality and consequent economic burden of influenza epidemics vary annually. Although typically falling within the winter months in Australia, the onset and severity of annual epidemics varies. Therefore, robust surveillance is needed to guide prevention and controls efforts.

Timely antiretroviral prophylaxis during pregnancy effectively reduces HIV mother-to-child transmission in eight counties in China: a prospective study during 2004-2011.

Abstract: This study investigates the improvement of the prevention of mother-to-child transmission (PMTCT) of Human Immunodeficiency Virus (HIV) in China during 2004–2011. A clinic-based prospective study was conducted among HIV-positive pregnant women and their children in eight counties across China. Associated factors of mother-to-child transmission were analyzed using regression analysis. A total of 1,387 HIV+ pregnant women and 1,377 HIV-exposed infants were enrolled. The proportion of pregnant women who received HIV testing increased significantly from 45.1% to 98.9% during 2004–2011. Among whom, the proportion that received antiretroviral (ARV) prophylaxis increased from 61% to 96%, and the corresponding coverage in children increased from 85% to 97% during the same period. In contrast, single-dose nevirapine treatment during delivery declined substantially from 97.9% to 12.7%. Vertical transmission of HIV declined from 11.1% (95% confidence interval [CI]: 5.7–23.3%) in 2004 to 1.2% (95% CI: 0.1–5.8%) in 2011. Women who had a vaginal delivery (compared to emergency caesarian section (odds ratio [OR] = 0.46; 0.23–0.96)) and mothers on multi-ARVs (OR = 0.11; 0.04–0.29) were less likely to transmit HIV to their newborns. Increasing HIV screening enabled timely HIV care and prophylaxis to reduce vertical transmission of HIV. Early and consistent treatment with multi-ARVs during pregnancy is vital for PMTCT.