Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/054403

Population effect of influenza vaccination under co-circulation of non-vaccine variants and the case for a multi-strain A/H3N2 vaccine component

Colin J Worby·Jacco Wallinga·Marc Lipsitch·Edward Goldstein
View original source ↗Export metadata ↓

Abstract

Some past epidemics of different influenza (sub)types (particularly A/H3N2) in the US saw co-circulation of vaccine-type and variant strains. There is evidence that natural infection with one influenza (sub)type offers short-term protection against infection with another influenza (sub)type (henceforth, cross-immunity). This suggests that such cross-immunity for strains within a (sub)type is expected to be strong. Therefore, while vaccination effective against one strain may reduce transmission of that strain, this may also lead to a reduction of the ability of the vaccine-type strain to suppress spread of a variant strain. It remains unclear what the joint effect of vaccination and cross-immunity is for co-circulating influenza strains, and what is the potential benefit of a bivalent vaccine that protects against both strains.\n\nWe simulated co-circulation of vaccine-type and variant strains under a variety of scenarios. In each scenario, we considered the case when the vaccine efficacy against the variant strain is lower than the efficacy against the vaccine-type strain (monovalent vaccine), as well the case when vaccine is equally efficacious against both strains (bivalent vaccine).\n\nAdministration of a bivalent vaccine results in a significant reduction in the overall incidence of infection compared to administration of a monovalent vaccine, even with lower coverage by the bivalent vaccine. Additionally, we found that the stronger is the degree of cross-immunity, the less beneficial is the increase in coverage levels for the monovalent vaccine, and the more beneficial is the introduction of the bivalent vaccine.\n\nOur work exhibits the limitations of influenza vaccines that have low efficacy against non-vaccine strains, and demonstrates the benefits of vaccines that offer good protection against multiple influenza strains. The results elucidate the need for guarding against the potential co-circulation of non-vaccine strains for an influenza (sub)type, at least during select seasons, possibly through inclusion of multiple strains within a (sub)type (particularly A/H3N2) in a vaccine.

Explore related subjects

EpidemiologyPreprint

Keep this discovery

BibTeXRIS

Colin J Worby, Jacco Wallinga, Marc Lipsitch, Edward Goldstein. 2016-05-20. Population effect of influenza vaccination under co-circulation of non-vaccine variants and the case for a multi-strain A/H3N2 vaccine component. https://doi.org/10.1101/054403

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2016

A risk stratification approach for improved interpretation of diagnostic accuracy statistics

Diagnostic accuracy statistics, including predictive values, risk-differences, Youdens index and Area Under the Curve (AUC), assess the promise of novel biomarkers proposed as diagnostic tests. We reinterpret these statistics in light of risk-stratification (how well a biomarker separates those at higher risk from those at lower risk) to better understand their implications for public-health programs. We introduce an intuitively simple statistic, Mean Risk Stratification (MRS): the average change in risk (pre-test vs. post-test) revealed for tested individuals. High MRS implies better risk separation achieved by testing. MRS demonstrates that conventional predictive values can mislead because they do not account for disease prevalence or test-positivity rates. Little risk-stratification is possible for rare diseases, demonstrating a \"high-bar\" to justify population-based screening. Importantly, we demonstrate that the risk-difference, Youdens index, and AUC measure only multiplicative relative gains in risk-stratification: AUC=0.6 achieves only 20% of maximum risk-stratification (AUC=0.9 achieves 80%). However, large relative gains in risk-stratification might not imply large absolute gains if disease is rare or if the test is rarely positive. We illustrate MRS by our experience comparing the performance of cervical cancer screening tests in China vs. the USA. The test with the worst AUC=0.72 in China (visual inspection with ascetic acid) provides twice the risk-stratification of the test with best AUC=0.83 in the USA (human papillomavirus and Pap cotesting) because China has three times more cervical precancer/cancer. MRS could be routinely calculated to better understand the clinical/public-health implications of standard diagnostic accuracy statistics.

Epidemiology↗
bioRxiv2016

Collider Scope: How selection bias can induce spurious associations

Large-scale cross-sectional and cohort studies have transformed our understanding of the genetic and environmental determinants of health outcomes. However, the representativeness of these samples may be limited - either through selection into studies, or by attrition from studies over time. Here we explore the potential impact of this selection bias on results obtained from these studies, from the perspective that this amounts to conditioning on a collider (i.e., a form of collider bias). While it is acknowledged that selection bias will have a strong effect on representativeness and prevalence estimates, it is often assumed that it should not have a strong impact on estimates of associations. We argue that because selection can induce collider bias (which occurs when two variables independently influence a third variable, and that third variable is conditioned upon), selection can lead to substantially biased estimates of associations. In particular, selection related to phenotypes can bias associations with genetic variants associated with those phenotypes. In simulations, we show that even modest influences on selection into, or attrition from, a study can generate biased and potentially misleading estimates of both phenotypic and genotypic associations. Our results highlight the value of knowing which population your study sample is representative of. If the factors influencing selection and attrition are known, they can be adjusted for. For example, having DNA available on most participants in a birth cohort study offers the possibility of investigating the extent to which polygenic scores predict subsequent participation, which in turn would enable sensitivity analyses of the extent to which bias might distort estimates.\n\nKey MessagesSelection bias (including selective attrition) may limit the representativeness of large-scale cross-sectional and cohort studies.\n\nThis selection bias may induce collider bias (which occurs when two variables independently influence a third variable, and that variable is conditioned upon).\n\nThis may lead to substantially biased estimates of associations, including of genetic associations, even when selection / attrition is relatively modest.

Epidemiology↗
bioRxiv2016

A comparative analysis of Chikungunya and Zika transmission

The recent global dissemination of Chikungunya and Zika has fostered public health concern worldwide. To better understand the drivers of transmission of these two arboviral diseases, we propose a joint analysis of Chikungunya and Zika epidemics in the same territories, taking into account the common epidemiological features of the epidemics: transmitted by the same vector, in the same environments, and observed by the same surveillance systems. We analyse eighteen outbreaks in French Polynesia and the French West Indies using a hierarchical time-dependent SIR model accounting for the effect of virus, location and weather on transmission, and based on a disease specific serial interval. We show that Chikungunya and Zika have similar transmission potential in the same territories (transmissibility ratio between Zika and Chikungunya of 1.04 [95% credible interval: 0.97; 1.13]), but that detection and reporting rates were different (around 19% for Zika and 40% for Chikungunya). Temperature variations between 22{degrees}C and 29{degrees}C did not alter transmission, but increased precipitation showed a dual effect, first reducing transmission after a two-week delay, then increasing it around five weeks later. The present study provides valuable information for risk assessment and introduces a modelling framework for the comparative analysis of arboviral infections that can be extended to other viruses and territories.

Epidemiology↗