An evidence synthesis approach to estimating the incidence of seasonal influenza in the Netherlands

Estimating the burden of adult hospitalized RSV infection using local and state data - methodology

Respiratory syncytial virus (RSV) is becoming increasingly recognized as a serious threat to vulnerable population subgroups. This study describes the statistical analysis plan for a retrospective cohort study of adults hospitalized for acute respiratory infection (ARI) to estimate the population burden of RSV especially for groups such as the elderly, pregnant women and solid organ transplant patients. Disease burden estimates are essential for setting vaccine policy, e.g., should RSV vaccine become available, burden estimates may inform recommendations to prioritize certain high-risk groups. The study population is residents of Allegheny County, Pennsylvania ≥18 years of age who were hospitalized in Pennsylvania during the period September 1, 2015–August 31, 2018. Data sources will include U.S. Census, Pennsylvania Health Care Cost Containment Council (PHC4) and the electronic medical record for the health system to which the hospitals belong. The algorithm involves: 1) ARI-associated hospitalizations in PHC4 data; 2) adjustment for ARI hospitalizations among county residents but admitted to hospitals outside the county; and 3) RSV detections from respiratory viral panels. Key sensitivity analyses will adjust for undertesting for viruses in the fall and spring quarters. The results will be population-based estimates, stratified by age and risk groups. Adjusting hospitalization data using a multiplier method is a simple means to estimate the impact of RSV in a given area. This algorithm can be applied to other health systems and localities to estimate RSV and other respiratory pathogen burden in adults, to estimate burden following introduction of RSV vaccine and to make cost-effectiveness estimates.

Consultations for Influenza-Like Illness in Primary Care in The Netherlands: A Regression Approach

OBJECTIVES

To estimate the general practitioner (GP) consultation rate attributable to influenza in The Netherlands.

METHODS

Regression analysis was performed on the weekly numbers of influenza-like illness (ILI) GP consultations and laboratory reports for influenza virus types A and B and 8 other pathogens over the period 2003-2014 (11 influenza seasons; week 40-20 of the following year).

RESULTS

In an average influenza season, 27% and 11% of ILI GP consultations were attributed to infection by influenza virus types A and B, respectively. Influenza is therefore responsible for approximately 107 000 GP consultations (651/100 000) each year in The Netherlands. GP consultation rates associated with influenza infection were highest in children under 5 years of age, at 667 of 100 000 for influenza A and 258 of 100 000 for influenza B. Influenza virus infection was found to be the predominant cause of ILI-related GP visits in all age groups except children under 5, in which respiratory syncytial virus (RSV) infection was found to be the main contributor.

CONCLUSIONS

The burden of influenza in terms of GP consultations is considerable. Overall, influenza is the main contributor to ILI. Although ILI symptoms in children under 5 years of age are most often associated with RSV infection, the majority of visits related to influenza occur among children under 5 years of age.

Quantifying the annual incidence and underestimation of seasonal influenza: A modelling approach

BACKGROUND

Seasonal influenza poses a significant public health and economic burden, associated with the outcome of infection and resulting complications. The true burden of the disease is difficult to capture due to the wide range of presentation, from asymptomatic cases to non-respiratory complications such as cardiovascular events, and its seasonal variability. An understanding of the magnitude of the true annual incidence of influenza is important to support prevention and control policy development and to evaluate the impact of preventative measures such as vaccination.

METHODS

We use a dynamic disease transmission model, laboratory-confirmed influenza surveillance data, and randomized-controlled trial (RCT) data to quantify the underestimation factor, expansion factor, and symptomatic influenza illnesses in the US and Canada during the 2011-2012 and 2012-2013 influenza seasons.

RESULTS

Based on 2 case definitions, we estimate between 0.42-3.2% and 0.33-1.2% of symptomatic influenza illnesses were laboratory-confirmed in Canada during the 2011-2012 and 2012-2013 seasons, respectively. In the US, we estimate between 0.08-0.61% and 0.07-0.33% of symptomatic influenza illnesses were laboratory-confirmed in the 2011-2012 and 2012-2013 seasons, respectively. We estimated the symptomatic influenza illnesses in Canada to be 0.32-2.4 million in 2011-2012 and 1.8-8.2 million in 2012-2013. In the US, we estimate the number of symptomatic influenza illnesses to be 4.4-34 million in 2011-2012 and 23-102 million in 2012-2013.

CONCLUSIONS

We illustrate that monitoring a representative group within a population may aid in effectively modelling the transmission of infectious diseases such as influenza. In particular, the utilization of RCTs in models may enhance the accuracy of epidemiological parameter estimation.

Climatic factors and long-term trends of influenza-like illness rates in The Netherlands, 1970-2016

BACKGROUND

Climatic factors affect the survival and transmissibility of respiratory viruses causing influenza-like illness (ILI), and we hypothesized that changes in absolute humidity and temperature may affect long-term trends of ILI incidence rate in temperate countries. We tested this hypothesis using ILI and meteorological time series in the Netherlands for the period 1970-2016.

METHODS

We described the long-term trends of ILI incidence, absolute humidity and temperature; modelled the association between climatic factors and ILI activity using negative binomial regression models; and assessed the strength of the association between the seasonal average absolute humidity (or temperature) and ILI incidence rate using the Spearman's rank correlation coefficient.

RESULTS

The ILI incidence rate declined from 1970 and reached a minimum in the season 2002-03, but started to increase again from the season 2003-04 onwards. In the negative binominal regression models, the weekly ILI count was inversely associated (p < 0.001) with 0- and 1-week lagged absolute humidity and temperature. After three decades of rising absolute humidity and temperature (1970-2000), the early 2000s represented a trend-reversal point for the climatic time series. The seasonal average ILI incidence rate and absolute humidity (or temperature) were strongly (inversely) correlated.

CONCLUSIONS

Our findings suggest that climate change may have played a role in the long-term trends of ILI incidence rates in the Netherlands, as we were able to show that lower humidity and temperature in a given week were associated with higher ILI incidence in the next week, there was a clear time point reversal in climatic parameters and ILI rates in the 2000s, and the average annual ILI incidence was inversely related to average annual temperatures and humidity.

The incidence of symptomatic infection with influenza virus in the Netherlands 2011/2012 through 2016/2017, estimated using Bayesian evidence synthesis

Due to differences in the circulation of influenza viruses, distribution and antigenic drift of A subtypes and B lineages, and susceptibility to infection in the population, the incidence of symptomatic influenza infection can vary widely between seasons and age-groups. Our goal was to estimate the symptomatic infection incidence in the Netherlands for the six seasons 2011/2012 through 2016/2017, using Bayesian evidence synthesis methodology to combine season-specific sentinel surveillance data on influenza-like illness (ILI), virus detections in sampled ILI cases and data on healthcare-seeking behaviour. Estimated age-aggregated incidence was 6.5 per 1000 persons (95% uncertainty interval (UI): 4.7–9.0) for season 2011/2012, 36.7 (95% UI: 31.2–42.8) for 2012/2013, 9.1 (95% UI: 6.3–12.9) for 2013/2014, 41.1 (95% UI: 35.0–47.7) for 2014/2015, 39.4 (95% UI: 33.4–46.1) for 2015/2016 and 27.8 (95% UI: 22.7–33.7) for season 2016/2017. Incidence varied substantially between age-groups (highest for the age-group <5 years: 23 to 47/1000, but relatively low for 65+ years: 2 to 34/1000 over the six seasons). Integration of all relevant data sources within an evidence synthesis framework has allowed the estimation – with appropriately quantified uncertainty – of the incidence of symptomatic influenza virus infection. These estimates provide valuable insight into the variation in influenza epidemics across seasons, by virus subtype and lineage, and between age-groups.

An Evidence Synthesis Approach to Estimating the Proportion of Influenza Among Influenza-like Illness Patients

BACKGROUND

Estimation of the national-level incidence of seasonal influenza is notoriously challenging. Surveillance of influenza-like illness is carried out in many countries using a variety of data sources, and several methods have been developed to estimate influenza incidence. Our aim was to obtain maximally informed estimates of the proportion of influenza-like illness that is true influenza using all available data.

METHODS

We combined data on weekly general practice sentinel surveillance consultation rates for influenza-like illness, virologic testing of sampled patients with influenza-like illness, and positive laboratory tests for influenza and other pathogens, applying Bayesian evidence synthesis to estimate the positive predictive value (PPV) of influenza-like illness as a test for influenza virus infection. We estimated the weekly number of influenza-like illness consultations attributable to influenza for nine influenza seasons, and for four age groups.

RESULTS

The estimated PPV for influenza in influenza-like illness patients was highest in the weeks surrounding seasonal peaks in influenza-like illness rates, dropping to near zero in between-peak periods. Overall, 14.1% (95% credible interval [CrI]: 13.5%, 14.8%) of influenza-like illness consultations were attributed to influenza infection; the estimated PPV was 50% (95% CrI: 48%, 53%) for the peak weeks and 5.8% during the summer periods.

CONCLUSIONS

The model quantifies the correspondence between influenza-like illness consultations and influenza at a weekly granularity. Even during peak periods, a substantial proportion of influenza-like illness-61%-was not attributed to influenza. The much lower proportion of influenza outside the peak periods reflects the greater circulation of other respiratory pathogens relative to influenza.

Comparison between influenza coded primary care consultations and national influenza incidence obtained by the General Practitioners Sentinel Network in Portugal from 2012 to 2017

Influenza is associated with severe illness, death, and economic burden. Sentinel surveillance systems have a central role in the community since they support public health interventions. This study aimed to describe and compare the influenza-coded primary care consultations with the reference index of influenza activity used in Portugal, General Practitioners Sentinel Network, from 2012 to 2017. An ecological time-series study was conducted using weekly R80-coded primary care consultations (according to the International Classification of Primary Care-2), weekly influenza-like illness (ILI) incidence rates from the General Practitioners Sentinel Network and Goldstein Index (GI). Good accordance between these three indicators was observed in the characterization of influenza activity regarding to start and length of the epidemic period, intensity of influenza activity, and influenza peak. A high correlation (>0.75) was obtained between weekly ILI incidence rates and weekly number of R80-coded primary care consultations during all five studied seasons. In 3 out of 5 seasons this correlation increased when weekly ILI incidence rates were multiplied for the percentage of influenza positive cases. A cross-correlation between weekly ILI incidence rates and the weekly number of R80-coded primary care consultations revealed that there was no lag between the rate curves of influenza incidence and the number of consultations in the 2012/13 and 2013/14 seasons. In the last three seasons, the weekly influenza incidence rates detected the influenza epidemic peak for about a week earlier. In the last season, the GI anticipated the detection of influenza peak for about a two-week period. Sentinel networks are fundamental elements in influenza surveillance that integrate clinical and virological data but often lack representativeness and are not able to provide regional and age groups estimates. Given the good correlation between weekly ILI incidence rate and weekly number of R80 consultations, primary care consultation coding system may be used to complement influenza surveillance data, namely, to monitor regional influenza activity. In the future, it would be interesting to analyse concurrent implementation of both surveillance systems with the integration of all available information.

Translation of the UK Pediatric Influenza Vaccination Programme in Primary Schools to 13 European Countries Using a Dynamic Transmission Model

Objectives: To simulate the impact of a pediatric influenza vaccination programme using quadrivalent live attenuated influenza vaccine (QLAIV) in Europe by applying coverage rates achieved in the United Kingdom during the 2014-2015 season and to compare the model outcomes to the UK results. Methods: We used a deterministic, age-structured, dynamic transmission model adapted to the demography, contact patterns and influenza incidence of 13 European countries, with a 10-year horizon. The reference strategy was the unchanged country-specific coverage rate, using quadrivalent inactivated vaccine (assumed efficacy against infection from 45% in 1-year-old children to 60% in healthy adults). In the evaluated strategy, 56.8% of 5-10-year-old children were additionally vaccinated with QLAIV (assumed efficacy 80%), as was the case in 2014-2015 in the United Kingdom's primary school pilot areas. Symptomatic influenza cases and associated medical resources (primary care consultations [PCC], hospitalization, intensive care unit [ICU] admissions) were calculated. The evaluated versus reference strategies were compared using odds ratios (ORs) for PCC in the target (aged 5-10-years) and non-target adult (aged >17 years) populations as well as number needed to vaccinate (NNV) with QLAIV to avert one PCC, hospitalization or ICU admission. Model outcomes, averaged over 10 seasons, were compared with published real-life data from the United Kingdom for the 2014-2015 season. Results: Over 13 countries and 10 years, the evaluated strategy prevented 32.8 million of symptomatic influenza cases (172.3 vs 205.2 million). The resulting range of ORs for PCC was 0.18-0.48 among children aged 5-10-years, and the published OR in the United Kingdom was 0.06 (95% confidence interval [0.01; 0.62]). In adults, the range of ORs for PCC was 0.60-0.91 (UK OR=0.41 [0.19; 0.86]). NNV ranges were 6-19 per averted PCC (UK NNV=16), 530-1524 per averted hospitalization (UK NNV=317) and 5298-15 241 per averted ICU admission (UK NNV=2205). Conclusions: Across a range of European countries, our model shows the beneficial direct and indirect impact of a paediatric vaccination programme using QLAIV in primary school-aged children, consistent with what was observed during a single season in the United Kingdom. Recommendations for the implementation of pediatric vaccination programmes are, therefore, supported in Europe.

Direct and Indirect Protection with Pediatric Quadrivalent Live-Attenuated Influenza Vaccination in Europe Estimated by a Dynamic Transmission Model

Objectives: To estimate the public health impact of annual vaccination of children with a quadrivalent live-attenuated influenza vaccine (QLAIV) across Europe. Methods: A deterministic, age-structured, dynamic model was used to simulate influenza transmission across 14 European countries, comparing current vaccination coverage using a quadrivalent inactivated vaccine (QIV) to a scenario whereby vaccination coverage was extended to 50% of 2-17 year-old children, using QLAIV. Differential equations described demographic changes, exposure to infectious individuals, recovery and immunity dynamics. For each country, the basic reproduction number (R0) was calibrated to published influenza incidence statistics. Assumed vaccine efficacy for children was 80% (QLAIV) and 59% (QIV). Symptomatic cases cumulated over 10 years were calculated per 100 000 person-years. One-way sensitivity analyses were conducted on QLAIV efficacy in 7-17 year-olds (59% instead of 80%), durations of natural (±3 years; base case: 6, 12 years for influenza A, B respectively) and QLAIV vaccine-induced immunity (100% immunity loss after 1 season; base case: 30%), and R0 (+/-10% around all-year average value). Results: Across countries, annual QLAIV vaccination additionally prevents 1366-3604 symptomatic cases per 100 000 population (average 2495 /100 000, ie, a reduction of 47.6% of the cases which occur in the reference scenario with QIV vaccination only). Among children (2-17 years), QLAIV prevents 551-1555 cases per 100 000 population (average 990 /100 000, ie, 67.2% of current cases). Among adults, QLAIV indirectly prevents 726-2047 cases per 100 000 population (average 1466 /100 000, ie, 40.0% of current cases). The most impactful drivers of total protection were duration of natural immunity against influenza A, R0 and QLAIV immunity duration and efficacy. In all evaluated scenarios, there was a large direct and even larger indirect protection compared with the reference scenario. Conclusions: The model highlights direct and indirect protection benefits when vaccinating healthy children with QLAIV in Europe, across a range of demographic structures, contact patterns and vaccination coverage rates.

Assessing Trends in Chlamydia Positivity and Gonorrhea Incidence and Their Associations With the Incidence of Pelvic Inflammatory Disease and Ectopic Pregnancy in Washington State, 1988-2010

BACKGROUND

Chlamydia and gonorrhea screening for women is beneficial if it prevents serious reproductive sequelae, such as pelvic inflammatory disease (PID) and ectopic pregnancy (EP). We assessed trends in PID and EP among women in Washington and their association with gonorrhea incidence and chlamydia positivity in a screened population of women over a 23 year period.

METHODS

Using data on chlamydia positivity from the Infertility Prevention Project, gonorrhea incidence from state surveillance, and PID and EP hospitalizations from hospital discharge records, we assessed trends in each condition over time. In addition, we estimated total incidence of PID and EP by incorporating information on outpatient-treated cases in alternative populations using a Bayesian approach that accounted for uncertainty in the estimates. We assessed associations between each infection and PID/EP using a linear regression model that accounts for year-to-year correlation in data points.

RESULTS

We observed substantial declines in both infections and in each outcome over time. For every 2% decrease in chlamydia positivity, there was a 35.7/100,000 decrease in estimated total PID incidence (P = 0.058) and 184.4/100,000 decrease in estimated total EP (P = 0.149). For every 32/100,000 decline in gonorrhea incidence, there was a 16.5/100,000 decrease in total PID (P = 0.292) and 159.8/100,000 decrease in total EP (P = 0.020). The associations with inpatient PID and EP were highly significant for both chlamydia and gonorrhea.

CONCLUSIONS

These ecological data note concurrent and substantial declines in chlamydia positivity and gonorrhea incidence, and in PID and EP incidence in Washington from 1988 to 2010 during a time when widespread chlamydia screening was ongoing.

Increased incidence of subarachnoid hemorrhage during cold temperatures and influenza epidemics

OBJECTIVE

This study investigated whether the increased incidence of aneurysmal subarachnoid hemorrhage (SAH) in winter is related to temperature or increased incidence of influenza. Such relationships may elucidate the pathogenesis of intracranial aneurysm rupture.

METHODS

A nationwide sample of 18,714 patients with SAH was linked with weekly temperature and influenza-like illness consultation data. Poisson regression analyses were used to calculate incidence density ratios (IDRs) with corresponding 95% CIs for the association of SAH incidence with temperature and influenza epidemics; IDRs were adjusted for study year (aIDR). In addition, SAH incidence data from 30 European population-based studies were linked with daily temperature data from the European Climate Assessment.

RESULTS

The aIDR for SAH during influenza epidemics was 1.061 (95% CI 1.022–1.101) in the univariable and 1.030 (95% CI 0.989–1.074) in the multivariable analysis. This association declined gradually during the weeks after epidemics. Per 1°C temperature drop, the aIDR was 1.005 (95% CI 1.003–1.008) in the univariable and 1.004 (95% CI 1.002–1.007) in the multivariable analysis. In the European population-based studies, the IDR was 1.143 (95% CI 1.129–1.157) per 1°C temperature drop.

CONCLUSIONS

The incidence of SAH is increased during cold temperatures and epidemic influenza. Future studies with individual patient data are needed to investigate causality between temperature or influenza and SAH.

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

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.

An evidence synthesis approach to estimating the incidence of symptomatic pertussis infection in the Netherlands, 2005-2011

BACKGROUND

Despite high vaccination coverage, infection with Bordetella pertussis is a current public health concern in the Netherlands and other European Union member states. Because surveillance data are subject to extensive under-ascertainment and under-reporting, incidence is difficult to determine. Our objective was to estimate the age-group specific incidence of symptomatic pertussis infection in the Netherlands over the period 2005-2011, using multi-parameter evidence synthesis.

METHODS

Age-specific seroconversion probabilities were estimated for 2007 using Netherlands population data stratified by age-group and cross-sectional population-wide serosurvey (PIENTER-2) data, with a sero-diagnostic cut-off of 125 EU/ml as a proxy for recent infection. Symptomatic probabilities were derived from a study of household contacts and from PIENTER-2. The annual number of symptomatic infected (SI) persons was estimated using evidence synthesis methods in a Bayesian framework, by combining the estimated incidence of infection with notification data and symptomatic probabilities.

RESULTS

An incidence rate of 128 SI cases per 10,000 population (95 % credible interval [CrI]: 110-150) was estimated for 2005, which decreased to 107 per 10,000 (95 % CrI: 91-126) for 2011. The degree of underestimation in statutory notified cases was age-dependent, ranging from 10-fold (10-19 years) to 69-fold (60+ years). The largest annual decreases in SI incidence rate over the study period were in the 1-4 and 5-9 years age-groups (24.3 %, 15.9 % per year, respectively).

CONCLUSIONS

By synthesising all available data, the incidence of symptomatic pertussis and the extent to which SI is underrepresented by notification data can be estimated. Such estimates are essential for disease burden computation and for informing public health priority-setting.

Enhancing Syndromic Surveillance With Online Respondent-Driven Detection

OBJECTIVES

We investigated the feasibility of combining an online chain recruitment method (respondent-driven detection) and participatory surveillance panels to collect previously undetected information on infectious diseases via social networks of participants.

METHODS

In 2014, volunteers from 2 large panels in the Netherlands were invited to complete a survey focusing on symptoms of upper respiratory tract infections and to invite 4 individuals they had met in the preceding 2 weeks to take part in the study. We compared sociodemographic characteristics among panel participants, individuals who volunteered for our survey, and individuals recruited via respondent-driven detection.

RESULTS

Starting from 1015 panel members, the survey spread through all provinces of the Netherlands and all age groups in 83 days. A total of 433 individuals completed the survey via peer recruitment. Participants who reported symptoms were 6.1% (95% confidence interval = 5.4, 6.9) more likely to invite contact persons than were participants who did not report symptoms. Participants with symptoms invited more symptomatic recruits to take part than did participants without symptoms.

CONCLUSIONS

Our findings suggest that online respondent-driven detection can enhance identification of symptomatic patients by making use of individuals' local social networks.

The impact of prior information on estimates of disease transmissibility using Bayesian tools

The basic reproductive number (R₀) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R₀ and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R₀ and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R^0 = 1.36–1.46, μ^ = 2.0–2.7, μ^ = mean of the SI). The estimates of R₀ and μ for the SARS outbreak ranged from 2.0–4.4 and 7.4–11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample.

Estimating influenza disease burden from population-based surveillance data in the United States

Annual estimates of the influenza disease burden provide information to evaluate programs and allocate resources. We used a multiplier method with routine population-based surveillance data on influenza hospitalization in the United States to correct for under-reporting and estimate the burden of influenza for seasons after the 2009 pandemic. Five sites of the Influenza Hospitalization Surveillance Network (FluSurv-NET) collected data on the frequency and sensitivity of influenza testing during two seasons to estimate under-detection. Population-based rates of influenza-associated hospitalization and Intensive Care Unit admission from 2010-2013 were extrapolated to the U.S. population from FluSurv-NET and corrected for under-detection. Influenza deaths were calculated using a ratio of deaths to hospitalizations. We estimated that influenza-related hospitalizations were under-detected during 2010-11 by a factor of 2.1 (95%CI 1.7-2.9) for age < 18 years, 3.1 (2.4-4.5) for ages 18-64 years, and 5.2 (95%CI 3.8-8.3) for age 65+. Results were similar in 2011-12. Extrapolated estimates for 3 seasons from 2010-2013 included: 114,192-624,435 hospitalizations, 18,491-95,390 ICU admissions, and 4,915-27,174 deaths per year; 54-70% of hospitalizations and 71-85% of deaths occurred among adults aged 65+. Influenza causes a substantial disease burden in the U.S. that varies by age and season. Periodic estimation of multipliers across multiple sites and age groups improves our understanding of influenza detection in sentinel surveillance systems. Adjusting surveillance data using a multiplier method is a relatively simple means to estimate the impact of influenza and the subsequent value of interventions to prevent influenza.

Comparison of contact patterns relevant for transmission of respiratory pathogens in Thailand and The Netherlands using respondent-driven sampling

Understanding infection dynamics of respiratory diseases requires the identification and quantification of behavioural, social and environmental factors that permit the transmission of these infections between humans. Little empirical information is available about contact patterns within real-world social networks, let alone on differences in these contact networks between populations that differ considerably on a socio-cultural level. Here we compared contact network data that were collected in The Netherlands and Thailand using a similar online respondent-driven method. By asking participants to recruit contact persons we studied network links relevant for the transmission of respiratory infections. We studied correlations between recruiter and recruited contacts to investigate mixing patterns in the observed social network components. In both countries, mixing patterns were assortative by demographic variables and random by total numbers of contacts. However, in Thailand participants reported overall more contacts which resulted in higher effective contact rates. Our findings provide new insights on numbers of contacts and mixing patterns in two different populations. These data could be used to improve parameterisation of mathematical models used to design control strategies. Although the spread of infections through populations depends on more factors, found similarities suggest that spread may be similar in The Netherlands and Thailand.

Discrimination of influenza infection (A/2009 H1N1) from prior exposure by antibody protein microarray analysis

Reliable discrimination of recent influenza A infection from previous exposure using hemagglutination inhibition (HI) or virus neutralization tests is currently not feasible. This is due to low sensitivity of the tests and the interference of antibody responses generated by previous infections. Here we investigate the diagnostic characteristics of a newly developed antibody (HA1) protein microarray using data from cross-sectional serological studies carried out before and after the pandemic of 2009. The data are analysed by mixture models, providing a probabilistic classification of sera (susceptible, prior-exposed, recently infected). Estimated sensitivity and specificity for identifying A/2009 infections are low using HI (66% and 51%), and high when using A/2009 microarray data alone or together with A/1918 microarray data (96% and 95%). As a heuristic, a high A/2009 to A/1918 antibody ratio (>1.05) is indicative of recent infection, while a low ratio is indicative of a pre-existing response, even if the A/2009 titer is high. We conclude that highly sensitive and specific classification of individual sera is possible using the protein microarray, thereby enabling precise estimation of age-specific infection attack rates in the population even if sample sizes are small.

Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods

BACKGROUND

Efficient and reliable surveillance and notification systems are vital for monitoring public health and disease outbreaks. However, most surveillance and notification systems are affected by a degree of underestimation (UE) and therefore uncertainty surrounds the 'true' incidence of disease affecting morbidity and mortality rates. Surveillance systems fail to capture cases at two distinct levels of the surveillance pyramid: from the community since not all cases seek healthcare (under-ascertainment), and at the healthcare-level, representing a failure to adequately report symptomatic cases that have sought medical advice (underreporting). There are several methods to estimate the extent of under-ascertainment and underreporting.

METHODS

Within the context of the ECDC-funded Burden of Communicable Diseases in Europe (BCoDE)-project, an extensive literature review was conducted to identify studies that estimate ascertainment or reporting rates for salmonellosis and campylobacteriosis in European Union Member States (MS) plus European Free Trade Area (EFTA) countries Iceland, Norway and Switzerland and four other OECD countries (USA, Canada, Australia and Japan). Multiplication factors (MFs), a measure of the magnitude of underestimation, were taken directly from the literature or derived (where the proportion of underestimated, under-ascertained, or underreported cases was known) and compared for the two pathogens.

RESULTS

MFs varied between and within diseases and countries, representing a need to carefully select the most appropriate MFs and methods for calculating them. The most appropriate MFs are often disease-, country-, age-, and sex-specific.

CONCLUSIONS

When routine data are used to make decisions on resource allocation or to estimate epidemiological parameters in populations, it becomes important to understand when, where and to what extent these data represent the true picture of disease, and in some instances (such as priority setting) it is necessary to adjust for underestimation. MFs can be used to adjust notification and surveillance data to provide more realistic estimates of incidence.