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Pebody, R.

Publications and source records attributed to Pebody, R..

2 recordsLinked to original sources
bioRxiv2018

Estimates for quality of life loss due to RSV

A number of vaccines against Respiratory Syncytial Virus (RSV) infection are approaching licensure. Deciding which RSV vaccine strategy, if any, to introduce, will partly depend on cost-effectiveness analyses, which compares the relative costs and health benefits of a potential vaccination programme. Health benefits are usually measured in Quality Adjusted Life Year (QALY) loss, however, there are no QALY loss estimates for RSV that have been determined using standardised instruments. Moreover, in children under the age of five years in whom severe RSV episodes predominantly occur, there are no appropriate standardised instruments to estimate QALY loss. We estimated the QALY loss due to RSV across all ages by developing a novel regression model which predicts the QALY loss without the use of standardised instruments. To do this, we conducted a surveillance study which targeted confirmed episodes in children under the age of five years (confirmed cases) and their household members who experienced symptoms of RSV during the same time (suspected cases.) All participants were asked to complete questions regarding their health during the infection, with the suspected cases aged 5-14 and 15+ years old additionally providing Health-Related Quality of Life (HR-QoL) loss estimates through completing EQ-5D-3L-Y and EQ-5D-3L instruments respectively. The questionnaire responses from the suspected cases were used to calibrate the regression model. The calibrated regression model then used other questionnaire responses to predict the HR-QoL loss without the use of EQ-5D instruments. The age-specific QALY loss was then calculated by multiplying the HR-QoL loss on the worst day predicted from the regression model, with estimates for the duration of infection from the questionnaires and a scaling factoring for disease severity. Our regression model for predicting HR-QoL loss estimates that for the worst day of infection, suspected RSV cases in persons five years and older who do and do not seek healthcare have an HR-QoL loss of 0{middle dot}616 (95% CI 0{middle dot}155-1{middle dot}371) and 0{middle dot}405 (95% CI 0{middle dot}111-1{middle dot}137) respectively. This leads to a QALY loss per RSV episode of 1{middle dot}950 x 10-3 (95% CI 0{middle dot}185 x 10-3 -9{middle dot}578 x 10-3) and 1{middle dot}543 x 10-3 (95% CI 0{middle dot}136 x 10-3 -6{middle dot}406 x 10-3) respectively. For confirmed cases in a child under the age of five years who sought healthcare, our model predicted a HR-QoL loss on the worst day of infection of 0{middle dot}820 (95% CI 0{middle dot}222-1{middle dot}450) resulting in a QALY loss per RSV episode of 3{middle dot}823 x 10-3 (95% CI 0{middle dot}492 x 10-3 -12{middle dot}766 x 10-3). Combing these results with previous estimates of RSV burden in the UK, we estimate the annual QALY loss of healthcare seeking RSV episodes as 1,199 for individuals aged five years and over and 1,441 for individuals under five years old. The QALY loss due to an RSV episode is less than the QALY loss due to an Influenza episode. These results have important implications for potential RSV vaccination programmes, which has so far focused on preventing infections in infants--where the highest reported disease burden lies. Future potential RSV vaccination programmes should also evaluate their impact on older children and adults, where there is a substantial but unsurveilled QALY loss.

epidemiology↗
bioRxiv2018

Unsupervised Extraction of Epidemic Syndromes from Participatory Influenza Surveillance Self-reported Symptoms

Seasonal influenza surveillance is usually carried out by sentinel general practitioners who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This practice for surveillance is generally affected by two main issues: i) reports are usually released with a lag of about one week or more, ii) the definition of a case of influenza-like illness based on patients symptoms varies from one surveillance system to the other, i.e. from one country to the other. The availability of novel data streams for disease surveillance can alleviate these issues; in this paper, we employed data from Influenzanet, a participatory web-based surveillance project which collects symptoms directly from the general population in real time. We developed an unsupervised probabilistic framework that combines time series analysis of symptoms counts and performs an algorithmic detection of groups of symptoms, hereafter called syndromes. Symptoms counts were collected through the participatory web-based surveillance platforms of a consortium called Influenzanet which is found to correlate with Influenza-like illness incidence as detected by sentinel doctors. Our aim is to suggest how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness temporal trends without relying on a specific case definition. We evaluated the performance of our framework by showing that the temporal trends of the detected syndromes closely follow the ILI incidence as reported by the traditional surveillance, and consist of combinations of symptoms that are compatible with the ILI definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.\n\nAuthor summaryThis study suggests how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness temporal trends without relying on a specific case definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.

epidemiology↗