Influenza Hospitalisations in England during the 2022/23 Season: do different data sources drive divergence in modelled waves? A comparison of surveillance and administrative data

Accurate and representative data is vital for precisely reporting the impact of influenza in healthcare systems. Northern hemisphere winter 2022/23 experienced the most substantial influenza wave since the COVID-19 pandemic began in 2020. Simultaneously, new data streams become available within health services because of the pandemic. Comparing these data, surveillance and administrative, supports the accurate monitoring of population level disease trends. We analysed admissions rates per capita from four different collection mechanisms covering National Health Service hospital Trusts in England over the winter 2022/23 wave. We adjust for difference in reporting and extracted key epidemic characteristics including the maximum admission rate, peak timing, cumulative season admissions and growth rates by fitting generalised additive models at national and regional levels. By modelling the admission rates per capita across surveillance and administrative data systems we show that different data measuring the epidemic produce different estimates of key quantities. Nationally and in most regions the data correspond well for the maximum admission rate, date of peak and growth rate, however, in subnational analysis discrepancies in estimates arose, particularly for the cumulative admission rate. This research shows that the choice of data used to measure seasonal influenza epidemics can influence analysis substantially at sub-national levels. For the admission rate per capita there is comparability in the sentinel surveillance approach (which has other important functions), rapid situational reports, operational databases and time lagged administrative data giving assurance in their combined value. Utilising multiple sources of data aids understanding of the impact of seasonal influenza epidemics in the population

Prophylactic antibiotics in elective hip and knee arthroplasty: an analysis of organisms reported to cause infections and national survey of clinical practice

Objectives: We wanted to investigate regional variations in the organisms reported to be causing peri-prosthetic infections and to report on prophylaxis regimens currently in use across England. Methods: Analysis of data routinely collected by Public Health England’s (PHE) national surgical site infection database on elective primary hip and knee arthroplasty procedures between April 2010 and March 2013 to investigate regional variations in causative organisms. A separate national survey of 145 hospital Trusts (groups of hospitals under local management) in England routinely performing primary hip and/or knee arthroplasty was carried out by standard email questionnaire. Results: Analysis of 189 858 elective primary hip and knee arthroplasty procedures and 1116 surgical site infections found statistically significant variations for some causative organism between regions. There was a 100% response rate to the prophylaxis questionnaire that showed substantial variation between individual trust guidelines. A number of regimens currently in use are inconsistent with the best available evidence. Conclusions: The approach towards antibiotic prophylaxis in elective arthroplasty nationwide reveals substantial variation without clear justification. Only seven causative organisms are responsible for 89% of infections affecting primary hip and knee arthroplasty, which cannot justify such widespread variation between prophylactic antibiotic policies. Cite this article: Bone Joint Res 2015;4:181–189

The impact of social and physical distancing measures on COVID-19 activity in England: findings from a multi-tiered surveillance system

BACKGROUND: A multi-tiered surveillance system based on influenza surveillance was adopted in the United Kingdom in the early stages of the coronavirus disease (COVID-19) epidemic to monitor different stages of the disease. Mandatory social and physical distancing measures (SPDM) were introduced on 23 March 2020 to attempt to limit transmission. AIM: To describe the impact of SPDM on COVID-19 activity as detected through the different surveillance systems. METHODS: Data from national population surveys, web-based indicators, syndromic surveillance, sentinel swabbing, respiratory outbreaks, secondary care admissions and mortality indicators from the start of the epidemic to week 18 2020 were used to identify the timing of peaks in surveillance indicators relative to the introduction of SPDM. This timing was compared with median time from symptom onset to different stages of illness and levels of care or interactions with healthcare services. RESULTS: The impact of SPDM was detected within 1 week through population surveys, web search indicators and sentinel swabbing reported by onset date. There were detectable impacts on syndromic surveillance indicators for difficulty breathing, influenza-like illness and COVID-19 coding at 2, 7 and 12 days respectively, hospitalisations and critical care admissions (both 12 days), laboratory positivity (14 days), deaths (17 days) and nursing home outbreaks (4 weeks). CONCLUSION: The impact of SPDM on COVID-19 activity was detectable within 1 week through community surveillance indicators, highlighting their importance in early detection of changes in activity. Community swabbing surveillance may be increasingly important as a specific indicator, should circulation of seasonal respiratory viruses increase

Improving feedback of surveillance data on antimicrobial consumption, resistance and stewardship in England: putting the data at your Fingertips.

The provision of better access to and use of surveillance data is a key component of the UK 5 Year Antimicrobial Resistance (AMR) Strategy Since April 2016, PHE has made data on practice (infection prevention and control; antimicrobial stewardship) and outcome (prevalence of AMR, antibiotic use and healthcare-associated infections) available through Fingertips, a publicly accessible web tool (https://fingertips.phe.org.uk/profile/amr-local-indicators). Fingertips provides access to a wide range of public health data presented as thematic profiles, with the above data being available through the 'AMR local indicators' profile. Local data on a range of indicators can be viewed at the level of National Health Service acute trusts, Clinical Commissioning Groups or general practitioner practices, all of which can be compared with the corresponding aggregate values for England to allow benchmarking. The data can be viewed in a range of formats including an overview showing counts and rates, interactive maps, spine charts and graphs that show temporal trends over a range of time scales or allow correlations between pairs of indicators. The aim of the AMR local indicators profile on Fingertips is to support the development of local action plans to optimize antibiotic prescribing and reduce AMR and healthcare-associated infections. Provision of access to relevant information in an easy to use format will help local stakeholders, including healthcare staff, commissioners, Directors of Public Health, academics and the public, to benchmark relevant local AMR data and to monitor the impact of local initiatives to tackle AMR over time

Cancer survival in the Strategic Health Authorities of England, 1997-2004

This dataset presents the latest one- and five-year age-standardised relative survival rates for cancers of the bladder, breast (in women), cervix, colon, lung, oesophagus, prostate and stomach with data for the government office regions (GOR) and strategic health authorities (SHA)

The interplay between susceptibility and vaccine effectiveness control the timing and size of an emerging seasonal influenza wave in England

Relaxing social distancing measures and reduced level of influenza over the last two seasons may lead to a winter 2022 influenza wave in England. We used an established model for influenza transmission and vaccination to evaluate the rolled out influenza immunisation programme over October to December 2022. Specifically, we explored how the interplay between pre-season population susceptibility and influenza vaccine efficacy control the timing and the size of a possible winter influenza wave. Our findings suggest that susceptibility affects the timing and the height of a potential influenza wave, with higher susceptibility leading to an earlier and larger influenza wave while vaccine efficacy controls the size of the peak of the influenza wave. With pre-season susceptibility higher than pre-COVID-19 levels, under the planned vaccine programme an early influenza epidemic wave is possible, its size dependent on vaccine effectiveness against the circulating strain. If pre-season susceptibility is low and similar to pre-COVID levels, the planned influenza vaccine programme with an effective vaccine could largely suppress a winter 2022 influenza outbreak in England