DiD IT?: a differences-in-differences investigation tool to quantify the impact of local incidents on public health using real-time syndromic surveillance health data.

Syndromic surveillance was originally developed to provide early warning compared to laboratory surveillance, but it is increasing used for real-time situational awareness. When a potential threat to public health is identified, a rapid assessment of its impact is required for public health management. When threats are localised, analysis is more complex as local trends need to be separated from national trends and differences compared to unaffected areas may be due to confounding factors such as deprivation or age distributions. Accounting for confounding factors usually requires an in-depth study, which takes time. Therefore, a tool is required which can provide a rapid estimate of local incidents using syndromic surveillance data.Here, we present 'DiD IT?', a new investigation tool designed to measure the impact of local threats to public health. 'DiD IT?' uses a difference-in-differences statistical approach to account for temporal and spatial confounding and provide a direct estimate of impact due to incidents. Temporal confounding differences are estimated by comparing unaffected locations during and outside of exposure periods. Whilst spatial confounding differences are estimated by comparing unaffected and exposed locations outside of the exposure period. Any remaining differences can be considered to be the direct effect of the local incident.We illustrate the potential utility of the tool through four examples of localised health protection incidents in England. The examples cover a range of data sources including general practitioner (GP) consultations, emergency department (ED) attendances and a telehealth call and online health symptom checker; and different types of incidents including, infectious disease outbreak, mass-gathering, extreme weather and an industrial fire. The examples use the UK Health Security Agency's ongoing real-time syndromic surveillance systems to show how results can be obtained in near real-time.The tool identified 700 additional online difficulty breathing assessments associated with a severe thunderstorm, 53 additional GP consultations during a mumps outbreak, 2-3 telehealth line calls following an industrial fire and that there was no significant increase in ED attendances during the G7 summit in 2021.DiD IT? can provide estimates for the direct impact of localised events in real-time as part of a syndromic surveillance system. Thus, it has the potential for enhancing surveillance and can be used to evaluate the effectiveness of extending national surveillance to a more granular local surveillance.

Understanding the impact of the COVID-19 pandemic response on GI infection surveillance trends in England, January 2020-April 2022.

Stepwise non-pharmaceutical interventions and health system changes implemented as part of the COVID-19 response have had implications on the incidence, diagnosis, and reporting of other communicable diseases. Here, we established the impact of the COVID-19 outbreak response on gastrointestinal (GI) infection trends using routinely collected surveillance data from six national English laboratory, outbreak, and syndromic surveillance systems using key dates of governmental policy to assign phases for comparison between pandemic and historic data. Following decreases across all indicators during the first lockdown (March-May 2020), bacterial and parasitic pathogens associated with foodborne or environmental transmission routes recovered rapidly between June and September 2020, while those associated with travel and/or person-to-person transmission remained lower than expected for 2021. High out-of-season norovirus activity was observed with the easing of lockdown measures between June and October 2021, with this trend reflected in laboratory and outbreak systems and syndromic surveillance indicators. Above expected increases in emergency department (ED) attendances may have reflected changes in health-seeking behaviour and provision. Differential reductions across specific GI pathogens are indicative of the underlying routes of transmission. These results provide further insight into the drivers for transmission, which can help inform control measures for GI infections.

Developing a sentinel syndromic surveillance system using school-absenteeism data, example monitoring absences over the 2020 COVID-19 pandemic.

This study describes the development of a pilot sentinel school absence syndromic surveillance system. Using data from a sample of schools in England the capability of this system to monitor the impact of disease on school absences in school-aged children is shown, using the coronavirus disease 2019 (COVID-19) pandemic period as an example. Data were obtained from an online app service used by schools and parents to report their children absent, including reasons/symptoms relating to absence. For 2019 and 2020, data were aggregated into daily counts of 'total' and 'cough' absence reports. There was a large increase in the number of absence reports in March 2020 compared to March 2019, corresponding to the first wave of the COVID-19 pandemic in England. Absence numbers then fell rapidly and remained low from late March 2020 until August 2020, while lockdown was in place in England. Compared to 2019, there was a large increase in the number of absence reports in September 2020 when schools re-opened in England, although the peak number of absences was smaller than in March 2020. This information can help provide context around the absence levels in schools associated with COVID-19. Also, the system has the potential for further development to monitor the impact of other conditions on school absence, e.g. gastrointestinal infections.

Investigating regional variation of respiratory infections in a general practice syndromic surveillance system.

BACKGROUND: Established surveillance systems can follow trends in community disease and illness over many years. However, within England there are known regional differences in healthcare utilisation, which can affect interpretation of trends. Here, we explore regional differences for a range of respiratory conditions using general practitioner (GP) consultation data. METHODS: Daily data for respiratory conditions were extracted from a national GP surveillance system. Average daily GP consultation rates per 100 000 registered patient population were calculated by each region of England and for each study year (2013-17). Consultation rates and incidence rate ratios were also calculated for each condition by deprivation quintile and by rural, urban, and conurbation groups. RESULTS: Upper and lower respiratory tract infections and asthma were higher in the North and the Midlands than in London and the South, were highest in the most deprived groups and tended to be higher in more urban areas. Influenza-like illness was highest in the least deprived and rural areas. CONCLUSIONS: There are consistent differences in GP consultation rates across the English regions. This work has improved our understanding and interpretation of GP surveillance data at regional level and will guide more accurate public health messages.

Describing the indirect impact of COVID-19 on healthcare utilisation using syndromic surveillance systems.

BACKGROUND: Since the end of January 2020, the coronavirus (COVID-19) pandemic has been responsible for a global health crisis. In England a number of non-pharmaceutical interventions have been introduced throughout the pandemic, including guidelines on healthcare attendance (for example, promoting remote consultations), increased handwashing and social distancing. These interventions are likely to have impacted the incidence of non-COVID-19 conditions as well as healthcare seeking behaviour. Syndromic Surveillance Systems offer the ability to monitor trends in healthcare usage over time. METHODS: This study describes the indirect impact of COVID-19 on healthcare utilisation using a range of syndromic indicators including eye conditions, mumps, fractures, herpes zoster and cardiac conditions. Data from the syndromic surveillance systems monitored by Public Health England were used to describe the number of contacts with NHS 111, general practitioner (GP) In Hours (GPIH) and Out-of-Hours (GPOOH), Ambulance and Emergency Department (ED) services over comparable periods before and during the pandemic. RESULTS: The peak pandemic period in 2020 (weeks 13-20), compared to the same period in 2019, displayed on average a 12% increase in NHS 111 calls, an 11% decrease in GPOOH consultations, and a 49% decrease in ED attendances. In the GP In Hours system, conjunctivitis consultations decreased by 64% and mumps consultations by 31%. There was a 49% reduction in attendance at EDs for fractures, and there was no longer any weekend increase in ED fracture attendances, with similar attendance patterns observed across each day of the week. There was a decrease in the number of ED attendances with diagnoses of myocardial ischaemia. CONCLUSION: The COVID-19 pandemic drastically impacted healthcare utilisation for non-COVID-19 conditions, due to a combination of a probable decrease in incidence of certain conditions and changes in healthcare seeking behaviour. Syndromic surveillance has a valuable role in describing and understanding these trends.

Comparison of statistical algorithms for daily syndromic surveillance aberration detection.

MOTIVATION: Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the 'rising activity, multilevel mixed effects, indicator emphasis' (RAMMIE) method and the improved quasi-Poisson regression-based method known as 'Farrington Flexible' both currently used at Public Health England, and the 'Early Aberration Reporting System' (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data. RESULTS: We conclude that amongst the algorithm variants that have a high specificity (i.e. >90%), Farrington Flexible has the highest sensitivity and specificity, whereas RAMMIE has the highest probability of outbreak detection and is the most timely, typically detecting outbreaks 2-3 days earlier. AVAILABILITY AND IMPLEMENTATION: R codes developed for this project are available through https://github.com/FelipeJColon/AlgorithmComparison. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The COVID-19 pandemic: a new challenge for syndromic surveillance.

The COVID-19 pandemic is exerting major pressures on society, health and social care services and science. Understanding the progression and current impact of the pandemic is fundamental to planning, management and mitigation of future impact on the population. Surveillance is the core function of any public health system, and a multi-component surveillance system for COVID-19 is essential to understand the burden across the different strata of any health system and the population. Many countries and public health bodies utilise 'syndromic surveillance' (using real-time, often non-specific symptom/preliminary diagnosis information collected during routine healthcare provision) to supplement public health surveillance programmes. The current COVID-19 pandemic has revealed a series of unprecedented challenges to syndromic surveillance including: the impact of media reporting during early stages of the pandemic; changes in healthcare-seeking behaviour resulting from government guidance on social distancing and accessing healthcare services; and changes in clinical coding and patient management systems. These have impacted on the presentation of syndromic outputs, with changes in denominators creating challenges for the interpretation of surveillance data. Monitoring changes in healthcare utilisation is key to interpreting COVID-19 surveillance data, which can then be used to better understand the impact of the pandemic on the population. Syndromic surveillance systems have had to adapt to encompass these changes, whilst also innovating by taking opportunities to work with data providers to establish new data feeds and develop new COVID-19 indicators. These developments are supporting the current public health response to COVID-19, and will also be instrumental in the continued and future fight against the disease.

Emergency department use during COVID-19 as described by syndromic surveillance.

On 12 March 2020 the UK entered the 'delay phase' of the COVID-19 pandemic response. The Public Health England Emergency Department Syndromic Surveillance System (EDSSS) carries out daily (near real-time) public health surveillance of emergency department (ED) attendances across England. This retrospective observational analysis of EDSSS data aimed to describe changes in ED attendances during March-April 2020, and identify the attendance types with the largest impact. Type 1 ED attendances were selected from 109 EDs that reported data to EDSSS for the period 1 January 2019 to 26 April 2020. The daily numbers of attendances were plotted by age group and acuity of presentation. The 2020 'COVID-19' period (12 March 2020 to 26 April 2020) attendances were compared with the equivalent 2019 'pre-COVID-19' period (14 March 2019 to 28 April 2019): in total; by hour and day of the week; age group(<1, 1-4, 15-14, 15-44, 45-64 and 65+ years); gender; acuity; and for selected syndromic indicators(acute respiratory infection, gastroenteritis, myocardial ischaemia). Daily ED attendances up to 11 March 2020 showed regular trends, highest on a Monday and reduced in children during school holidays. From 12 March 2020 ED attendances decreased across all age groups, all acuity levels, on all days and times. Across age groups the greatest percentage reductions were seen in school age children (5-14 years). By acuity, the greatest reduction occurred in the less severe presentations. Syndromic indicators showed that the greatest reductions were in non-respiratory indicators, which fell by 44-67% during 2020 COVID-19, while acute respiratory infection was reduced by -4.4% (95% CI -9.5% to 0.6%). ED attendances in England have been particularly affected during the COVID-19 pandemic due to changes in healthcare seeking behaviour. EDSSS has enabled real-time daily monitoring of these changes, which are made publicly available to facilitate action. The EDSSS provides valuable surveillance of ED attendances in England. The flexibility of EDSSS allowed rapid development of new indicators (including COVID-19-like) and reporting methods.

Syndromic surveillance: two decades experience of sustainable systems - its people not just data!

Syndromic surveillance is a form of surveillance that generates information for public health action by collecting, analysing and interpreting routine health-related data on symptoms and clinical signs reported by patients and clinicians rather than being based on microbiologically or clinically confirmed cases. In England, a suite of national real-time syndromic surveillance systems (SSS) have been developed over the last 20 years, utilising data from a variety of health care settings (a telehealth triage system, general practice and emergency departments). The real-time systems in England have been used for early detection (e.g. seasonal influenza), for situational awareness (e.g. describing the size and demographics of the impact of a heatwave) and for reassurance of lack of impact on population health of mass gatherings (e.g. the London 2012 Olympic and Paralympic Games).We highlight the lessons learnt from running SSS, for nearly two decades, and propose questions and issues still to be addressed. We feel that syndromic surveillance is an example of the use of 'big data', but contend that the focus for sustainable and useful systems should be on the added value of such systems and the importance of people working together to maximise the value for the public health of syndromic surveillance services.

Potential added value of the new emergency care dataset to ED-based public health surveillance in England: an initial concept analysis.

INTRODUCTION: For the London Olympic and Paralympic Games in 2012, a sentinel ED syndromic surveillance system was established to enhance public health surveillance by obtaining data from a selected network of EDs, focusing on London. In 2017, a new national standard Emergency Care Dataset was introduced, which enabled Public Health England (PHE) to initiate the expansion of their sentinel system to national coverage. Prior to this initiative, we estimated the added value, and potential additional resource use, of an expansion of the sentinel surveillance system. METHODS: The detection capabilities of the sentinel and national systems were compared using the aberration detection methods currently used by PHE. Different scenarios were used to measure the impact on health at a local, subnational and national level, including improvements to sensitivity and timeliness, along with changes in specificity. RESULTS: The biggest added value was found to be for detecting local impacts, with an increase in sensitivity of over 80%. There were also improvements found at a national level with outbreaks being detected earlier and smaller impacts being detectable. However, the increased number of local sites will also increase the number of false alarms likely to be generated. CONCLUSION: We have quantified the added value of national ED syndromic surveillance systems, showing how they will enable detection of more localised events. Furthermore, national systems add value in enabling timelier public health interventions. Finally, we have highlighted areas where extra resource may be required to manage improvements in detection coverage.

Google search patterns monitoring the daily health impact of heatwaves in England: How do the findings compare to established syndromic surveillance systems from 2013 to 2017?

One of the implications of climate change is a predicted increase in frequent and severe heatwaves. The impact of heatwaves on the health of the population is captured through real-time syndromic healthcare surveillance systems monitored daily in England during the summer months. Internet search data could potentially provide improved timeliness and help to assess the wider population health impact of heat by capturing a population sub-group who are symptomatic but do not seek healthcare. A retrospective observational study was carried out from June 2013 to September 2017 in England to compare daily trends in validated syndromic surveillance heat-related morbidity indicators against symptom-based heatwave related Google search terms. The degree of correlation was determined with Spearman correlation coefficients and lag assessment was carried out to determine timeliness. Daily increases in frequency in Google search terms during heatwave events correlated well with validated syndromic indicators. Correlation coefficients between search term frequency and syndromic indicators from 2013 to 2017 were highest with the telehealth service NHS 111 (range of 0.684-0.900 by search term). Lag analysis revealed a similar timeliness between the data sources, suggesting Google data did not provide a delayed or earlier signal in the context of England's syndromic surveillance systems. This work highlights the potential benefits for countries which lack established public health surveillance systems to monitor heat-related morbidity and the use of internet search data to assess the wider population health impact of exposure to heat.

A standardized methodology for the surveillance of antimicrobial prescribing linked to clinical indications in primary care.

Background: A key component of strategies to reduce antimicrobial resistance is better antimicrobial prescribing. The majority of antibiotics are prescribed in primary care. While many existing surveillance systems can monitor trends in the quantities of antibiotics prescribed in this setting, it can be difficult to monitor the quality of prescribing as data on the condition for which prescriptions are issued are often not available. We devised a standardized methodology to facilitate the monitoring of condition-specific antibiotic prescribing in primary care. Methods: We used a large computerized general practitioner database to develop a standardized methodology for routine monitoring of antimicrobial prescribing linked to clinical indications in primary care in the UK. Outputs included prescribing rate by syndrome and percentages of consultations with antibiotic prescription, for recommended antibiotic, and of recommended treatment length. Results: The standardized methodology can monitor trends in proportions of common infections for which antibiotics were prescribed, the specific drugs prescribed and duration of treatment. These data can be used to help assess the appropriateness of antibiotic prescribing and to assess the impact of prescribing guidelines. Conclusions: We present a standardized methodology that could be applied to any suitable national or local database and adapted for use in other countries.

A methodological framework for the evaluation of syndromic surveillance systems: a case study of England.

BACKGROUND: Syndromic surveillance complements traditional public health surveillance by collecting and analysing health indicators in near real time. The rationale of syndromic surveillance is that it may detect health threats faster than traditional surveillance systems permitting more timely, and hence potentially more effective public health action. The effectiveness of syndromic surveillance largely relies on the methods used to detect aberrations. Very few studies have evaluated the performance of syndromic surveillance systems and consequently little is known about the types of events that such systems can and cannot detect. METHODS: We introduce a framework for the evaluation of syndromic surveillance systems that can be used in any setting based upon the use of simulated scenarios. For a range of scenarios this allows the time and probability of detection to be determined and uncertainty is fully incorporated. In addition, we demonstrate how such a framework can model the benefits of increases in the number of centres reporting syndromic data and also determine the minimum size of outbreaks that can or cannot be detected. Here, we demonstrate its utility using simulations of national influenza outbreaks and localised outbreaks of cryptosporidiosis. RESULTS: Influenza outbreaks are consistently detected with larger outbreaks being detected in a more timely manner. Small cryptosporidiosis outbreaks (<1000 symptomatic individuals) are unlikely to be detected. We also demonstrate the advantages of having multiple syndromic data streams (e.g. emergency attendance data, telephone helpline data, general practice consultation data) as different streams are able to detect different outbreak types with different efficacy (e.g. emergency attendance data are useful for the detection of pandemic influenza but not for outbreaks of cryptosporidiosis). We also highlight that for any one disease, the utility of data streams may vary geographically, and that the detection ability of syndromic surveillance varies seasonally (e.g. an influenza outbreak starting in July is detected sooner than one starting later in the year). We argue that our framework constitutes a useful tool for public health emergency preparedness in multiple settings. CONCLUSIONS: The proposed framework allows the exhaustive evaluation of any syndromic surveillance system and constitutes a useful tool for emergency preparedness and response.

Retrospective Observational Study of Atypical Winter Respiratory Illness Season Using Real-Time Syndromic Surveillance, England, 2014-15.

During winter 2014-15, England experienced severe strains on acute health services. We investigated whether syndromic surveillance could contribute to understanding of the unusually high level of healthcare needs. We compared trends for several respiratory syndromic indicators from that winter to historical baselines. Cumulative and mean incidence rates were compared by winter and age group. All-age influenza-like illness was at expected levels; however, severe asthma and pneumonia levels were above those expected. Across several respiratory indicators, cumulative incidence rates during 2014-15 were similar to those of previous years, but higher for older persons; we saw increased rates of acute respiratory disease, including influenza like illness, severe asthma, and pneumonia, in the 65-74- and >75-year age groups. Age group-specific statistical algorithms may provide insights into the burden on health services and improve early warning in future winters.

Novel public health risk assessment process developed to support syndromic surveillance for the 2012 Olympic and Paralympic Games.

Background: Syndromic surveillance aims to provide early warning and real time estimates of the extent of incidents; and reassurance about lack of impact of mass gatherings. We describe a novel public health risk assessment process to ensure those leading the response to the 2012 Olympic Games were alerted to unusual activity that was of potential public health importance, and not inundated with multiple statistical 'alarms'. Methods: Statistical alarms were assessed to identify those which needed to result in 'alerts' as reliably as possible. There was no previously developed method for this. We identified factors that increased our concern about an alarm suggesting that an 'alert' should be made. Results: Between 2 July and 12 September 2012, 350 674 signals were analysed resulting in 4118 statistical alarms. Using the risk assessment process, 122 'alerts' were communicated to Olympic incident directors. Conclusions: Use of a novel risk assessment process enabled the interpretation of large number of statistical alarms in a manageable way for the period of a sustained mass gathering. This risk assessment process guided the prioritization and could be readily adapted to other surveillance systems. The process, which is novel to our knowledge, continues as a legacy of the Games.

Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats.

BACKGROUND: As service provision and patient behaviour varies by day, healthcare data used for public health surveillance can exhibit large day of the week effects. These regular effects are further complicated by the impact of public holidays. Real-time syndromic surveillance requires the daily analysis of a range of healthcare data sources, including family doctor consultations (called general practitioners, or GPs, in the UK). Failure to adjust for such reporting biases during analysis of syndromic GP surveillance data could lead to misinterpretations including false alarms or delays in the detection of outbreaks. The simplest smoothing method to remove a day of the week effect from daily time series data is a 7-day moving average. Public Health England developed the working day moving average in an attempt also to remove public holiday effects from daily GP data. However, neither of these methods adequately account for the combination of day of the week and public holiday effects. METHODS: The extended working day moving average was developed. This is a further data-driven method for adding a smooth trend curve to a time series graph of daily healthcare data, that aims to take both public holiday and day of the week effects into account. It is based on the assumption that the number of people seeking healthcare services is a combination of illness levels/severity and the ability or desire of patients to seek healthcare each day. The extended working day moving average was compared to the seven-day and working day moving averages through application to data from two syndromic indicators from the GP in-hours syndromic surveillance system managed by Public Health England. RESULTS: The extended working day moving average successfully smoothed the syndromic healthcare data by taking into account the combined day of the week and public holiday effects. In comparison, the seven-day and working day moving averages were unable to account for all these effects, which led to misleading smoothing curves. CONCLUSIONS: The results from this study make it possible to identify trends and unusual activity in syndromic surveillance data from GP services in real-time independently of the effects caused by day of the week and public holidays, thereby improving the public health action resulting from the analysis of these data.

The application of a novel 'rising activity, multi-level mixed effects, indicator emphasis' (RAMMIE) method for syndromic surveillance in England.

MOTIVATION: Syndromic surveillance is the real-time collection and interpretation of data to allow the early identification of public health threats and their impact, enabling public health action. The 'rising activity, multi-level mixed effects, indicator emphasis' method was developed to provide a single robust method enabling detection of unusual activity across a wide range of syndromes, nationally and locally. RESULTS: The method is shown here to have a high sensitivity (92%) and specificity (99%) compared to previous methods, whilst halving the time taken to detect increased activity to 1.3 days. AVAILABILITY AND IMPLEMENTATION: The method has been applied successfully to syndromic surveillance systems in England providing realistic models for baseline activity and utilizing prioritization rules to ensure a manageable number of 'alarms' each day. CONTACT: roger.morbey@phe.gov.uk.

Assessing the Likely Impact of a Rotavirus Vaccination Program in England: The Contribution of Syndromic Surveillance.

BACKGROUND: In July 2013, a rotavirus vaccination program for 2- to 3-month-olds was introduced in the United Kingdom. We present an initial impact analysis of this new vaccine program using national syndromic surveillance systems. METHODS: General practitioner (GP) in-hours, GP out-of-hours, and emergency department (ED) syndromic surveillance systems were used to monitor GP consultations and ED visits for gastroenteritis, diarrhea, and vomiting. Data were stratified by age group and compared between pre- and postvaccine-year rotavirus seasons. Incidence rate ratios (IRRs) and percentage ratios were calculated for GP in-hours consultations and GP out-of-hours and ED data, respectively. RESULTS: There was a significant reduction in gastroenteritis, diarrhea, and vomiting GP in-hours consultations in children aged 0-4 years when comparing the rotavirus season in the pre- and postvaccine years (P < .001 for all indicators). IRRs illustrated a 26%-33% and 23%-31% decrease in gastroenteritis incidence in the <1 and 1-4 years age groups, respectively, across the syndromic surveillance systems. There was also an 8% decrease recorded in the 5-14 years age group in the GP in-hours and ED systems. CONCLUSIONS: Syndromic surveillance revealed a marked decline in gastroenteritis, coinciding with the introduction of the new rotavirus vaccine program in England. The largest reduction in disease was observed in infants, although some impact was also demonstrated in children aged 1-4 and 5-14 years, suggesting possible herd protection in older age groups. This study was limited to the first postvaccine year, and further analysis is required to assess the longer-term impact of the vaccine.

Using real-time syndromic surveillance systems to help explore the acute impact of the air pollution incident of March/April 2014 in England.

During March and early April 2014 there was widespread poor air quality across the United Kingdom. Public Health England used existing syndromic surveillance systems to monitor community health during the period. Short lived statistically significant rises in a variety of respiratory conditions, including asthma and wheeze, were detected. This incident has demonstrated the value of real-time syndromic surveillance systems, during an air pollution episode, for helping to explore the impact of poor air quality on community health in real-time.

Trends in antibiotic prescribing in primary care for clinical syndromes subject to national recommendations to reduce antibiotic resistance, UK 1995-2011: analysis of a large database of primary care consultations.

OBJECTIVES: To measure trends in antibiotic prescribing in UK primary care in relation to nationally recommended best practice. PATIENTS AND METHODS: A descriptive study linking individual patient data on diagnosis and prescription in a large primary care database, covering 537 UK general practices during 1995-2011. RESULTS: The proportion of cough/cold episodes for which antibiotics were prescribed decreased from 47% in 1995 to 36% in 1999, before increasing to 51% in 2011. There was marked variation by primary care practice in 2011 [10th-90th percentile range (TNPR) 32%-65%]. Antibiotic prescribing for sore throats fell from 77% in 1995 to 62% in 1999 and then stayed broadly stable (TNPR 45%-78%). Where antibiotics were prescribed for sore throat, recommended antibiotics were used in 69% of cases in 2011 (64% in 1995). The use of recommended short-course trimethoprim for urinary tract infection (UTI) in women aged 16-74 years increased from 8% in 1995 to 50% in 2011; however, a quarter of practices prescribed short courses in ≤16% of episodes in 2011. For otitis media, 85% of prescriptions were for recommended antibiotics in 2011, increasing from 77% in 1995. All these changes in annual prescribing were highly statistically significant (P < 0.001). CONCLUSIONS: The implementation of national guidelines in UK primary care has had mixed success, with prescribing for coughs/colds, both in total and as a proportion of consultations, now being greater than before recommendations were made to reduce it. Extensive variation by practice suggests that there is significant scope to improve prescribing, particularly for coughs/colds and for UTIs.