GP coding behaviour for non-specific clinical presentations: a pilot study.

BACKGROUND: Clinical coding is an integral part of primary care. Disease incidence studies based on primary care electronic health records (EHRs) rely on the accuracy of these codes. Current code validation methods are not appropriate for non-specific conditions and provide limited information about GPs' decision-making behaviour around coding. Qualitative methods could offer insight into decision-making behaviour around coding of patients with non-specific conditions. AIM: To investigate the decision-making behaviour of GPs when applying Read codes to non-specific clinical presentations, using Lyme disease as a case example. DESIGN & SETTING: A pilot study was undertaken, involving masked semi-structured interviews of eight GPs in the North West of England. METHOD: Semi-structured interviews were carried out based on 11 clinical cases representative of Lyme disease presentations. Discrete answers were described descriptively. Interview transcripts were analysed using a thematic approach. RESULTS: Themes underpinning GPs' coding behaviour included: GP personal and professional experience; clinical evidence; diagnostic uncertainty; professional integrity and defensive practice; and patient-sourced health information and beliefs. GPs placed Lyme disease on their differential diagnosis list for five cases; in only two cases would GPs select a Lyme disease related Read code. CONCLUSION: GPs were reluctant to code with specific diagnostic Read codes when they were presented with patients with vague or unfamiliar symptomology. This masked questionnaire methodology offers a new approach to validate incidence figures, based on Read codes of non-specific conditions. The reluctance to code poses many problems for primary care EHRs research. Further research is needed to understand what drives GPs' coding behaviour.

A Fully Integrated Real-Time Detection, Diagnosis, and Control of Community Diarrheal Disease Clusters and Outbreaks (the INTEGRATE Project): Protocol for an Enhanced Surveillance System.

BACKGROUND: Diarrheal disease, which affects 1 in 4 people in the United Kingdom annually, is the most common cause of outbreaks in community and health care settings. Traditional surveillance methods tend to detect point-source outbreaks of diarrhea and vomiting; they are less effective at identifying low-level and intermittent food supply contamination. Furthermore, it can take up to 9 weeks for infections to be confirmed, reducing slow-burn outbreak recognition, potentially impacting hundreds or thousands of people over wide geographical areas. There is a need to address fundamental problems in traditional diarrheal disease surveillance because of underreporting and subsequent unconfirmed infection by patients and general practitioners (GPs); varying submission practices and selective testing of samples in laboratories; limitations in traditional microbiological diagnostics, meaning that the timeliness of sample testing and etiology of most cases remains unknown; and poorly integrated human and animal surveillance systems, meaning that identification of zoonoses is delayed or missed. OBJECTIVE: This study aims to detect anomalous patterns in the incidence of gastrointestinal disease in the (human) community; to target sampling; to test traditional diagnostic methods against rapid, modern, and sensitive molecular and genomic microbiology methods that identify and characterize responsible pathogens rapidly and more completely; and to determine the cost-effectiveness of rapid, modern, sensitive molecular and genomic microbiology methods. METHODS: Syndromic surveillance will be used to aid identification of anomalous patterns in microbiological events based on temporal associations, demographic similarities among patients and animals, and changes in trends in acute gastroenteritis cases using a point process statistical model. Stool samples will be obtained from patients' consulting GPs, to improve the timeliness of cluster detection and characterize the pathogens responsible, allowing health protection professionals to investigate and control outbreaks quickly, limiting their size and impact. The cost-effectiveness of the proposed system will be examined using formal cost-utility analysis to inform decisions on national implementation. RESULTS: The project commenced on April 1, 2013. Favorable approval was obtained from the Research Ethics Committee on June 15, 2015, and the first patient was recruited on October 13, 2015, with 1407 patients recruited and samples processed using traditional laboratory techniques as of March 2017. CONCLUSIONS: The overall aim of this study is to create a new One Health paradigm for detecting and investigating diarrhea and vomiting in the community in near-real time, shifting from passive human surveillance and management of laboratory-confirmed infection toward an integrated, interdisciplinary enhanced surveillance system including management of people with symptoms. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13941.

Characteristics and patient pathways of Lyme disease patients: a retrospective analysis of hospital episode data in England and Wales (1998-2015).

BACKGROUND: Lyme disease is a tick-borne disease of increasing global importance. There is scant information on Lyme disease patient demographics in England and Wales, and how they interact with the National Health Service (NHS). Our aims were to explore the demographic characteristics of Lyme disease patients within the Hospital Episode Statistics (HES) and Patient Episode Database for Wales (PEDW), and to describe patient pathways. METHODS: Data from 1st January 1998 to 31st December 2015 was retrieved from the two administrative hospital datasets (HES and PEDW), based on patients coded with Lyme disease. Information was collected on demographic characteristics, home address and case management. Incidence rates were calculated, and demographics compared to the national population. RESULTS: Within HES and PEDW, 2361 patients were coded with Lyme disease. There was a significant increase (p < 0.01) in incidence from 0.08 cases/100,000 in 1998, to 0.53 cases/100,000 in 2015. There was a bimodal age distribution, patients were predominantly female, white and from areas of low deprivation. New cases peaked annually in August, with higher incidence rates in southern central and western England. Within hospital admission data (n = 2066), most cases were either referred from primary care (28.8%, n = 596) or admitted via accident and emergency (A&E) (29.5%, n = 610). This population entering secondary care through A&E suggest a poor understanding of the recommended care pathways for symptoms related to Lyme disease by the general population. CONCLUSIONS: These data can be used to inform future investigations into Lyme disease burden, and patient management within the NHS. They provide demographic information for clinicians to target public health messaging or interventions.

Mapping tweets to a known disease epidemiology; a case study of Lyme disease in the United Kingdom and Republic of Ireland.

BACKGROUND: Analysis of social media is an emerging method with potential as a tool for disease surveillance. Twitter may offer a route for surveillance by using tweeting habits as a proxy for disease incidence. Previous work has focused on temporal patterns and have proven to be successful. However, the identification of geographical patterns has been limited by a combination of Twitter's data collection policies and by exploring diseases that have a high prevalence and high levels of awareness with the public. We propose that, by performing a restricted geographical search strategy on a disease with a relatively low incidence, one may be able to explore spatial patterns. Here, Lyme disease in the United Kingdom and the Republic of Ireland is used as a case example. OBJECTIVE: To explore whether the tweeting habits of British and Irish Twitter users matched the known spatio-temporal epidemiology of Lyme disease in these respective countries. METHODS: All Tweets containing the word 'Lyme' were collected between the 1st of July 2017 and the 30th June 2018, restricted by geography (a 375-mile radius around the geographical centre of Great Britain) and by language (English-only tweets). Tweets were removed which referred to locations that included 'Lyme' within their name (e.g. Lyme Regis). Only original tweets were analysed. Daily and monthly time series were created and compared to national Lyme disease surveillance figures. A map of the number of Twitter users tweeting about Lyme disease per 100,000 population per local authority was created. This was formerly compared to national surveillance data for England and Wales using an exploratory spatial data analysis approach. RESULTS: During the study period, 13,757 original tweets containing the word 'Lyme', and excluding place names relating to Lyme, were collected. The mean number of daily tweets was 38 (range: 12-276). There was strong seasonality with the highest number of tweets in the summer, this matched the known epidemiology of Lyme disease. Of the 5212 of users who tweeted about Lyme disease, 51.8% had a user profile location that could be matched to a local authority in the United Kingdom or Republic of Ireland. The mean number of Twitter users tweeting about Lyme disease per 100,000 population per local authority was 3.7. The areas with the highest incidence were south-west England and the Highlands of Scotland. When comparing these figures to English and Welsh Lyme disease surveillance figures they showed a significant positive spatial correlation (p = 0.002). CONCLUSIONS: The tempo-spatial pattern of Twitter users tweeting about Lyme disease matches the known disease epidemiology. The degree of geographical concordance between Twitter users' locations and national surveillance reports, indicate that Twitter has the potential to be used in to identify potential disease hotspots based on the levels of social media 'noise'. There is scope for further work to test the robustness of Twitter as an adjunct 'measure of concern' disease surveillance tool. However, caution must be taken as national media stories can skew data and Twitter users may not provide reliable facts in the data that they share on the platform.

Evaluating the Efficacy of Knowledge-Transfer Interventions on Animal Health Knowledge of Rural Working Equid Owners in Central Ethiopia: A Cluster-Randomized Controlled Trial.

The objectives of this study were to evaluate the efficacy of several knowledge-transfer interventions about donkey health, utilizing a cluster-randomized controlled trial (c-RCT), on the long-term knowledge change (~6 months post intervention) of Ethiopian rural working equid owners. Knowledge transfer interventions included: an audio programme, a village meeting and a diagrammatic hand-out, which were also compared to a control group, which received no intervention. All interventions addressed identical learning objectives. Thirty-two villages were randomly selected and interventions randomly assigned to blocks of eight villages. All participants in a village received the same intervention, and knowledge levels were assessed by questionnaire administration both pre and post intervention. Data analysis included multilevel linear and logistic regression models (allowing for clustering of individuals within villages) to evaluate the change in knowledge between the different knowledge-transfer interventions, and to look at other factors associated with change in knowledge. A total of 516 randomly selected participants completed pre-intervention questionnaires, 476 undertook a post-dissemination questionnaire ~6 months later, a follow-up response rate of 92%. All interventions significantly improved the overall knowledge score on the post intervention questionnaire compared to the control group, with the diagrammatic hand-out [coefficient (coef) 10.0, S.E. = 0.5] and the village meeting (coef 8.5, S.E = 0.5) having a significantly greater impact than the audio programme (coef 4.0, S.E = 0.5). There were differences in learning across interventions, learning objectives, age and education levels of the participants. Participants with higher levels of formal education had greater knowledge change but this varied across interventions. In conclusion, knowledge of donkey health was substantially increased by a diagrammatic hand-out and the impact of this simple, low-cost intervention should be further evaluated in other communities in low-income countries. This study should assist in the design and development of effective knowledge-transfer materials for adult learning for rural villagers in low-income countries.

Large-scale spatial and temporal genetic diversity of feline calicivirus.

Feline calicivirus (FCV) is an important pathogen of domestic cats and a frequently used model of human caliciviruses. Here we use an epidemiologically rigorous sampling framework to describe for the first time the phylodynamics of a calicivirus at regional and national scales. A large number of FCV strains cocirculated in the United Kingdom at the national and community levels, with no strain comprising more than 5% and 14% of these populations, respectively. The majority of strains exhibited a relatively restricted geographical range, with only two strains (one field virus and one vaccine virus) spreading further than 100 km. None of the field strains were identified outside the United Kingdom. Temporally, while some strains persisted locally for the majority of the study, others may have become locally extinct. Evolutionary analysis revealed a radial phylogeny with little bootstrap support for nodes above the strain level. In most cases, spatially and temporally diverse strains intermingled in the phylogeny. Together, these data suggest that current FCV evolution is not associated with selective competition among strains. Rather, the genetic and antigenic landscape in each geographical location is highly complex, with many strains cocirculating. These variants likely exist at the community level by a combination of de novo evolution and occasional gene flow from the wider national population. This complexity provides a benchmark, for the first time, against which vaccine cross-protection at both local and national levels can be judged.

Identifying temporal variation in reported births, deaths and movements of cattle in Britain.

BACKGROUND: The accuracy of predicting disease occurrence using epidemic models relies on an understanding of the system or population under investigation. At the time of the Foot and Mouth disease (FMD) outbreak of 2001, there were limited reports in the literature as to the cattle population structure in Britain. In this paper we examine the temporal patterns of cattle births, deaths, imports and movements occurring within Britain, reported to the Department for the Environment, Food and Rural Affairs (DEFRA) through the British Cattle Movement service (BCMS) during the period 1st January 2002 to 28th February 2005. RESULTS: In Britain, the number of reported cattle births exhibit strong seasonality characterised by a large spring peak followed by a smaller autumn peak. Other event types also exhibit strong seasonal trends; both the reported number of cattle slaughtered and "on-farm" cattle deaths increase during the final part of the year. After allowing for seasonal components by smoothing the data, we illustrate that there is very little remaining non-seasonal trend in the number of cattle births, "on-farm" deaths, slaughterhouse deaths, on- and off-movements. However after allowing for seasonal fluctuations the number of cattle imports has been decreasing since 2002. Reporting of movements, births and deaths was more frequent on certain days of the week. For instance, greater numbers of cattle were slaughtered on Tuesdays, Wednesdays and Thursdays. Evidence for digit preference was found in the reporting of births and "on-farm" deaths with particular bias towards over reporting on the 1st, 10th and 20th of each month. CONCLUSION: This study provides insight into the population and movement dynamics of the British cattle population. Although the population is in constant flux, seasonal and long term trends can be identified in the number of reported births, deaths and movements of cattle. Incorporating this temporal variation in epidemic disease modelling may result in more accurate model predictions and may usefully inform future surveillance strategies.