Data source

Around 98% of the UK population is registered with a GP.[18] The Health Improvement Network (THIN) is a database containing longitudinal primary care records from around 6% of the UK population, registered with GP practices that use VISION patient management software and have agreed to contribute data to THIN.[19] THIN contains data on prescriptions and diagnoses, together with additional demographic information. The registered THIN population is broadly representative of UK demography, and general practices contributing data to THIN are representative of UK general practices in terms of prescribing and consultation frequency.[20, 21]

Diagnoses and symptoms are entered in primary care electronic records using Read codes [22] by the GP, usually during patient consultations. Read codes are a coding system used to record clinical summary information. Two systematic reviews [23, 24] found that a high proportion of diagnoses recorded as Read codes in electronic medical records were confirmed using internal validation, or external validation using GP questionnaires or paper medical records. Prescriptions are entered using drug codes that map onto chapters of the British National Formulary.[25]

Severe outcomes of influenza are more common in persons of low socio-economic status,[26, 27] and consultations rates for lower respiratory tract infections are higher among individuals living in deprived areas.[28] We therefore examined the effect of socio-economic deprivation on ILI consultations, using quintiles of Townsend scores. The Townsend score is a small area based measure of multiple deprivation based on data from the 2001 Census. It takes into account property and car ownership, overcrowding and unemployment in the resident population of a small area consisting of approximately 150 households. In THIN, the Townsend score is linked to a patient through their postcode of residence by the data providers; however the actual postcodes and lower level geographies are not made available to researchers due to concerns regarding anonymity.

Study population and period

We included all individuals registered in a contributing THIN practice from birth up to 99 years inclusive, who were registered for at least seven days at any point during the study period, 2^nd October 1995 to 19^th May 2013. Patient electronic records were included from the date at which practices met quality criteria regarding data entry.[29, 30] We examined consultations occurring in the winter seasons 1995/1996 to 2012/2013, where a winter season is defined as Monday week 40 (beginning of October) in year x to Sunday in week 20 (middle of May) in year x+1.[31]

Definition of influenza-like illness (ILI)

We used established methods based on word searches of the Read code dictionary,[32] to create a code list for ILI (S1 Table), including codes for influenza-like illness, influenza and positive swab results for influenza. These codes were chosen as they indicate clinicians’ willingness to record a specific diagnosis of ILI or influenza infection (even if only suspected). We also included prescriptions of neuraminidase inhibitors (NIs, oseltamivir or zanamivir) as indicators of ILI.

We compared trends in recording of specific ILI codes to trends in recording of cough or fever (S1 Table), as an indicator of recording of non-specific symptoms of respiratory tract infections. We chose fever or cough as these two symptoms are recommended for community influenza surveillance by the World Health Organisation,[8] although only a minority of cough or fever episodes would be expected to be caused by influenza.[11] Since it is rare for GPs to record two different diagnoses during a consultation, we did not examine recording of cough or fever during the same consultation. We included only the first ILI record per person within each season as infection with two different influenza strains during a season is rare,[33] and only a small minority (around 1%) of persons consulting with ILI consulted twice or more during a season. Likewise, we only included the first cough or fever consultation per person per season in order to carry out similar comparisons over time between specific and non-specific diagnoses.

Statistical methods

Ethics

All data were anonymised. THIN data collection has been approved by the South East NHS Multicentre Research Ethics Committee. The analyses for this study were approved by the Scientific Review Committee of the data providers (CSD Medical Research, now IMS Health), study reference number SRC 14–004.

Time trends in ILI recorded consultations, 1995–2013

ILI consultation rates decreased by 52% during the study period (Fig 1); from 32.5/100,000 pw in the first three seasons, 1995/96, 1996/97 and 1997/98, (95% CI 32.1, 32.9) to 15.5/100,000 pw (95% CI 15.4, 15.7) in the last three seasons (2010/11, 2011/12 and 2012/13). This decline occurred prior to 2002/3 (S3 Table). After this date ILI consultation rates remained largely stable, apart from year-on-year variations and the peak in consultations during the 2009/10 pandemic (Fig 1). In contrast, consultation rates for non-specific cough or fever symptoms increased by 40% from 169.4/100,000 pw (95% CI 168.6, 170.3) to 237.7/100,000 pw (95% CI 237.2, 238.2) between the first three and the last three seasons (Fig 2, S3 Table).

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Fig 1. Consultation rates for ILI (per 100,000 person-weeks) by age group and winter season, 1995–2013.

https://doi.org/10.1371/journal.pone.0138659.g001

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Fig 2. Consultation rates for cough or fever symptoms (per 100,000 person-weeks) by age group and winter season, 1995–2013*.

*Note difference in scale on y-axis cf. Fig 1.

https://doi.org/10.1371/journal.pone.0138659.g002

A cubic spline with four internal knots at 2002/03, 2004/05, 2007/08 and 2009/10 best explained the time trend in ILI consultations, based on minimisation of the AIC. Including the age group: season, and age group: pandemic indicator interaction terms significantly improved model fit, implying that the observed trends over time in ILI recorded consultations vary significantly by age group. S1 Fig shows the fit of the final models. An overall decline prior to 2002/3 in ILI consultations was observed in all age groups, but the largest declines were observed in individuals aged 65 years or above. Children aged less than 15 years experienced the highest peak in consultations during the 2009–2010 pandemic.

Recent recoding of ILI according to socio-demographic characteristics, 2010–2013

Overall, ILI consultation rates were higher among women than men, but this difference varied by age group (an age group: gender interaction term significantly improved model fit based on minimisation of AIC). Consultation rates were higher in women than in men among individuals aged between 15 and 74 years. Only small gender differences in consultation rates were observed in children aged less than 15 years and persons aged over 75 years (Table 1). Individuals living in more deprived areas had higher consultation rates (incidence rate ratio comparing highest versus lowest Townsend deprivation quintile 1.25 (1.14, 1.37).

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Table 1. Observed consultation rates and adjusted incidence rate ratios by age group, gender, winter season and Townsend quintile, 2010–2013^*.

https://doi.org/10.1371/journal.pone.0138659.t001

Only 0.5% of the registered population had a record of at least one ILI consultation during the last three winters (combined) of the study period. Even in the age, gender and deprivation groups with the highest consultation rates, the proportion with one or more ILI consultations during a winter season did not exceed 1% (S2 Fig).

Practice-level variation in ILI recorded consultations, 2010–2013

Practice-level ILI consultation rates varied between 0.1/100,000 pw to 154.3/100,000 pw. Of 507 practices contributing data in the three year period, 60 (11.8%) had SCRs which fell outside the 95% control limits of the funnel plot, whereas only 25 practices would be expected to by chance (Fig 3). Ten of 60 of the outlier practices had rates above the 95% control limit (17%) and 50 below.

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Fig 3. Funnel plot of standardised ILI consultation ratios (SCRs) by practice, 2010/11-2012/13*.

*SCRs are adjusted for age group, sex, Townsend quintile and winter season.

https://doi.org/10.1371/journal.pone.0138659.g003

Strength and limitations

This was a large study of over seven million individuals, demographically representative of the UK population, across 18 winters. The large study size and long period of data collection allowed examination of consultation rates according to population subgroups, between practices and over time.

THIN is a database collected for clinical management, not research. This leads to some limitations. THIN only covers individuals who present to their GP. However a substantial proportion of people with ILI do not consult their GP,[11] therefore the ILI burden seen and recorded in primary care is an underestimate of the true population burden. We used the Townsend score, a small-area level indicator of deprivation, and not an individual level measure of a person’s socio-economic circumstances. This could result in misclassification of an individual’s deprivation status, in turn leading the observed association between deprivation level and ILI consultation rates to be underestimated.

Interpretation

There are few studies of temporal trends in ILI consultations in the UK, and no repeated community or seroprevalence studies of influenza covering the whole study period. Declining ILI consultation rates have been observed in a sentinel surveillance network in England between 1966 and 2006, including during the late 1990s, but the magnitude of the decline was not reported, with no age-specific analyses.[16] Contemporary surveillance reports noted a reduction in influenza activity in England and Wales in the early 2000s compared to the late 1990s based on both primary care surveillance and laboratory data. [40] Declines in ILI consultation rates during the 1990s and 2000s have also been observed in primary care or outpatient settings in the Netherlands,[14] New Zealand [13] and Taiwan.[41]

Since not only influenza causes ILI, factors unrelated to influenza activity are also likely to contribute to the observed decline in ILI consultations. One likely explanation for the reduction in ILI consultation rates is changes coding by GPs, in favour of less specific codes. This is supported by our finding of a concurrent 40% increase in coding of cough or fever symptoms, indicating that it is not the propensity to consult GPs with common symptoms of respiratory tract infections which are driving the trends. Universal influenza vaccination for individuals aged 65 years and over was introduced in the UK in 2000.[42] It is possible that the introduction of influenza vaccine could lead GPs to be more reluctant to use codes specifically mentioning influenza and (such as the codes for ‘influenza-like’ or ‘flu-like’ illness) in vaccinated individuals. However, since declines in ILI consultation rates were observed in all age groups, with the main decline observed during the late 1990s, this is an unlikely explanation of our findings.

An increase in the use of non-specific codes in primary care databases has also been observed for certain chronic conditions.[43] In addition, UK public health information campaigns since the late 1990s encouraged self-management of symptoms of respiratory tract infections in order to reduce antibiotic prescribing.[44] An increase in the use of codes for less specific respiratory tract infections and ill-defined symptoms in children have been linked to GPs’ decision making regarding whether to describe antibiotics for symptoms of likely viral origin.[45]

We observed large between-practice differences in ILI coded consultation rates, most likely reflecting differences in GP recording preferences in the absence of standard diagnostic criteria for ILI. Local variation in the propensity to consult primary care or variation in local population infection rates may also contribute to the observed variation. More detailed data on practice locations and their proximity are required to determine the potential role of local outbreaks of respiratory infection in driving these differences.

Only a very minor proportion of registered individuals have an ILI recorded in their GP records during a winter season. This is to be expected from a recent community study, where even among confirmed, symptomatic influenza cases, only 21% consult their GP.[11] Apart from the 2009/10 pandemic, the highest ILI recorded consultation rates were observed in adults. This finding was also reported by Meier et al for UK primary care [17], but in New Zealand [13] and the Netherlands [14] ILI consultation rates were highest in children. Lower ILI consultation rates in children than adults are in contrast to community and seroepidemiological studies of influenza, in which children tend to have the highest infection rates.[11, 46, 47] Similarly, studies estimating the impact of influenza on hospital admissions in a number of countries have found the highest impact among young children as well as older adults.[48, 49] This discrepancy reflects differences in GPs’ willingness to assign specific ILI codes in young children with respiratory symptoms in whom competing diagnoses are harder to rule out. In contrast clear diagnostic guidelines had been issued during the 2009/10 pandemic; this is also when ILI consultation rates were highest among children. The reluctance to code ILI in children could lead to an underestimate of the burden of ILI, and therefore also the severity of influenza epidemics in children. A qualitative or questionnaire study examining why GPs decide to enter a specific ILI code in the absence of standard diagnostic criteria is required to understand these differences by age group.

Women were more likely to have an ILI diagnosis recorded than men among older children and adults. A higher ILI consultation rate in women than men has also been reported in a previous study.[15] In the UK, adult women are more likely to present to primary care than men. [50] However, women were also more likely to report ILI symptoms in an internet based community survey, even after adjusting for contact with children, suggesting other contributing factors including sex-based immunological differences. [51]

Rates of recorded ILI in primary care have declined in all age groups in the UK since 1995, whilst recording of less specific symptoms, cough or fever, has increased. The age pattern of ILI recorded in primary care differs from the age pattern of ILI and influenza observed in community settings in the UK. Our results suggest that differences in GP coding habits may explain these results. Policy makers and researchers need to be aware of the importance of GP coding behaviour. Observed differences in ILI consultations rates in electronic primary care records over time and between groups may not reflect true variability in the severity of influenza epidemics in the population.