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. 2012 Jul 12;2(4):e001179.
doi: 10.1136/bmjopen-2012-001179. Print 2012.

Campylobacter epidemiology: a descriptive study reviewing 1 million cases in England and Wales between 1989 and 2011

Gordon L Nichols  1 , Judith F Richardson, Samuel K Sheppard, Chris Lane, Christophe Sarran
Affiliations

Campylobacter epidemiology: a descriptive study reviewing 1 million cases in England and Wales between 1989 and 2011

Gordon L Nichols et al. BMJ Open. .

Abstract

Objectives: To review Campylobacter cases in England and Wales over 2 decades and examine the main factors/mechanisms driving the changing epidemiology.

Design: A descriptive study of Campylobacter patients between 1989 and 2011. Cases over 3 years were linked anonymously to postcode, population density, deprivation indices and census data. Cases over 5 years were anonymously linked to local weather exposure estimates.

Setting: Patients were from general practice, hospital and environmental health investigations through primary diagnostic laboratories across England and Wales.

Participants: There were 1 109 406 cases.

Outcome measures: Description of changes in Campylobacter epidemiology over 23 years and how the main drivers may influence these.

Results: There was an increase in Campylobacter cases over the past 23 years, with the largest increase in people over 50 years. Changes in the underlying population have contributed to this, including the impacts of population increases after World War I, World War II and the 'baby boom' of the 1960s. A recent increase in risk or ascertainment within this population has caused an increase in cases in all age groups from 2004 to 2011. The seasonal increase in cases between weeks 18 (Early May) and 22 (Early June) was consistent across ages, years and regions and was most marked in children and in more rural regions. Campylobacter prevalence by week in each region correlated with temperature 2 weeks before. There were higher prevalences in areas with a low population density, low deprivation and lower percentage of people of ethnic origin. Data from sero-phage and multilocus sequence typing show a few common types and many uncommon types.

Conclusions: The drivers/mechanisms influencing seasonality, age distribution, population density, socioeconomic and long-term differences are diverse and their relative contributions remain to be established. Surveillance and typing provide insights into Campylobacter epidemiology and sources of infection, providing a sound basis for targeted interventions.

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Conflict of interest statement

Competing interests: None.

Figures

Figure 1
Figure 1
Age distribution. Data from 1 109 406 laboratory-confirmed cases reported in England and Wales to national surveillance between 1989 and 2011. (A) Campylobacter isolates from patients with and without meningitis in different age groups as a percentage of all cases, 1989–2009. (B) Campylobacter isolates from fatal and non-fatal cases in different age groups as a percentage of all cases, 1989–2009. (C) Campylobacter cases per year from 1989 to 2011 by age (2011 data provisional). (D) Campylobacter cases per year 1989–2010 as a percentage of cases in different age groups over 45 years. (E) Campylobacter cases by age and sex, 1989–2009. (F) Campylobacter cases by age as a percentage of all cases for London and all other areas, 1898–2009. (G) Campylobacter cases in people who had recently returned from abroad and all other cases, 1989–2009. (H) Age distribution of Salmonella and Campylobacter. (I) Change in age distribution over four 5-year time periods. (J) Campylobacter age and sex distribution in 2008. (K) Population age and sex distribution in 2008. (L) Campylobacter cases per 100 000 in 2008 by age and sex. (M) Campylobacter cases per 1-year age group as a percentage of all cases per year, 1989–2009. (N) Campylobacter prevalence per 100 000 per year between 1989 and 2009 by age and sex. CSF, cerebrospinal fluid.
Figure 2
Figure 2
The weekly distribution of Campylobacter cases as a percentage of all cases over different age groups (unadjusted data).
Figure 3
Figure 3
Typing. Typing data on cases of Campylobacter from England and Wales, 1989–2009. Data in the HPA data set (29 081 isolates) includes the number of Campylobacter jejuni and Campylobacter coli combined serotype (HS) and phage type (PT) combinations (HS/PT) against the number of isolates in each type (A) the separate HS and PT types for C jejuni and C coli (B). The PubMLST database (1394 isolates) shows the number of types from human cases against the number of patient isolates. The figures represent individual sequenced genes for uncA (C), tkt (D), pgm (E), glyA (F), gltA (G), glnA (H) and aspA (I), with an average of the seven genes (J) and the individual ST and CC (K).
Figure 4
Figure 4
Resistance, spatial, social and temporal distribution. (A) Resistance of Campylobacter isolates to antibiotics between 1989 and 2009. (B) Campylobacter cases per day before and after bank holidays in England and Wales, 1989–2009. The dotted line represents an estimate of the cases that would have occurred if there had not been a bank holiday. (C) Time series of Campylobacter by day of year 1989–2009 showing unmodified data (OPIE raw), data adjusted for day of week (OPIE), for bank holidays (adjusted) and also adjusted for long-term trend (normalised). (D) Campylobacter cases by age and week of year. (E) Seasonal distribution of cases over and under 10 years of age (normalised data). (F) Regional differences in seasonal distribution (normalised data). (G and H) Campylobacter prevalence per 100 000 for 2009 based on the medium-level lower super output areas (MLSOA) (H) Campylobacter cases in England and Wales per thousand tonnes of poultry in the UK. (I) Campylobacter cases per 100 000 in 2009 by population density and completeness of postcode reporting. (J) Campylobacter prevalence per 100 000 per year and the Index of Multiple Deprivation (IMD) score. Differences between 2007 and 2009 reflect improving postcode reporting as cases cannot be included in the figures without a postcode. (K) Campylobacter prevalence by week in each region against the local temperature 2 weeks before the case specimen date (2005–2009).
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