Susana Monge1, Peter Teunis2, Ingrid Friesema2, Eelco Franz2, Wim Ang3, Wilfrid van Pelt2, Lapo Mughini-Gras4. 1. Centre for Infectious Disease Control Netherlands (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands; European Programme for Intervention Epidemiology Training (EPIET), European Centre for Disease Prevention and Control, (ECDC), Stockholm, Sweden. Electronic address: susana.monge@rivm.nl. 2. Centre for Infectious Disease Control Netherlands (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands. 3. Department of Medical Microbiology and Infection Control, VU University Medical Center Amsterdam, the Netherlands. 4. Centre for Infectious Disease Control Netherlands (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands; Utrecht University, Faculty of Veterinary Medicine, Department of Infectious Diseases and Immunology, Utrecht, The Netherlands.
Abstract
BACKGROUND: We aimed to estimate population-level exposure to Campylobacter and associated risk factors, using three approaches for serological data analysis. METHODS: Nationwide, population-based serosurvey in the Netherlands (Feb 2006-Jun 2007). Anti-Campylobacter IgG, IgM and IgA were measured using ELISA, and analysed via: a) seroincidence estimation, using reference values of antibody peak levels and decay rates over-time after Campylobacter exposure; b) two normal distributions of true positives/negatives fitted to the IgG distribution to derive seroprevalence and individual probability of being positive/negative; and c) IgG levels. Risk factors were analysed using multiple linear regressions. RESULTS: From 1559 respondents, seroincidence was estimated at 1.61 infections/person-year (95%CI:1.58-1.64) and seroprevalence at 68.1% (65.4-70.9). The three approaches identified similar risk factors, although seroincidence had higher power and results were interpretable as risk: seroincidence was higher in females [exp(b) = 1.07(1.04-1.11)], older ages [vs. 15-34 years; for < 5, 5-14, 35-54 and 55-70 years: 0.60(0.58-0.63), 0.74(0.71-0.78), 1.08(1.03-1.13) and 1.08(1.01-1.16), respectively], non-Dutch background [Moroccan/Turkish: 1.25(1.14-1.37); Caribbean: 1.14(1.03-1.25)], low socioeconomic status [1.05(1.01-1.10)], traveling outside Europe [1.05(1.01-1.09)], and eating undercooked meat [1.04(1.01-1.08)]. CONCLUSION: Campylobacter exposure is much higher than clinical infection rates, but risk factors are similar to those previously described.Seroincidence is a powerful measure to study Campylobacter epidemiology, and is preferred over other methods.
BACKGROUND: We aimed to estimate population-level exposure to Campylobacter and associated risk factors, using three approaches for serological data analysis. METHODS: Nationwide, population-based serosurvey in the Netherlands (Feb 2006-Jun 2007). Anti-Campylobacter IgG, IgM and IgA were measured using ELISA, and analysed via: a) seroincidence estimation, using reference values of antibody peak levels and decay rates over-time after Campylobacter exposure; b) two normal distributions of true positives/negatives fitted to the IgG distribution to derive seroprevalence and individual probability of being positive/negative; and c) IgG levels. Risk factors were analysed using multiple linear regressions. RESULTS: From 1559 respondents, seroincidence was estimated at 1.61 infections/person-year (95%CI:1.58-1.64) and seroprevalence at 68.1% (65.4-70.9). The three approaches identified similar risk factors, although seroincidence had higher power and results were interpretable as risk: seroincidence was higher in females [exp(b) = 1.07(1.04-1.11)], older ages [vs. 15-34 years; for < 5, 5-14, 35-54 and 55-70 years: 0.60(0.58-0.63), 0.74(0.71-0.78), 1.08(1.03-1.13) and 1.08(1.01-1.16), respectively], non-Dutch background [Moroccan/Turkish: 1.25(1.14-1.37); Caribbean: 1.14(1.03-1.25)], low socioeconomic status [1.05(1.01-1.10)], traveling outside Europe [1.05(1.01-1.09)], and eating undercooked meat [1.04(1.01-1.08)]. CONCLUSION: Campylobacter exposure is much higher than clinical infection rates, but risk factors are similar to those previously described.Seroincidence is a powerful measure to study Campylobacter epidemiology, and is preferred over other methods.
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