Iris Pigeot

Leibniz-Institute of Prevention Research and Epidemiology, Bremen, Bremen, Germany

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Publications (201)248.79 Total impact

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    ABSTRACT: Data privacy is a major concern in spatial epidemiology because exact residential locations or parts of participants' addresses such as street or zip codes are used to perform geospatial analyses. To overcome this concern, different levels of aggregation such as census districts or zip code areas are mainly used, though any spatial aggregation leads to a loss of spatial variability. For the assessment of urban opportunities for physical activity that was conducted in the IDEFICS (Identification and prevention of dietary- and lifestyle-induced health effects in children and infants) study, macrolevel analyses were performed, but the use of exact residential addresses for micro-level analyses was not permitted by the responsible office for data protection. We therefore implemented a spatial blurring to anonymise address coordinates depending on the underlying population density. We added a standard Gaussian distributed error to individual address coordinates with the variance [Formula: see text] depending on the population density and on the chosen k-anonymity. 1000 random point locations were generated and repeatedly blurred 100 times to obtain anonymised locations. For each location 1 km network-dependent neighbourhoods were used to calculate walkability indices. Indices of blurred locations were compared to indices based on their sampling origins to determine the effect of spatial blurring on the assessment of the built environment. Spatial blurring decreased with increasing population density. Similarly, mean differences in walkability indices also decreased with increasing population density. In particular for densely-populated areas with at least 1500 residents per km², differences between blurred locations and their sampling origins were small and did not affect the assessment of the built environment after spatial blurring. This approach allowed the investigation of the built environment at a microlevel using individual network-dependent neighbourhoods, while ensuring data protection requirements. Minor influence of spatial blurring on the assessment of walkability was found that slightly affected the assessment of the built environment in sparsely-populated areas. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
    BMJ Open 03/2015; 5(3):e006481. DOI:10.1136/bmjopen-2014-006481 · 2.06 Impact Factor
  • Revista Andaluza de Medicina del Deporte 03/2015; 8(1):43. DOI:10.1016/j.ramd.2014.10.061
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    ABSTRACT: Some German cohort studies have already linked secondary and registry data with primary data from interviews and medical examinations. This offers the opportunity to obtain more valid information by taking advantage of the strengths of these data synergistically and overcome their individual weaknesses at the same time. The potential and the requirements for linking secondary and registry data with primary data from cohort studies is described generally and illustrated by the example of the "German National Cohort" (GNC). The transfer and usage of secondary and registry data require that administrative and logistic efforts be made over the whole study period. In addition, rigid data protection regulations for using social data have to be observed. The particular strengths of secondary and registry data, namely their objectivity and independence from recall bias, add to the strengths of newly collected primary data and improve the assessment of morbidity endpoints, exposure history and need of patient care. Moreover, new insights on quality and on the added value of linking different data sources may be obtained. © Georg Thieme Verlag KG Stuttgart · New York.
    Das Gesundheitswesen 02/2015; 77(2):118 - 119. DOI:10.1055/s-0034-1396805 · 0.62 Impact Factor
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    ABSTRACT: Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.
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    ABSTRACT: Exploring changes in children's diet over time and the relationship between these changes and socio-economic status (SES) may help to understand the impact of social inequalities on dietary patterns. The aim of the present study was to describe dietary patterns by applying a cluster analysis to 9301 children participating in the baseline (2 –9 years old) and follow-up (4– 11 years old) surveys of the Identification and Prevention of Dietary-and Lifestyle-induced Health Effects in Children and Infants Study, and to describe the cluster memberships of these children over time and their association with SES. We applied the K-means clustering algorithm based on the similarities between the relative frequencies of consumption of forty-two food items. The following three consistent clusters were obtained at baseline and follow-up: processed (higher frequency of consumption of snacks and fast food); sweet (higher frequency of consumption of sweet foods and sweetened drinks); healthy (higher frequency of consumption of fruits, vegetables and wholemeal products). Children with higher-educated mothers and fathers and the highest household income were more likely to be allocated to the healthy cluster at baseline and follow-up and less likely to be allocated to the sweet cluster. Migrants were more likely to be allocated to the processed cluster at baseline and follow-up. Applying the cluster analysis to derive dietary patterns at the two time points allowed us to identify groups of children from a lower socio-economic background presenting persistently unhealthier dietary profiles. This finding reflects the need for healthy eating interventions specifically targeting children from lower socio-economic backgrounds.
    The British journal of nutrition 01/2015; 113(03). DOI:10.1017/S0007114514003663 · 3.34 Impact Factor
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    ABSTRACT: The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. 1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.
    PLoS ONE 12/2014; 9(12):e114089. DOI:10.1371/journal.pone.0114089 · 3.53 Impact Factor
  • Iris Pigeot, Benedikt Buchner
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    ABSTRACT: Für die Erforschung von Volkskrankheiten spielen epidemiologische Studien mit einem Life-Course Ansatz eine entscheidende Rolle. Dank langfristiger Beobachtungen auf Individualebene kann das longitudinale Risikoprofil der gesundheitlichen, wirtschaftlichen und sozialen Situation einer Population beurteilt werden. Allerdings setzt dies eine Verknüpfung von verschiedenen Datenqualitäten voraus, die erhebliche Herausforderungen nicht nur auf der Ebene der Informatik und der statistischen Modellbildung, sondern vor allem auch mit Blick auf die Einhaltung datenschutzrechtlicher Grundprinzipien birgt.
    Datenschutz und Datensicherheit - DuD 12/2014; 38(12). DOI:10.1007/s11623-014-0325-0
  • Ronja Foraita, M Jäger, I Pigeot
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    ABSTRACT: The rapidly developing genotyping technology has led to the detection of many genetic factors that contribute to the pathogenesis of complex diseases. From this, the aim arose to use these results to offer tailored preventive measures or therapies based on an individual genetic profile. For this purpose, genetic tests are being developed that should allow us to identify individuals who belong to a high risk group with respect to a certain disease due to their genetic predisposition. Such tests are often based on known genetic risk factors that have been identified in genome-wide association studies. Typically, the effect estimates obtained from these studies are further used to construct a genetic risk measure to predict a certain phenotype. This paper describes several statistical and methodological challenges that must be coped with when establishing a genetic prediction model: Starting with the goal to obtain unbiased effect estimates to identify appropriate genetic risk predictors, genetic risk measures must be developed, and the predictive value of a new genetic test must be established. These key requirements of a statistical risk prediction in genetics will be discussed in three sections and finally discussed from a public health perspective.
    Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 11/2014; DOI:10.1007/s00103-014-2091-4 · 1.01 Impact Factor
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    ABSTRACT: Features of the built environment that may influence physical activity (PA) levels are commonly captured using a so-called walkability index. Since such indices typically describe opportunities for walking in everyday life of adults, they might not be applicable to assess urban opportunities for PA in children. Particularly, the spatial availability of recreational facilities may have an impact on PA in children and should be additionally considered. We linked individual data of 400 2- to 9-year-old children recruited in the European IDEFICS study to geographic data of one German study region, based on individual network-dependent neighborhoods. Environmental features of the walkability concept and the availability of recreational facilities, i.e. playgrounds, green spaces, and parks, were measured. Relevant features were combined to a moveability index that should capture urban opportunities for PA in children. A gamma log-regression model was used to model linear and non-linear effects of individual variables on accelerometer-based moderate-to-vigorous physical activity (MVPA) stratified by pre-school children (
    Journal of Urban Health 11/2014; 92(1). DOI:10.1007/s11524-014-9915-2 · 1.94 Impact Factor
  • Occupational and Environmental Medicine 10/2014; DOI:10.1136/oemed-2014-102630 · 3.23 Impact Factor
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    ABSTRACT: 10,11 , H Pohlabeln 10 , W Ahrens 10,11 and D Molnár 1 on behalf of the IDEFICS consortium BACKGROUND/OBJECTIVES: To address behaviours associated with childhood obesity, certain target values are recommended that should be met to improve children's health. In the IDEFICS (Identification and prevention of Dietary-and lifestyle-induced health Effects in Children and infantS) study such lifestyle recommendations were conveyed as six key messages. Here, we investigate the adherence of European children to these messages. METHODS: The IDEFICS intervention was based on the intervention mapping approach with the following six targets: increase water consumption (to replace sugar-containing beverages), increase fruit/vegetable consumption, reduce daily screen time, increase daily physical activity, improve the quality of family life and ensure adequate sleep duration. Internationally recommended target values were applied to determine the prevalence of children meeting these targets. RESULTS: In a cohort of 18 745 children participating in the IDEFICS baseline survey or newly recruited during follow-up, data on the above lifestyle behaviours were collected for a varying number of 8302 to 17 212 children. Information on all six behaviours was available for 5140 children. Although 52.5% of the cohort was classified in the highest category of water consumption, only 8.8% met the target of an intake of fruits/vegetables five times a day. The prevalence of children adhering to the recommendation regarding total screen time—below 1 h for pre-school children and 2 h for school children—was 51.1%. The recommended amount of at least 60 min of moderate-to-vigorous physical activity per day was fulfilled by 15.2%. Family life of the child measured by various indicators was considered as satisfactory in 22.8%. Nocturnal sleep duration of 11 (10) hours or more in pre-school (school) children was achieved by 37.9%. In general, children in northern countries and younger children showed better adherence to the recommendations. Only 1.1% of the children adhered to at least five of these recommendations. CONCLUSIONS: Current adherence of children to lifestyle recommendations to prevent childhood obesity is low where observed differences with respect to country, age and gender call for targeted intervention.
    International Journal of Obesity 09/2014; 38145. DOI:10.1038/ijo.2014.145 · 5.39 Impact Factor
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    ABSTRACT: To characterise the nutritional status in children with obesity or wasting conditions, European anthropometric reference values for body composition measures beyond the body mass index (BMI) are needed. Differentiated assessment of body composition in children has long been hampered by the lack of appropriate references.
    International journal of obesity (2005) 09/2014; 38 Suppl 2:S15-25. DOI:10.1038/ijo.2014.131 · 5.39 Impact Factor
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    ABSTRACT: To establish age- and sex-specific reference values for serum leptin and adiponectin in normal-weight 3.0-8.9-year old European children.
    International journal of obesity (2005) 09/2014; 38 Suppl 2:S32-8. DOI:10.1038/ijo.2014.133 · 5.39 Impact Factor
  • W Ahrens, L A Moreno, I Pigeot
    International journal of obesity (2005) 09/2014; 38 Suppl 2:S2-3. DOI:10.1038/ijo.2014.129 · 5.39 Impact Factor
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    ABSTRACT: 12 on behalf of the IDEFICS and the I.Family consortia OBJECTIVE: Valid estimates of population intakes are essential for monitoring trends as well as for nutritional interventions, but such data are rare in young children. In particular, the problem of misreporting in dietary data is usually not accounted for. Therefore, this study aims to provide accurate estimates of intake distributions in European children. DESIGN: Cross-sectional setting-based multi-centre study. SUBJECTS: A total of 9560 children aged 2–9 years from eight European countries with at least one 24-h dietary recall (24-HDR). METHODS: The 24-HDRs were classified in three reporting groups based on age-and sex-specific Goldberg cutoffs (underreports, plausible reports, overreports). Only plausible reports were considered in the final analysis (N = 8611 children). The National Cancer Institute (NCI)-Method was applied to estimate population distributions of usual intakes correcting for the variance inflation in short-term dietary data. RESULTS: The prevalence of underreporting (9.5%) was higher compared with overreporting (3.4%). Exclusion of misreports resulted in a shift of the energy and absolute macronutrient intake distributions to the right, and further led to the exclusion of extreme values, that is, mean values and lower percentiles increased, whereas upper percentiles decreased. The distributions of relative macronutrient intakes (% energy intake from fat/carbohydrates/proteins) remained almost unchanged when excluding misreports. Application of the NCI-Method resulted in markedly narrower intake distributions compared with estimates based on single 24-HDRs. Mean percentages of usual energy intake from fat, carbohydrates and proteins were 32.2, 52.1 and 15.7%, respectively, suggesting the majority of European children are complying with common macronutrient intake recommendations. In contrast, total water intake (mean: 1216.7 ml per day) lay below the recommended value for 490% of the children. CONCLUSION: This study provides recent estimates of intake distributions of European children correcting for misreporting as well as for the daily variation in dietary data. These data may help to assess the adequacy of young children's diets in Europe.
    International Journal of Obesity 09/2014; 38142(38):115-123. DOI:10.1038/ijo.2014.142 · 5.39 Impact Factor
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    ABSTRACT: Quantitative ultrasound measurements and bone metabolic markers can help to monitor bone health and to detect impaired skeletal development. Population-based reference values for children may serve as a basis for preventive measures to reduce the risk of osteoporosis and osteoporotic fractures in later life. This is the first paper providing age-, sex- and height-specific reference values for bone stiffness index (SI) and serum carboxy-terminal cross-linking telopeptide of type I collagen (CTX) in healthy, apparently prepubertal children.
    International Journal of Obesity 09/2014; 38:76-85. DOI:10.1038/ijo.2014.138 · 5.39 Impact Factor
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    ABSTRACT: Despite documented benefits of a Mediterranean-like dietary pattern, there is a lack of knowledge about how children from different European countries compare with each other in relation to the adherence to this pattern. In response to this need, we calculated the Mediterranean diet score (MDS) in 2-9-year-old children from the Identification and prevention of dietary- and lifestyle-induced health effects in children and infants (IDEFICS) eight-country study.
    International journal of obesity (2005) 09/2014; 38 Suppl 2:S108-14. DOI:10.1038/ijo.2014.141 · 5.39 Impact Factor
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    ABSTRACT: 10 on behalf of the IDEFICS consortium OBJECTIVES: To describe energy density (ED; kcal g − 1) of dietary intake of European children. METHODS: From 16 228 children who participated in the IDEFICS (Identification and prevention of Dietary-and lifestyle-induced health EFfects In Children and infantS) baseline examination, 8551 children with 24-h dietary recalls (24-HDR), with plausible reported energy intakes and complete covariate information were included in the present analysis. ED was calculated using two methods: (1) ED including solid foods (ED F) and (2) ED including solid foods and energy-containing beverages (ED F&B). Beverage energy was calculated in kcal per day. Dietary characteristics and body mass index (BMI) z-score of children aged 2 to o 6 years and 6 to o 10 years were compared between children with an overall ED F below the o 25th percentile, between the 25th and 75th percentile as well as above the 475th percentile. Standardised regression coefficients were estimated to assess the association between dietary characteristics, BMI z-score and ED of the diet. RESULTS: Children with low ED F and ED F&B diets consumed less energy but higher quantity of food and beverages than children with high ED F and ED F&B diets. Consumption of caloric beverages decreased with increasing ED F&B of the diet owing to the relatively low ED of the beverages, in relation to solid foods. Generally, children with low ED F and ED F&B diets showed healthier food choices than peers with higher ED F and ED F&B diets. In this sample, ED F and ED F&B were not associated with BMI z-score. CONCLUSION: Health promotion strategies should proclaim lower ED diets by means of foods with high water and low fat content and mainly fruit and vegetable components. Excluding caloric beverages from ED F calculation is a useful method to avoid misinterpretation of true exposure to a high energy dense diet. We recommend excluding caloric beverages from ED F calculation when investigating the effect of ED on a certain (health) outcome.
    International Journal of Obesity 09/2014; 38143. DOI:10.1038/ijo.2014.143 · 5.39 Impact Factor
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    ABSTRACT: To estimate the prevalence of the metabolic syndrome (MetS) using reference standards obtained in European children and to develop a quantitative MetS score and describe its distribution in children.
    International journal of obesity (2005) 09/2014; 38 Suppl 2:S4-S14. DOI:10.1038/ijo.2014.130 · 5.39 Impact Factor

Publication Stats

1k Citations
248.79 Total Impact Points

Institutions

  • 2012–2015
    • Leibniz-Institute of Prevention Research and Epidemiology
      Bremen, Bremen, Germany
    • Humboldt-Universität zu Berlin
      Berlín, Berlin, Germany
  • 2002–2015
    • Universität Bremen
      • • Faculty 03: Mathematics/Computer Science
      • • Bremen Institute for Prevention Research and Social Medicine (BIPS)
      Bremen, Bremen, Germany
  • 2014
    • University of Zaragoza
      • Department of Pediatrics, Radiology and Physical Medicine
      Caesaraugusta, Aragon, Spain
  • 1999–2001
    • Technische Universität München
      München, Bavaria, Germany
    • Ludwig-Maximilian-University of Munich
      • Institut für Statistik
      München, Bavaria, Germany
  • 2000
    • University of Wisconsin–Madison
      Madison, Wisconsin, United States
  • 1991–1996
    • Technische Universität Dortmund
      • Chair of Computer Science XII
      Dortmund, North Rhine-Westphalia, Germany