Arnaud Chiolero

University of Zurich, Zürich, Zurich, Switzerland

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Publications (108)540.56 Total impact

  • Blood Pressure Monitoring 12/2014; 19(6):371. · 1.18 Impact Factor
  • Arnaud Chiolero
    BMJ Clinical Research 11/2014; 349:g7078. · 14.09 Impact Factor
  • Arnaud Chiolero, Jay S Kaufman
    The Lancet 06/2014; 383(9934):2042. · 39.21 Impact Factor
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    ABSTRACT: Although dual-energy X-ray absorptiometry (DEXA) is the preferred method to estimate adiposity, body mass index (BMI) is often used as a proxy. However, the ability of BMI to measure adiposity change among youth is poorly evidenced. This study explored which metrics of BMI change have the highest correlations with different metrics of DEXA change.
    Archives of Disease in Childhood 05/2014; · 2.91 Impact Factor
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    ABSTRACT: In contrast to obesity, information on the health risks of underweight is sparse. We examined the long-term association between underweight and mortality by considering factors possibly influencing this relationship. We included 31,578 individuals aged 25-74 years, who participated in population based health studies between 1977 and 1993 and were followed-up for survival until 2008 by record linkage with the Swiss National Cohort (SNC). Body Mass Index (BMI) was calculated from measured (53% of study population) or self-reported height and weight. Underweight was defined as BMI < 18.5kg/m2. Cox regression models were used to determine mortality Hazard Ratios (HR) of underweight vs. normal weight (BMI 18.5- < 25.0kg/m2). Covariates were study, sex, smoking, healthy eating proxy, sports frequency, and educational level. Underweight individuals represented 3.0% of the total study population (n = 945), and were mostly women (89.9%). Compared to normal weight, underweight was associated with increased all-cause mortality (HR: 1.37; 95%CI: 1.14-1.65). Increased risk was apparent in both sexes, regardless of smoking status, and mainly driven by excess death from external causes (HR: 3.18; 1.96-5.17), but not cancer, cardiovascular or respiratory diseases. The HR were 1.16 (0.88-1.53) in studies with measured BMI and 1.59 (1.24-2.05) with self-reported BMI. The increased risk of dying of underweight people was mainly due to an increased mortality risk from external causes. Using self-reported BMI may lead to an overestimation of mortality risk associated with underweight.
    BMC Public Health 04/2014; 14(1):371. · 2.32 Impact Factor
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    Arnaud Chiolero, Pascal Bovet, George S. Stergiou
    Journal of Clinical Hypertension 04/2014; · 2.36 Impact Factor
  • Arnaud Chiolero, Gilles Paradis, Jay S Kaufman
    American journal of epidemiology 03/2014; · 4.98 Impact Factor
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    ABSTRACT: The diagnosis of hypertension in children is difficult because of the multiple sex-, age-, and height-specific thresholds to define elevated blood pressure (BP). Blood pressure-to-height ratio (BPHR) has been proposed to facilitate the identification of elevated BP in children. We assessed the performance of BPHR at a single screening visit to identify children with hypertension that is sustained elevated BP. In a school-based study conducted in Switzerland, BP was measured at up to three visits in 5207 children. Children had hypertension if BP was elevated at the three visits. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for the identification of hypertension were assessed for different thresholds of BPHR. The ability of BPHR at a single screening visit to discriminate children with and without hypertension was evaluated with receiver operating characteristic (ROC) curve analyses. The prevalence of systolic/diastolic hypertension was 2.2%. Systolic BPHR had a better performance to identify hypertension compared with diastolic BPHR (area under the ROC curve: 0.95 vs. 0.84). The highest performance was obtained with a systolic BPHR threshold set at 0.80 mmHg/cm (sensitivity: 98%; specificity: 85%; PPV: 12%; and NPV: 100%) and a diastolic BPHR threshold set at 0.45 mmHg/cm (sensitivity: 79%; specificity: 70%; PPV: 5%; and NPV: 99%). The PPV was higher among tall or overweight children. BPHR at a single screening visit had a high performance to identify hypertension in children, although the low prevalence of hypertension led to a low PPV.
    Journal of Hypertension 03/2014; · 4.22 Impact Factor
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    ABSTRACT: Control of blood pressure (BP) remains a major challenge in primary care. Innovative interventions to improve BP control are therefore needed. By updating and combining data from 2 previous systematic reviews, we assess the effect of pharmacist interventions on BP and identify potential determinants of heterogeneity. Randomized controlled trials (RCTs) assessing the effect of pharmacist interventions on BP among outpatients with or without diabetes were identified from MEDLINE, EMBASE, CINAHL, and CENTRAL databases. Weighted mean differences in BP were estimated using random effect models. Prediction intervals (PI) were computed to better express uncertainties in the effect estimates. Thirty-nine RCTs were included with 14 224 patients. Pharmacist interventions mainly included patient education, feedback to physician, and medication management. Compared with usual care, pharmacist interventions showed greater reduction in systolic BP (-7.6 mm Hg, 95% CI: -9.0 to -6.3; I(2)=67%) and diastolic BP (-3.9 mm Hg, 95% CI: -5.1 to -2.8; I(2)=83%). The 95% PI ranged from -13.9 to -1.4 mm Hg for systolic BP and from -9.9 to +2.0 mm Hg for diastolic BP. The effect tended to be larger if the intervention was led by the pharmacist and was done at least monthly. Pharmacist interventions - alone or in collaboration with other healthcare professionals - improved BP management. Nevertheless, pharmacist interventions had differential effects on BP, from very large to modest or no effect; and determinants of heterogeneity could not be identified. Determining the most efficient, cost-effective, and least time-consuming intervention should be addressed with further research.
    Journal of the American Heart Association. 03/2014; 3(2):e000718.
  • Arnaud Chiolero
    Journal of Hypertension 03/2014; 32(3):477-9. · 4.22 Impact Factor
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    David Faeh, Lucienne Roh, Fred Paccaud, Arnaud Chiolero
    Epidemiology (Cambridge, Mass.) 01/2014; 25(1):156-8. · 6.18 Impact Factor
  • Arnaud Chiolero, Fred Paccaud, Luc Fornerod
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    ABSTRACT: To describe the goals and methods of contemporary public health surveillance and to present the activities of the Observatoire Valaisan de la Santé (OVS), a tool unique in Switzerland to conduct health surveillance for the population of a canton.
    Santé publique (Vandoeuvre-lès-Nancy, France). 01/2014; 26(1):75-84.
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    ABSTRACT: Background: Accurately measuring childhood adiposity change has important clinical management and public health surveillance implications. Dual-energy X-ray absorptiometry (DEXA) is the method of choice to estimate fat mass, but is cost-prohibitive and proxies such as body mass index (BMI) are oftentimes used. However, limited data exist on the validity of BMI metrics to measure adiposity change among youth. Objective: Compare correlations between different BMI change metrics with different DEXA change metrics. Methods: Data were from QUALITY, a prospective cohort of children 8-10 years-old at recruitment from Qubec, Canada (n=557). Height and weight were measured by trained nurses at baseline (2008) and follow-up (2010). Metrics of BMI change were: raw unit, adjusted for median BMI (BMI%), and age-sex-adjusted with the US CDC growth curves expressed as Z-scores (BMIz) or percentiles (BMI%ile). Metrics of DEXA change were raw unit (total fat mass: (TFM), percent (TFM%), height-adjusted (fat mass index; FMI) and internally age-sex-adjusted (TFMz). Spearman rank correlations between BMI change metrics and DEXA change metrics were derived. Results: Correlations ranged from 0.59 to 0.86. TFM change correlated most highly with BMI change (r=0.86), TFM% change with BMI% and BMIz changes (r=0.65), TFMz change with BMI% change (r=0.80), and FMI change with BMI and BMI% changes (r=0.83). Correlations with BMI%ile change were consistently the lowest for all DEXA change metrics. Conclusions: In 8-10 year-old children followed-up over 2 years, changes in BMI (raw unit) or in BMI% are the best proxies for changes in TFM or in FMI. BMI%ile performs less well.
    141st APHA Annual Meeting and Exposition 2013; 11/2013
  • Arnaud Chiolero
    Epidemiology (Cambridge, Mass.) 11/2013; 24(6):938-9. · 6.18 Impact Factor
  • Arnaud Chiolero, Gilles Paradis, Jay S Kaufman
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    ABSTRACT: To estimate the possible direct effect of birth weight on blood pressure, it is conventional to condition on the mediator, current weight. Such conditioning can induce bias. Our aim was to assess the potential biasing effect of U, an unmeasured common cause of current weight and blood pressure, on the estimate of the controlled direct effect of birth weight on blood pressure, with the help of sensitivity analyses. We used data from a school-based study conducted in Switzerland in 2005-2006 (n = 3,762; mean age = 12.3 years). A small negative association was observed between birth weight and systolic blood pressure (linear regression coefficient βbw = -0.3 mmHg/kg, 95% confidence interval: -0.9, 0.3). The association was strengthened upon adjustment for current weight (βbw|C = -1.5 mmHg/kg, 95% confidence interval: -2.1, -0.9). Sensitivity analyses revealed that the negative conditional association was explained by U only if U was relatively strongly associated with blood pressure and if there was a large difference in the prevalence of U between low-birth weight and normal-birth weight children. This weakens the hypothesis that the negative relationship between birth weight and blood pressure arises only from collider-stratification bias induced by conditioning on current weight.
    American journal of epidemiology 10/2013; · 4.98 Impact Factor
  • Arnaud Chiolero, Valérie Santschi, Fred Paccaud
    The European Journal of Public Health 04/2013; · 2.52 Impact Factor
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    ABSTRACT: OBJECTIVE:: Identification of children with elevated blood pressure (BP) is difficult because of the multiple sex, age, and height-specific thresholds to define elevated BP. We propose a simple set of absolute height-specific BP thresholds and evaluate their performance to identify children with elevated BP in two different populations. METHODS:: Using the 95th sex, age, and relative-height BP US thresholds to define elevated BP in children (standard criteria), we derived a set of (non sex- and non age-specific) absolute height-specific BP thresholds for 11 height categories by 10 cm increments. Using data from large school-based surveys conducted in Switzerland (N = 5207; 2621 boys, 2586 girls; age range: 10.1-14.9 years) and in the Seychelles (N = 25 759; 13 048 boys, 12 711 girls; age range: 4.4-18.8 years), we evaluated the performance of these height-specific thresholds to identify children with elevated BP. We also derived sex-specific absolute height-specific BP thresholds and compared their performance. RESULTS:: In the Swiss and the Seychelles surveys, the prevalence of elevated BP (standard criteria) was 11.4 and 9.1%, respectively. The height-specific thresholds to identify elevated BP had a sensitivity of 80 and 84%, a specificity of 99 and 99%, a positive predictive value of 92 and 91%, and a negative predictive value of 97 and 98%, respectively. Performance of sex-specific absolute height-specific BP thresholds was similar. CONCLUSION:: A simple table of height-specific BP thresholds allowed identifying children with elevated BP with high sensitivity and excellent specificity.
    Journal of Hypertension 04/2013; · 4.22 Impact Factor
  • Arnaud Chiolero, Gilles Paradis
    Paediatrics & child health 02/2013; 18(2):63-4. · 1.55 Impact Factor
  • Arnaud Chiolero, Pascal Bovet, Michel Burnier
    Journal of Hypertension 02/2013; 31(2):426. · 4.22 Impact Factor
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    Arnaud Chiolero, Pascal Bovet, Gilles Paradis
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    ABSTRACT: Although screening for elevated blood pressure (BP) in adults is beneficial, evidence of its beneficial effects in children is not clear. Elevated BP in children is associated with atherosclerosis early in life and tracks across the life course. However, because of the high variability in BP, tracking is weak, and having an elevated BP in childhood has a low predictive value for having elevated BP later in life. The absolute risk of cardiovascular diseases associated with a given level of BP in childhood and the long-term effect of treatment beginning in childhood are not known. No study has experimentally evaluated the benefits and harm of BP screening in children. One modeling study indicates that BP screen-and-treat strategies in adolescents are moderately cost-effective but less cost-effective than population-wide interventions to decrease BP for the reduction of coronary heart diseases. The US National Heart, Lung, and Blood Institute and the European Society of Hypertension recommend that children 3 years of age and older have their BP measured during every health care visit. According to the US Preventive Services Task Force, there is no sufficient evidence to recommend for or against screening, but their recommendations have to be updated. Whether the benefits of universal BP screening in children outweigh the harm has to be determined. Studies are needed to assess the absolute risk of cardiovascular diseases associated with elevated BP in childhood, to evaluate how to simplify the identification of elevated BP, to evaluate the long-term benefits and harm of treatment beginning in childhood, and to compare universal and targeted screening strategies.
    JAMA pediatrics. 01/2013;

Publication Stats

2k Citations
540.56 Total Impact Points


  • 2014
    • University of Zurich
      Zürich, Zurich, Switzerland
  • 2000–2014
    • University Hospital of Lausanne
      • • Institut universitaire de médecine sociale et préventive
      • • Service de neurologie
      Lausanne, Vaud, Switzerland
  • 2004–2013
    • University of Lausanne
      • • Institute of Social and Preventive Medicine
      • • Faculté de biologie et de médecine (FBM)
      Lausanne, Vaud, Switzerland
  • 2009–2012
    • McGill University
      • Department of Epidemiology, Biostatistics and Occupational Health
      Montréal, Quebec, Canada
    • Université de Montréal
      • Department of Pediatrics
      Montréal, Quebec, Canada
  • 2011
    • University of Alberta
      • School of Public Health
      Edmonton, Alberta, Canada
  • 1998–2002
    • Policlinique Médicale Universitaire Lausanne
      Lausanne, Vaud, Switzerland