Predicting risk of substantial weight gain in German adults—a multi-center cohort approach
Autor: | Barbara Thorand, Rudolf Kaaks, Ursula Bachlechner, Karin Halina Greiser, Susanne Vogt, Henry Völzke, Annika Steffen, Jasmine Neamat-Allah, Annette Peters, Christa Scheidt-Nave, Heiner Boeing, Marjolein Haftenberger, Anja Schienkiewitz, Till Ittermann, Sabine Schipf, Ute Nöthlings |
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Rok vydání: | 2016 |
Předmět: |
Adult
Male medicine.medical_specialty Adolescent Population Overweight Weight Gain Cohort Studies Young Adult 03 medical and health sciences 0302 clinical medicine Risk Factors Germany Positive predicative value medicine Humans Cutoff Obesity 030212 general & internal medicine education Life Style Aged Proportional Hazards Models education.field_of_study Framingham Risk Score business.industry 030503 health policy & services Public Health Environmental and Occupational Health Middle Aged Diet Cohort Physical therapy Female medicine.symptom 0305 other medical science business Weight gain Cohort study |
Zdroj: | The European Journal of Public Health Eur. J. Public Health 27, 768-774 (2017) |
ISSN: | 1464-360X 1101-1262 |
Popis: | Background: A risk-targeted prevention strategy may efficiently utilize limited resources available for prevention of overweight and obesity. Likewise, more efficient intervention trials could be designed if selection of subjects was based on risk. The aim of the study was to develop a risk score predicting substantial weight gain among German adults. Methods: We developed the risk score using information on 15 socio-demographic, dietary and lifestyle factors from 32 204 participants of five population-based German cohort studies. Substantial weight gain was defined as gaining 10% of weight between baseline and follow-up ( > 6 years apart). The cases were censored according to the theoretical point in time when the threshold of 10% baseline-based weight gain was crossed assuming linearity of weight gain. Beta coefficients derived from proportional hazards regression were used as weights to compute the risk score as a linear combination of the predictors. Cross-validation was used to evaluate the score's discriminatory accuracy. Results: The cross-validated c index (95% CI) was 0.71 (0.67-0.75). A cutoff value of 475 score points yielded a sensitivity of 71% and a specificity of 63%. The corresponding positive and negative predictive values were 10.4% and 97.6%, respectively. Conclusions: The proposed risk score may support healthcare providers in decision making and referral and facilitate an efficient selection of subjects into intervention trials. |
Databáze: | OpenAIRE |
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