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01.12.2014 | Original Article | Ausgabe 6/2014

Journal of Public Health 6/2014

Duration of untreated illness and predictors of late treatment initiation in anorexia nervosa

Journal of Public Health > Ausgabe 6/2014
Angelika Weigel, Maddalena Rossi, Hanna Wendt, Karolin Neubauer, Kathrin von Rad, Anne Daubmann, Georg Romer, Bernd Löwe, Antje Gumz



Little is known about the duration of untreated illness (DUI) in anorexia nervosa (AN) and the factors which predict a later initiation of treatment. Practitioners emphasize the importance of a lack of insight into the disorder as well as treatment motivation; however, this has not yet been empirically investigated. This study aimed to quantify the mean DUI in AN and to identify predictors of late treatment initiation.

Subject and methods

Female patients with AN completed a survey and a semi-structured interview. Data about eating pathology (SCID- eating disorder section), sociodemographic variables (e.g. level of education, health insurance status) and personality styles (PSSI-K) were examined. A multivariate Poisson regression was used to identify predictors of late treatment initiation.


The sample consisted of 58 female AN patients (mean age = 22.3 years, SD = 7.8). The results revealed a mean DUI of 31.8 months (SD = 71.4) and the strongest predictors of late treatment initiation were a statutory health insurance, health-care-system-related factors (e.g. waiting periods) as well as a lack of insight into the disorder and treatment motivation.


A substantial time passes between the onset of the disorder and the beginning of recommended treatment in AN patients due to external (e.g. health insurance status) and internal (lack of insight into the disorder) reasons. Public health interventions are needed and should be optimised to be specific to target groups (e.g. according to age, parents and/or peers) to improve awareness of AN, facilitate insight into the disorder, increase knowledge about treatment options and the motivation to initiate treatment as well as to optimize patient flow into specialist care.

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