Antidepressants and public health in Iceland

Helgason, Tómas; Tómasson, Helgí; Zoëga, Tómas

doi:10.1192/bjp.184.2.157

Cited by 97 publications

(21 citation statements)

References 17 publications

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“…3 Previous research suggests that national suicide rates declined as SSRI use increased over time in Sweden; similar findings have been obtained for Finland, Norway, and Hungary (Isacsson, 2000;Rihmer et al, 2001;Ohberg et al, 1998). In contrast, Italy did not appear to experience a change in total suicide rates from pre-existing trends following growth in SSRI use (Barbui et al, 1999), while data from Iceland reveals no relationship between the volume of sales of anti-depressant medications more generally and suicide rates (Helgason et al, 2004). ber of children filling prescriptions for anti-depressant medication divided by the number of children receiving medication of any type (p. 979).…”

Section: Introductionmentioning

confidence: 63%

Anti-depressants, suicide, and drug regulation

Ludwig

Marcotte

2005

J. Pol. Anal. Manage.

131

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Section: Introductionmentioning

confidence: 63%

Anti-depressants, suicide, and drug regulation

Ludwig

Marcotte

2005

J. Pol. Anal. Manage.

131

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“…Depression is related to worse outcome with respect to weight loss in a clinical setting [9] , and depressed individuals are often excluded from clinical trials. The use of antidepressants has increased dramatically in Iceland in the last decades [10] . It should be noted that antidepressants are prescribed for a variety of psychiatric conditions other than depression (e.g.…”

Section: Discussionmentioning

confidence: 99%

Predictors of Dropping Out in a Weight Loss Intervention Trial

2010

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Background/Aim: Dropouts in intervention trials can affect the statistical power of the study. The aim was to assess predictors of dropping out in a weight loss trial. Methods: Each subject (n = 114) was instructed to follow a diet, energy-restricted by 30% from estimated energy expenditure (approximately 600 kcal/day), for 8 consecutive weeks. A detailed meal plan as well as recipe booklets and instructions were provided by a nutritionist. Results: Antidepressants were used by 19.2% of the dropouts, compared with 1.8% of the completers (p < 0.001). Additionally, those dropping out consumed 35% more soft drinks in total and 44% more diet (artificially sweetened) soft drinks at baseline (habitual intake) than those completing the trial (p = 0.012). Dropouts (18.6%) were on average 1.9 years younger than those completing the study, but the difference was of borderline significance (p = 0.085). Conclusions: The use of antidepressants and heavy habitual soft drink consumption might be considered as exclusion criteria when preparing weight loss intervention trials including dietary restrictions.

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“…Some examples with panel data include the impact of European institutional changes on business cycles (Canova, Ciccarelli, and Ortega 2012), the coupling between sensory and intellectual functioning (Ghisletta and Lindenberger 2005), or the analysis of bidirectional links between children's delinquency and the quality of parent-child relationships (Keijsers, Loeber, Branje, and Meeus 2011). Examples of single subject approaches are studies on the decline in pneumonia rates in the USA after a vaccine introduction (Grijalva, Nuorti, Arbogast, Martin, Edwards, and Griffin 2007), or the lack of a relationship between antidepressant sales and public health in Iceland (Helgason, Tomasson, and Zoega 2004). At present, applications of dynamic models in the social and behavioral sciences are almost exclusively limited to discrete time models.…”

Section: Introductionmentioning

confidence: 99%

Continuous Time Structural Equation Modeling with R Package ctsem

Driver¹,

Oud²,

Voelkle³

2017

J. Stat. Soft.

195

253

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We introduce ctsem, an R package for continuous time structural equation modeling of panel (N > 1) and time series (N = 1) data, using full information maximum likelihood. Most dynamic models (e.g., cross-lagged panel models) in the social and behavioural sciences are discrete time models. An assumption of discrete time models is that time intervals between measurements are equal, and that all subjects were assessed at the same intervals. Violations of this assumption are often ignored due to the difficulty of accounting for varying time intervals, therefore parameter estimates can be biased and the time course of effects becomes ambiguous. By using stochastic differential equations to estimate an underlying continuous process, continuous time models allow for any pattern of measurement occasions. By interfacing to OpenMx, ctsem combines the flexible specification of structural equation models with the enhanced data gathering opportunities and improved estimation of continuous time models. ctsem can estimate relationships over time for multiple latent processes, measured by multiple noisy indicators with varying time intervals between observations. Within and between effects are estimated simultaneously by modeling both observed covariates and unobserved heterogeneity. Exogenous shocks with different shapes, group differences, higher order diffusion effects and oscillating processes can all be simply modeled. We first introduce and define continuous time models, then show how to specify and estimate a range of continuous time models using ctsem.

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Antidepressants and public health in Iceland

Abstract: The dramatic increase in the sales of antidepressants has not had any marked impact on the selected public health measures. Obviously, better treatment for depressive disorders is still needed in order to reduce the burden caused by them.

Cited by 97 publications

References 17 publications

Anti-depressants, suicide, and drug regulation

Anti-depressants, suicide, and drug regulation

Predictors of Dropping Out in a Weight Loss Intervention Trial

Continuous Time Structural Equation Modeling with R Package ctsem

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