BackgroundQuantitative indicators are needed in order to define priorities, plan policies and evaluate public health interventions in mental health. The aim of this study was to assess the contribution of a large and exhaustive French national administrative database to study and monitor treated depression by comparing the prevalence and characteristics of the population using significant healthcare resources for depression as identified by different estimation methods and sources and to discuss the advantages and drawbacks of these methods.MethodsThis study included the French population covered by the main health insurance scheme in 2012 (Régime général, 86% of the insured French population). Data were extracted from the French health insurance claim database (SNIIRAM), which contains information on all reimbursements, including treatments and hospital stays in France. The following distinct sources of the SNIIRAM were used to select persons with depression: diagnoses of long-term or costly conditions, data from national hospital claims and data concerning all national health insurance reimbursements for drugs.ResultsIn 2012, we included 58,753,200 individuals covered by the main health insurance scheme; 271,275 individuals had full coverage for depression; 179,470 individuals had been admitted to a psychiatric hospital and 66,595 individuals admitted to a general hospital with a diagnosis of depression during a 2-year timeframe and 144,670 individuals had more than three reimbursements for antidepressants during the study year (with a history of hospitalisation for depression during the past 5 years). Only 16% of individuals were selected by more than one source.ConclusionsWe propose an algorithm that includes persons recently hospitalised for depression, or with a history of hospitalisation for depression and still taking antidepressants, or with full coverage for depression as a specific long-term or costly condition, yielding a prevalence estimate of 0.93% or 544,105 individuals. Changes in the case selection methodology have major consequences on the frequency count and characteristics of the selected population, and consequently on the conclusions that can be drawn from the data, emphasizing the importance of defining the characteristics of the target population before the study in order to produce relevant results.

Health economic evaluation studies are widely used in public health to assess health strategies in terms of their cost-effectiveness and inform public policies. We developed an R package for writing Markov models for health economic evaluations which implements the modelling and reporting features described in reference textbooks and guidelines: deterministic and probabilistic sensitivity analysis, heterogeneity analysis, time dependency on state-time and model-time (semi-Markov and non-homogeneous Markov models), etc. In this paper we illustrate the features of heemod by building and analysing an example Markov model. We then explain the design and the underlying implementation of the package.

Objective To assess the cost-effectiveness of acupuncture for pelvic girdle and low back pain (PGLBP) during pregnancy. Design Pragmatic-open-label randomised controlled trial. Setting Five maternity hospitals Population Pregnant women with PGLBP Method 1:1 randomization to standard care or standard care plus acupuncture (5 sessions by an acupuncturist midwife). Main outcome measure Efficacy: proportion of days with self-assessed pain by numerical rating scale (NRS) ≤ 4/10. Cost effectiveness (societal viewpoint, time horizon: pregnancy): incremental cost per days with NRS ≤ 4/10. Indirect non-healthcare costs included daily compensations for sick leave and productivity loss caused by absenteeism or presenteeism. Results 96 women were allocated to acupuncture and 103 to standard care (total 199). The proportion of days with NRS ≤ 4/10 was greater in the acupuncture group than in the standard care group (61% vs 48%, p = 0.007). The mean Oswestry disability score was lower in the acupuncture group than with standard care alone (33 versus 38, Δ = 5, 95% CI: 0.8 to 9, p = 0.02). Average total costs were higher in the control group (€2947) than in the acupuncture group (€2635, Δ = —€312, 95% CI: -966 to +325), resulting from the higher indirect costs of absenteeism and presenteeism. Acupuncture was a dominant strategy when both healthcare and non-healthcare costs were included. Costs for the health system (employer and out-of-pocket costs excluded) were slightly higher for acupuncture (€1512 versus €1452, Δ = €60, 95% CI: -272 to +470). Conclusion Acupuncture was a dominant strategy when accounting for employer costs. A 100% probability of cost-effectiveness was obtained for a willingness to pay of €100 per days with pain NRS ≤ 4.