Evolution of public funding since primary care research was considered as a priority research domain in france.

PURPOSE: Annually, the French Ministry of Health funds clinical research projects based on a national call for projects. Since 2013, the Ministry has prioritized funding of primary care. Projects selected for funding are made public without distinguishing the specific area of research. The objective of this study was to identify and describe the evolution of the primary care research projects funded by the Ministry of Health between 2013 and 2019. METHOD: We reviewed all of the 1796 medical research projects funded between 2013 and 2019 and categorized projects as primary care projects by using a list of specific keywords. This list was established through two approaches: (1) selected by an expert committee, the RECaP primary care working group, and (2) using an automated textual analysis of published articles in the field. The keywords were used to screen the titles of the medical research projects funded. The abstracts (at www. CLINICALTRIALS: gov ) or details (from project leaders) were then analyzed by two independent reviewers to determine true primary care projects. RESULTS: Finally, 49 primary care projects were identified, representing 2.7% of all medical research projects funded, without any significant change over the period. These projects were predominantly interventional (69%), with a median number of patients expected per project of 902. CONCLUSION: Despite the prioritization of primary care research in 2013 by the French ministry of health, the number and proportion of projects funded remains low, with no significant change over the years. TRIAL REGISTRATION: Not applicable.

Characteristics of self-management education and support programmes for people with chronic diseases delivered by primary care teams: a rapid review.

BACKGROUND: Primary care actors can play a major role in developing and promoting access to Self-Management Education and Support (SMES) programmes for people with chronic disease. We reviewed studies on SMES programmes in primary care by focusing on the following dimensions: models of SMES programmes in primary care, SMES team's composition, and participants' characteristics. METHODS: For this mixed-methods rapid review, we searched the PubMed and Cochrane Library databases to identify articles in English and French that assessed a SMES programme in primary care for four main chronic diseases (diabetes, cancer, cardiovascular disease and/or respiratory chronic disease) and published between 1 January 2013 and 31 December 2021. We excluded articles on non-original research and reviews. We evaluated the quality of the selected studies using the Mixed Methods Appraisal Tool. We reported the study results following the PRISMA guidelines. RESULTS: We included 68 studies in the analysis. In 46/68 studies, a SMES model was described by focusing mainly on the organisational dimension (n = 24). The Chronic Care Model was the most used organisational model (n = 9). Only three studies described a multi-dimension model. In general, the SMES team was composed of two healthcare providers (mainly nurses), and partnerships with community actors were rarely reported. Participants were mainly patients with only one chronic disease. Only 20% of the described programmes took into account multimorbidity. Our rapid review focused on two databases and did not identify the SMES programme outcomes. CONCLUSIONS: Our findings highlight the limited implication of community actors and the infrequent inclusion of multimorbidity in the SMES programmes, despite the recommendations to develop a more interdisciplinary approach in SMES programmes. This rapid review identified areas of improvement for SMES programme development in primary care, especially the privileged place of nurses in their promotion. TRIAL REGISTRATION: PROSPERO 2021 CRD42021268290 .

Meta-analyses frequently pooled different study types together: a meta-epidemiological study.

OBJECTIVE: To evaluate the characteristics of therapeutic meta-analyses including both observational studies and randomized controlled trials (RCTs), how these studies were combined and whether there were differences in treatment effects. STUDY DESIGN AND SETTING: Meta-epidemiological study of meta-analyses, including both observational studies and RCTs. We searched MEDLINE for the five leading journals of each medical category according to Journal Citation Reports) and Cochrane Database of Systematic Reviews, from 2014 to 2018 for eligible meta-analyses and extracted how observational studies and RCTs were combined and results for each study. RESULTS: Of the 102 included meta-analyses, observational studies and RCTs were combined together without a subgroup analysis in 39 (38%) and with subgroup analysis in 15 (15%); they were pooled separately for the same outcome in 11 (11%) and not for the same outcome in 9 (9%). In 28 (27%) meta-analyses, only RCTs were combined, with a qualitative description of observational studies. Treatment effect estimates did not differ between observational studies and RCTs (ratio of estimates = 0.98 [95% confidence interval 0.80-1.21]), with substantial heterogeneity (I2 = 59%). CONCLUSION: Many meta-analyses, including both observational studies and RCTs pool results from both study types. Although treatment effects did not differ between them on average, we identified situations for which estimates differed.

Reporting of statistically significant results at ClinicalTrials.gov for completed superiority randomized controlled trials.

BACKGROUND: Publication bias and other reporting bias have been well documented for journal articles, but no study has evaluated the nature of results posted at ClinicalTrials.gov. We aimed to assess how many randomized controlled trials (RCTs) with results posted at ClinicalTrials.gov report statistically significant results and whether the proportion of trials with significant results differs when no treatment effect estimate or p-value is posted. METHODS: We searched ClinicalTrials.gov in June 2015 for all studies with results posted. We included completed RCTs with a superiority hypothesis and considered results for the first primary outcome with results posted. For each trial, we assessed whether a treatment effect estimate and/or p-value was reported at ClinicalTrials.gov and if yes, whether results were statistically significant. If no treatment effect estimate or p-value was reported, we calculated the treatment effect and corresponding p-value using results per arm posted at ClinicalTrials.gov when sufficient data were reported. RESULTS: From the 17,536 studies with results posted at ClinicalTrials.gov, we identified 2823 completed phase 3 or 4 randomized trials with a superiority hypothesis. Of these, 1400 (50%) reported a treatment effect estimate and/or p-value. Results were statistically significant for 844 trials (60%), with a median p-value of 0.01 (Q1-Q3: 0.001-0.26). For the 1423 trials with no treatment effect estimate or p-value posted, we could calculate the treatment effect and corresponding p-value using results reported per arm for 929 (65%). For 494 trials (35%), p-values could not be calculated mainly because of insufficient reporting, censored data, or repeated measurements over time. For the 929 trials we could calculate p-values, we found statistically significant results for 342 (37%), with a median p-value of 0.19 (Q1-Q3: 0.005-0.59). CONCLUSIONS: Half of the trials with results posted at ClinicalTrials.gov reported a treatment effect estimate and/or p-value, with significant results for 60% of these. p-values could be calculated from results reported per arm at ClinicalTrials.gov for only 65% of the other trials. The proportion of significant results was much lower for these trials, which suggests a selective posting of treatment effect estimates and/or p-values when results are statistically significant.