Gross Domestic Product (GDP) and productivity of schizophrenia trials: an ecological study

The 5000 randomised controlled trials (RCTs) in the Cochrane Schizophrenia Group's database affords an opportunity to research for variables related to the differences between nations of their output of schizophrenia trials. Ecological study – investigating the relationship between four economic/demographic variables and number of schizophrenia RCTs per country. The variable with closest correlation was used to predict the expected number of studies. GDP closely correlated with schizophrenia trial output, with 76% of the total variation about the Y explained by the regression line (r = 0.87, 95% CI 0.79 to 0.92, r2 = 0.76). Many countries have a strong tradition of schizophrenia trials, exceeding their predicted output. All nations with no identified trial output had GDPs that predicted zero trial activity. Several nations with relatively small GDPs are, nevertheless, highly productive of trials. Some wealthy countries seem either not to have produced the expected number of randomised trials or not to have disseminated them to the English-speaking world. This hypothesis-generating study could not investigate causal relationships, but suggests, that for those seeking all relevant studies, expending effort searching the scientific literature of Germany, Italy, France, Brazil and Japan may be a good investment

Pitfalls of using the risk ratio in meta‐analysis

For meta-analysis of studies that report outcomes as binomial proportions, the most popular measure of effect is the odds ratio (OR), usually analyzed as log(OR). Many meta-analyses use the risk ratio (RR) and its logarithm, because of its simpler interpretation. Although log(OR) and log(RR) are both unbounded, use of log(RR) must ensure that estimates are compatible with study-level event rates in the interval (0, 1). These complications pose a particular challenge for random-effects models, both in applications and in generating data for simulations. As background we review the conventional random-effects model and then binomial generalized linear mixed models (GLMMs) with the logit link function, which do not have these complications. We then focus on log-binomial models and explore implications of using them; theoretical calculations and simulation show evidence of biases. The main competitors to the binomial GLMMs use the beta-binomial (BB) distribution, either in BB regression or by maximizing a BB likelihood; a simulation produces mixed results. Two examples and an examination of Cochrane meta-analyses that used RR suggest bias in the results from the conventional inverse-variance-weighted approach. Finally, we comment on other measures of effect that have range restrictions, including risk difference, and outline further research

The utilization of antidepressants and benzodiazepines among people with major depression in Canada

Objective: Although clinical guidelines recommend monotherapy with antidepressants (ADs) for major depression, polypharmacy with benzodiazepines (BDZs) remains an issue. Risks associated with such treatments include tolerance and dependence, among others. We assessed the prevalence and determinants of AD and BDZ utilization among Canadians who experienced a major depressive episode (MDE) in the previous 12 months, and determined the association of seeing a psychiatrist on the utilization of ADs and BDZs. Method: Data were drawn from the 2002 Canadian Community Health Survey: Health and Well-Being, a nationally representative sample of Canadians aged 15 years and older. Descriptive statistics quantified utilization, while logistic regression identified factors associated with utilization, such as sociodemographic characteristics or type of physician seen. Sampling weights and bootstrap variance estimations were used for all analysis. Results: The overall prevalence of AD and BDZ utilization was 49.3% of respondents who experienced an MDE in the past 12 months and reported AD use. Key determinants of utilization were younger age and unemployment in the past week (OR 2.6; P < 0.001). Being seen by a psychiatrist increased utilization (OR 2.5; P < 0.001), possibly because psychiatrists were seeing patients with severe depression. Conclusion: A large proportion of people with past-year MDEs utilized ADs and BDZs. It is unclear how much of this is appropriate given that evidence-based clinical guidelines recommend monotherapy with ADs in the treatment of major depression

Reimagining the journey to recovery: The COVID-19 pandemic and global mental health

In this editorial, guest editors Vikram Patel, Daisy Fancourt, Lola Kola, and Toshi Furukawa discuss the contents of the special issue on the pandemic and global mental health, highlighting key themes and providing important context

The placebo effect and its determinants in fibromyalgia: meta-analysis of randomized controlled trials

The aims of this study were to determine whether placebo treatment in randomised controlled trials (RCTs) is effective for fibromyalgia and to identify possible determinants of the magnitude of any such placebo effect. A systematic literature search was undertaken for RCTs in people with fibromyalgia that included a placebo and/or a no-treatment (observation only or waiting list) control group. Placebo effect size (ES) for pain and other outcomes was measured as the improvement of each outcome from baseline divided by the standard deviation of the change from baseline. This effect was compared with changes in the no-treatment control groups. Meta-analysis was undertaken to combine data from different studies. Subgroup analysis was conducted to identify possible determinants of the placebo ES. A total of 3912 studies were identified from the literature search. After scrutiny, 229 trials met the inclusion criteria. Participants who received placebo in the RCTs experienced significantly better improvements in pain, fatigue, sleep quality, physical function, and other main outcomes than those receiving no treatment. The ES of placebo for pain relief was clinically moderate (0.53, 95%CI 0.48 to 0.57). The ES increased with increasing strength of the active treatment, increasing participant age and higher baseline pain severity, but decreased in RCTS with more women and with longer duration of fibromyalgia. In addition, placebo treatment in RCTs is effective in fibromyalgia. A number of factors (expected strength of treatment, age, gender, disease duration) appear to influence the magnitude of the placebo effect in this condition

Glial Cell Line-Derived Neurotrophic Factor (GDNF) as a Novel Candidate Gene of Anxiety.

Glial cell line-derived neurotrophic factor (GDNF) is a neurotrophic factor for dopaminergic neurons with promising therapeutic potential in Parkinson's disease. A few association analyses between GDNF gene polymorphisms and psychiatric disorders such as schizophrenia, attention deficit hyperactivity disorder and drug abuse have also been published but little is known about any effects of these polymorphisms on mood characteristics such as anxiety and depression. Here we present an association study between eight (rs1981844, rs3812047, rs3096140, rs2973041, rs2910702, rs1549250, rs2973050 and rs11111) GDNF single nucleotide polymorphisms (SNPs) and anxiety and depression scores measured by the Hospital Anxiety and Depression Scale (HADS) on 708 Caucasian young adults with no psychiatric history. Results of the allele-wise single marker association analyses provided significant effects of two single nucleotide polymorphisms on anxiety scores following the Bonferroni correction for multiple testing (p = 0.00070 and p = 0.00138 for rs3812047 and rs3096140, respectively), while no such result was obtained on depression scores. Haplotype analysis confirmed the role of these SNPs; mean anxiety scores raised according to the number of risk alleles present in the haplotypes (p = 0.00029). A significant sex-gene interaction was also observed since the effect of the rs3812047 A allele as a risk factor of anxiety was more pronounced in males. In conclusion, this is the first demonstration of a significant association between the GDNF gene and mood characteristics demonstrated by the association of two SNPs of the GDNF gene (rs3812047 and rs3096140) and individual variability of anxiety using self-report data from a non-clinical sample

A Bayesian dose-response meta-analysis model: simulation study and application

Dose-response models express the effect of different dose or exposure levels on a specific outcome. In meta-analysis, where aggregated-level data is available, dose-response evidence is synthesized using either one-stage or two-stage models in a frequentist setting. We propose a hierarchical dose-response model implemented in a Bayesian framework. We present the model with cubic dose-response shapes for a dichotomous outcome and take into account heterogeneity due to variability in the dose-response shape. We develop our Bayesian model assuming normal or binomial likelihood and accounting for exposures grouped in clusters. We implement these models in R using JAGS and we compare our approach to the one-stage dose-response meta-analysis model in a simulation study. We found that the Bayesian dose-response model with binomial likelihood has slightly lower bias than the Bayesian model with the normal likelihood and the frequentist one-stage model. However, all three models perform very well and give practically identical results. We also re-analyze the data from 60 randomized controlled trials (15,984 participants) examining the efficacy (response) of various doses of antidepressant drugs. All models suggest that the dose-response curve increases between zero dose and 40 mg of fluoxetine-equivalent dose, and thereafter is constant. We draw the same conclusion when we take into account the fact that five different antidepressants have been studied in the included trials. We show that implementation of the hierarchical model in Bayesian framework has similar performance to, but overcomes some of the limitations of the frequentist approaches and offers maximum flexibility to accommodate features of the data

How to Obtain NNT from Cohen's d: Comparison of Two Methods

Background: In the literature we find many indices of size of treatment effect (effect size: ES). The preferred index of treatment effect in evidence-based medicine is the number needed to treat (NNT), while the most common one in the medical literature is Cohen’s d when the outcome is continuous. There is confusion about how to convert Cohen’s d into NNT. Methods: We conducted meta-analyses of individual patient data from 10 randomized controlled trials of second generation antipsychotics for schizophrenia (n = 4278) to produce Cohen’s d and NNTs for various definitions of response, using cutoffs of 10 % through 90 % reduction on the symptom severity scale. These actual NNTs were compared with NNTs calculated from Cohen’s d according to two proposed methods in the literature (Kraemer, et al., Biological Psychiatry, 2006; Furukawa, Lancet, 1999). Results: NNTs from Kraemer’s method overlapped with the actual NNTs in 56%, while those based on Furukawa’s method fell within the observed ranges of NNTs in 97 % of the examined instances. For various definitions of response corresponding with 10 % through 70 % symptom reduction where we observed a non-small number of responders, the degree of agreement for the former method was at a chance level (ANOVA ICC of 0.12, p = 0.22) but that for the latter method was ANOVA ICC of 0.86 (95%CI: 0.55 to 0.95, p,0.01)

Comparing methods for estimating patient‐specific treatment effects in individual patient data meta‐analysis

Meta-analysis of individual patient data (IPD) is increasingly used to synthesize data from multiple trials. IPD meta-analysis offers several advantages over meta-analyzing aggregate data, including the capacity to individualize treatment recommendations. Trials usually collect information on many patient characteristics. Some of these covariates may strongly interact with treatment (and thus be associated with treatment effect modification) while others may have little effect. It is currently unclear whether a systematic approach to the selection of treatment-covariate interactions in an IPD meta-analysis can lead to better estimates of patient-specific treatment effects. We aimed to answer this question by comparing in simulations the standard approach to IPD meta-analysis (no variable selection, all treatment-covariate interactions included in the model) with six alternative methods: stepwise regression, and five regression methods that perform shrinkage on treatment-covariate interactions, that is, least absolute shrinkage and selection operator (LASSO), ridge, adaptive LASSO, Bayesian LASSO, and stochastic search variable selection. Exploring a range of scenarios, we found that shrinkage methods performed well for both continuous and dichotomous outcomes, for a variety of settings. In most scenarios, these methods gave lower mean squared error of the patient-specific treatment effect as compared with the standard approach and stepwise regression. We illustrate the application of these methods in two datasets from cardiology and psychiatry. We recommend that future IPD meta-analysis that aim to estimate patient-specific treatment effects using multiple effect modifiers should use shrinkage methods, whereas stepwise regression should be avoided

Diagnostic delay for giant cell arteritis – a systematic review and meta-analysis

Background Giant cell arteritis (GCA), if untreated, can lead to blindness and stroke. The study’s objectives were to (1) determine a new evidence-based benchmark of the extent of diagnostic delay for GCA and (2) examine the role of GCA-specific characteristics on diagnostic delay. Methods Medical literature databases were searched from inception to November 2015. Articles were included if reporting a time-period of diagnostic delay between onset of GCA symptoms and diagnosis. Two reviewers assessed the quality of the final articles and extracted data from these. Random-effects meta-analysis was used to pool the mean time-period (95% confidence interval (CI)) between GCA symptom onset and diagnosis, and the delay observed for GCA-specific characteristics. Heterogeneity was assessed by I 2 and by 95% prediction interval (PI). Results Of 4128 articles initially identified, 16 provided data for meta-analysis. Mean diagnostic delay was 9.0 weeks (95% CI, 6.5 to 11.4) between symptom onset and GCA diagnosis (I 2 = 96.0%; P < 0.001; 95% PI, 0 to 19.2 weeks). Patients with a cranial presentation of GCA received a diagnosis after 7.7 (95% CI, 2.7 to 12.8) weeks (I 2 = 98.4%; P < 0.001; 95% PI, 0 to 27.6 weeks) and those with non-cranial GCA after 17.6 (95% CI, 9.7 to 25.5) weeks (I 2 = 96.6%; P < 0.001; 95% PI, 0 to 46.1 weeks). Conclusions The mean delay from symptom onset to GCA diagnosis was 9 weeks, or longer when cranial symptoms were absent. Our research provides an evidence-based benchmark for diagnostic delay of GCA and supports the need for improved public awareness and fast-track diagnostic pathways