Sex differences in symptomatology in people with schizophrenia and other psychotic disorders: protocol for a systematic review and pairwise meta-analysis of observational studies.
Marta Ferrer-QuinteroMarina Verdaguer-RodriguezMarina Esteban SanjustoClara Serra-ArumíJudith UsallSusana OchoaIrene BighelliHelena García-MieresPublished in: BJPsych open (2022)
Sex differences in symptomatology in people with psychosis have been studied extensively in recent decades. Although studies have pointed to such differences, to date there is no review that has performed a systematic search and quantitative synthesis. In this paper, we describe the protocol for a pairwise meta-analysis comparing a range of symptom outcome measures between men and women diagnosed with a psychotic spectrum disorder at different stages of the disorder (PROSPERO registration number CRD42021264942). In August 2021 we conducted systematic searches of PsychInfo, PubMed, Web of Science, Scopus and Dialnet to identify observational studies that report data on symptoms for males and females separately. Two independent reviewers will conduct literature searches, select studies, extract data, assess the risk of bias and assess outcome quality. To assess the effect size of all outcome measures, we will conduct pairwise meta-analysis using random-effects models. The quality of studies will be evaluated using a National Heart, Lung and Blood Institute's quality assessment tool and the confidence in the results will be evaluated using the GRADE tool. Meta-regression and sensitivity analyses will be conducted to assess the robustness of the findings. No ethical problems are foreseen. Results from this study will be published in peer-reviewed journals and presented at relevant conferences.
Keyphrases
- case control
- systematic review
- bipolar disorder
- quality improvement
- meta analyses
- randomized controlled trial
- spectrum disorder
- electronic health record
- public health
- mental health
- multidrug resistant
- machine learning
- atrial fibrillation
- high resolution
- depressive symptoms
- mass spectrometry
- physical activity
- data analysis
- deep learning
- artificial intelligence
- sleep quality