1.6% of women and girls suffer from premenstrual dysphoric disorder, new data shows

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In a recent study published in the Journal of Affective Disorders, a group of researchers measured the point prevalence of premenstrual dysphoric disorder (PMDD) using both confirmed and provisional diagnosis.

Study: The prevalence of premenstrual dysphoric disorder: Systematic review and meta-analysis. Image Credit: GBALLGIGGSPHOTO/Shutterstock.com
Study: The prevalence of premenstrual dysphoric disorder: Systematic review and meta-analysis. Image Credit: GBALLGIGGSPHOTO/Shutterstock.com

Background 

PMDD, a significant mental health concern, has evolved from a condition for further study in earlier Diagnostic and Statistical Manual of Mental Disorders (DSM) editions to a recognized disorder in DSM-5 and International Classification of Diseases (ICD)-11. This disorder, characterized by severe emotional and physical symptoms tied to the menstrual cycle, is now understood to severely impact quality of life and is associated with increased suicidality. Diagnosis hinges on specific timing and severity of symptoms, with DSM-5 and ICD-11 emphasizing luteal phase symptoms, including emotional changes and physical discomfort.

The diagnosis process, ideally based on two menstrual cycles' prospective symptom ratings, varies between confirmed and provisional, depending on the method of symptom reporting. Further research is needed to understand better PMDD's etiology, treatment efficacy, and long-term impacts on mental and physical health.

About the study

The researchers conducted a thorough search for observational studies that examined the prevalence of PMDD among females from menarche to menopause, adhering to either DSM or ICD diagnostic criteria. Studies relying solely on self-diagnosis were excluded due to their unreliability. They also excluded studies focusing on participants from health services like gynecological clinics to avoid inflated prevalence figures. When encountering multiple studies from the same sample, the one with the largest sample size was selected. There were no language restrictions, and Google Translate was employed for non-English reports.

The search extended across several databases - Embase, PsycINFO, MEDLINE, and PubMed - from their inception until March 2021. Two authors independently screened study abstracts, selecting relevant full-texts for detailed examination. The search incorporated various terms related to PMDD to ensure a comprehensive literature review. 

For data analysis, two authors extracted crucial information like study setting, sample size, and method of diagnosis, resolving any disagreements through discussion with a third author. Each study was assessed for bias using a specific risk of bias tool. 

Statistical analyses were performed using R Statistical Software, employing a random-effects model to account for variations across studies. They assessed heterogeneity using the I2 statistic and explored its potential sources with subgroup analysis and meta-regression. The studies were further categorized based on the diagnosis method (provisional or confirmed) and the use of questionnaires. 

Study results 

In the present comprehensive search, the researchers identified 12,340 records, eventually narrowing down to 44 studies that met their criteria. These studies represented 48 independent samples with a total of 50,659 participants, ranging from 62 to 8,694 per sample and an average sample size of 1151. The age range of participants varied between 14.3 and 38.6 years. These studies were globally distributed, with a notable number from Asia, Europe, and North America. Most studies used DSM criteria for diagnosis, with no studies using the ICD-11 criteria.

The researchers noted that only a minority of the studies employed prospective ratings for confirmed diagnosis of PMDD. There was a notable difference in the mean sample size between studies with confirmed (798) and provisional (1255) diagnoses. Interestingly, one study reported no cases of PMDD in its sample. The researchers found that the pooled prevalence of PMDD was 3.2% in confirmed diagnoses and 7.7% in provisional diagnoses, albeit with significant heterogeneity (I2 = 99%).

The risk of bias varied among the studies, with scores ranging from 3 to 9. Notably, all studies were consistent in their data collection methods and directly obtained data from participants. A higher prevalence was observed in studies with provisional diagnosis, African samples, and university settings. Also, a negative correlation between prevalence rates and risk of bias was observed.

The use of questionnaires in diagnosing PMDD also influenced the prevalence rates. Studies using recognized questionnaires reported a lower prevalence compared to those that did not use a standard questionnaire. Among the studies that reported provisional diagnosis, 22 used a recognized questionnaire.

A particular study by de la Gándara Martín and de Diego Herrero in 1996, which reported an unusually high prevalence of 30.5%, was considered an outlier and excluded from further analysis. This adjustment led to a reduced pooled prevalence of 2.7% and a decrease in heterogeneity.

Focusing solely on community-based samples that strictly adhered to DSM criteria, including tracking symptoms over two cycles, resulted in a more refined analysis. This restriction to six samples revealed a pooled prevalence of 1.6%, with significantly lower heterogeneity (I2 = 26%). This finding underscores the importance of strict adherence to diagnostic criteria and sample selection in determining the prevalence of PMDD. 

Journal reference:
Vijay Kumar Malesu

Written by

Vijay Kumar Malesu

Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.    

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