Gender Difference in the Prevalence of Insomnia: A Meta-Analysis of Observational Studies

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Abstract

Objective: Insomnia is a major health challenge in the general population, but the results of the gender differences in the epidemiology of insomnia have been mixed. This is a meta-analysis to examine the gender difference in the prevalence of insomnia among the general population.

Methods:Two reviewers independently searched relevant publications in PubMed, EMBASE, PsycINFO, Web of Science from their inception to 16 April 2019. Studies that reported the gender-based prevalence of insomnia according to the international diagnostic criteria were included for analyses using the random-effects model.

Results:Eventually 13 articles were included in the meta-analysis. The pooled prevalence of insomnia in the general population was 22.0% [ n = 22,980, 95% confidence interval (CI): 17.0–28.0%], and females had a significantly higher prevalence of insomnia compared with males (OR = 1.58, 95% CI: 1.35, 1.85, Z = 5.63, p < 0.0001). Subgroup analyses showed that greater gender difference was associated with the use of case-control study design and consecutive sampling method. Meta-regression analyses also revealed that higher proportion of females and better study quality were significantly associated with greater gender difference.

Conclusions:This meta-analysis found that the prevalence of insomnia in females was significantly higher than males in the included studies. Due to the negative effects of insomnia on health, regular screening, and effective interventions should be implemented in the general population particularly for females.

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Julian P T Higgins, Simon G Thompson (2002)

The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.