Caregiver Burden among Caregivers of Patients with Mental Illness: A Systematic Review and Meta-Analysis

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Abstract

Due to the increasing importance of caregivers in the treatment outcomes of patients with mental illness, this study aimed to systematically review studies investigating the former’s caregiver burden and determine its prevalence. An open search, without filters, was conducted. Articles were selected from Medline, Scopus, and PubMed from inception to 30 April 2022 using the PRISMA protocol. Subgroup analyses examined the between-group differences by study setting, measurements used, and disorder type. A total of 5034 caregivers from 23 countries were included in this review. Thirty-nine studies were included in the systematic review, and, among them, twenty-six were deemed eligible for meta-analysis. The overall pooled prevalence of caregiver burden among caregivers of individuals with mental illness was 31.67% (95% CI = 26.22–37.12). Pooled prevalence was the highest among care recipients receiving treatment in a hospital setting (36.06%; 95% CI = 22.50–49.63), followed by the community and clinic settings. Caregiver prevalence values were higher for burden measured using the Zarit Burden Interview (38.05%; 95% CI = 27.68–48.43). compared with other instruments, and for carers of care recipients with psychosis (35.88%; 95% CI = 27.03–44.72) compared with those without. Thus, targeted interventions should focus on caregivers of patients in hospital settings and with psychotic symptoms.

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Measuring inconsistency in meta-analyses.

Julian P. T. Higgins, Simon Thompson, Jonathan Deeks … (2003)

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Estimating the mean and variance from the median, range, and the size of a sample

Stela Pudar Hozo, Benjamin Djulbegovic, Iztok Hozo (2005)

Background Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. Methods In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. Results We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. Conclusion Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.