In recent years, meta-analysis has evolved to a critically important field of Statistics, and has significant applications in Medicine and Health Sciences. In this work we briefly present existing methodologies to conduct meta-analysis along with any discussion and recent developments accompanying them. Undoubtedly, studies brought together in a systematic review will differ in one way or another. This yields a considerable amount of variability, any kind of which may be termed heterogeneity. To this end, reports of meta-analyses commonly present a statistical test of heterogeneity when attempting to establish whether the included studies are indeed similar in terms of the reported output or not. We intend to provide an overview of the topic, discuss the potential sources of heterogeneity commonly met in the literature and provide useful guidelines on how to address this issue and to detect heterogeneity. Moreover, we review the recent developments in the Bayesian approach along with the various graphical tools and statistical software that are currently available to the analyst. In addition, we discuss sensitivity analysis issues and other approaches of understanding the causes of heterogeneity. Finally, we explore heterogeneity in meta-analysis for time to event data in a nutshell, pointing out its unique characteristics.

Purpose: To identify the effect of frailty and geriatric syndromes on the quality of life (QoL), of older adults receiving home care, taking into consideration their socioeconomic and homebound status, including multi-comorbidities. Patients and Methods: This cross-sectional study enrolled elders aged (≥65) years old, registered members of “Help at Home” programs in the Reference Region of Crete, from March to May 2019. Participants were screened using the WHOQOL-BREF for Quality of Life, geriatric syndromes such as frailty using the SHARE-Frailty Index (SHARE-Fi), the Montreal Cognitive Assessment (MoCA), for cognitive function and the Geriatric Depression Scale (GDS), for the assessment of depression. Results: The mean age of the 301 participants was 78.45 (±7.87) years old. The prevalence of frailty was 38.5%, severe depression 13.6%, cognitive dysfunction 87.8% and severe comorbidity 70.6%. Intriguingly, none of the participants (0%) was identified as free of comorbidity (CCI = 0–1). The overall QoL (ranging from 4–20) of the study participants was 13.24 (±4.09). The bivariate analysis showed that overall QoL significantly differed among older adults with frailty (15.91 vs. 11.56, p < 0.001), cognitive dysfunction (15.42 vs. 12.90, p < 0.001), depression (14.90 vs. 9.31, p < 0.001), and disability in Activities of Daily Living (13.67 vs. 10.67, p = 0.002), compared to non-frail, normal cognition and depression, and independent elders, respectively. Multiple linear regression models revealed that frail and depressive elders reported significantly lower QoL (β = −2.65, p < 0.001 and (β = −5.71, p < 0.001), compared to non-frail and older adults with no depressive symptoms, respectively, despite the fact that this association was not significant for older adults with dementia (β = −2.25, p = 0.159), even after adjusting for potential confounding effects (age, gender, comorbidity, homebound status, etc.). Conclusion: frailty and geriatric syndromes including comorbidities are important risk factors for “poor” QoL among older adults receiving home-based healthcare.

Purpose: To evaluate frailty and its relationship with geriatric syndromes in the context of socioeconomic variables. Patients and Methods: In this cross-sectional study, elderly people aged 65 years old and over who received homecare in the reference region of Crete, Greece, were enrolled. Geriatric syndromes such as frailty, dementia, and depression were evaluated using the SHARE-Frailty Index (SHARE-Fi), the Montreal Cognitive Assessment (MoCA), and the Geriatric Depression Scale (GDS), respectively. Level of education, annual individual income, disability in Activities of Daily Living (ADL) and homebound status were also assessed as ‘socioeconomic factors.’ Results: The mean age of 301 participants was 78.45 (±7.87) years old. A proportion of 38.5% was identified as frail. A multiple logistic regression model revealed that elderly people with cognitive dysfunction were more likely to be frail (OR = 1.65; 95% CI: 0.55–4.98, p = 0.469) compared to those with normal cognition, but this association was not significant. Although elderly people with mild depression were significantly more likely to be frail (OR = 2.62; CI: 1.33–5.17, p = 0.005) compared to those with normal depression, the association for elderly people with severe depression (OR = 2.05, CI: 0.80–5.24, p = 0.134) was not significant. Additionally, comorbidity (OR = 1.06, CI: 0.49–2.27, p = 0.876) was not associated with frailty, suggesting that comorbidity is not a risk factor for frailty. In addition, patients with mild depression were significantly more likely to report frailty (OR = 2.62, CI:1.33–5.17, p = 0.005) compared to those with normal depression, whereas elders with an annual individual income (>EUR 4500) were less likely to be frail (OR = 0.45, CI: 0.25–0.83, p = 0.011) compared to those with <EUR 4500 per year. Conclusions: Our data analysis shows that higher annual individual income and mild depression were independently associated with frailty, suggesting that a lower poverty threshold and mild depression are risk factors for frailty.