There is still much controversy regarding the epidemiology of endometriosis. The objective of this work is to conduct a systematic review, and if possible, proceed with a meta-analysis of studies that have analyzed the incidence and prevalence of this condition among women in the general population. The inclusion criteria were papers published after 1997 that had reported data of the incidence or prevalence of endometriosis. The PubMed search engine was used to identify papers meeting the inclusion criteria from 1997 to 2019, with an additional manual search for the identification of potentially eligible studies. The search was limited to papers published in English. The risk of bias was assessed according to the Joanna Briggs Institute Critical Appraisal Checklist. As a result, 27 papers, which included a total of 28,660,652 women, were classified according to the type of design and sources of information in five subgroups. Pooled estimates of prevalence for studies with self-reported data were 0.05 (95% CI: 0.03; 0.06), 0.01 for population-based integrated information systems (95% CI: 0.01; 0.02), and 0.04 (95% CI 0.04; 0.05) in studies using other designs. The pooled incidence rate of endometriosis was: 1.36 per 1000 person-years (PY) (95% CI: 1.09; 1.63) for studies based on hospital discharges, 3.53 per 1000 PY (95% CI: 2.06; 4.99) for cohort studies, and 1.89 per 1000 PY (95% CI: 1.42; 2.37) for population-based integrated information systems. Meta-analysis indicated high heterogeneity based on I-squared statistics. This significant variability may not only be due to methodological issues and the specific limitations of the different designs and data analyzed, including case definitions and subject selection strategies, but also to the inherent heterogeneity of endometriosis. Epidemiological studies with appropriate study designs remain necessary to provide a valid estimation of the population burden of endometriosis.

Diabetes Mellitus is a chronic and lifelong disease that incurs a huge burden to healthcare systems. Its prevalence is on the rise worldwide. Diabetes is more complex than the classification of Type 1 and 2 may suggest. The purpose of this systematic review was to identify the research studies that tried to find new sub-groups of diabetes patients by using unsupervised learning methods. The search was conducted on Pubmed and Medline databases by two independent researchers. All time publications on cluster analysis of diabetes patients were selected and analysed. Among fourteen studies that were included in the final review, five studies found five identical clusters: Severe Autoimmune Diabetes; Severe Insulin-Deficient Diabetes; Severe Insulin-Resistant Diabetes; Mild Obesity-Related Diabetes; and Mild Age-Related Diabetes. In addition, two studies found the same clusters, except Severe Autoimmune Diabetes cluster. Results of other studies differed from one to another and were less consistent. Cluster analysis enabled finding non-classic heterogeneity in diabetes, but there is still a necessity to explore and validate the capabilities of cluster analysis in more diverse and wider populations.

Type 2 diabetes mellitus (T2DM) is a serious public health problem. A large proportion of patients with T2DM are unaware of their condition. People with undiagnosed T2DM are at a greater risk of developing complications, whereas prediabetes has an elevated risk of becoming T2DM. The aim of this study is to estimate the prevalence of impaired fasting glucose (IFG), undiagnosed and prior-diagnosed T2DM in Kazakhstan. A cross-sectional study was conducted in four geographically remote regions using the WHO STEP survey instrument. The status of T2DM of 4,753 participants was determined using the WHO diagnostic criteria based on fasting plasma glucose (FPG) level. As a result, the survey-weighted prevalence of IFG was 1.9% (95% CI 1.1%; 3.5%) and of T2DM was 8.0% (95% CI 3.8; 15.9). A total of 54% of T2DM have been newly diagnosed with T2DM. Being 55–64 years old (OR = 2.71, 95% CI 1.12; 6.60) and having lowered HDL-C level (OR = 3.72, 95% CI 1.68; 8.23) were found to be independent predictors for IFG. Being older than 45 years, a female (OR = 0.57, 95% CI 0.39; 0.83), having high waist circumference, was associated with newly diagnosed T2DM. Whereas, the age older than 45 years, high waist circumference, and family history of diabetes (OR = 2.42, 95% CI 1.64; 3.54) were associated with preexisting T2DM. This study shows a high prevalence of IFG and a high proportion of newly diagnosed T2DM in Kazakhstan. A series of risk factors identified in the study may be used to strengthen appropriate identification of IFG or undiagnosed patients in healthcare settings to deliver either preventive or therapeutic interventions aimed to reduce the incidence of T2DM or the delay of their complications. Further longitudinal studies are needed to confirm these associations in our population.

(1) Background: Health services that were already under pressure before the COVID-19 pandemic to maximize its impact on population health, have not only the imperative to remain resilient and sustainable and be prepared for future waves of the virus, but to take advantage of the learnings from the pandemic to re-configure and support the greatest possible improvements. (2) Methods: A review of articles published by the Special Issue on Population Health and Health Services to identify main drivers for improving the contribution of health services on population health is conducted. (3) Health services have to focus not just on providing the best care to health problems but to improve its focus on health promotion and disease prevention. (4) Conclusions: Implementing innovative but complex solutions to address the problems can hardly be achieved without a multilevel and multisectoral deliberative debate. The CHRODIS PLUS policy dialog method can help standardize policy-making procedures and improve network governance, offering a proven method to strengthen the impact of health services on population health, which in the post-COVID era is more necessary than ever.

Diabetes Mellitus is a chronic and lifelong disease that incurs a huge burden to healthcare systems. Its prevalence is on the rise worldwide. Diabetes is more complex than the classification of Type 1 and 2 may suggest. The purpose of this systematic review was to identify the research studies that tried to find new sub-groups of diabetes patients by using unsupervised learning methods. The search was conducted on Pubmed and Medline databases by two independent researchers. All time publications on cluster analysis of diabetes patients were selected and analysed. Among fourteen studies that were included in the final review, five studies found five identical clusters: Severe Autoimmune Diabetes; Severe Insulin-Deficient Diabetes; Severe Insulin-Resistant Diabetes; Mild Obesity-Related Diabetes; and Mild Age-Related Diabetes. In addition, two studies found the same clusters, except Severe Autoimmune Diabetes cluster. Results of other studies differed from one to another and were less consistent. Cluster analysis enabled finding non-classic heterogeneity in diabetes, but there is still a necessity to explore and validate the capabilities of cluster analysis in more diverse and wider populations.