Interventions to maintain essential services for maternal, newborn, child, and adolescent health during the COVID-19 pandemic: A scoping review of evidence from low- and middle-income countries.

Background: The coronavirus disease 2019 (COVID-19) pandemic had challenged health systems worldwide, including those in low- and middle-income countries (LMICs). Aside from measures to control the pandemic, efforts were made to continue the provision and use of essential services. At that time, information was not organised and readily available to guide country-level decision-making.

The future burden of type 2 diabetes in Belgium: a microsimulation model.

Objective: To forecast the annual burden of type 2 diabetes and related socio-demographic disparities in Belgium until 2030.

Methods: This study utilized a discrete-event transition microsimulation model. A synthetic population was created using 2018 national register data of the Belgian population aged 0-80 years, along with the national representative prevalence of diabetes risk factors obtained from the latest (2018) Belgian Health Interview and Examination Surveys using Multiple Imputation by Chained Equations (MICE) as inputs to the Simulation of Synthetic Complex Data (simPop) model.

Quantifying Overlapping Forms of Malnutrition Across Latin America: A Systematic Literature Review and Meta-Analysis of Prevalence Estimates.

Estimating the prevalence of double burden of malnutrition (DBM) is challenging in the Latin American and Caribbean (LAC) region where various DBM typologies (e.g., obesity and stunting) are heterogeneous and estimates are scattered across literature This study aimed to assess the prevalence of DBM typologies in the LAC region.

Dynamics of the double burden of malnutrition in Guatemala: a secondary data analysis of the demographic and health surveys from 1998-2015.

Objective: We estimated the prevalence and time trends of the double burden of malnutrition (DBM) in Guatemala and explored its occurrence based on socio-demographic factors.

Study Design: This was a secondary data analysis using information from four Demographic and Health Surveys covering the period 1998-2015.

Methods: The unit of analysis was the household within which information was gathered from women 18-49 years and their children, 6-59 months.

Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe.

Background: Breast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups.