Objectives: Further improvements in the health of mothers and children depend, in part, on collecting, analyzing, and interpreting relevant data correctly. Despite consistent efforts to improve data capacity and use during the past two decades, the need persists for a model set of maternal and child health (MCH) indicators to guide decisions about health conditions to be monitored, elements to be included in data sets, and definitions of measures. This article describes development, key characteristics, and major applications of a set of MCH Model Indicators (MCH MI) created to address these needs.
Methods: A conceptual model with five domains was created to organize and guide development of the indicators. The development process included systematic specification of concepts, formulas, age/gender groups, and data sources, as well as recommendations for frequency of surveillance. Information sources included published reports and expert opinion.
Results: There are 217 indicators distributed across domains as follows: 75 health status, 9 contextual characteristics, 16 health systems capacity and adequacy, 49 risk/protective status, and 68 health and related services. Twenty of the indicators, all of them in the health status domain, are recommended for routine surveillance.
Conclusions: The indicators can be used to identify and address MCH problems, to complement and expand other sets of MCH indicators, to serve as standards for consistent definitions, to provide guidance for creation and revision of MCH and related data bases, and to provide a foundation for the development of related sets of indicators. Some of the indicators require further development, but the total MCH MI package constitutes a solid foundation for subsequent work, as well as for ongoing modifications that are essential if the Model Indicators are to remain responsive to MCH needs.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1023/a:1022311524144 | DOI Listing |
JMIR Med Inform
January 2025
Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Background: Many tools have been developed to predict the risk of diabetes in a population without diabetes; however, these tools have shortcomings that include the omission of race, inclusion of variables that are not readily available to patients, and low sensitivity or specificity.
Objective: We aimed to develop and validate an easy, systematic index for predicting diabetes risk in the Asian population.
Methods: We collected the data from the NAGALA (NAfld [nonalcoholic fatty liver disease] in the Gifu Area, Longitudinal Analysis) database.
J Cheminform
January 2025
School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea.
G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening.
View Article and Find Full Text PDFCardiovasc Diabetol
January 2025
Department of Cardiology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, People's Republic of China.
Background: Hypertension (HTN) is a global public health concern and a major risk factor for cardiovascular disease (CVD) and mortality. Insulin resistance (IR) plays a crucial role in HTN-related metabolic dysfunction, but its assessment remains challenging. The triglyceride-glucose (TyG) index and its derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) have emerged as reliable IR markers.
View Article and Find Full Text PDFCardiovasc Diabetol
January 2025
Department of Cardiology, the First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan 2nd Road, Guangzhou, 510080, China.
Background: Triglyceride-glucose-BMI (TyG-BMI) index is a surrogate marker of insulin resistance and an important predictor of cardiovascular disease. However, the predictive value of TyG-BMI index in the progression of non-severe aortic stenosis (AS) is still unclear.
Methods: The present retrospective observational study was conducted using patient data from Aortic valve diseases RISk facTOr assessmenT andprognosis modeL construction (ARISTOTLE).
J Cardiothorac Surg
January 2025
Department of Paediatrics, Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr. D. Y. Patil Vidyapeeth, Maharashtra, Pune, 411018, India.
Background: Proton pump inhibitors (PPIs) are commonly used for managing gastroesophageal disorders but concerns about their potential association with increased stroke risk have emerged, especially among patients with pre-existing cardiovascular conditions such as acute coronary syndrome (ACS). This systematic review and meta-analysis aim to assess the risk of stroke associated with PPI use, stratified by the presence or absence of pre-existing CVD.
Methods: This review was conducted following the PRISMA guidelines and included studies up to March 2024 from PubMed, Embase, and Web of Science.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!