Large-scale food fortification (LSFF) programs have potential to improve a population's nutritional status. Though their success depends heavily on the prevailing policy environment, few tools exist to understand this environment. To address this gap, we develop a novel framework to define and assess the policy enabling environment for LSFF. This easy-to-apply framework can be used in any setting to track progress and identify next steps for continued improvements. The policy enabling environment is conceptualized as having three domains-policy agenda setting, policy implementation, and policy monitoring and evaluation-each of which is captured through indicators that can be evaluated using existing documentation, key informant interviews, and/or a survey of stakeholder perceptions. To validate the framework and demonstrate how it can be operationalized, we apply it in Kenya, where a mandatory LSFF program for salt has been in place since 1978, and a program for packaged maize and wheat flours and vegetable oils was introduced in 2012. Per our assessment, Kenya has achieved the greatest success within the domain of policy agenda setting, has realized moderate success in policy implementation, and has a weaker record in policy monitoring and evaluation. The positive trajectory for many indicators points to a promising future for Kenya's LSFF program. This assessment yields policy implications for Kenya to improve its policy environment for LSFF, especially around financial sustainability of the program; ways to improve the processes for surveillance and enforcement; efforts to support fortification among medium-and small-scale millers; and a need to improve the data landscape.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098474 | PMC |
http://dx.doi.org/10.1371/journal.pgph.0003211 | DOI Listing |
J Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Center for Management, University of Münster, Münster, Germany.
Background: Telemedicine is transforming health care by enabling remote diagnosis, consultation, and treatment. Despite rapid adoption during the COVID-19 pandemic, telemedicine uptake among health care professionals (HCPs) remains inconsistent due to perceived risks and lack of tailored policies. Existing studies focus on patient perspectives or general adoption factors, neglecting the complex interplay of contextual variables and trust constructs influencing HCPs' telemedicine adoption.
View Article and Find Full Text PDFEffective communication is crucial in pediatric palliative care and is essential to facilitate shared decision making between families and the health care team. This study explored the communication preferences of caregivers and health care specialists in Central-Eastern Europe, a region with unique cultural and health care dynamics. Through qualitative interviews, key communication style preferences and barriers were identified.
View Article and Find Full Text PDFPLoS Pathog
January 2025
Department of Pathology, Case Western Reserve University, Cleveland, Ohio, United States of America.
The intracellular protozoan Toxoplasma gondii manipulates host cell signaling to avoid targeting by autophagosomes and lysosomal degradation. Epidermal Growth Factor Receptor (EGFR) is a mediator of this survival strategy. However, EGFR expression is limited in the brain and retina, organs affected in toxoplasmosis.
View Article and Find Full Text PDFPLoS One
January 2025
Institute of Visual Informatics, The National University of Malaysia (UKM), Bangi, Malaysia.
Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!