A Global Campaign Against Epilepsy Demonstration Project
Handle Antimicrobials with care, we can help!
An introduction to 90-90-90 in South Africa
Presentation is current through November 21, 2014 and will be updated every Friday by 5pm. For the most up-to-date information, please visit www.cdc.gov/ebola.
*Presentation contains materials from CDC, MSF, and WHO
This study is a theory-driven analysis of the socio-demographic determinants of maternal care seeking in Kenya. Specifically, it examines predisposing, enabling, and need factors potentially associated with use of antenatal care (ANC), health facility delivery, and timely postnatal care (PNC).This s...tudy uses data from the 2014 Kenya Demographic and Health Survey (KDHS) conducted among women age 15-49 with a live birth in the five years preceding the survey. It includes data from all 47 counties of Kenya, grouped contiguously into 12regions.We apply Andersen’s Behavioral Model of Health Services Use to examine socio-demographic predictors of health service use.We estimate logistic regression models for adequate use of ANC (defined as attending at least four ANC visits, starting in the first three months of pregnancy), delivery in a health facility, and PNC within 48 hours of delivery.
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Tanzania is prone to refugee influxes, often of long duration. Despite facing its own economic challenges, for decades Tanzania has welcomed thousands of refugees fleeing conflicts in neighboring countries of Great Lakes Region. The counties geographic proximity to the strifetorn Congo Basin is resp...onsible in part for the ease access of displaced populations. As well Tanzania was an early signatory in the region to international agreements on the rights and welfare of refugee and asylum seekers As of December, 2018, Tanzania host some 284,300 camp-based refugees, 77% of who are children and woman, in Nduta, Nyarugusu and Mtendeli Refugee Camps in Kigoma region in Northwest Tanzania. About 74% are from Burundi, and the remaining 26% are primarily from Democratic republic of Congo.
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The classification of digital health interventions (DHIs) categorizes the different ways in which digital and mobile technologies are being used to support health system needs. Historically, the diverse communities working in digital health—including government stakeholders, technologists, clinic...ians, implementers, network operators, researchers, donors— have lacked a mutually understandable language with which to assess and articulate functionality. A shared and standardized vocabulary was recognized as necessary to identify gaps and duplication, evaluate effectiveness, and facilitate alignment across different digital health implementations. Targeted primarily at public health audiences, this Classification framework aims to promote an accessible and bridging language for health program planners to articulate functionalities of digital health implementations.
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Since the discovery of insulin nearly 100 years ago, advances in diabetes treatments and therapies have transformed the lives of people
with diabetes (PwD), notably reducing the daily burden of its management.
Newer technologies, including those driven by artificial intelligence, have the potentia...l to further improve the quality of life of PwD and help
identify and diagnose people at risk of developing Type 2 diabetes and diabetes-related complications early. However, medical and technological advances alone are not enough to fix the diabetes challenge. It is also critical to acknowledge the complexity and the seriousness of diabetes, its impact on the quality of life and well-being of over 32 million PwD in the EU and the financial burden it represents for health systems and society at large.
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