Health and population

Health and population dynamics are intertwined, embodying an intricate relationship with significant implications on the Sustainable Development Goals (SDGs). Health is fundamentally at the center of these 17 global goals, aimed to transform the world by 2030. Specifically, Goal 3 endeavors to "Ensure healthy lives and promote well-being for all at all ages." It acknowledges that health is pivotal to human life quality, social cohesion, and sustainable development. Inextricably linked to this are the complexities of population dynamics, including growth rates, age structure, fertility and mortality rates, and migration patterns.

With the world's population projected to exceed 9.7 billion by 2050, the pressure on health systems will undoubtedly escalate. The demographic transition, with an aging population and an increasing prevalence of non-communicable diseases, poses new challenges for health systems globally. Additionally, areas with high fertility rates often overlap with extreme poverty, resulting in heightened health risks, including higher maternal and child mortality rates, malnutrition, and infectious diseases.

Moreover, rapid urbanization and migration present both opportunities and threats to health. While urban areas may provide better access to healthcare, they also harbor risks of disease transmission, air and water pollution, and social determinants of health like inadequate housing and social inequality. Simultaneously, migrants often face disproportionate health risks due to unstable living conditions, exploitation, and limited access to healthcare services.

Achieving the SDGs will necessitate comprehensive approaches that consider the intricate interplay of health and population dynamics. It means strengthening health systems, promoting universal health coverage, and addressing social determinants of health. It also implies crafting policies that recognize demographic realities and foster an environment conducive to sustainable development. Only by understanding and harnessing these dynamics can the world meaningfully progress towards realizing the SDGs, ensuring healthy lives and well-being for all.

This book chapter advances SDG #3 and #10 by providing therapeutic strategies that can be employed in clinical trials for AD in DS will be discussed as well as their underlying scientific rationale.
This book chapter advances SDG #3 and #10 by reviewing deep brain stimulation as a treatment for AD patients, reviewing the recent studies and issues associated with the treatment.
This book chapter advances SDG #3 and #10 by providing a brief history of PET imaging and the radiotracers that have had a significant impact for measuring the three signature AD-related neuropathologies related to AD and provides an overview of the research utilizing PET imaging in the DS population
This book chapter advances SDG #3 and #10 by discussing the advantages of performing genetic studies in people with DS, and then discussing the role of reported genes that are known to be associated with AD risk in adults with DS or in the general population. It also discusses how future longitudinal multiomic and imaging study can enhance our understanding of the biology of AD.
Elsevier,

Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021

This book chapter advances SDG #3 and #10 by discussing the operational aspects of deep learning solutions for Alzheimer’s disease, including the review of the advantages and limitations of using deep learning, and future directions on the applications of deep learning to Alzheimer’s disease.
This book chapter advances SDG #3 and #10 by systematically appraises the concepts and promising benefits of AI technology within healthcare for AD risk prediction across communities, and its possible concerns to be tackled prior to large-scale implementation.
Elsevier,

Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021

This book chapter advances SDG #3 and #10 by outlining the applications of big data to Alzheimer’s disease research as well as common methods used to collect and analyze big data. It also explores how big data research could be used to treat, diagnose, and understand Alzheimer’s disease.
Elsevier,

Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021

This book chapter advances SDG #3 and #10 by providing evidence that behavioral treatments are more effective than most pharmacological therapies at managing depression in Alzheimer’s disease.
This book chapter advances SDG #3 and #10 by reviewing the observed epidemiological links between normal and abnormal diurnal and seasonal rhythmicity, cognitive impairment, and ADRD. Then reviewing normal diurnal and seasonal rhythms of brain epigenetic modification and gene expression in model organisms. Finally, reviewing evidence for diurnal and seasonal rhythms of epigenetic modification and gene expression the human brain in aging, Alzheimer's disease, and other brain disorders.
This book chapter advances SDG #3 and #10 by stressing that a population health approach and a focus on promoting equity in health and access to care are critical to reducing the risk of AD and other dementias.

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