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.

COVID-19, Obesity, and Structural Racism.
This study supports SDG 3 and 10 by reporting that Māori and Pacific people with type 2 diabetes have consistently poorer health outcomes than European patients, indicating the need for specific policies and interventions to better manage type 2 diabetes in these subpopulations.
Elsevier,

The Lancet Global Health, Volume 9, Issue 4, April 2021, Pages e489–e551

This Lancet Global Health Commission advances addresses SDG 3 directly, and SDGs 1, 2, 4, 5, 8 and 10 indirectly, by comprehensively demonstrating how improving eye health by treating and preventing vision impairment and vision loss can not only advance SDG 3—improving health and wellbeing for all—but also contribute to poverty reduction, zero hunger, quality education, gender equality, and decent work and economic growth. The findings of this report frame eye health as a development issue and highlight that, with a growing ageing population globally, urgent and concerted action is needed to meet unmet eye health needs globally, including incorporating equitable eye care into countries’ universal health coverage plans.
Background: In 2016, of the estimated 257 million people living with chronic hepatitis B virus (HBV) infection worldwide, only a small proportion was diagnosed and treated. The insufficiency of information on the proportion of people infected with HBV who are eligible for treatment limits the interpretation of global treatment coverage. We aimed to estimate the proportion of people with chronic HBV infection who were eligible for antiviral treatment worldwide, based on the WHO 2015 guidelines.
An Article in support of SDG 3, showing that the age-adjusted prevalence of blindness has reduced over the past three decades, yet due to population growth, progress is not keeping pace with needs and vision impairment remains an urgent and increasingly important public health priority.
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 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.

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