1  Introduction


1.1 The Concept of Health

The concept of being “well” has evolved over the years and is not solely confined to physical health. Traditionally, health metrics focused on factors like life expectancy, mortality rates, and the prevalence of diseases. However, the understanding of well-being has expanded to include broader dimensions, incorporating mental health, social factors, and overall life satisfaction.

In recent years, there has been an increased emphasis on holistic well-being, recognizing that health is not merely the absence of disease but a state of complete physical, mental, and social well-being. This perspective aligns with the 1946 World Health Organization’s constitution principles1 of health which states: “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.”

1.1.1 The Culture

The cultural context significantly influences the values, norms, and expectations within a society, and these, in turn, contribute to people’s understanding of what constitutes a good life. Education, media, legal systems, and community practices all play pivotal roles in shaping individuals’ perceptions of well-being. Cultural norms and values often define what is considered acceptable or taboo, influencing individuals’ behaviors and aspirations. Understanding these cultural nuances is crucial when developing health metrics and policies that aim to capture and improve well-being. It emphasizes the need for a holistic approach that considers not only physical health but also the social, cultural, and psychological dimensions of individuals and communities.

Achieving a global mean or standard for well-being involves considering not just the diversity of cultures but also acknowledging the common elements that contribute to human happiness and health. Behavioral education, more specifically the process of teaching individuals how to modify their behaviors in order to improve their overall well-being, plays a pivotal role in this process. Promoting positive behaviors that are conducive to both individual and societal well-being can be a universal goal. Understanding what constitutes “good behavior” in the context of health and happiness is, indeed, central to standardizing well-being metrics.

Moreover, this aligns with the idea that health is closely tied to happiness. Cultivating positive behaviors, such as maintaining a healthy lifestyle, fostering social connections, and contributing to one’s community, can significantly impact both health and happiness. These behaviors can be universal, yet their interpretation and emphasis might vary based on cultural contexts.

1.1.2 A Global Perspective

In essence, achieving global standardization in well-being requires a multi-faceted approach that includes education, social support systems, and a collective effort to reshape cultural narratives. It’s a complex task, but incremental changes in education and social structures can contribute to a more harmonized vision of well-being across diverse cultures.

The aim of this book is to provide an extensive information about the way this well-being can be statically measured. Public health metrics, such as Years of Life Lost (YLL) and Years lived with Disability (YLD), are examples of the key metrics discussed and used throughout this book. They are expressed in number of years of life lost or years lived with disabilities, and their sum represents a crucial value named Disability Adjusted Life Years (DALYs). DALYs are generally used for ranking the health status of a population. The book covers the history of the development of the health metrics and aims at stimulating reasoning to suggests alternatives by providing a comprehensive manual to make reference to if you get lost among the plethora of information. The hope is to provide insights for health researchers and policymakers.

To be more specific, the book compares the metrics used to summarize the health status of a population across different locations. It tests prediction levels using key models. The initial tools are {tidymodels} and {INLA} for modelling, but other machine learning packages like {mlr3} and {caret} are also tested as alternative tools.

In practice, the data will include information about humanity such as age, sex, life expectancy, mortality, and risk levels. The interesting part is related to the identification of the influence of some of the most dangerous infectious diseases on the values of health metrics of a population, and this is done to practice the variation to eventually predict through model transfer techniques the same pattern procedure on other countries. It’s not the only application; more examples of model transfer applications are needed in research2.

A focus on the impact of recent infectious disease outbreaks, such as SARS-Covid19, on the state of health of the population, will be provided along with the most affected locations to compare results of both deterministic and stochastic (Bayesian) models. Risk factor analysis is another important aspect covered in this book. It aims at identifying connections that could lead to an increase in the number of DALYs for specific populations. Additionally, it looks at providing suggestions for public health policy and practice3.

1.2 The Structure of the Book

The book is structured with an alternation of text and chunks of code, primarily in the R programming language; hints for translations in Python are provided in Appendix C. The book encourages readers to actively engage with real-world case studies, transforming theoretical concepts into practical skills. This hands-on approach enables readers to become practitioners, applying the methods learned directly to relevant scenarios.

The material supports full exploratory data analysis and model visualization, offering code for generating compelling spatial visualizations using packages such as {ggplot2}, {leaflet}, {sf}, and {terra}, among others, which allow for extensive user customization. The inclusion of these tools is intended to unlock the full potential of the R language, broadening the reader’s understanding of both spatial and health metrics.

To ensure reproducibility, the book-project employs the {renv} package, which provides a snapshot of the specific versions of all packages used during the writing of the book Appendix B. This enables readers to restore all R package libraries to the exact versions they were in at the time of writing, ensuring that all code examples work as intended regardless of future package updates. This detail is crucial for readers who wish to recreate the analysis or adapt it to their own datasets.

This book is designed for practitioners at early stages of their careers and graduate students in STEM fields, yet it remains a valuable resource for experts who seek to have all the tools in one place for quick reference. The overarching goal of this book is to contribute to the scientific development of health metrics and evaluation, providing a comprehensive resource that supports both learning and application in this important field4.

  1. “Constitution of the World Health Organization,” n.d., https://www.who.int/about/accountability/governance/constitution.↩︎

  2. Kirstin Roster, Colm Connaughton, and Francisco A. Rodrigues, “Forecasting New Diseases in Low-Data Settings Using Transfer Learning,” Chaos, Solitons, and Fractals 161 (August 2022): 112306, doi:10.1016/j.chaos.2022.112306.↩︎

  3. Theo Vos et al., “Global Burden of 369 Diseases and Injuries in 204 Countries and Territories, 19902019: A Systematic Analysis for the Global Burden of Disease Study 2019,” The Lancet 396, no. 10258 (October 2020): 1204–22, doi:10.1016/s0140-6736(20)30925-9.↩︎

  4. Christopher JL Murray and Julio Frenk, “Health Metrics and Evaluation: Strengthening the Science,” The Lancet 371, no. 9619 (April 5, 2008): 1191–99, doi:10.1016/S0140-6736(08)60526-7.↩︎