This chapter proposes a framework for estimating the investment in human capital from health improvement or activities that improve life expectancy and reduce morbidity rates. The measurement framework builds on and extends the Jorgenson-Fraumeni income-based approach for estimating human capital to account for the effect of health on human capital. This economic approach to measuring health human capital differs from the welfare-based approach that estimates the economic effect of health improvements on the quality of life and well-being of individuals. The framework is then implemented for Canada, and the investment in health human capital for the period from 1970 to 2020 is estimated. The estimated investment in health human capital based on the income approach was found to be lower than health expenditures in Canada. This suggests that much of the health expenditures should be classified as consumption rather than as an investment that increases earnings.

For decades, researchers have tried to identify ecological and biological correlates of longevity, often using life expectancy and maximum lifespan as the gold standards. The recent increase in demographic data collected in non-model species has also led researchers to develop alternative metrics of longevity, especially in comparative analyses (e.g. 90% longevity). As a result, studies focused on longevity rely on heterogeneous statistical methodologies and use a variety of longevity metrics that are not always clearly defined. This lack of clarity has led to confusion in the interpretation of results and makes it difficult to compare results across studies. This chapter discusses the statistical interpretation of each metric and highlights potential biases associated with the missus of longevity metrics; conducts a systematic review of the various longevity metrics used across the scientific literature and analyses the content of scientific articles on longevity using topic modelling methodology; and illustrates, using two examples, the importance of selecting the appropriate metric based on the research question. Based on these insights, it provides a list of recommendations aimed at helping researchers to think carefully about the choice of metrics when studying longevity.

The Global South, that groups together low- or middle- income countries mainly located in Africa, Asia and Latin America, concentrates most of the world population. Population ageing, caused by the demographic transition and a large decrease in fertility and mortality rates, make these countries face numerous challenges. Among regions in the Global South, the differences in life expectancy at birth were still large in 2022: almost 74 years in Latin America and the Caribbean but only 60 years in sub-Saharan Africa, with some countries barely exceeding 50. Due to many factors that play on health transition, high mortality countries suffer a cumulative burden from both infectious and non-communicable diseases (NCDs). In addition, the lack of old-age mortality data is a dramatic issue when studying longevity in these countries.

During the nineteenth century, Iberia entered the path towards modern economic growth. Although industrialization occurred later than in other Western European countries, economic progress ultimately led to an unprecedented improvement in the standards of living. This chapter aims to analyse the evolution of such advances and, when possible, compare Iberia with its Western European counterparts. In so doing, it presents several indicators capturing different dimensions of well-being, average income, consumption patterns, height, life expectancy, and a synthetic measure, the Human Development Index (HDI). Income distribution is examined by looking at alternative inequality indicators: Gini coefficient, the extraction ratio and top income shares. Based on this information the long-run evolution of economic inequality is assessed. All in all, the evidence presented shows that economic progress and well-being significantly improved in Iberia since mid-nineteenth century, although this happened at a slower pace than in Western Europe.

That differences in health outcomes exist between groups is unsurprising and, in some cases, seems subject to ‘natural law’. Such ‘common sense’, arguably unavoidable differences are termed ‘health disparities’ – a term usually understood to be value-neutral. By contrast, more complex differences in health outcomes which seem to derive from differences in opportunities or systemic bias are deemed ‘unfair’ and are referred to as ‘health inequalities’ or ‘health inequities’.

This chapter delves further into how we describe health inequalities and different measures and data that illustrate these differences. Causes and mechanisms of inequality are explored, followed by examples of inequality across groups with certain population characteristics, including ethnicity; gender, sexual orientation and gender identity; disability; and socially excluded groups. Finally, approaches and strategies for reducing health inequalities are presented, with potential actions described at the micro-, meso- and macro-levels.

Best places in the world to grow old based on income, employment, health, education, and environment. In Sweden, health has improved in the older population over the last decades, so Sweden’s health care needs have decreased overall. Sweden has the largest health care workforce in the world serving citizens over 65. 94 % of people over 65 live at home! Elders receive in-home assistance when needed. Only 4% of all care—health care or home care-is paid for by patients themselves. Municipal fixers—people who can come and do chores to help reduce falls, such as change a lightbulb. They come to your home. Totally free. If your needs are high enough, someone can come in every two hours around the clock to help care for you—totally without cost to you. High satisfaction. No stigma around dementia. Swedish government develops list of drugs that older people should not be prescribed. Sweden has implemented community-based care and practical approaches to older adult safety.

We provide food for thought on some pressing questions about health inequalities – why some of us maintain good health into old age, and the inequity of infectious and Non-Communicable Diseases, both very relevant now to COVID-19. We use historical perspectives and modern examples to discuss the population explosion, social determinants of health and how development over the first 1,000 days influences later health. Some ideas are likely to be quite novel to the reader, such as the risk of disease being increased by ‘mismatch’ between our developmental environment and where and how we live later. This takes the story across the globe, from high- to low-income countries, where early development is often less healthy but economic progress is changing environments fast. Can young people in such settings escape, or has the anvil on which their bodies were forged in early life left them with unalterable inequalities? We ask who needs to ‘own’ these problems and why solutions to them have been slow to emerge. The wider, global perspective, sets the scene for the final chapter which focuses on what we can all do as individuals now that we know some of the secrets of our first 1,000 days.

Population ageing is a result of increased life expectancy (lower mortality) and decreased fertility rates (UN, 2019). For the first time in history, in 2018, people aged 65 years or over outnumbered children under five years of age (UN, 2019). The world’s population is ageing. The United Nations (UN) (2019) estimates that, globally, the number of people aged 65 years and over will increase from 693 million in 2019 to 1.6 billion in 2050 and 2.5billion in 2100. Australia’s population is also ageing. In 2018, about 3.9 million Australians were aged 65 years and over, representing 16% of the total estimated population (Australian Institute of Health and Welfare (AIHW) 2020a). Healthy ageing is gaining momentum as an important goal for societies experiencing population ageing. This chapter presents the public health issues relevant to the wellbeing of older people, now and in the future.

How has Augmented Human Development been distributed across countries? Chapter 3 offers an answer. It presents long-run inequality trends for AHDI and its dimensions and examines gains across the distribution using growth incidence curves, in absolute and relative terms. Augmented human development inequality declined since 1900. In the long run, countries in the middle and lower deciles obtained larger relative gains over the last century. Over time, changes in the international distribution of augmented human development largely depended on the behaviour of schooling and civil and political liberties, even though life expectancy was inequality’s main driver until the 1920s since the uneven diffusion of new medical knowledge and technology and health practices in the early stages of the epidemiological transition provoked unequal life expectancy gains. The global spread of schooling and the diffusion of epidemiological transition made a substantial contribution to reducing AHD inequality between the 1920s and the early 1980s. The rise of authoritarian political regimes partly offset AHD inequality decline, since its dispersion only fell from the 1970s. These findings are at odds with the evolution of per capita income dispersion that increased until the late twentieth century and only fell since 1990. (198 words)

Chapter 1 addresses the challenge of moving from an abstract concept, human development, to an empirical measure, the AHDI. The chapter discusses the measurement of human development, examining each of its dimensions: access to knowledge, a healthy life, and other aspects of well-being leading to a meaningful life, and exploring the reduced forms of these dimensions used as proxies. Then, it proposes a new, augmented human development index that combines achievements in terms of health and education, and material welfare in a context of freedom of choice and, therefore, satisfies the capabilities approach. In order to allow for its bounded nature and quality improvements, the new AHDI, unlike the HDI, derives the proxies for health and education, namely, life expectancy at birth and years of schooling, as Kakwani indices that transform them non-linearly, so increases at higher level represent higher achievements than similar increases at a lower level. Moreover, the AHDI adds a crucial dimension, civil and political liberties, to proxy agency and freedom. As in the HDI, the four indices are combined using unweighted geometric average to obtain the AHDI, as all of them are considered indispensable.

Did augmented human development improve in Latin America since 1870, what drove it, and did the gap with the OECD widen? Chapter 5 addresses these questions. Latin America presents sustained AHD gains since the late nineteenth century, especially during the 1940s and 1950s and from 1970 onwards, the 1980s in particular. AHD advance was not restricted to phases of economic progress, i.e., the 1940–1980 phase of state-led growth, but extended to the globalisation backlash (1914–1950) and the ‘lost decade’ (1980s). Schooling, as a result of the diffusion of new ideas, nation-building, and urbanisation, and life expectancy, due to the spread of the epidemiological transition, drove AHD over the long run and accounted for catching up to the OECD until 1960, while civil and political liberties did so in the 1980s. The rise of life expectancy before drugs spread internationally since 1950 points to the diffusion of new medical knowledge that through hygienic practices and low-cost public health measures helped eradicating communicable diseases and played a major role in reducing infant and maternal mortality.

A trite, if apt, metaphor for the American health care and insurance system is a battleship that has been sailing in a particular direction for many years, with many of us as free riders in a direction we do not prefer. That direction is characterized by spending growth that outpaces virtually any other sectoral trend in the economy, and by quality and outcome measures that, at best, improve little and, at worst, deteriorate. The battleship takes up 18 percent of gross domestic product (GDP), furnishes employment to nearly 15 percent of the workforce, and consumes a large share of federal and state governmental budgets (Figure 2.1). Even if we could figure out how to cut the power, this dreadnought would continue to coast in the same direction for the foreseeable future. The obvious conclusion is that it has been and will continue to be hard to turn the vessel to go in a different direction. As of this writing, the novel coronavirus pandemic has affected the use of care as well, putting many “normal” services on hold to accommodate sick patients. And while it is too early to conclusively confirm the effect of the pandemic on spending trends, there is likely to be an effect (although even the direction is not known). Once the pandemic stabilizes, consumption of health care services will probably not return exactly to past behaviors, but there will be a strong tendency to slide back. What might help to avoid doing so, and most importantly, what evidence can be currently offered or generated to support efforts to change course?

In Chapter 2, trends in Augmented Human Development and its dimensions are presented and compared to those of GDP per head. Then, a breakdown of AHDI gains into their dimensions’ contribution is carried out, and some explanatory hypotheses proposed. Augmented human development improved significantly in the world since 1870, especially over 1913–1980, but significant room for improvement remains. Although AHDI and real per capita GDP exhibit similar progress over the long run, their pace does not match over the different phases of its evolution. Major gains in augmented human development were achieved across the board during the economic globalisation backlash of the first half of the twentieth century. AHD progress was driven by its non-income dimensions. Life expectancy at birth was the main contributor over time, even though its main contribution took place over 1920–1970, as the epidemiological transition diffused internationally. Schooling, mostly public, stimulated by new social views and nation-building, made a steady contribution over time, while civil and political liberties led AHD gains in the last two decades of the twentieth century as authoritarian regimes gave way to the expansion of liberal democracy.

Chapter 6 assesses long-run augmented human development in Africa. Augmented human development experienced sustained gains since 1880, faster between 1920 and 1960, under colonial rule, and at the turn of the century, but remains at the bottom of the world distribution, although the northern and southern regions forged ahead while the rest stayed behind. AHD grew twice as much as per capita GDP, thriving at times of poor economic performance and, unlike GDP per head that fell behind from a higher relative position, AHD was catching up to the OECD since the late 1920s. Schooling was the main driver of AHD gains and catching up, with life expectancy making a significant contribution in the interwar in the early stage of the epidemiological transition, as the diffusion of health practices prevented infectious disease spread and helped reduce infant and maternal mortality. Civil and political liberties made a contribution both at the time of independence and in the 1990s. AHD long-run performance does not support either the pessimistic view of the colonial era or the depiction of ‘lost decades’ for the post-independence era, but there is still a long way to go from an international perspective

Chapter 4 investigates Augmented Human Development across world regions and focuses on the differences between advanced countries (the OECD) and the rest of the world over time. It takes a closer look at world regions, examining the contribution of each dimension to AHD gains and how they affect world distribution. Finally, it investigates catching up to the OECD in the regions of the Rest and what drives it. Augmented human development achieved substantial but unevenly distributed gains across world regions. Life expectancy and schooling drove AHD in both the OECD and the Rest. Although the absolute gap between the OECD and the Rest deepened over time, the gap shrank in relative terms since the late 1920s, at odds with the increasing relative gap in terms of GDP per head. The gap between the OECD and the Rest dominated AHD international distribution until the mid-twentieth century. Life expectancy and civil and political rights were its main drivers of the Rest’s catching up to the OECD. Up to 1970, stronger catching up took place up to 1970, as the epidemiological transition spread and, again, in the 1990s, when liberties expanded in the Rest.