Conceptualising population health: from mechanistic thinking to complexity science

The mechanistic interpretation of reality can be traced to the influential work by René Descartes and Sir Isaac Newton. Their theories were able to accurately predict most physical phenomena relating to motion, optics and gravity. This paradigm had at least three principles and approaches: reductionism, linearity and hierarchy. These ideas appear to have influenced social scientists and the discourse on population health. In contrast, Complexity Science takes a more holistic view of systems. It views natural systems as being 'open', with fuzzy borders, constantly adapting to cope with pressures from the environment. These are called Complex Adaptive Systems (CAS). The sub-systems within it lack stable hierarchies, and the roles of agency keep changing. The interactions with the environment and among sub-systems are non-linear interactions and lead to self-organisation and emergent properties. Theoretical frameworks such as epi+demos+cracy and the ecosocial approach to health have implicitly used some of these concepts of interacting dynamic sub-systems. Using Complexity Science we can view population health outcomes as an emergent property of CAS, which has numerous dynamic non-linear interactions among its interconnected sub-systems or agents. In order to appreciate these sub-systems and determinants, one should acquire a basic knowledge of diverse disciplines and interact with experts from different disciplines. Strategies to improve health should be multi-pronged, and take into account the diversity of actors, determinants and contexts. The dynamic nature of the system requires that the interventions are constantly monitored to provide early feedback to a flexible system that takes quick corrections

A New Trichotomous Measure of World-system Position Using the International Trade Network

Snyder and Kick's (1979) measure of world-system position continues to serve as the premier trichotomous network indicator of a state's location in the capitalist world economy. In this study, we identify several problems with this orthodox measure concerning its age, informal construction, and incorporation of inappropriate networks. We introduce a trichotomous network measure of world-system position that addresses these concerns, applying Borgatti and Everett's (1999) core/periphery model to a three-tiered partition using international trade data. Our trichotomous measure of the trade network identifies an expanded core, consisting of an old orthodox core joined by a set of upwardly mobile states. We estimate the effect of world-system position on economic growth and find that our trade measure significantly outperforms Snyder and Kick's orthodox measure. When controlling for human capital, the strong effects of our trade measure persist, while the weaker effects estimated by the orthodox measure largely disappear. Moreover, our models with human capital reveal that states economically converge within world-system zones, while continuing to diverge between zones.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Understanding unequal ageing: towards a synthesis of intersectionality and life course analyses

Intersectionality has received an increasing amount of attention in health inequalities research in recent years. It suggests that treating social characteristics separately—mainly age, gender, ethnicity, and socio-economic position—does not match the reality that people simultaneously embody multiple characteristics and are therefore potentially subject to multiple forms of discrimination. Yet the intersectionality literature has paid very little attention to the nature of ageing or the life course, and gerontology has rarely incorporated insights from intersectionality. In this paper, we aim to illustrate how intersectionality might be synthesised with a life course perspective to deliver novel insights into unequal ageing, especially with respect to health. First we provide an overview of how intersectionality can be used in research on inequality, focusing on intersectional subgroups, discrimination, categorisation, and individual heterogeneity. We cover two key approaches—the use of interaction terms in conventional models and multilevel models which are particularly focussed on granular subgroup differences. In advancing a conceptual dialogue with the life course perspective, we discuss the concepts of roles, life stages, transitions, age/cohort, cumulative disadvantage/advantage, and trajectories. We conclude that the synergies between intersectionality and the life course hold exciting opportunities to bring new insights to unequal ageing and its attendant health inequalities

Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low- and middle-income countries

<p>Abstract</p> <p>Background</p> <p>An important contribution of the social determinants of health perspective has been to inquire about non-medical determinants of population health. Among these, labour market regulations are of vital significance. In this study, we investigate the labour market regulations among low- and middle-income countries (LMICs) and propose a labour market taxonomy to further understand population health in a global context.</p> <p>Methods</p> <p>Using Gross National Product per capita, we classify 113 countries into either low-income (n = 71) or middle-income (n = 42) strata. Principal component analysis of three standardized indicators of labour market inequality and poverty is used to construct 2 factor scores. Factor score reliability is evaluated with Cronbach's alpha. Using these scores, we conduct a hierarchical cluster analysis to produce a labour market taxonomy, conduct zero-order correlations, and create box plots to test their associations with adult mortality, healthy life expectancy, infant mortality, maternal mortality, neonatal mortality, under-5 mortality, and years of life lost to communicable and non-communicable diseases. Labour market and health data are retrieved from the International Labour Organization's Key Indicators of Labour Markets and World Health Organization's Statistical Information System.</p> <p>Results</p> <p>Six labour market clusters emerged: Residual (n = 16), Emerging (n = 16), Informal (n = 10), Post-Communist (n = 18), Less Successful Informal (n = 22), and Insecure (n = 31). Primary findings indicate: (i) labour market poverty and population health is correlated in both LMICs; (ii) association between labour market inequality and health indicators is significant only in low-income countries; (iii) Emerging (e.g., East Asian and Eastern European countries) and Insecure (e.g., sub-Saharan African nations) clusters are the most advantaged and disadvantaged, respectively, with the remaining clusters experiencing levels of population health consistent with their labour market characteristics.</p> <p>Conclusions</p> <p>The labour market regulations of LMICs appear to be important social determinant of population health. This study demonstrates the heuristic value of understanding the labour markets of LMICs and their health effects using exploratory taxonomy approaches.</p

Signalling Demand for Foreign Investment: Postsocialist Countries in the Global Bilateral Investment Treaties Network

A unique dataset on bilateral investment treaties provides a novel source of evidence on the link between neoliberal globalisation and market transition. We argue that postsocialist countries of Europe and Eurasia, more than other developing regions in the world, signed such treaties to signal demand for foreign investment in the spirit of neoliberalism. We calculated the density of the whole BIT network since its inception in 1959 to 2009, and density and centrality of different regional blocks within it, and found strong support for our argument. Yet, even if bilateral investment treaties are designed to promote foreign direct investment, dynamic panel regression models show that signing them does not automatically translate into foreign direct investment inflows for postsocialist European and Eurasian countries in the 1990–2010 period