During the past two decades, the public health community’s attention has been drawn increasingly to the social determinants of health (SDH)—the factors apart from medical care that can be influenced by social policies and shape health in powerful ways. We use “medical care” rather than “health care” to refer to clinical services, to avoid potential confusion between “health” and “health care.” The World Health Organization’s Commission on the Social Determinants of Health has defined SDH as “the conditions in which people are born, grow, live, work and age” and “the fundamental drivers of these conditions.” The term “social determinants” often evokes factors such as health-related features of neighborhoods (e.g., walkability, recreational areas, and accessibility of healthful foods), which can influence health-related behaviors. Evidence has accumulated, however, pointing to socioeconomic factors such as income, wealth, and education as the fundamental causes of a wide range of health outcomes. This article broadly reviews some of the knowledge accumulated to date that highlights the importance of social—and particularly socioeconomic— factors in shaping health, and plausible pathways and biological mechanisms that may explain their effects. We also discuss challenges to advancing this knowledge and how they might be overcome.

https://journals.sagepub.com/doi/pdf/10.1177/00333549141291S206

The Social Determinants of Health: It's Time to Consider the Causes of the Causes:

Complex systems abound in public health. Complex systems are made up of heterogeneous elements that interact with one another, have emergent properties that are not explained by understanding the individual elements of the system, persist over time, and adapt to changing circumstances. Public health is starting to use results from systems science studies to shape practice and policy, for example in preparing for global pandemics. However, systems science study designs and analytic methods remain underutilized and are not widely featured in public health curricula or training. In this review we present an argument for the utility of systems science methods in public health, introduce three important systems science methods (system dynamics, network analysis, and agent-based modeling), and provide three case studies in which these methods have been used to answer important public health science questions in the areas of infectious disease, tobacco control, and obesity.

Systems science methods in public health: dynamics, networks, and agents.

Preface Chapter 1: Does Epidemiologic Theory Exist? - on science, data, and explaining disease distribution Chapter 2: Health in the Balance: early theories about patterns of disease occurrence Chapter 3: Epidemiology Emerges: early theories and debating determinants of disease distribution - poison, filth, class & race (1600-1900) Chapter 4: Epidemiology Expands: germs, genes, and the (social) environment (1900-1950) Chapter 5: Contemporary Mainstream Epidemiologic Theory: biomedical & lifestyle Chapter 6: Social Epidemiologic Alternatives: sociopolitical and psychosocial frameworks Chapter 7: Ecosocial Theory of Disease Distribution: embodying societal & ecologic context Chapter 8: Epidemiologic Theory Counts: harm, knowledge, action and the people's health

Epidemiology and the People's Health: Theory and Context

Injury prediction is one of the most challenging issues in sports and a key component for injury prevention. Sports injuries aetiology investigations have assumed a reductionist view in which a phenomenon has been simplified into units and analysed as the sum of its basic parts and causality has been seen in a linear and unidirectional way. This reductionist approach relies on correlation and regression analyses and, despite the vast effort to predict sports injuries, it has been limited in its ability to successfully identify predictive factors. The majority of human health conditions are complex. In this sense, the multifactorial complex nature of sports injuries arises not from the linear interaction between isolated and predictive factors, but from the complex interaction among a web of determinants. Thus, the aim of this conceptual paper was to propose a complex system model for sports injuries and to demonstrate how the implementation of complex system thinking may allow us to better address the complex nature of the sports injuries aetiology. According to this model, we should identify features that are hallmarks of complex systems, such as the pattern of relationships (interactions) among determinants, the regularities (profiles) that simultaneously characterise and constrain the phenomenon and the emerging pattern that arises from the complex web of determinants. In sports practice, this emerging pattern may be related to injury occurrence or adaptation. This novel view of preventive intervention relies on the identification of regularities or risk profile, moving from risk factors to risk pattern recognition.

https://bjsm.bmj.com/content/bjsports/50/21/1309.full.pdf

Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition—narrative review and new concept

Nonmedical prescription opioid misuse remains a growing public problem in need of action and is concentrated in areas of US states with large rural populations such as Kentucky, West Virginia, Alaska, and Oklahoma. We developed hypotheses regarding the influence of 4 factors: (1) greater opioid prescription in rural areas, creating availability from which illegal markets can arise; (2) an out-migration of young adults; (3) greater rural social and kinship network connections, which may facilitate drug diversion and distribution; and (4) economic stressors that may create vulnerability to drug use more generally. A systematic consideration of the contexts that create differences in availability, access, and preferences is critical to understanding how drug use context varies across geography.

Understanding the Rural–Urban Differences in Nonmedical Prescription Opioid Use and Abuse in the United States

Identifying biological and behavioural causes of diseases has been one of the central concerns of epidemiology for the past half century. This has led to the development of increasingly sophisticated conceptual and analytical approaches focused on the isolation of single causes of disease states. However, the growing recognition that (i) factors at multiple levels, including biological, behavioural and group levels may influence health and disease, and (ii) that the interrelation among these factors often includes dynamic feedback and changes over time challenges this dominant epidemiological paradigm. Using obesity as an example, we discuss how the adoption of complex systems dynamic models allows us to take into account the causes of disease at multiple levels, reciprocal relations and interrelation between causes that characterize the causation of obesity. We also discuss some of the key difficulties that the discipline faces in incorporating these methods into non-infectious disease epidemiology. We conclude with a discussion of a potential way forward.

/pdf/causal-thinking-and-complex-system-approaches-in-ivtboxodg8.pdf

Causal thinking and complex system approaches in epidemiology

The investigations of Rosenquist (1) and of Gibson and Howard (2) on the urinary excretion of endogenous uric acid and of Gettler and Lindeman (3) on the uric acid content of the blood in pernicious anemia suggest a disturbanc.e of the purine metabolism in this disease. The results of these studies are characterized by their variability and by the frequency of abnormally high values for uric acid in the blood and urine of patients with pernicious anemia. These investigations were performed before the discovery by Minot, Murphy, Cohn and their associates (4) (5) (6) (7) (8) of the specific therapeutic effect of liver and of extracts of liver in pernicious anemia. The remarkable improvement in patients following the daily ingestion of small amounts of liver extract suggests an association of the disease process with some unknown disorder of metabolism and indicates the importance of a reexamination and reevaluation of the existing knowledge of the metabolic manifestations of this disease. Fortunately liver and liver extract, aside from their remedial value, provide a method for the experimental control of the course of the disease. Remissions may be produced at will and consequently may be anticipated for experimental purposes. This paper deals with the behavior of the endogenous uric acid metabolism during the early stages of remission in patients with pernicious anemia.

/pdf/the-endogenous-uric-acid-metabolism-in-pernicious-anemia-5f1tbmdec2.pdf

Matthew C. Riddle

Papers

Causal thinking and complex system approaches in epidemiology

The endogenous uric acid metabolism in pernicious anemia