We begin with a brief description and analysis of the mechanistic model, its influence on social sciences and on the way we perceive population health. This is followed by an outline of the new paradigm influenced by Complexity Science and an exploration as to how it sheds light on our understanding of determinants of health.
Elements of the Newtonian approach in determinants of population health
Some of the previous and current discourses on determinants approach to population health appear to implicitly and explicitly accept a mechanistic view of reality (i.e. reductionism, linearity and hierarchy). Three such examples are described below.
The first is Durkheim's hypothesis that suicides are a product of social influence [
6]. His analysis and interpretation showed that rates of suicide reflected social structures that went beyond individual psychological circumstances. Higher rates of suicide in certain communities (in contrast to others) were explained by the relationship between the individual and the moral community. Using variations in these relationships, he proposed three types of suicide: altruistic suicides (i.e. individuals are under intense social control and commit suicide as a matter of honour or duty), egoistical suicide (where the collective acts of society are weak and there is lack of social integration, leading to individuals taking their lives, almost as an act of defiant independence) and anomic suicides (when unrealistic goals set by people, and people's detachment from attainable societal norms, lead to situations in which they cannot cope with life stresses). The hypotheses of Durkheim can be considered as examples of linearity: variations in two interacting factors (i.e. the individual and the moral community) that explain three types of suicide. They also illustrate reductionism: a complex social phenomenon is being telescoped to two main 'agents' in order to explain different outcomes. This does not completely negate the usefulness of such analyses, but indicates that one must be cautious when applying such hypotheses to different social environments and be prepared to accept wide variations in associated features and outcomes.
The second example is the explanations given for the findings in the Black Report of 1980. The Report compiled data from the 1950s and 1970s and showed that life span and morbidity rates from non-communicable diseases were strongly related to measures of social and economic position, which were termed 'social class' [
7]. Further exploration of the Whitehall studies of British civil servants revealed the stepwise nature of health inequalities, whereby with each drop in the social status, there was a higher risk of coronary artery disease mortality. This became recognised as the social gradient in health outcomes, in contrast to a dichotomous threshold effect in which the poor have worse indices than the better off [
8]. The social gradient was explained using three pathways: (a) low income and its material consequences; (b) a 'cultural-behavioural' explanation in which low-income groups shared a culture that promoted health-damaging behaviours (e.g. acceptance of tobacco smoking as a social norm); and (c) those who were ill or diseased were 'selected' to find themselves in the lower socioeconomic groups, analogous to Darwinian natural selection. This process could operate in the opposite direction too, with the more capable and intelligent moving towards a higher social class. These explanations imply a hierarchy of factors (e.g. health-damaging behaviour) and linearity of outcomes, (e.g. shared cultures promoted adverse health behaviours) and a reductionist approach by attempting to dissect and identify individual factors that are responsible for outcomes.
The third example is from more recent studies that reported disparities in rates of non-communicable diseases (NCDs), communicable diseases and injuries according to social status or socio-economic status or occupations [
9]. Several hypotheses are advanced to explain these health inequalities that entail linearity, hierarchy and reductionism. These include:
a) the psychosocial theory: the psychological stresses experienced at home and work place as a result of low social status lead to higher morbidity and mortality rates, predominantly through the autonomic nervous system and hypothalamo-pituitary-adrenal axis;
b) Neo-materialists' explanations: empirical evidence suggests that countries with narrower income inequalities provide easier access to public health, education, and social support. As a result these countries have less social exclusion and narrower disparities in health outcomes;
c) Life course explanations: adverse health influences commencing from foetal life (e.g. from poor maternal nutrition leading to growth retardation), through infancy, childhood, adolescence and young adulthood lead to higher rates of NCDs in later adult life and in the elderly.
A feature of Durkheim's hypothesis and the Black Report is that they cross disciplinary boundaries. For example, Durkheim attempted to explain an epidemiological finding on suicide rates using psychological and social factors, while the Black Report dwells into social class and behavioural science to partly explain the social stratification of illness. The more recent psychosocial theory, neo-materialists' explanations and life course approaches have further increased the interactions with other disciplines. For example, the psychosocial theory incorporates studies from sociology (social status), psychology, immunology and neuro-endocrinology (i.e. the stress pathway) and support from primate research [
10]. Although these are healthy deviations from pure reductionism and a hierarchy of contributing factors, features of Newtonian mechanistic views remain. Research is often directed at investigating single or multiple risk factors of diseases and their proximate or distal determinants. Statistical models and epidemiological analysis is extensively used in this discourse, in which linearity is a fundamental assumption to predict future health outcomes. Hierarchy also forms a part of the discourse. Although less visible than reductionism and linearity, it is seen during analyses involving hierarchical modelling and when a strict process of prioritisation or classification is applied to population health policies.
These mechanistic explanations are convenient for conceptualisation and to plan intervention policies, though they are not necessarily a true reflection of reality. Examples include the prescriptive reports by the World Bank and the UK's Department of Health programme for action on health inequalities, and the more pragmatic and flexible Report of the WHO's Commission on Social Determinants of Health [
11‐
13]. In its recommendations, the World Bank advocated several measures that assumed linearity, for example, that economic growth would have a trickle-down effect on health improvement at the household levels [
13]. Its recommendations were based on principles of reductionism, i.e. quantifying the burden of illness using Disability Adjusted Life Years, which used a single index to quantify human suffering from different causes. In contrast, the WHO's Commission on Social Determinants of Health has used a more pragmatic approach. It emphasises contextual factors in explaining health inequalities such as governance, macroeconomic policies, social policies, public policies, culture and societal values [
11,
14]. The broad recommendations (i.e. improving daily living conditions, tackling the inequitable distribution of power, money and resources, and measuring and understanding the problem and assessing the impact of action) are supplemented with numerous examples and success stories from local studies.
The next section gives an overview of Complexity Science and discusses its potential impact on our understanding of population health.