Biology and Health Inequality

D espite continued growth in the global economy, good health remains elusive for a large proportion of the world's population. Health inequalities between rich and poor countries are based on historical patterns of development and exploitation that leave millions of people starved of basic material and social amenities. In those rich countries where absolute poverty is largely absent, continuing health inequality is more difficult to understand. The discipline of biology has an important contribution to make in this regard. Studying the pathways responsible for translating social differences into biological differences and chronic disease is an intrinsically challenging pursuit. It is also an ethically compelling application of life science. One of the root observations leading to the work I will describe arose from the first Whitehall study, which analyzed cardiovascular disease among 17,530 civil servants working in London 30 years ago [1]. In this population of middle-aged men, all of whom were wage-earners in stable jobs, there was an inverse relationship between social status and coronary heart disease (CHD) mortality: the lower the employment grade, the higher the risk of death. A ten-year follow-up of the cohort showed that there was a steep inverse relation between grade of employment and death from all causes—CHD and noncoronary causes [2]. The relative risk of death due to CHD was 2.2 in clerical compared with senior administrative staff, and 1.6 for those in the intermediate professional and executive grade. This working community shared a benign environment at the time of the baseline screening examination in 1968, important features of which included growing prosperity, the welfare state, and free health care. Nevertheless, a health hierarchy persisted that produced a greater than 2-fold difference in mortality, even across social strata limited to men in secure, non-manual employment. Mortality gradients in the study reflected those in United Kingdom social class mortality data in the 1970s, which continue today. The then-novel technique of multivariate modeling shed light on some of the causes of the inverse social gradient in CHD in the first Whitehall study and helped to shape the future research agenda. When conventional coronary risk factors—such as smoking, serum cholesterol, and blood pressure—were controlled for one-third of the gradient was explained. Importantly, serum cholesterol levels were slightly higher in men in the higher employment grades [1]. The distribution of serum cholesterol in a population is a key determinant of its background level of risk, but it did …