Public health research has increasingly shown that socioeconomic status (SES) is a strong predictor of one’s health, particularly when it comes to the risk of chronic and noncommunicable diseases. Chronic diseases are the leading cause of death worldwide, and almost all countries have inequities in risk factors and in access to health resources.
Reducing these inequities requires a fresh perspective on research and global policy creation, says Onyebuchi Arah, professor of epidemiology at the UCLA Fielding School of Public Health and affiliated professor of statistics at the university.
When examining policies to reduce the prevalence of chronic diseases among poorer populations worldwide, “we need to move away from the individualized approach, or at least augment it with a more collective approach,” says Arah. “This is very important, given the times that we live in. When you think about social movements around discrimination, climate, and violence—these are all collective things, beyond the individual level, that shape who we are.”
On Wednesday, October 23, Arah will visit the School of Public Health for the Public Health Forum “On the Global Socioeconomic Context of Chronic Diseases.”
Ahead of the event, Arah spoke more about his research methodologies and the global policy implications for reducing health disparities in chronic conditions among low-SES populations.
How do you approach social and clinical epidemiology?
I am very much driven by methods—how we know what we know, and how we study what we study. I’m drawn to causal inference—the idea of using the modern machinery of graphical models of causality and the potential outcomes framework. I apply these techniques to social or clinical epidemiology. Related to this, I employ systems science framework and tools to go beyond integrating various datasets and information sources within what we are beginning to call data fusion, to conduct realistic what-if analyses in virtual labs that can inform interventions or policies.
As part of my interest in that methodology, I’m drawn to what I call my skeptical side—I don’t believe everything I see or read. I think about alternative explanations for what we think we consider to be a signal or a causal effect. I work on methods for bias analyses, where I look for alternative explanations that may be due to factors that weren’t measured, were measured with error, or were the wrong samples used in studies. I’ve contributed to the literature on bias analyses and developed formulas for conducting more principled quantitative bias analyses.
In your global research, are there any major differences that you’ve observed among countries, in terms of how one’s SES impacts the prevalence and burden of chronic diseases?
Every country does have socioeconomic inequalities and inequities in health to some degree. Societies have these problems no matter how wealthy they are, albeit to varying degrees.
But different countries have different ways of responding to these inequities. The impacts of social or socioeconomic exposures on health and chronic diseases tend to be harsher in environments that have higher inequalities. The effects of education or income on one’s health tend to be more pronounced in poorer countries. The economic context in which these issues exist is such that, within countries, you see patterns that are disturbing. No opportunities for work, no access to green parks, no access to safety nets—these exposures are more pronounced in poorer countries. Further, the risk factors that exist in many societies have far more disproportionate effects within societies that have suffered historical events that have led to bigger inequalities or socioeconomic gaps than you find in wealthier Western European countries and North America. For example, the effects in Eastern Europe are still present after the fall of the Soviet Union.
Based on your research, at what policy level is there a need for the greatest change in the way we address socioeconomic status and health?
You have to think about the resources that you have in each context. In some cases, it makes sense to have a combination of federal and local policies and interventions. In other places, the only politically expedient tools you have may be far more removed from the problem. What’s important is that whenever you create policies that affect exposures at a collective level, you’re going to have a cascading effect on outcomes other than the one you might have used to justify your action. No matter what level or combination of levels you use, you should expect multiple outcomes and unintended consequences—which could be a good thing in the sense that it leads to bigger policies that are administered to groups of people, beyond the individual level, where they may be more impactful. This moves us to examine the collective society as a whole, rather than blaming individuals for their health behaviors or outcomes.
What are the research gaps or next steps in studying the global effect of SES on chronic and non-communicable diseases?
In epidemiology, we haven’t been very creative in leveraging more natural experiments, triangulations, and multigenerational cohort data where we can obtain different kinds of effects that might lend credibility to the kinds of designs and analyses that we conduct. Therefore, what we’re trying to do in Northern Europe is to collect more longitudinal information, so that we can have more information on not just individuals, but their parents and grandparents, in a way that we haven’t been able to do in most parts of the world. We’re hoping that this longitudinal follow-up about socioeconomic exposure will help us address cross-sectional exposures over time from other communities, and we use them to relate to subsequent outcomes in those populations. If we move to a design where we’re able to measure some of these factors over a longer period, then we might have a life-course perspective that would allow us to compare results from non-life-course or non-longitudinal data settings. This might inform us empirically about some explanations that we’ve seen before and whether we should believe them. And if they’re not to be believed, what are they, and where are they coming from? That’s the $4 million question.