[Ed Note: This is a continuation of our discussion about the development of health communication and health information technology objectives for Healthy People 2020. I invited Dr. Vish Viswanath from the Harvard School of Public Health to talk about his research into communication inequalities and their potential role in population-based disease prevention and health promotion. What follows is an edited version of what he sent me.]
That there are profound disparities among different racial/ethnic and socio-economic groups across the health care continuum is now a well-established theme accepted by academics, advocates and policy makers engaged in health policy. These disparities manifest on a range of issues: preventable behavioral risk factors including tobacco use, diet and physical activity; screening, detection and treatment; and quality of life associated with survivorship and treatment at the end-of-life. Even though a number of possible explanations for these disparities have been identified, two key social determinants, race and ethnicity and socioeconomic position (SEP), have stood out, and have influence that spans the disease prevention and health promotion continuum. However, the specific mechanisms and processes that connect these social determinants to individual and population health remain unclear.
We propose that the connection between social determinants and disparities in health outcomes might be traced to disparities in information and communications, and that communications is a central thread that could link social determinants of class and race to health disparities. Communication inequalities are differences among social groups in accessing, seeking, processing and using health information. Communication inequalities may act as a significant deterrent to obtaining and processing information, in using the information to make prevention, treatment and survivorship-related decisions, and in establishing relationships with providers - all of which impact prevention and treatment outcomes. Communication inequalities are a disturbing, yet potentially modifiable, counterpart to health disparities and, may have a profound and invidious impact on health outcomes.
We have articulated a framework, The Structural influence Model (SIM) of Health Communication, that identifies how communication inequalities may influence health disparities. Our model posits that disparities in health outcomes at least in part be explained by understanding how (a) social determinants (e.g. SEP and race) (b) may lead to differential communication processes (e.g.) such as access to and use of information channels, attention to health content, knowledge and comprehension and a capacity to act on relevant information resulting in differential communication and clinical outcomes.
For example, some populations lack Internet access in the first place. Those who use the Internet as a source for health information tend to be college educated, have higher incomes, are younger and are less likely to be from a racial or ethnic minority group. There is no doubt that the Internet and other forms of “new media” are rapidly changing the way people approach their health and their relationships with their providers, but many people are still disconnected, and this “digital divide” may further propagate communication inequalities and therefore health outcomes.
Another facet of communication inequality that needs to be further explored is the patient-provider relationship. There is ample evidence that providers are likely to communicate differently with minority and low SEP patients compared to white or high SEP patients, thus placing low SEP and minority patients at a disadvantage. Understanding this crucial relationship may illuminate some of the communication issues minorities and low-SEP patients face.
There is much more to be learned about the ways in which communication inequality impacts prevention, treatment, survivorship and outcomes. Researchers, designers of public health campaigns and clinicians need to know more about the fundamental mechanisms, processes and outcomes before interventions can be developed for clinical applications.