br Methods br Results br Discussion

Methods
Results
Discussion As expected, the baseline mean SBP values were substantially higher in the low-income group. Further, there were no differences in SBP means by smoking status only among the low-income group, while the mean SBP was 1.7mmHg higher in smokers vs. non-smokers in the high-income group. This suggests that SBP inequalities between income groups are not wholly attributable to the observed smoking status. Low-income populations have a historically greater risk of chronic health conditions due to well-known factors that reinforce the income gradient in health, such as fewer material and informational resources to prevent disease (Link & Phelan, 1995), and greater psychosocial stressors (Siegrist & Marmot, 2004). A thorough investigation of income inequalities and health is beyond the scope of this paper, but we mention it to emphasize the point that, a researcher or policy maker who chooses to define a high-risk anti fungal based solely on the prevalence of “modifiable” risk factors (e.g., smoking status) rather than more distal social definitions of risk, would overlook significant causes of inequalities in health outcomes. This issue was raised by Rose himself, when he warned that, by ignoring underlying causes of illness, any intervention will do little to address health inequalities in the long-term (Rose, 1985). An additional benefit of a structural focus is that it reduces the potential for victim blaming and stigmatization that can occur when individuals are identified as high-risk based on presumed behavioral characteristics. This was a prominent critique of early formulations of high-risk intervention strategies (Labonté, 1994). A high-risk population can be defined by any number of criteria. One may choose a definition based on socio-demographic groups such as age, sex, race/ethnicity, income, or neighborhood; or high-risk may be defined using “modifiable” risk behaviors, such as smoking, physical activity, substance use, or other comorbid conditions. There is not always a clear distinction between what is or is not modifiable (e.g., social isolation (Berkman et al., 2003; Pantell et al., 2013). For these reasons, it is important to consider the assumptions and implications of how we define who is at high-risk. Focusing on what is modifiable may allow for a more well-defined intervention, but this may lead one to address the most proximate causes of disease, rather than thinking about risk in terms of macrosocial or structural determinants (Krieger & Davey Smith, 2016; Schwartz, Prins, Campbell, & Gatto, 2015). In fact, structural determinants that are unaccounted for may affect compliance or adherence with an intervention, as has been posited as an explanation for inconsistent and unexpected findings in several large intervention studies (Multiple Risk Factor Intervention Trial Research Group, 1982; Orr et al., 2003). The behavior of individuals is affected by the political, economic, and cultural contexts in which they live, and this must be taken into account for any intervention to be successful. A more general limitation of the high-risk approach, as discussed by Rose (1985), is the difficulty that researchers face in predicting individual risk for disease. In our simulations, despite modeling a well-established risk factor for hypertension, the act of reducing the prevalence of individual exposure to smoking did little to reduce the overall population mean SBP, especially when primary prevention was attempted for the entire population. As the underlying risk in the high-risk sample increased, so too did the efficacy of the primary prevention strategies. It seems that the effect of the high-risk primary prevention strategy was driven mainly by decreases within the low-income cases. The efficacy of these strategies for an individual is limited by our ability to predict the individual risk of disease, which is in part dependent on the prevalence of the causal factors that interact with that exposure to cause disease. In other words, the magnitude of the effect of an exposure on disease is dependent on the prevalence of the causal factors that interact with that exposure. Though we are able to predict health in populations with much more certainty than we can predict health in individuals, we will improve our ability to predict an individual\'s risk for disease by understanding how multiple risk factors interact to cause disease. Our predictive ability might also increase by defining our high-risk population in more narrow terms (e.g., individuals “exposed” to both smoking and low-income status), but do so knowing that the absolute number of cases we can prevent will likely decrease as the population becomes smaller. For example, the primary prevention strategy decreased hypertension among low-income individuals by 30.7% vs. 23.6% in the general population. However, the general population in this example was comprised of 3393 individuals, so a decrease of 23.6% prevented 171 cases of hypertension, while the low-income population is comprised of 1358 individuals, so a decrease of 30.7% prevented only 70 cases of hypertension. Additionally, high-risk strategies imply that high-risk individuals must be identified and consent to participating in an intervention, both of which may be difficult and expensive.