ELAINE HERNANDEZ
Indiana University

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WHERE AND HOW WE INTERVENE to shape health inequalities are important questions. On one hand, policy interventions could restructure systems that generate unequal access to resources, for example the educational system, theoretically altering educational inequalities in health (Hummer and Hernandez 2013). These approaches yield important benefits, particularly if applied to children early in the life course (Zajacova and Lawrence 2018). On the other hand, upstream policy interventions could neutralize individuals’ abilities to use their resources by implementing a policy that targets population health regardless of socioeconomic position. My objective is to use two empirical examples to demonstrate how policies that target groups uniformly—regardless of socioeconomic, racial, or gender differences—hold the potential to ameliorate or even prevent health inequalities. 

 

Understanding how policies affect health inequalities at the population level necessitates the use of large datasets, yet it is important not to overlook the value of studying micro-level interactions. For instance, health care providers are a primary conduit for new health information and they are integral for the translation of new medical knowledge. Depending on how they implement new health protocols at the clinic level, they may play a role in the formation of health inequalities.

To understand the intricacies of this process, I enrolled prima gravida women (pregnant for the first time) in four clinics to participate in a study. Nearing the end of their pregnancies, I selected a stratified random sample to participate in in-depth interviews, and conducted in-depth interviews with their health care providers (nurses and physicians). In the early 2010s, two new prenatal supplements had emerged as topics of conversation among patients and their providers: omega-3 fatty acid (O3FA) and vitamin D. For pregnant women, the O3FA supplement represented the most up-to-date way to ensure that they were doing everything possible to promote brain and eye development of their fetus. Unsurprisingly, women with the highest levels of education were most informed about and most inclined to use the supplement. Providers acted in concert, explaining that they would discuss the O3FA supplement with women who they felt could (1) understand the information and (2) afford to purchase it.

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The protocol for vitamin D supplementation offered a direct contrast to O3FA supplementation. One of the women’s clinics implemented a universal vitamin D screening protocol for pregnant women. Unlike the O3FA supplement, every patient had their vitamin D levels tested in early pregnancy and were prescribed vitamin D if their levels were low (prescribed medications were covered by Medicaid). In-depth interviews with patients and providers revealed how they dedicated little time to researching vitamin D or evaluating whether women could afford the supplement, and women were treated alike.

What can we learn about health inequalities from this example? First, it offers evidence that the uniformity of providers’ recommendations, as well as individual differences between patients, contribute to (or prevent) educational differences in health behaviors. Second, it emphasizes the need to understand how the dissemination of new health information through clinic-level protocol and patient-provider behavior is associated with the formation of health disparities. Finally, the findings underscore the role of institutional level changes (i.e., clinic-level protocol) in preventing unequal (1) provider recommendations and (2) patient health behaviors, and, potentially, (3) health outcomes.

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Understanding how clinic level protocols, or lack thereof, diffuse at the patient-provider level offers a foundation to examine whether other universal policies have the potential to attenuate or prevent health inequalities. Prior examples suggest that upstream policy interventions narrow gaps in socioeconomic, racial, or gender health inequalities. In the U.S., examples occur within different geographic or institutional boundaries (i.e., national, state, city) or within specific institutions such as health care clinics or schools. After implementation, universal policies or interventions reduced disparities across various outcomes: cereal fortification reduced inequalities in folate status (Dowd and Aiello 2008), and mandatory seat belt laws decreased inequalities in seat belt use (Harper et al. 2014), much like I found in clinics (Hernandez 2013). For smoking specifically, prior research found that smoking bans narrowed gender differences in smoking (Vuolo, Kadowaki, and Kelly 2016). The unequal diffusion of medical innovation provides another vantage point to estimate whether policies that apply universally affect health inequalities (Capewell and Graham 2010). In these instances, the equitable implementation of best-practice interventions across groups hold the potential to eliminate differences in outcomes such as coronary heart disease (Kivimäki et al. 2008) and colorectal cancer (Clouston et al. 2016).

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To test whether universal policies affect the magnitude of specific health inequalities, colleagues and I used tobacco clean air acts (i.e., smoking bans) to examine educational inequalities in smoking (Hernandez, Vuolo, Frizzell, and Kelly 2019). Rather than attempting to motivate behavioral change at the individual level—for example through educational campaigns about the risks of smoking—smoking bans “move upstream” by equitably limiting the spaces where people can smoke. This approach counteracts the capacity for some to employ individual flexible resources to their advantage more than others (McLaren et al. 2010).  

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We anticipated two potential results. On one hand, for the aforementioned reasons smoking bans may narrow educational gradients in smoking. However, we were also wary that they could exacerbate these inequalities. Although smoking bans uniformly restrict everyone from smoking in certain spaces–such as bars or restaurants–they intensify the denormalization of smoking by symbolically signaling that smoking is abnormal (Kelly 2009; Kelly et al. 2018; Stuber et al. 2008). Smokers must segregate themselves from others, typically outside of social spaces. Public health policymakers strategically use smoking bans to alter injunctive norms around smoking (Cialdini et al. 1990) and normative beliefs about whether others approve or disapprove of it (Bell et al. 2010). Yet, current or former smokers with higher levels of education are more susceptible to smoker-related stigma (Stuber et al. 2008). Thus, denormalization may have influenced behavior unequally across educational groups, motivating well-educated people to quit at higher rates.

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Using data from the National Longitudinal Survey of Youth 1997 (NLSY97), a large, nationally representative cohort study, we employed within-person analyses to examine whether exposure to smoking bans at the city-level changed the association between education and smoking. Importantly, we examine smoking rates during young adulthood, a key point in the life course when smoking habits form and begin to contribute to broader inequalities in health and mortality (Pampel et al. 2014; Hummer and Hernandez 2013). If people are going to change their smoking habits—a highly addictive behavior—they are more likely to do so earlier in the life course.

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Did smoking bans affect educational gradients in smoking? We found no evidence that the denormalization of smoking exacerbated educational gradients in smoking. We find that living in a city that implemented a smoking ban prompted greater reductions in smoking among young adults with the lowest parental or individual educational attainment, resulted in a narrowing of the educational-smoking gradient (see figure). This result remained after accounting for a wide array of individual and city-level characteristics. The consistency of these findings, supports the notion that smoking bans bypass individual resources, resulting in narrower educational inequalities in smoking. They also assuage concerns that denormalization accentuates educational inequalities in smoking.