A Public Health of Consequence: Let’s Ask About Who Benefits

A public health of consequence calls for research that generates knowledge about the types of interventions that improve population health.¹ In parallel, a public health of consequence also should call for research to examine whether interventions benefit everyone equally and to identify the structural factors that constrain subgroups from benefitting from such interventions. Such a framework will help us better understand how interventions can most effectively ensure equitable access to health for all.

Building on the work of Geoffrey Rose,^2,3 a particular emphasis of consequentialist public health is placed on the power of universal prevention strategies to shift the distribution of health in populations. Based on the assumption that most cases of disease arise from the sector of the population that has an average level of risk, and not from the small sector of the population at high risk, intervening on the whole population is expected to improve the distribution of risk for all, generating the greatest improvement in population health. Hence, evaluating the average causal effect that universal social and health policies have on population health is a central component of a public health of consequence.

A focus on the average causal effect of universal policies is unarguably a key mandate of public health, but two critical pieces of the puzzle are often missing—questions about who benefits from universal policies and what effect such policies have on social inequalities in health. Because policies are often implemented within contexts in which structural factors give rise to different distributions of risk across social groups, the benefits of such policies also tend to follow the social distribution of risk.^4,5 Those social groups with the lowest initial levels of risk are also the ones to derive the greatest benefit from interventions.

We illustrated this point with a simulation model: we tested a hypothetical intervention to improve collective efficacy across all New York City neighborhoods as a way to reduce rates of violent victimization.⁶ Although such an intervention could indeed reduce overall rates of victimization, it did nothing to shift Black and White inequalities in victimization. The only way to shift such inequalities was to shift patterns of racial residential segregation—that is, those structural factors that gave rise to the differential distributions of victimization risk across Black and White people. This study suggested that to improve health and reduce inequalities, joint investment in health policies that promoted specific public health improvements (i.e., universal violence prevention interventions) and social policies that weakened socioeconomic differences in access to public health improvements (i.e., redistributive tax policies, fair housing policies) was necessary.

This point is not only a hypothetical one. Two examples from drug policy illustrate the types of issues that can arise when universal policies are implemented in a social vacuum. The first example relates to the current opioid epidemic. Fatal overdoses related to drug use are the leading cause of injury death in the United States, largely as a result of prescription opioid overdoses. Prescription drug monitoring programs, state-level databases to which pharmacy dispensers and medical providers report prescription information when certain medications are prescribed and dispensed, have been advanced as tools to reduce prescription opioid–related harm. Prescription drug monitoring programs may benefit high-income populations, who tend to access prescription opioids through their medical providers and who can afford to access evidence-based treatment referrals for opioid dependence. Low-income groups, by contrast, are less likely to access prescription opioids through medical channels, and they often cannot afford treatment. When the prescription opioid supply is regulated but no concurrent investment is made in providing equitable access to treatment, low-income prescription opioid users may start using heroin, which is cheaper and more available. This can exacerbate the problem of opioid abuse in the most affected group rather than improving it as the policy intended.

The second example relates to marijuana legalization. Since 1996, 29 states and the District of Columbia have legalized marijuana in some form. One of the stated purposes of marijuana legalization was to reduce racial/ethnic inequalities in drug arrest rates. Yet arrest statistics in Colorado since recreational marijuana use was legalized in 2012 suggest that this has not occurred.⁷ Black individuals were twice as likely to be arrested for marijuana before legalization, but they are three times as likely to be arrested following legalization. Among juveniles, the rates are particularly stark: White juvenile arrests declined by 9% from 2012 to 2014, whereas arrests increased by 52% among Black juveniles.⁷ Other states have seen a similar trend following marijuana-related legal reform—a decline in overall marijuana arrests but no change or an increase in racial/ethnic inequalities in arrests. Reasons for this are unclear but may be related to racial/ethnic differences in enforcement of drug-related offenses, as well as racial/ethnic differences in access to the legal marijuana market.

These examples illustrate the importance of considering the social context in which universal policies are implemented. If universal policies are enacted in a social vacuum—that is, without asking questions about who benefits from such policies and without accounting for the fundamental drivers of health that might constrain policy benefits in certain social groups—universal policies may indeed result in the greatest improvement in overall health, but they may do nothing to shift social inequalities in health or may even exacerbate such inequalities.

So how can we hold our research accountable to a public health of consequence? A public health of consequence cares about both average and heterogeneous effects of universal policies and holds the effect of universal policies on health inequalities as a central concern.

In the previously discussed first example on drug policy, we can ask not only “What is the average effect of implementing prescription drug monitoring programs on opioid-related harm?” but also “Who benefits the most from a universal policy such as prescription drug monitoring programs?”; “Who benefits the least, and what structural factors modify the benefit different groups can draw from prescription drug monitoring programs?”; and “What kinds of supportive interventions do we need to carry out in conjunction with the universal prescription drug monitoring program policy, to ensure that the needs of vulnerable groups are adequately addressed?” Furthermore, and particularly in the case of policies that partly aim to reduce social inequalities, we can ask “To what extent did implementation of the policy shift the distribution of social inequalities in health?” and “If social inequalities did not change, what role did underlying, structural drivers of health play in this?”

By asking such questions, our work as public health researchers has the potential to inform policymakers about the types of policies that can produce the greatest population health effect and about the types of structural factors we need to address to ensure that the benefit of universal policies is truly universal.