摘要：Objectives. As a case study of the impact of universal versus targeted interventions on population health and health inequalities, we used simulations to examine (1) whether universal or targeted manipulations of collective efficacy better reduced population-level rates and racial/ethnic inequalities in violent victimization; and (2) whether experiments reduced disparities without addressing fundamental causes. Methods. We applied agent-based simulation techniques to the specific example of an intervention on neighborhood collective efficacy to reduce population-level rates and racial/ethnic inequalities in violent victimization. The agent population consisted of 4000 individuals aged 18 years and older with sociodemographic characteristics assigned to match distributions of the adult population in New York City according to the 2000 US Census. Results. Universal experiments reduced rates of victimization more than targeted experiments. However, neither experiment reduced inequalities. To reduce inequalities, it was necessary to eliminate racial/ethnic residential segregation. Conclusions. These simulations support the use of universal intervention but suggest that it is not possible to address inequalities in health without first addressing fundamental causes. The work of Geoffrey Rose transformed our conception of public health prevention efforts. Rose introduced the notion of a universal strategy of prevention, which targets a whole population regardless of variation in individuals’ risk status. 1,2 This strategy is grounded on 2 important assumptions: (1) the distribution of risk in a population is shaped by contextual conditions that differ between populations, and (2) most cases arise from the large population with only an average level of risk, rather than from the small population at high risk. 1,2 Although each individual at average risk has a low probability of disease incidence, so many are exposed that the number of cases arising from this group is large. Thus, intervening on the entire population improves the risk distribution for all, resulting in the most effective improvement in population health. Rose differentiated such a universal strategy from the targeted strategy, which dominates much of biomedicine to this day. The targeted strategy identifies and intervenes on individuals with high disease risk. This strategy is appropriate to the individuals treated, as it is tailored to their specific risk factors. However, because it does not deal with the root of the problem by shifting the population risk distribution, a targeted strategy must continue indefinitely treating those at highest risk. 3 Rose’s strategy of universal intervention has been criticized for not addressing the structural factors that lead to different distributions of risk between social groups, such that those with the lowest initial level of risk are the first to derive benefits from universal interventions, potentially exacerbating health inequalities. 4–6 This has been seen in interventions in areas such as smoking prevention, smoking cessation, cervical cancer screening, and neonatal intensive care whereby a universal intervention was associated with attendant widening of intergroup differences in health. 7–9 Such a view is consistent with fundamental cause theory, which argues that higher social status, as indexed by knowledge, money, power, social connectedness, and prestige is always associated with better access to resources that optimize health, even though health and its predictors may change with time. 10–12 Hence, an intervention may shift the mean distribution of disease, but if the intervention fails to address the underlying economic and political forces that lead to a different risk exposure across social groups, those with more resources (and thus lower initial risk) will benefit more from the intervention so that inequalities may increase with the intervention. Questions about the effect of universal versus targeted prevention strategies on population health and health inequalities, and the role that fundamental causes play in population health, are critical to the articulation of effective public health planning strategies. Although an energetic debate exists about the potential merits and shortcomings of targeted versus universal interventions, 4,13–15 we are not aware of any empirical tests that examine the impact of universal versus targeted public health interventions on both population-level rates of disease and inequalities in disease. We aimed to fill this gap by quantifying the impact of universal and targeted interventions on both population health and health inequalities and testing whether it was possible for interventions to effectively address population health and health inequalities without addressing fundamental causes of health. Empirical testing of these questions would require large-scale population-based experiments that manipulate social exposures. Such experiments are prohibitively expensive or logistically impossible to implement. We instead addressed these questions through the use of agent-based simulation modeling that allowed us to simulate large populations in silico. We used a case study to test the impact of universal versus targeted interventions on population health and health inequalities: manipulating collective efficacy to reduce both population-level rates and racial/ethnic inequalities in violent victimization. The concept of collective efficacy arises from social disorganization theory and involves the ability of community residents to collectively harness resources and effectively respond to negative situations for the benefit of the community (informal social control), combined with the degree to which community residents mutually trust and respect each other (social cohesion). 16 Collective efficacy has been consistently associated with reduced neighborhood victimization across observational studies in the United States and other countries. 16–21 Interventions are currently under way in cities across the United States and other countries to mobilize collective efficacy as a way to improve public health. 22–26 We used collective efficacy and victimization for our case study because the focus of intervention (i.e., collective efficacy) and the health indicator (i.e., violent victimization) are socially distributed, and the role of fundamental causes of health is particularly relevant in this case. Collective efficacy arises in more stable, less economically disadvantaged neighborhoods. 16,17,27,28 Victimization, in turn, is racially and economically patterned: in 1980–2008, Blacks were disproportionately represented as homicide victims and offenders. They were 6 times more likely to die from homicide than were Whites, and the offending rate was 8 times higher among Blacks than among Whites. 29 An important determinant of the elevated rates of homicide among Blacks is the disproportionate segregation of Blacks into economically disadvantaged neighborhoods, 30–36 where there are lower levels of protective social processes such as collective efficacy as well as exposure to multiple other risk factors for violent victimization. 37,38 Hence, racial residential segregation is a fundamental cause of violent victimization as well as multiple other correlated health-related problems. 37 We used in silico experiments that capitalize on innovative complex systems approaches to answer 2 major questions: (1) what is the comparative impact of universal versus targeted experimental manipulations of collective efficacy on population-level rates of violent victimization and on Black–White inequalities in victimization? and (2) when the level of racial residential segregation is altered, does the impact of collective efficacy on population-level rates of violent victimization and of Black–White inequalities in victimization change? We used agent-based modeling (ABM) to simulate a series of in silico neighborhood experiments. Because ABMs consist of simulations that follow prescribed rules about the characteristics of agents, their networks, contexts, and behaviors, investigators can simulate scenarios in which only 1 aspect of the initial conditions is changed, thus allowing us to conduct counterfactual neighborhood policy “experiments” without issues of resource costs or ethical concerns. These in silico experiments can serve as a first step to build the evidence base on tractable interventions that can then be tested in community-randomized trials.
