By contrast population strategies focus on universal strateg
By contrast, gonadotropin releasing hormone receptor strategies focus on universal strategies that affect all individuals, regardless of baseline risk of developing disease. This universality also has advantages and disadvantages. A population strategy typically seeks to decrease population-level risk of disease, which is why vaccination programs strive for herd immunity by maximizing population coverage of vaccines. At the individual level, most individuals would never get the disease with or without the vaccination. Rose referred to this as the “prevention paradox” (Rose, 1985, p. 432). This can make the implementation of a population-level intervention very difficult, especially in the short-term. Indeed, recent calls to increase the tax on sugar sweetened beverages in New York City were met with staunch resistance and ultimately defeated, in part because opponents believed that beverage choice was an individual decision, and the risk conferred by high fructose corn syrup is not perceived as worth the increased cost by the individual consumer (Brownell et al., 2009; Gollust, Barry, & Niederdeppe, 2014). When they do succeed, however, population strategies can be powerfully sustainable drivers of healthy behavior change. For example, workplace smoking bans have been shown to encourage smokers to quit or to reduce tobacco consumption (Fichtenberg & Glantz, 2002). Further, it is important to consider the differences in effects of primary vs. secondary prevention, both in terms of equity, cost-effectiveness, and timing (McLeod, Blakely, Kvizhinadze, & Harris, 2014). Primary prevention programs may be more cost-effective in the long-term as individuals are prevented from ever getting disease. Since secondary prevention programs seek to treat those with disease, it may seem as though they are more cost-effective in the shorter-term. The impact on health equity may also vary by strategy, and the most equitable interventions likely include a combination of primary prevention supplemented with secondary treatment (Blakely et al., 2015). There are several limitations to these simulations. First, the parameters we set for our strategies and their impact were likely based on overly optimistic assumptions. In reality, the targets of such interventions would likely be more modest. As such, we are presenting the maximum impact of intervention rather than the actualized impact. Also, we assumed that the changes would be permanent, namely that no smokers would relapse. Further, we lowered the threshold for high SBP and removed outliers, thus results may not generalize to the broader population, or to individuals with high SBP diagnosed in clinical care. Overall, while our approach led to a simplification of individual\'s health behavior, our goal was to contrast different strategies under an explicit set of assumptions. We believe that the results of these comparisons should serve to further stimulate discussion about the differing impacts of health interventions. Additionally, part of the decision to impute smoker\'s SBP independent of their baseline SBP was to account for some of the unintended consequences of removing this exposure; for some individuals quitting smoking may lead to other health outcomes, which themselves may be risk factors for hypertension (e.g., weight gain (Williamson et al., 1991)). Also, part of the decrease in SD from the interventions was due to the use of mean imputation (Donders, van der Heijden, Stijnen, & Moons, 2006). These methods could be improved with the integration of multiple imputation techniques for modeling a more realistic impact of these interventions in the population. While many of the assumptions and parameters we presented are subject to debate, the purpose of this paper was to illustrate what questions and considerations might be necessary for practitioners to address as they begin to plan an intervention. Finally, the analysis did not account for financial considerations of these strategies. Cost-effectiveness is a significant consideration in planning health interventions, and is often what gets the most attention in these types of analyses. We argue that questions about maximizing both efficiency and equity should also be core considerations that inform how we may develop and implement population health interventions. In fact, a consideration of intervention effects within various population subgroups should be included in cost-effectiveness models (McLeod et al., 2014). It is critical to consider the impact of an intervention on health equity, including a thoughtful and transparent consideration of when subgroup differences in health indicators (e.g., life expectancy) may be appropriate to use, and when differences may reflect the consequences of discrimination. In addition, there are numerous other decisions that policy developers must make as they consider evidence to inform interventions, each of which rests on value-laden assumptions. For example, choosing to measure relative vs. absolute health inequalities can lead to very different interpretations and implication (Mechanic, 2002). Also influential is the use and estimation of statistical weights to represent those population sub-groups in large population surveys (Pearcy & Keppel, 2002).