Government-Driven Incentives to Improve Health
Summary and Keywords
Worldwide, key behavioral risk factors for ill-health and premature death include smoking, alcohol, too much or too little of several dietary factors, and low physical activity. At least three structural factors (biological attributes and functions, population size and structure, and wealth and income disparities) modify the global impact that the risk factors have on health; without accounting for these structural drivers, the effect of government-driven incentives to act on the behavioral risk factors for improved health will be suboptimal. The risk factors and their impact on health are further driven by malleable circumstantial drivers, including technological developments, exposure to products, social influences and attitudes, and potency of products. Government-driven incentives, which can be both positive and negative, can act on the circumstantial drivers and can impact on the behavioral risk factors to improve or worsen health. Government-driven incentives include a range of policies and measures: policies that reduce exposure; regulation of the private sector; research and development to reduce potency; resource allocation for advice and treatment; direct incentives on individual behavior; and, managing co-benefits and adverse side effects. Within a framework of government-driven whole-of-society approaches to improve health, an accountability system is needed to identify who or what causes what harm to health to whom. A health footprint, modeled on the carbon footprint is proposed as the accounting system.
Governments have a role to protect and promote the health of the people. This role is well summed by then mayor Michael Bloomberg’s address to the United Nations General Assembly High Level Meeting on Non-Communicable Diseases in 2011 (Bloomberg, 2011), in which he said in part:
First, we’ve learned that changing the social and physical environment is far more effective than changing individual behavior alone. Making workplaces and places of entertainment smoke-free; reconfiguring city streets to make them safer; creating ways for consumers to find healthy food. Such social and physical changes that make the healthiest route are also the ones easiest to follow.
Second, and very importantly in today’s world, healthy solutions are not necessarily costly solutions. Far from it. New York’s smoke-free air act, our restrictions on trans fats, and our requirements concerning calorie posting in restaurants cost virtually nothing in public funds to implement. And raising cigarette taxes raises public revenues.
Third, collaboration with the private sector—as in the national salt reduction initiative—and with non-government organizations—as in traffic safety efforts worldwide—are very important. Collaboration across borders, among national and local governments and agencies, is also critical. The challenges before us are too vast and complex for individual governments to overcome alone.
But fourth and finally, while government action is not sufficient alone, it is nevertheless absolutely essential. There are powers only governments can exercise, policies only governments can mandate and enforce, and results only governments can achieve. To halt the worldwide epidemic of noncommunicable diseases, governments at all levels must make healthy solutions the default social option.
That is, ultimately, government’s highest duty. And one of the spiritual founders of the United Nations—America’s Franklin Delano Roosevelt—once put it: “The state’s paramount concern should be the health of its people.’ So why don’t we all resolve to renew our efforts now to address the worldwide crisis of non-communicable disease, and bring better health, and greater hope, to all the people of our good Earth.”
In implementing government-driven incentives, governments have a spectrum of options, ranging from prohibition to doing nothing (Nuffield Council on Bioethics, 2007), notably:
Eliminate choice. Regulate in such a way as to eliminate choice entirely—for example, prohibiting products.
Restrict choice. Regulate in such a way as to restrict the options available to people with the aim of protecting them—for example, removing unhealthy ingredients from foods,- or removing unhealthy foods from shops or restaurants.
Guide choice through disincentives. Put fiscal and other disincentives in place to influence people not to pursue certain activities—for example, imposing taxes on cigarettes.
Guide choices through incentives. Offer regulations that guide choices by fiscal and other incentives—for example, offering tax breaks for the purchase of bicycles that are used as a means of traveling to work.
Guide choices through changing the default policy. Make changes in menus—for example, in a restaurant, instead of providing chips as a standard side dish (with healthier options available), changing menus to provide a healthier option as standard (with chips as an option available).
Enable choice. Enable individuals to change their behaviors—for example, offering participation in “stop smoking” programs, building cycle lanes, or providing free fruit in schools.
Provide information. Inform and educate the public—for example, encouraging people to walk more or to eat more fruit and vegetables every day.
Do nothing or simply monitor the current situation. Doing nothing is, of course, doing something—nothing, which can be a cause of ill-health and premature death.
The structure for this discussion is the drivers’ diagram shown in Figure 1. At the center of the diagram is the health footprint, the accounting system for identifying the determinants of the behavioral risk factor-related harm and the management tool to evaluate opportunities by the public and private sectors and civil society to reduce harm. The health footprint measures the impact of a range of structural and circumstantial drivers of impaired health and the policies and measures that affect them, thus accounting for who and what causes the harm done by behavioral risk factors. The largest contributors are both the private sector through the products and services they provide and governments at all levels through the policies and programs they implement or do not implement.
The footprint can be operationalized by disability-adjusted life years (DALYs)—a summary measure of years lost due to ill-health and premature death (Ezzati et al., 2004). Worldwide, over the period 1990–2015, noncommunicable diseases (NCDs), including cancers, cardiovascular diseases, and mental and behavioral disorders, surpassed communicable, maternal, neonatal and nutritional diseases (Group 1 causes) as the preeminent cause of DALYs (GBD 2015 DALYs and HALE Collaborators, 2016). Group 1 causes fell from 1.2 billion DALYs in 1990 to 742 million in 2015. NCDs increased from 1.1 billion DALYs in 1990 to 1.5 billion in 2015, responsible for 60% of all DALYs. Injury DALYs remained stable over this time, accounting for about 10% of all DALYs.
Behind the DALYs are behavioral, environmental, and metabolic risk factors (GBD 2015 Risk Factors Collaborators, 2016). In rank order, the top ten risk factors for 2015, by million DALYs, were:
1. high systolic blood pressure (212)
2. smoking (149)
3. high fasting plasma glucose (143)
4. high body mass index (120)
5. childhood undernutrition (113)
6. ambient particulate matter (103)
7. high total cholesterol (89)
8. household air pollution (86)
9. alcohol use (85)
10. high sodium (83).
The Global Burden of Disease (GBD) assessments break diet down into individual components, including high sodium (ranked 10th), low whole grains (11th), low fruit (13th), low nuts and seeds (17th), low vegetables (20th), low omega-3 (23rd), high processed meat (27th), and high trans-fat (30th). When all dietary components are considered together, dietary risks rank as the first risk factor, with absolute number of diet-related DALYs increasing by 8.9% in absolute terms and by 2.1% per person over the period 2005–2015. Low physical activity is ranked 21st. The metabolic risk factors (high systolic blood pressure, high fasting plasma glucose, high body mass index (BMI), and high total cholesterol) are, of course, impacted by the behavioral risk factors. The present discussion focuses on the four main behavioral risk factors—alcohol, diet, physical activity, and smoking—with emphasis given to alcohol and smoking to illustrate many of the themes.
Structural drivers of harm include biological attributes and functions, population size and structure, and levels of wealth and income disparities within jurisdictions. The harm done by behavioral risk factors can be decomposed by population size (number of people), population structure (sex and age), and socioeconomic development. All the structural drivers determine the size of the health footprint, which, in turn, is influenced by biological attributes and functions (e.g., genes and “hard-wiring” in the brain).
Circumstantial drivers refer to the processes, mechanisms, and characteristics that influence harm, sometimes through the structural drivers and sometimes not. Circumstantial drivers include risk factor potency and exposure, and the technological developments that might influence them, as well as social influences and attitudes. Included in the policies and measures level are policies that reduce risk factor exposure (including low physical activity); actions that promote research and development to reduce risk factor potency; the co-benefits and adverse side effects of policies and measures; incentives for healthy individual behavior; resource allocation for advice and treatment; and regulation of the private sector. Policies and measures affect the circumstantial drivers. The structural and circumstantial drivers may, in turn, influence policies and measures.
Structural Drivers of Harm
Structural drivers are those demographic and biological determinants that governments need to account for when setting incentives for improved health.
Population Size and Structure
A different picture is portrayed depending on whether one presents changes in the absolute number of behavioral risk-related DALYs over time or changes in the age standardized DALY rate over time (GBD 2015 Risk Factors Collaborators, 2016):
• For smoking, the absolute number of smoking-related DALYs increased by 16.9% during the years 1990–2005 and by 1.0% during 2005–2015, whereas age standardized DALYs decreased by 17.7% during 1990–2005 and by 21.3% during 2005–2015.
• For alcohol, the absolute number of DALYs increased by 28.6% during the years 1990–2005 and decreased by 1.2% during 2005–2015, whereas age-standardized DALYs decreased by 4.7% during 1990–2005 and by 17.9% during 2005–2015.
• For high BMI (taken as a surrogate metabolic marker of diet), the absolute number of DALYs increased by 54.7% during the years 1990–2005 and by 22.0% during 2005–2015, whereas age-standardized DALYs increased by 8.4% during 1990–2005 and decreased by 4.9% during 2005–2015.
• For low physical activity, the absolute number of DALYs increased by 32.0% during the years 1990–2005 and by 17.4% during 2005–2015, whereas age standardized DALYs decreased by 8.3% during 1990–2005 and by 9.6% during 2005–2015.
Thus, while there might be some improvements over time per person (as measured by age-standardized rates), such improvements are wiped out at a global level by changes due to population ageing and population growth, which for the above four risk factors are roughly equal causes for the increases in the absolute number of DALYs. (The exception is alcohol, for which changes in population size are a larger structural driver than changes in population ageing.) Of course, there are other important drivers of changes in the absolute number of DALYs, including changes in the patterns of diseases on which the risks operate and changes in risk factor exposure.
The implication of the structural drivers of population ageing and of population growth (without direct policies to change them) is that government-driven incentives need to take them into account when planning to influence the health impact of risk factors. An illustrative example is smoking and the impact of the Framework Convention on Tobacco Control (FCTC). Although the proportion of the population who are smoking has decreased significantly over recent years, with the growing global population the actual number of current daily smokers has increased and will continue to increase (Ng et al., 2014). This increase highlights the need to persist in the development and optimization of interventions targeting tobacco cessation through the FCTC. In general, as populations age and grow, there is a greater need to impact exposure, the more direct and malleable circumstantial driver amenable to change.
Levels of Wealth and Income Disparities
Levels of wealth and income drive harm through two mechanisms: influencing risk factor exposure and influencing harm at the same level of exposure.
The GBD studies have derived indices of exposure and indices of socioeconomic development (GBD 2015 Risk Factors Collaborators, 2016). Exposure is estimated differently for each risk factor and presented as a summary exposure value (SEV), which ranges from 0% (no risk exposure in a population) to 100% (entire population has maximum possible risk).
• For smoking, SEV is a combination of the proportion of the population with cumulative exposure to tobacco smoking and the proportion of the population who currently smoke. For smoking, age-standardized SEV dropped from 29.0% in 1990 to 21.0% in 2015 for men and from 8.7% to 6.2% for women.
• For alcohol, SEV is a combination of average daily alcohol consumption of pure alcohol (measured in g/day) in current drinkers who had consumed alcohol during the past 12 months, and the proportion of the population reporting binge consumption of at least 60 g for males and 48 g for females of pure alcohol on a single occasion. For alcohol, SEV remained stable for men (from 10.9% in 1990 to 10.7% in 2015) but dropped from 5.9% to 5.1% for women.
• For physical activity, the standard measurement is average metabolic equivalent minutes (MET) per week of total activity at work, at home, transport related, and recreational. For low physical activity, SEV remained stable for both men (from 45.3% in 1990 to 46.3% in 2015) and women (from 39.9% to 39.4%).
• The SEV for BMI is measured in kg/m2. For high BMI, SEV increased from 3.6% in 1990 to 5.0% in 2015 for men and from 4.6% to 6.2% for women.
The sociodemographic index (SDI) is based on estimates of lag-dependent income per capita, average educational attainment over the age of 15 years, and total fertility rate, scaled from zero to one. The relationship between SDI and SEV varies by risk factor.
• For high BMI, for the world and for all regions of countries, the higher the SDI, the higher the SEV.
• For alcohol, a similar pattern emerges, except within the region of highest income countries, in which there is a tendency for SEV to decrease as SDI increases.
• For smoking, for the world as SDI increases, SEV increases. However, within all regions of countries, as SDI goes up, SEV goes down.
• [Data for low physical activity was not presented in the GBD results].
The implication for government incentives is clear. As SDI increases in low SDI countries and regions, SEV will increase. Therefore, government incentives need to be stepped up to account for the SDI changes.
Within countries, health outcomes are driven by inequalities, with, for example, people with lower incomes worse off than people with higher incomes (Wilkinson & Pickett, 2009). Further, for the same given level of exposure, poorer people experience poorer health. For example, for the same amount of alcohol consumed, people with lower incomes have higher alcohol-related deaths than people with higher incomes (Room et al., 2015). The implication for government incentives is twofold: policies that reduce income inequalities will reduce risk-factor-related DALYs, independent of changes in exposure; and policies that reduce exposure will reduce health inequalities.
Biological Attributes and Functions
Understanding human evolutionary behavior and the common mismatch between the way we run our lives in present times and the way our lives were run in the environment in which we evolved can provide better pointers as to what needs to be done to reduce ill-health and premature death from noncommunicable diseases (Lieberman, 2013).
When environmental circumstances change, previously beneficial phenotypes can become mismatched with our genetic heritage (Greaves, 2014). With breast cancer, amenorrhea was the norm in the environment in which humans evolved, with early pregnancy and many years of lactation. In present-day society, delayed first pregnancy and minimal breast feeding, powerful risk factors for breast cancer, has become the norm (Anderson, 1983). With respect to physical activity, presently we live largely in non-moving (walking, running) societies—yet, humans were designed by evolution to move (Lieberman et al., 2009, 2011). In fact, the human foot is exquisitely designed for running, so much so that, together with specialized muscle function in humans, a fit long-distance runner can outpace a horse over long distances (Bramble & Lieberman, 2004).
More broadly, an evolutionary understanding of behavior recognizes that human behavior evolved to provide responses that were adaptive in the environment in which we evolved. In other words, behavior is embodied and situated within environments (Aunger & Curtis, 2015). Importantly for behavior change, factors other than cognition are considered active and present in the moment of behavior, having independent causal influence on activity (Aunger & Curtis, 2016). In this sense, the physical and social environment has its own structure, playing a formative role in behavior production, rather than being a set of barriers. The consequences of this approach include the recognition that campaigns that emphasize enhancing knowledge or altering attitudes through education have little effect on behavior, since behavior is more readily influenced by automatic (e.g., habitual) processes and environmental factors.
Biological attributes and functions can predispose us to certain behaviors, as well as impact exposure and harm, independent of exposure. Examples of predisposition are hard-wiring that predisposes humans to seek out alcohol and nicotine. An example of impacting harm is variation in genes that affect alcohol dehydrogenase, the enzyme that metabolizes ingested alcohol to acetaldehyde.
Hard-Wiring that Seeks out Nicotine and Alcohol
The idea that human exposure to nicotine and alcohol did not occur until late in human evolution—thus leaving our species inexperienced—is often posited as one of the reasons that these substances cause so much harm (Sullivan & Hagen, 2002, 2015). However, evidence suggests otherwise. Many substances consumed today are not evolutionary novelties (Sullivan & Hagen, 2015; Dudley, 2014). In the story of terrestrial life over the last 400 million years or so, one ongoing theme has been the “battle” between plants and the animals that eat them. Of many defense mechanisms, plants produce numerous chemicals, including nicotine, all of which are potent neurotoxins that deter consumption of plant tissues by animals (Sullivan & Hagen, 2015). From an evolutionary perspective, we thus find natural selection for compounds that discourage consumption of the plant via punishment of potential consumers. By contrast, there has been no natural selection for expression of psychoactive compounds that encourage consumption (Sullivan et al., 2008).
Counterbalancing the development of plant neurotoxins, though, plant-eating animals have evolved to counterexploit the plants’ production of drugs, including exploiting the antiparasitic properties of some drugs (Sullivan & Hagen, 2015). In a human context, present-day examples of pharmacophagy may be seen with Congo Basin hunter-gatherers, among whom the quantity of nicotine consumed is titrated against intestinal worm burden—the higher the intake, the lower the worm burden. In individuals treated with the anti-worm drug abendazole, the number of nicotine-containing cigarettes smoked is reduced (Roulette et al., 2014). (The same is also true for cannabis; see Roulette et al., 2016).
In the case of ethanol, its presence within ripe fruit suggests low-level but chronic dietary exposure for all fruit-eating animals, with volatilized alcohols potentially serving in olfactory localization of nutritional resources (Dudley, 2014). Molecular evolutionary studies indicate that our human ancestors modified an oral digestive enzyme capable of rapidly metabolizing ethanol near the time that they began extensively using the forest floor, about 10 million years ago (Carrigan et al., 2015). By contrast, the same alcohol dehydrogenase in our more ancient and mostly tree-dwelling ancestors did not oxidize ethanol as efficiently; humans have retained the fast-acting enzyme to this day. This evolutionary switch suggests that exposure to dietary sources of ethanol became more common as hominids adapted to bipedal life on the ground. Ripe fruits accumulating on the forest floor could provide substantially more ethanol cues and result in greater caloric gain relative to fruits ripening within the forest canopy (Dudley, 2014).
This evolutionary evidence does not imply that humans evolved to specifically consume nicotine, for example, or that nicotine use is beneficial in the modern world. What is novel in the modern world is the high degree of availability, together with the high concentration of nicotine and routes of consumption that promote intoxication. What is different with alcohol in the modern world is the novel availability through fermentative technology enabling humans to consume it in beverage form, devoid of food bulk, with higher ethanol content than that which characterizes fruit fermenting in the wild.
The evolutionary evidence has two implications for government incentives: first, policies that prohibit the use of alcohol and nicotine (as well as illegal drugs; see Anderson et al., 2017) are likely to fail because people have a biological predisposition to seek out such chemicals; and, second, in present-day society, drug-delivery systems have been developed that are beyond what is reflected in the natural environment, particularly with respect to levels of potency, availability, and taste, which could be argued as being the more central drivers of harm. Potency is largely determined by producer organizations operating in markets, which, from the perspective of overall societal well-being, are inadequately managed (Schmidt, 2015). Better regulation of potency can become a major opportunity for additional policy interventions and government incentives.
Some researchers argue that bold new strategies are required to reduce and, ultimately, eliminate the use of nicotine (McDaniel et al., 2015; Novotny, 2015). While such an approach is likely to reduce tobacco-related harm, if it goes too far toward prohibition, there is a risk of adverse side effects beyond the health domain. A prohibitionary approach for nicotine reflects neither the evolutionary evidence nor the lessons learned from prohibition of other drugs (Kleiman et al., 2015) and alcohol (McGirr, 2016).
An Example of Genetic Influence—ADH1B
A genetic variant (rs1229984) in the gene ADH1B affects an enzyme that metabolizes alcohol in the body, increasing the rate of metabolism and, by resulting in flushing (Brooks et al., 2009), is associated with lower levels of alcohol consumption and risks of heavy drinking (Peng et al., 2010; Holmes et al., 2014). Since variants in the ADH1B gene lead to increased levels of the carcinogen acetaldehyde, heavy drinkers who carry the variant have increased risk of gastrointestinal cancers (see Shield & Rehm, 2015). At the same time, there is evidence that variants in the gene can protect against cardiovascular disease (Holmes et al., 2014), calling into question alcohol’s impact in reducing the risk of coronary heart disease (Roerecke & Rehm, 2011, 2014; Naimi et al., 2016; Stockwell et al., 2016).
This genetic example illustrates an important implication for government incentives. Given the increased risk of cancer in the presence of the genetic variant and drinking alcohol, both individuals and the societies in which a high proportion of individuals carry the genetic variant (e.g., in Asia), should receive stepped-up individual and environmental support to consume less alcohol.
Circumstantial Drivers of Harm
Surrounding the structural drivers of Figure 1 are the circumstantial drivers, those that are malleable or amenable to change. They include the potency and exposure of products and services, technological developments to manage the potency and exposure of products and services, and social influences and attitudes as drivers of exposure and harm, on which government incentives to improve health may act.
One way to express potency used by toxicologists and those who assess the safety of consumed products is the Benchmark Dose (BMD) (Crump, 1984). BMD10 is the benchmark dose in which an adverse event (commonly death) occurs in 10% of subjects (commonly animals) given a one-off dose of the drug. BMD10 is normally calculated from LD50 (Lethal Dose), the amount of a material, given all at once, which causes the death of 50% (one-half) of a group of test animals, by dividing LD50 by 10.2. BMD10 is expressed as a mean with a 95% confidence interval. The “L” in BMDL10 indicates that the chosen value is the lower level of the 95% confidence interval. The lower dose is taken for precautionary reasons. Thus, BMDL10 is the dose at the lower level of the 95% confidence interval at which 10% of animals taking that dose in one go die. For a 70 kg adult, the equivalent BMDL10 works out at 0.21 gram for nicotine and 37.3 grams for alcohol. Thus, the quantity of drug in a standard unit of consumption, (e.g., the nicotine content of e-cigarette) or a standard package of alcohol (e.g., a can of beer) is a powerful, but modifiable, driver of harm.
Data from direct exposure, as obtained from surveys or municipal wastewater analyses (Ryu et al., 2016), can be combined with potency, the benchmark dose, to estimate the margin of exposure. Surveys in Europe, for example, have estimated daily exposure among adult users of alcohol and nicotine as 34 grams for alcohol and 26.25 mg for nicotine (Lachenmeier et al., 2015). Knowing both the potency through the benchmark dose and the exposure estimated from surveys, the margin of exposure (MOE) can be calculated as follows:
The MOE is the ratio of the benchmark dose divided by the exposure dose. A MOE of 100 means that an individual is consuming 1/100th of the benchmark dose (defined above as the dose that kills 10% of animals when taken in one go). A MOE of 1 means that the individual is consuming the benchmark dose. Toxicology-based risk assessment uses different MOE thresholds as guidelines, depending on whether the benchmark dose is derived from animal or human studies. For example, for carcinogens in food products, when derived from animal studies, MOEs should be higher than 10,000, whereas when derived from human studies they should be higher than 1000 (EFSA, 2005). Differing MOEs are often set for differing health outcomes, with lower MOEs for noncancer outcomes, compared with cancer outcomes. According to the typical interpretation of MOEs derived from animal experiments, MOE less than 10 is judged to pose “high risk,” while MOE less than 100 is judged as “risk.” MOEs above 100 are often judged as acceptable because the value of 100 corresponds to the default 100-fold uncertainty factor, which has been historically used in regulatory toxicology. The factor of 100 is based on scientific judgment and represents the product of two separate 10-fold factors that allow for interspecies differences and human variability (EFSA, 2005; WHO, 2009). When the toxicological endpoint is based on human data and not on animal experiments, as has been done for alcohol in relation to liver cirrhosis (Lachenmeier et al., 2011), MOEs above 10 would be judged acceptable and MOEs below 1 as “high risk.”
Margins of exposure have been estimated for European populations (Lachenmeier et al., 2015) in which the MOE is inherent in the drug itself; it does not account for the harms that arise from drug delivery systems, for example, smoked tobacco. For individual users, nicotine has a margin of exposure of 7.5 (95% CI 2.7 to 14.0) and alcohol 1.3 (95% CI 0.6 to 2.7). In other words, nicotine users are using the drug at a level of 7.5 times the benchmark dose, and alcohol users, just over the benchmark dose. The low MOE for alcohol (and thus high risk) is due to the high levels of exposure among European adults. Alcohol and tobacco policies could thus be evaluated for their impact on MOE, with a target that all policies should achieve MOEs of no less than 100 or 10, depending on the BMDL10 data source (animal or human).
The implications for government incentives are that policies could achieve their result by reducing either the exposure or the potency of the consumed product through technological development of less potent drugs or smaller amounts of the drug in the standard ingestion unit. Margins of exposure could also be calculated for exposure to salt and sugar (publications have not been found) to guide policy and government-driven incentives. Margins of exposure can also be applied to behaviors that do not involve consumption of products. For example, with respect to physical activity, a benchmark dose for the inverse of physical activity could be estimated for a health outcome such as premature death. Since exposure can be measured, a MOE for the inverse of physical activity could be calculated.
Because of the influence of social networks and the health gains that can be achieved, exposure should be manipulated for populations as a whole (see Rose, 1992). The added value of changing behaviors for those with high levels of exposure depends on the nature of the risk curves.
• For tobacco, the age-standardized rates of all-cause mortality increase linearly with number of cigarettes smoked per day, with lowest risk occurring for never smokers (Banks et al., 2015). In this case, any reduction in exposure at any level of cigarettes smoked per day theoretically brings the same health gain.
• For alcohol, the risk curves are exponential (Shield & Rehm, 2015). In this case, greater health gain can be achieved with the same absolute reductions of grams of alcohol consumed per heavier drinker than per lighter drinker.
• For overweight, beyond a BMI of 25 kg/m2, the risk of death increases exponentially with increased BMI (Global BMI Mortality Collaboration, 2016). In this case, greater health gain can be achieved with the same absolute reductions of BMI per higher BMI than lower BMI above 25 kg/m2.
• For physical activity, the standard measurement is metabolic equivalent minutes (MET) of total activity. Below 3000 MET per week (depending on the disease studied), the risk curves for cardiovascular diseases tend to be exponential the lower the MET (Kyu et al., 2016). Beyond 3000 MET, the risk curves flatten, with little extra health gain. Achieving 3000 MET would require the equivalent of performing all the following activities: climbing stairs for 10 minutes, vacuuming for 15 minutes, gardening for 20 minutes, running for 20 minutes, and walking or cycling for transportation for 25 minutes on a daily basis. In this case, greater health gain can also be achieved by the same absolute increase in physical activity per low physical activity than per high physical activity (Ekelund et al., 2016), at least up to 3000 MET per week.
While the whole population should be addressed to manipulate risk factor exposure (partly because of the impact of social networks and social norms), in order to achieve proportionally greater health gain, government incentives should particularly act on heavier drinkers, those with higher BMI, and those with lower physical activity.
Technological developments can lead to alterations in the potency/exposure ratio, and thus margins of exposure, by reducing the content of harmful chemicals in standard delivery units, changing the size of standard delivery units, or, in the case of physical activity, altering footwear.
Salt reduction initiatives (McLaren et al., 2016; Wong et al., 2016; Trieu et al., 2016; Christoforou et al., 2016; Tariq et al., 2016; Hyseni et al., 2016), in which the food industry reduces the salt content of processed foods (He at al., 2013), exemplify a technological development to reduce potency per delivery unit or portion and exposure. Spearheaded by government action in the United Kingdom, mean estimated salt intake for UK adults fell from 9·5 g in 2000/01 to 8·1 g in 2011, with a predicted saving of almost 9000 lives a year from strokes and heart attacks (He et al., 2013). Overall, in the UK, the food industry recorded about a 30% reduction in the amount of salt added (Wyness et al., 2012). This reduction was obtained by setting incremental targets for each food group, with a specified deadline to be achieved using maximum and average or sales-weighted average targets. Since there has been a gradual, progressive reduction in salt, the UK population has adjusted to the taste of lower salt concentrations. No loss of sales or switching between products has occurred as a result of salt reduction or addition of salt at the table (Sutherland et al., 2013). The continued success of the initiative is dependent on enforcing and ensuring food industry compliance (MacGregor et al., 2015) and ongoing technological solutions (Lacey et al., 2016). Similar initiatives are being started with sugar (Hashem et al., 2016; Public Health England 2015), food portion size (Hollands et al., 2015; Crino et al., 2016) and alcohol content (Rehm et al., 2016). With regard to alcohol, moves have also been initiated to reduce potency by manipulating the molecule itself to less harmful variants (Nutt, 2014).
Harm can also be impacted by modes of risk factor delivery. This is illustrated by nicotine, which, as of 2017, remains a hotly debated topic. While nicotine itself is not a harm-free drug (Baumung et al., 2016), over the last one hundred years, the harm has largely derived from its mode of delivery—smoked tobacco. Nicotine replacement therapies (gums, patches, etc.) have been used as treatment products to quit smoking rather than as replacement products to smoked tobacco. Technological developments have led to electronic nicotine delivery systems (ENDS) (e-cigarettes) as widespread alternative delivery systems to smoked tobacco, with best estimates showing e-cigarettes to be 95% less harmful to health than smoked cigarettes (see McNeill et al., 2015; Tobacco Advisory Group of Royal College of Physicians, 2016). There is much debate over their regulatory framework, bordering between regulations for medicinal products, tobacco products, or general product safety rules (see McNeill et al., 2015).
Margins of exposure analyses can be used to clarify the approach. For example, analyses of German e-cigarette liquids found that tobacco-specific toxicants and trace nicotine impurities were below levels likely to cause harm. This finding suggests that at least, from this perspective, e-cigarettes are likely less harmful than smoked tobacco (Hahn et al., 2014). Analyses of nicotine, glycerol, 1,2-propanediol, ethylene glycol, 1,3-propanediol, thujone, and ethyl vanillin found that nicotine was the only compound, for which the complete distribution was below an MOE of 10, and on average below 0.1. From all other compounds, only ethylene glycol may have reached MOEs below 100 in about 50% of cases and 1,2-propanediol in worst cases. This result indicates that it is nicotine that is the toxic drug in e-cigarettes. Nicotine levels in e-cigarettes can be set, regulated, and monitored (Baumung et al., 2016; Dixit, 2016; Azzopardi et al., 2016; O’Connell et al., 2016).
Opponents of ENDS worry that ENDS are additive or gateway products to smoked tobacco, rather than replacement products. Although there is evidence that adult smokers who use ENDS are less likely to quit smoking than those not using ENDS (Kalkhoran & Glantz, 2016), this success depends in part on the ENDS design: smokers who use “open” devices (i.e., refillable “tank” systems) are more likely to quit smoking than users of “closed” devices (i.e., prefilled cartridges, “cigalikes”) (Chen et al., 2016). Incentives can be set (e.g., through price and availability) to steer users toward ENDS and away from smoked tobacco.
Another technological advancement is the development of footwear that enhances the efficiency of running. Building on the evidence that humans are exquisitely designed for running, shoe manufacturers have developed minimalist footwear to mimic barefoot running, while avoiding the risks of puncture wounds from barefoot running. Meta-analyses find benefits to running economy from wearing minimalist footwear, compared with conventional running shoes, with no difference in economy between minimalist footwear and barefoot running (Fuller et al., 2014; Cheung & Ngai, 2016).
Social Influences and Attitudes
Social influences and attitudes are drivers of harm related to risk factor. Humans are hard-wired social animals (Wilson, 2014). We are unusual in that we form longstanding, nonreproductive unions with unrelated individuals—friends (Christakis & Fowler, 2014). Cooperation is a defining feature of these friendships (Bowles & Gintis, 2011; Apicella et al., 2012). We also learn from and influence each other, leading to an exceptional reliance on cultural transmission (Pagel, 2012). We form social networks that have a significant effect on individual behaviors, such as tobacco use (Christakis & Fowler, 2008), alcohol intake (Rosenquist, Murabito, Fowler, & Christakis, 2010), obesity (Christakis & Fowler, 2007), loneliness (Cacioppo, Fowler, & Christakis, 2009), happiness (Fowler & Christakis, 2008), and cooperative social behavior (Fowler & Christakis, 2010).
The opposite consequence of social networks is social exclusion. Also hard-wired, possibly to avoid poor social exchange partners and risk of contact with communicable pathogens, social exclusion is a driver of stigma and social isolation (Kurzban & Leary, 2001; Oaten et al., 2011), themselves both independent risk factors for poorer health (Hawkley & Capitanio, 2015), particularly with respect to heavy users of alcohol (Stoll & Anderson, 2015) and, more recently with respect to cigarette smokers (Evans-Polce et al., 2015). The stigma may cause more harm than the risk factor itself (Room, 2011; Schmidt et al., 2010; Schomerus et al., 2011; Moskalewicz & Klingemann, 2015).
Government incentives could act on social networks, creating social norms that promote, for example, not smoking in public places and not engaging in the harmful use of alcohol. Government incentives could act against stigma through providing better access to advice and treatment, when needed, with sympathetic portrayal of successfully treated individuals (McGinty et al., 2015). Government policy could also help demonstrate that use of risk factors exists within continua, rather than being dichotomized into those with problems and those without (Rose, 1992; Anderson et al., 2017). The concept of a continuum can be less stigmatizing that that of a dichotomy.
Policies and Measures
Government policies and measures (and their lack of them) that act on the circumstantial drivers are themselves drivers of harm. It is through their policies and measures that governments at all levels (from local to global) can incentivize behavior that reduces risk factor exposure. Governments themselves, of course, need to be incentivized to do this. A health footprint is one means to spur government action (as well as private-sector action).
Policies that Reduce Exposure
Government policies that regulate price, availability, and commercial communications are the most impactful in altering exposure to risk factors (World Economic Forum, 2011; World Health Organization, 2013).
The most effective strategy for reducing tobacco uptake and promoting tobacco cessation is to increase the price of tobacco products (Sassi et al., 2013; Savedoff & Alwaygn, 2015; World Health Organization, 2015; Hoffman & Tan, 2015; Carroll et al., 2016). Each 10% increase in the price of cigarettes is associated with 2% to 8% reductions in tobacco use in both high- and low-income countries (International Agency for Research on Cancer (IARC), 2011). Price increases appear to be most effective among younger adults and persons of lower socioeconomic status. Similarly, with alcohol, systematic reviews and meta-analyses find that increases in the price and taxation of alcohol reduce consumption and alcohol-related harm for all groups of drinkers, and in high-, middle-, and low-income countries (Wagenaar et al., 2009, 2010; Sornpaisarn et al., 2013; Elder et al., 2010; Martineau et al., 2013; Fitzgerald et al., 2016). There is also evidence, though not as strong, that taxes on sugar-sweetened beverages and foods high in fat can reduce consumption, and subsidies of fruit and vegetable consumption can increase consumption, with modest follow through to reductions in excess body weight (Cecchini et al., 2010; Eyles et al., 2012; Sassi et al., 2013; Cabrera Escobar et al., 2013; World Health Organization, 2015; Afshin et al., 2015). Governments can incentivize less use of cigarettes, alcohol, sugar, and unhealthy fats by increasing their price through taxes, and they can incentivize greater use of fruit and vegetables by reducing their price through “negative” taxes.
Physical availability is considered here as one of the best buys, rather than broader environmental support, which is considered later. An example of the impact of manipulating direct physical availability is given for alcohol. Systematic reviews and individual studies find that greater alcohol outlet density is associated with increased alcohol consumption and harms, including injuries, violence, and crime (Bryden et al., 2012; Gmel et al., 2016; Fone et al., 2016; Morrison et al., 2016; de Vocht et al., 2016). Further, systematic reviews and individual studies find that restrictions on days and hours of sale reduce consumption and harm (Hahn et al., 2010; Duailibi et al., 2007; Kypri et al., 2011; Rossow & Norström, 2011; Wittman, 2016a, 2016b).
With respect to advertising, a mixed picture emerges. While a wealth of evidence demonstrates the impact of advertising and marketing on smoking (Hoffman & Tan, 2015; Carroll et al., 2016), alcohol (Gallet, 2007; Booth et al., 2014; Bryden et al., 2012; Stautz et al., 2016), and eating behavior (Afshin et al., 2015), reviews of the impact of advertising bans show little impact on smoking behavior (Mozaffarian et al., 2012; Quentin et al., 2007; Capella et al., 2008), alcohol consumption (Siegfried et al., 2014) and diet (Afshin et al., 2015). However, with tobacco, it has been argued that, although systematic reviews have not conclusively shown that banning tobacco marketing is an effective tobacco control measure, substantial evidence exists on the harmful consequences of unregulated advertising on smoking behavior. Thus, advertising restrictions should be implemented (Saffer & Chaloupka, 2000; Blecher, 2008). The same argument would apply to alcohol and certain elements of diet.
Incentivizing Individual Behavior
Among opportunities for directly incentivizing individual behavior are direct financial incentives; physical and social environmental support; and improving health literacy.
Direct Financial Incentives
A systematic review identified 34 studies that assessed how direct financial incentives to adults changed their behavior in relation to smoking cessation and diet and physical activity. The incentives were mostly offered alongside concurrent components to change behavior (counseling, self-help material, professional advice, and nicotine replacement therapy; (Mantzari et al., 2015)). Personal financial incentives were found to increase smoking cessation for up to 18 months from the time the first incentives were given. Improved cessation rates were sustained for two to three months after the incentives were removed. Incentives also increased the attainment of target indicators of combinations of healthier eating and physical activity for up to 12 months from the start of giving incentives, but they were not sustained after the incentives were removed. For physical activity alone, financial incentives were not found to lead to increased target levels of physical activity. In general, it seemed that the more socioeconomically deprived participants were more responsive to incentives than their less socioeconomically deprived counterparts. Some evidence also showed that higher-value incentives were associated with a higher increase in smoking cessation than lower-value incentives. An additional systematic review that focused on financial incentives for physical activity found no impact for unconditional incentives, but did find an impact of rewards conditional on improvements in physical activity behavior (Barte & Wendel-Vos, 2015).
The implication of the findings is that financial incentives in a variety of forms and offered through a variety of channels, including health insurance companies, can, at least for the time that they are offered, influence behavior in favor of health, particularly when conditional on improvements in behavior (Anderson et al., 2011).
Cities can be natural units for providing physical environmental support promoting health (De Leeuw et al., 2015; Farrington et al., 2015; Reeve et al., 2015; Shakeshaft et al., 2012; Perlman et al., 2016; Moreland-Russell et al., 2016; Sisnowski et al., 2016). Although cities do not have full jurisdictional responsibilities for all health policy issues that national governments have, they often have greater flexibility and are an important site for innovative environmental measures that make healthier choices easier choices , shifting social norms in the process (Reeve et al., 2015). Cities are members of many networks, including Healthy Cities networks, which are natural vehicles for deployment to full scale globally.
In terms of environmental support, urban design, for example, can have a major impact on enhanced physical activity (Anderson et al., 2011; Giles-Corti et al., 2016; Stevenson et al., 2016; Sallis et al., 2016a). An analysis of 14 cities worldwide found that the difference in physical activity between participants living in the least and most activity-friendly neighborhoods ranged from 68 min/week to 89 min/week (Sallis et al., 2016b). Municipalities with active living-oriented provisions (e.g., sidewalks, bike–pedestrian connectivity, mixed use, bike lanes; Leider et al., 2016) and municipalities with safer physical environments (Rachele et al., 2016) have higher rates of physical activity than those without.
Municipalities can also impact the local food environment in favor of health (Anderson et al., 2011). Unfortunately, despite a few isolated high-profile innovations (for example, New York city under Major Bloomberg [Sisnowski et al., 2016; Reeve et al., 2015]), the overall evidence base for impact is limited (Afshin et al., 2015).
Smoking bans and restrictions in public spaces, workplaces, and residences have reduced smoking prevalence and cigarette consumption and have led to increased cessation of smoking (Hoffman & Tan, 2015; Carroll et al., 2016). A review of smoke-free policies in the workplace found an absolute reduction in smoking prevalence of 3.4%, reduced cigarette consumption by 2.2 cigarettes per day, increased quit attempts by 4.1%, and increased successful cessation by 6.4% (Hopkins et al., 2010). More broadly, smoke-free legislation promotes denormalization of cigarettes by limiting their presence in public places (Tan & Glantz, 2012). Further, it has been found to be associated with an overall 13% reduction in rates of acute heart attacks (OR: 0.87, 95% CI: 0.84–0.91) (Lin et al., 2013). A systematic review and meta-analysis found greater reductions in hospitalization rates for cardiovascular diseases with comprehensive versus partial smoke-free air laws around the world (for comprehensive laws, RR: 0.86, 95% CI: 0.83–0.89; for partial laws, RR: 0.92, 95% CI: 0.85–0.98; Jones et al., 2014).
Social Environment and Social Norms
In terms of the social environment, people’s health behavior is influenced by social norms—what they see or hear of others doing (Rimal & Lapinski, 2015; Miller & Prentice, 2016). There is evidence that social norms can be changed in favor of improved health, particularly on the group level and through social networks (Hakulinen et al., 2015; Kubacki et al., 2015; Previte et al., 2015). For example, there are associations between increases in negative attitudes to harmful use of alcohol and decreases in alcohol consumption at the societal level (Bloomfield et al., 2016; Callinan et al., 2014). It should be remembered, however, that attempting to pin down social norms relating to health behaviors at a national or whole-of-society level is almost impossible, and there are likely to be multiple interconnected and overlapping cultures in any given society (Savic et al., 2016; Ally et al., 2016; Room et al., 2016). Important changes in cultural practices have occurred in particular settings or contexts (such as the denormalization of drinking while driving). Moreover, cultures that influence health behavior encompass various subcultures or social worlds that interact with and influence the broader cultural position of behaviors that impact on health.
Experiences with traffic safety and smoking reveal that there are three main factors for success in changing social norms (Gielen & Green, 2015):
1. Reciprocal determinism: As people are influenced by their social, economic, physical, and media environments, through changes in social norms they also can exert agency, will, and effort in changing, resisting, and adapting to these environments through their behavior. Pathways in changing social norms work as much through secondary transmissions within groups of people as through direct influence on individuals (Livingood et al., 2016; McCartney et al., 2016a).
2. Research, monitoring, surveillance, and evaluation: It is essential to have various sources of periodically monitored implementation and surveillance on outcomes, including behavior, and other drivers of behavior (McCartney et al., 2016b; Nadler, 2016). Outcomes should be monitored over time and between jurisdictions, enabling the more successful programs to change social norms to be emulated by other jurisdictions. Providing data is an important element in shifting social norms.
3. Comprehensive and culturally appropriate interventions: Comprehensive approaches incorporate multiple strategies and methods that are variously relevant and acceptable to the unique subgroups within a target population (Schudson & Baykurt, 2016). Highly cohesive groups or groups sharing a salient social identity are more likely to be influenced by targeted social norms programs (Neighbors et al., 2004). An important condition that boosts the effectiveness of these programs is the presence of factors that allow the modified psychological states of participants to convert into behavior. For example, an intention to drink less following a program will be more likely to translate into behavior if conditions such as availability of low- or no-alcohol beers are in place to make that possible. Changing social norms should also be based on campaigns that support the positive steps that consumers are taking in relation to their behavior (Previte et al., 2015). Programs to change social norms also need to consider whether a behavior is enacted spontaneously or after deliberation. If it is spontaneous, whichever attitude or norm is most salient will likely have a direct effect on behavior. If the behaviour follows deliberation, behavioral, individual, and contextual attributes will influence the extent to which norms shape behavioral intentions and subsequent behavior (Chung & Rimal, 2016).
Governments can incentivize changes in social norms through combinations of intensive mass media campaigns that are coupled with (that is, not done on their own) changes in regulation and physical environmental support.
Improving Health Literacy
Improving health literacy takes many forms ranging from package labeling to mass media and, more recently, to digital-based technologies, including wearable sensors.
Health literacy can be defined as “the personal characteristics and social resources needed for individuals and communities to access, understand, appraise and use information and services to make decisions about health” (Dodson et al., 2015, p. 12). Health literacy includes the capacity to communicate, assert, and enact these decisions. Health literacy is a critical empowerment strategy designed to increase people’s control over their health, their ability to seek out information, and their willingness to take responsibility for their health (Kickbusch & Maag, 2008). Health literacy includes functional skills to understand and use text and numbers in the medical setting; interactive skills to actively participate in everyday activities, to extract information and derive meaning from different forms of communication, and to apply new information to changing circumstances; and critical skills to analyze information and to use this information to exert greater control over life events and situations (Nutbeam, 2000). Distributed health literacy refers to the health literacy skills within a group wherein group members can become “literacy mediators,” sharing their skills with other group members (Wagner et al., 1986; Edwards et al., 2013).
Low health literacy is common (Rowlands et al., 2015; HLS-EU Consortium, 2012; Berkman et al., 2011), and is a strong predictor of impaired health (HLS-EU Consortium, 2012; Bostock & Steptoe, 2012) and low engagement with preventive care Berkman et al., 2011). There is evidence that consumers lack sufficient health literacy. For example, with alcohol, consumers have difficulty understanding alcohol content labeling and drinking guidelines (Kerr & Stockwell, 2012), and consumers have little awareness of the links between alcohol consumption and risk of cancer (Buykx et al., 2015). Improving health literacy in terms of ability to access, understand, appraise, and use information and services to make decisions about health are common elements of educational and social norms programs that seek to improve health. Such programs, which are integral components of brief advice interventions delivered by primary health care providers, are known to have wide impact.
Health literacy can be improved in at least three ways: through health guidance and warning labels on packages; through mass media campaigns; and through digital technology.
Health Warnings on Packages
Health warnings on cigarette packs are a core tobacco control strategy put forward by the global Framework Convention on Tobacco control, over time progressing through strengthened phases (Hiilamo et al., 2014), with each strengthened phase, including changes from text to pictorial warnings, having greater impacts on attempts to quit smoking and reductions in smoking (Noar et al., 2016a, 2016b, 2016c). In a clinical trial, smokers randomized to have pictorial warnings on their cigarette packs had higher quit rates compared to those randomized to text-only warnings (Brewer et al., 2016). Plain packaging, which removes company branding and employs standard packaging and lettering, effectively strips tobacco companies of their proprietary marketing. Population studies in Australia show that introduction of plain packaging was associated with significantly reduced appeal of cigarette packs and increased motivation to quit smoking among both adolescents and adults (Wakefield et al., 2015; White et al., 2015).
A systematic review of nine studies of food labeling found evidence that food labeling would increase the number of people selecting a healthier food product by 18%, with traffic light systems appearing more effective than other food labeling systems (Cecchini & Warin, 2016).
Providing better labeling information, including health warnings, on alcohol containers may increase awareness of the risks and content of products, though it may not reduce harmful consumption (Wilkinson et al., 2009; Wilkinson & Room, 2009; Tam & Greenfield, 2010; Kerr & Stockwell, 2012; Pettigrew et al., 2014; Knai et al., 2015; Osiowy et al., 2015). Labeling of alcohol containers has public support and could play a role in shifting social norms to reduce harmful alcohol use when integrated with other broader social messaging campaigns (Thomas et al., 2014) and when implemented within broader alcohol policies (Louise et al., 2015).
Reviews have been presented to determine what makes an effective label (Martin-Moreno et al., 2013; Al-Hamdani, 2013; Pettigrew et al., 2015; Purmehdi et al., 2016). Health warning labels aimed at moderation and/or cessation tend to display a generally diminishing cascade of effects from attention, comprehension, recall, judgment to behavior. Labels targeting lower use show stronger effects on behavior than labels aimed at moderation and/or cessation, although they also display a downward trend for attention, comprehension, recall, and judgment. Labels are found to have increased impact when the labels are preactivated by means of an integrated communication strategy. Both social influence and exposure frequency increase the impact of labels. Other reviews have emphasized the need to use enhancing characteristics such as pictorial warning labels to achieve high levels of recall (Mostafa, 2016). Consumers’ recall can be increased through the design of simple messages that make the importance of information explicit rather than leaving it to the consumer’s inferences.
The implication for government incentives is that mandated health information, health guidance, and health warning labels should be applied to all food and beverage products, alcohol products, and nicotine delivery devices. The content and format of such labels should be based on evidence of likely best impact.
Mass Media Campaigns
Mass media campaigns, either alone or as part of a multicomponent intervention, provide a tool for disseminating evidence-based health behavior targets at community and national levels. Mass media campaigns can impact behavior when they are implemented as part of multicomponent programs regarding dietary improvements (Afshin et al., 2015), smoking prevalence (Hoffman & Tan, 2015; Carroll et al., 2016), and, to a lesser extent, alcohol (Elder et al., 2004; Martineau et al., 2013). As standalone interventions, however, the evidence suggests that mass media campaigns are ineffective in changing behavior, with the possible exception of short-term changes in diet (Afshin et al., 2015).
Government incentives imply that mass media campaigns should be implemented, but only as part of multicomponent programs, reinforcing other incentives and environmental supports. Mass media campaigns, when implemented alone, cannot be expected to change behavior.
The literature on digital technology is rapidly expanding and is forever changing. However, as suggested by a range of systematic reviews, it can be stated that people engage with digital intervention to change behavior, using known techniques that increase engagement (Alkhaldi et al., 2016) and that digital interventions can result in meaningful changes in health behaviors (Sawesi et al., 2016), although the extent to which these are maintained remains to be fully tested (Hermsen et al., 2016). Positive outcomes are applicable to diet and physical activity (Hermsen et al., 2016), as well as alcohol (Kaner et al., 2015) and smoking (Graham et al., 2016).
Wearable sensors, including, more recently, smart watches (Reeder & David, 2016), have largely been used to enhance physical activity, although now increasingly used to engage other health-related behaviors (Lobelo et al., 2016; Reeder & David, 2016). A systematic review of 12 studies on the impact of wearable accelerometers on physical activity and weight loss found small but significant effects for increasing physical activity and for weight loss (Goode et al., 2017).
An important role of government may be to provide standards and regulation for evidence-based components of digital technology, thereby ensuring that they best serve their purpose in incentivizing individual behavior for health (Kostkova, 2015).
Research and Development to Reduce Potency
Technological developments are important in reducing the potency of delivered food and beverage portions, including alcoholic drinks, and thus reducing exposure; in providing safer delivery systems, as with ENDS; and in promoting more efficient footwear promoting physical activity and running. While technological research and development continues to be largely in the hands of the private sector, governments can incentivize such developments through smart taxation policies or through directions imposed by government-financed research. For example, taxation of alcoholic products can be linearly set to the number of grams of alcohol sold in the container. This would favor production of, for example, lower alcohol-containing beer products, as their shelf price would be cheaper and thus more likely to be purchased. Government research funds could be designed to promote closer links between academia and the private sector, with required outcomes, including the development of new health-promoting products.
Resource Allocation for Advice and Treatment
No matter what policy or measure is in place to act on the underlying drivers, many individuals who are heavy users of alcohol or nicotine, have a high BMI, or engage in little physical activity will find it difficult to change their behavior over time. Matching this need will require the availability of advice and treatment programs, particularly delivered through primary health care facilities.
There is considerable evidence for the positive impact of primary health care-based screening and advice on harmful alcohol use (Álvarez-Bueno et al., 2015; Platt et al., 2016), smoking cessation (Stead et al., 2013), and, to a lesser extent, obesity (LeBlanc et al., 2011; Levine et al., 2015) and physical activity (Vijay et al., 2015; Gagliardi et al., 2015; Attwood et al., 2016).
The challenge for all these programs is one of implementation, as, for the most part, the availability of risk-factor-related advice and treatment programs remain poorly delivered, with large gaps between potential need and offers of advice and treatment (Carroll et al., 2016; OECD, 2015; Aveyard et al., 2016). However, substantial evidence exists that these gaps can be overcome through a range of structural and organizational strategies, including training and support and financial reimbursement strategies to primary health care providers (Medves et al., 2010; Grimshaw et al., 2004; Krause et al., 2014; Baker et al., 2015; Flottorp et al., 2013; Wensing et al., 2014; Keurhorst et al., 2015).
As an incentive to reduce risk factor exposure, governments would do well to invest in primary health care-based screening and brief advice prograes to reduce exposure to smoking, harmful alcohol use, overweight, and low physical activity. Such investments are not only highly cost effective, but, for many jurisdictions, cost saving (Carroll et al., 2016; OECD, 2015).
Co-Benefits and Adverse Side Effects
Government policies that incentivize healthier behavior need to maximize co-benefits across a range of sectors and to minimize adverse side effects. Two illustrations are health policies and greenhouse gas emissions as co-benefits and prohibition as adverse side effects.
A clear illustration of co-benefits from health policies is in the areas of greenhouse gas emissions (Haines et al., 2009) and climate change (Watts et al., 2015), arguably the greatest threat to human existence (Klein, 2014). Policies that favor urban active travel (Nieuwenhuijsen et al., 2016) increase physical activity and improve health, while reducing greenhouse gas emissions (Woodcock et al., 2009). Policies that shift diets from animal- to plant-based sources improve health and reduce greenhouse gas emissions (Friel et al., 2009; Aleksandrowicz et al., 2016).
An example of adverse side effects of policies is when they go too far—advocating prohibition. The experience of prohibition of alcohol in the 1930s demonstrated that, while the policy brought health gains, the gains came at the expense of social exclusion, criminalization, loss of personal security, and diminished sustainability of well-being over time, with long-lasting consequences (Stoll & Anderson, 2015; McGirr, 2016). Nicotine reduction policies would be advised to avoid going the route of prohibition.
Regulating Private Sector
Transnational corporations operating in the food and beverage, tobacco, and alcohol sectors have an enormous detrimental impact on health, mediated both through their products and their impact on the policy environment (Baum et al., 2016; Ulucanlar et al., 2016), what has been termed “vetocracy” (Fukuyama, 2014). The importance of transnational corporations is illustrated by the fact that of the 100 governments and corporations with the highest annual revenues in 2014, 63 were corporations and 37 were governments (Baum et al., 2016).
Corporate power (Solana & Saz-Carranza, 2016), through multiple channels of influence, can hinder evidence-based policy decisions (Anderson et al., 2017; Miller et al., 2017) and thus increase harm. Corporate strategies often include attempts to influence civil society, science, and the media, as part of a wider aim to manage and, if possible, capture institutions that set policy. Transparency is insufficient, given that the multiplicity of channels of corporate power is poorly recognized and understood by policymakers. There are also inadequate rules in place to ensure level playing fields for discussions and equitable decision making across all actors (Miller et al., 2017).
One of the more extensively studied industries is the tobacco industry, which continues to fight public health efforts in the face of overwhelming evidence of the negative health consequences of tobacco use (Gilmore et al., 2015). The industry invests billions of dollars in marketing each year and has used deception in many forms to promote smoking initiation among youth and to maintain a high prevalence of tobacco use. This record of behavior led to characterization of the tobacco industry as a disease “vector”—the mechanism by which the toxic exposure to tobacco is transmitted to its victim, the global population (Gilmore, 2012). An important step in combatting the tobacco industry as a disease vector is to denormalize the industry by informing the public of its manipulative behavior and harmful influence (Gilmore et al., 2015).
Another step is to act globally. The FCTC represents a comprehensive approach to global tobacco control and is the successful amalgamation of several international human rights proposals to address the global epidemic of tobacco use. With membership composed of 168 signatories and 180 parties, the FCTC is the most widely endorsed treaty in United Nations history. In 2008, the World Health Organization (WHO) summarized global tobacco control efforts under the FCTC in six strategies—MPOWER (WHO, 2008): Monitor implemented policies, Protect individuals from second-hand smoke, Offer cessation assistance, Warn about the health consequences of tobacco, Enforce bans on marketing, and Raise taxes and prices on tobacco products. It is estimated that implementation of MPOWER policies reduced the number of smokers globally by 15 million in 2010, with a corresponding 7.5 million tobacco-attributable deaths averted (Levy et al., 2013). However, despite the FCTC, the actual number of current daily smokers has increased and is predicted to increase further from 967 million in 2012 (Ng et al., 2014) to some 1.1 billion smokers in 2025 (Bilano et al., 2015), thus highlighting a need for intensive stepped-up action in implementing the FCTC.
To regulate the private sector, in addition to managing the market through regulations on price, availability, and advertising, governance structures need to have the capabilities and expertise to supervise industry movements to shape legislation and regulations related to risk factors, including regulating and restricting political lobbying. One of the difficulties here is that political change in difficult areas, such as lifestyle risk factors, is highly dependent on collective behavior decisions (Granovetter, 1978) and influenced by what has been termed specular interaction (Coceht, 2015), in which a politician’s acts may be less determined by his or her own conviction than by his or her evaluation of the strength of the belief among rivals and friends.
As has been noted, the absolute number of disability-adjusted life years (DALYs) due to noncommunicable diseases (NCDs) increased from 1.1 billion in 1990 to 1.5 billion in 2015. Behind these changes are risk factors, for all of which risk-related DALYs increased in absolute terms between 1990 and 2015: smoking by about 15%, alcohol by about 25%, low physical activity by over 50%, and high BMI by just under 100%. These risk factors and their related DALYs are all driven by a range of drivers, illustrated in Figure 1 and already discussed. Given known changes in the structural determinants (including population size and age and socioeconomic development), exposure to the risk factors and their related DALYs are set to continue to increase, unless more decisive action is taken. While much has been done to incentivize governments to take more concerted action, including provision of evidence of effectiveness, cost effectiveness, and financial returns on investment, clearly this is not enough and more needs to be done.
One method to spur further action is to use the health footprint, at the center of the interconnections of Figure 1, as the accounting system for identifying the determinants of risk factor-related health and the management tool to evaluate opportunities by both the public and the private sectors to reduce harm.
Footprints were developed in the ecological field as a measure of human demand on ecosystems (Rees, 1992). They have since developed in a range of areas, including water footprints (Hoekstra, 2013) that measure water utilization and carbon footprints that apportion greenhouse gas emissions (normally carbon dioxide, CO2 and methane, CH4) to a certain activity, product, or population (Wright et al., 2011). The central reason for estimating a carbon footprint is to help reduce the risk of climate change through enabling targeted and effective reductions of greenhouse gas emissions (Williams et al., 2012). The health footprint can be defined as a measure of the total amount of risk factor attributable to the DALYs; (Ezzati et al., 2004) of a defined population, sector, or action within the spatial (e.g., jurisdiction) and temporal boundary (e.g., stated year, such as 2016) of the population, sector, or action of interest. It can be calculated using standard risk factor-related DALY methodologies of the GBD Study (GBD 2015 DALYs and HALE Collaborators, 2016) and of the WHO (Ezzati et al., 2004).
Nations, Regions, Cities
Jurisdictions at differing levels—supranational, national, regional and city level—can influence risk factor exposure through the policies and programs implemented or not implemented. For example, the introduction of smoke-free public places as happened in the 2000s led to reductions in smoking as well as in harm to the smoker and to those surrounding the smoker (Bettcher & da Costa e Silva, 2013). Reducing taxes on alcohol, as happened in Finland in 2004, led to an increase in alcohol consumption, alcohol-related deaths and health inequalities, which subsequently reversed, when taxes were increased in 2008 (Österberg, 2012).
Jurisdictional entities can be ranked according to their overall health footprint, in order to identify the countries that contribute most to risk factor attributable ill-health and premature death and, therefore, where best health gain could be achieved for groupings of countries as a whole. This ranking could be supplemented with health footprint estimates per capita to ensure that targeted country approaches can be implemented so as to reduce health inequalities between countries. Apportioning health footprints by country and by per capita will enable jurisdictions to facilitate policy planning; to consider the need for strengthened policy for a particular population (e.g., those with younger versus older populations, those with gender disparities, or those with specific genetic profiles); and to monitor the outcomes of policies and programs over time. For example, European Union (EU) countries have been ranked by an alcohol-attributable health footprint for the population up to age 65 years (Anderson et al., 2017). To improve the EU’s health as a whole, with associated productivity gains (OECD, 2015), a European-wide policy could target the top five contributing countries (Germany, France, the United Kingdom, Poland, and Romania), considering how to reduce these countries’ alcohol-attributable footprint to the level (= DALY rate) of Italy. Were this level to be achieved, the EU’s alcohol-attributable DALYs could be reduced from 4.8 million to 2.7 million.
Jurisdictional footprints could be developed for what might be termed “policy attributable health footprints,” which estimate the health footprint between current policy and ideal health policy. This would address the question: “Were the country to implement strengthened or new policies compared to present policies, what would be the improvement in the health footprint?” (see OECD, 2015). Conversely, failure to implement the evidence-based policy apportions accountability for the failure.
A range of sectors are involved in risk factors encompassed by the health footprint. Sectors include producer organizations, retail organizations, such as large supermarket chains, and service provider companies, such as the advertising and marketing industries. There is considerable overlap between sectors, and estimates will need to determine appropriate boundaries for health footprint calculations. For the sector and company calculations, a counterfactual scenario could be constructed in which a hypothetical situation is taken for comparison where the products and services to evaluate do not exist. For example, the health footprint of a major beer producer for the year 2012 was estimated to have contributed 3.34 million alcohol-attributable DALYs, 3.4% of all alcohol-attributable DALYs, and 0.13% of all DALYs (Anderson et al., 2017). The company could choose to commit to reducing its health footprint by 10% to 3 million alcohol-attributable DALYs over the next five years. One way to achieve this goal is to remove alcohol from the market through lower alcohol concentration products (Rehm et al., 2016).
Numerous structural drivers of harm operate at the biological and population levels, and need to be accounted for when designing and implementing government incentives to promote health.
Lifestyle risk factor exposure and lifestyle-attributable disability-adjusted life years are all set to increase in absolute terms as populations grow and age and as low-income populations grow more prosperous. Thus, government incentives cannot stand still—they need to be stepped up to level off and reduce the absolute numbers of people who lose unnecessary years of health and life.
There is a biological mismatch between the environment in which humans evolved and the way we live now. This applies to all lifestyle risk factors, but it is particularly acute with regard to physical activity. Humans have evolved to move (walk and run). Without adequate movement, humans are predisposed to ill-health and premature death. Governments can incentivize increased personal movement, particularly through urban design that both promotes personal active transport and penalizes motorized transport.
For some risk factors, such as alcohol and nicotine, there are powerful built-in evolutionary drivers of use. Government incentives need to take these drivers into account in managing potency and exposure of alcohol and nicotine, which, for a substantial proportion of the population, are far too high. Caution should be taken in moving toward prohibition of these products, as adverse consequences in well-being domains beyond health, and in excess of health, are likely to ensue.
A range of malleable circumstantial drivers can impact lifestyle-related harm. Potency and exposure are important, and their ratio (margin of exposure) can be used as a policy benchmark. Illustrations given for alcohol and nicotine could be developed for salt and sugar and low physical activity as guides to strengthened and alternative policies and actions. For example, technological developments can reduce potency and exposure and can produce less harmful consumed portions and safer modes of risk factor delivery, as well as more efficient physical activity footwear. Government-led actions could incentivize such technological developments through normative actions and requests, backed up by threats of strengthened regulations.
Social networks have been shown to operate positively and negatively as drivers of harm. Social exclusion, social stigma, discrimination, and prejudice are particularly rife with respect to alcohol, often causing more harm to people than alcohol itself. By example and through the policies and services they provide, governments can reduce social exclusion and stigma.
The presence and absence of government-led policies and measures drive harm. Policies and measures that reduce exposure, incentivize individual behavior, promote research and development for less potency and exposure of lifestyle risk factors, and ensure universal access to advice and treatment can all act as incentives to reduce harm and improve health. Conversely, the absence of these policies and measures increases harm. In addition to governments at all levels from local to global, the private sector is a major driver of harm through the products it produces and through its vetoing power of effective policies and measures that can reduce harm. This requires regulation not only of the products, but also of the vetoing power of the private sector. International legally binding agreements are proposed as one way to better regulate the private sector. Government-implemented policies and measures can bring many co-benefits outside of the health sector (e.g., mitigation of greenhouse gas emissions), but, if inappropriately implemented can also bring many adverse consequences outside of the health sector (i.e., prohibition of the consumption of alcohol and nicotine).
Finally, in general, governments are lagging in their responses to providing incentives to improve health. The health footprint is introduced as the accountability and monitoring tool to drive improved health action by governments, and, indeed the private sector. Governments and companies should report their health footprints and choose to commit to reducing them by a specified amount over a five- to ten-year timeframe.
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