Government budgets are tight during the recession, with cuts to public health budgets being announced on almost daily basis. What strategies are available to enhance revenues for public welfare programs–for the kinds of health and education expenses that won’t “pay for themselves”(at least in the short term), and therefore are often the first to get slashed in hard times? Raising tax rates among the wealthy, and introducing new taxes like a Robin Hood Tax, have been widely discussed. But some researchers have also studied entirely new revenue-generating strategies for social welfare programs that don’t rely on taxes—including a popular pay-for-performance scheme based on “social impact bonds” (SIBs).

How they work

A SIB is one of many “payment by results” plans. Just like other types of bonds (for instance, the municipal bonds we invest in to fund a local community college), SIBs involve private investors paying for a particular program that funds some social welfare operation. But SIBs are organized such that if the social welfare program is successful, there should be some net savings to the government and benefits to society.

For example, if a public health program prevents diabetes by successfully sustaining a weight loss intervention, the government should save money that would have otherwise been spent through Medicaid or Medicare on future hospitalizations caused by diabetes. As part of a SIB, the government agrees to pay a portion of these savings back to the investors who funded the weight loss program. And just like any investment, if the program fails, the investors lose money—theoretically attracting investors towards the most effective social welfare programs.

The SIB mechanism has a few interesting limitations and poses some potential adverse consequences. First, there must be some way that the government and investors can agree on the savings rate from the social program. Hence, in our diabetes example, it may be challenging and debatable how to determine the number of diabetes cases that would have occurred if the program hadn’t happened (i.e., what is the counterfactual? How can we really know what would have happened if the program hadn’t existed?). Similarly, methadone replacement programs may reduce incarceration rates and unemployment or disability expenditures for the government, but someone needs to determine how many heroin users would have gone to prison or claimed unemployment and disability checks if the methadone program hadn’t existed. Any estimate of these end-points could be highly contested. Hence, SIBs may be limited to situations where the savings generated by a prevention program are very proximal in time to the intervention, and discrete enough to easily catalogue.

But SIBs also have an attractive market power. In theory, if social welfare programs are ineffective at improving outcomes, then investors won’t invest in them–reducing the prevalence of ineffective government programs and improving funding available for good programs. SIBs also allow providers of the social program to be paid in advance, relieving the up-front infrastructure costs of a social program from the government, and shifting some of the risk to private investors. The providers of the services don’t bear financial risk, allowing non-profit organizations to potentially try higher-risk projects than they would have otherwise, even during critical periods like the recession when government grants are hard to find. For example, if food bank programs are especially needed during the recession, then food service organizations could find investment funds even when government grants decline due to budget deficits.

This all sounds good in theory, but we’ve heard about market failures (the inability of the market to capture what is socially optimal), and in the case of SIBs it’s not clear that every good social welfare program will result in a net downstream cost savings for the government. Food bank programs, for example, may not result in any ultimate cost savings. Similarly, it’s commonly known that disease prevention programs may not really result in an economic cost savings–they can delay diseases better than preventing them altogether, and often result in the disease manifesting in older age (a social benefit, avoiding disability among younger people), but do not necessarily reduce ultimate medical costs to a significant degree (a subject that generated much contention during the recent US healthcare reform debates).

One additional concern is the idea that SIBs may undermine the role of democratically-elected government officials and their programs in favor of a privatized patchwork of non-government organization initiatives. While an ineffective government program at least has the theoretical possibility of being reformed (as elected officials can be voted out of office), an entirely private set of operations will likely be governed by whatever interests are most concerning to wealthy investors. This means that social welfare programs may be redirected towards only “popular” initiatives—e.g., children’s programs may receive many investors, but not the methadone program that public health officials determine necessary based on their higher degree of information about public health needs than private investor groups—or whatever programs can be most easily shown to produce dramatic cost savings, even if they are not the most socially-important programs in a given community.

Evidence to date

SIBs are a novel strategy, and therefore little data is available from which to evaluate them so far. One of the first SIB implementations took place in 2010 in the United Kingdom, where the Ministry of Justice created a SIB to reduce recidivism among prisoners. The Ministry allowed a financial intermediary called Social Finance to raise several million pounds of investments from private individuals and charities. These investments were directed to pay for interventions among incarcerated criminals serving prison sentences of less than 12 months at one of the country’s prisons. The Ministry agreed that if reconviction rates fell by at least 10% (compared to a matched control group, for each cohort of 1,000 criminals released from the prison), the Ministry of Justice would make a payment to the investors.

An initiation evaluation of the program by an independent research body (the RAND Corporation’s European branch) identified some key early lessons from the SIB, especially with regard to its effectiveness at reducing reconvictions, increasing efficiency, and balancing costs and benefits. The RAND research revealed that investors engaging in the SIB program found contractual relationships they entered into quite complex (an understandable issue given the novelty of the program, but a concern if transparent market mechanisms are intended to clearly direct investors towards valuable and important investments and away from likely-unworthy schemes). Despite this fact, the RAND researchers observed that many charities who invested in the SIB actually did so with their endowments, not by giving a grant. This suggests that the charities were viewing the SIB as a form of socially-responsible investment, not simply another donation. Furthermore, the SIB attracted a number of investors who hadn’t previously given money for any venture in the criminal justice realm, suggesting perhaps that the novelty of the instrument attracted new funds and interest in the sector.

But in terms of outcomes measures, results were more modest. It may be too early to tell, but UK program turned out to be too small to deliver substantial “cashable” savings (monetised benefits). The ability of the SIB model to ultimately lead to identifiable savings for the government has yet to be shown. And to maximize the ability of the project to detect an effect, there was much debate; the development of a methodologically robust outcome measure for the SIB, which had the confidence of all stakeholders, was a time-consuming and analytically complex process according to RAND’s technical report. It was difficult to establish that any statistically significant impact was achieved, and choosing a measure that could show statistically=significant changes would itself be time-consuming and potentially expensive to catalog (as those who participate in randomized controlled trials can attest).

A risk in the scheme is that providers of the service focus on members of the target group who are the easiest to help, i.e., cherry picking. In the case of the UK SIB, a comparison with a control group was required to determine how much impact the program had, but this cannot be rolled out nationally if the program is successful and meant to be available for everyone; hence future SIBs must other ways of measuring counterfactuals (for example, before-and-after measures) to determine how much investors should receive in return.

Lessons for other “social entrepreneurship” operations

The SIB is like several other “social entrepreneurship” projects—they all sound good in theory, especially using market forces to influence theoretical choices on the part of investors and implementors—but their implementation in practice is fraught with real obstacles and severe constraints. It’s not clear if we like these schemes simply because they sound like novel applications of capitalism—something other than traditional government. But if that’s the case, we may risk creating a patchwork of dozens or hundreds of different social welfare programs with competing overlapping priorities and disparate outcomes measures that are difficult to assess and harder to sustain investments over time. Like microcredit programs and “conditional cash transfer” schemes, we ultimately need to evaluate SIBs against their true counterfactual: to compare their outcomes against the outcomes from government social services that receive the same amount of money. Otherwise, we risk introducing a fad that ultimately undermines traditional approaches to reducing poverty, inequality or social problems–reducing government oversight and control while claiming to solve chronic problems faced by social welfare programs that may simply have been insufficiently funded.

It’s nevertheless worth conducting well-controlled evaluations of these projects to see which have merit. In the US, Social Impact Bonds have been called Pay for Success Bonds, and a report from the Center for American Progress has analyzed their potential. There are several other reports on the theory behind SIBs, such as Social Finance’s Technical Guide to developing these bonds, the Young Foundation’s proposal to expand these bonds, and Impact Economy’s review of the bonds in the context of global philanthropy. They all make interesting philosophical reading; we’re just waiting to see whether they can ultimately report real results…

From T-shirts to MacBooks–the new sweatshop scene

College students first drew attention to the problem of clothing sweatshops by  demanding that several universities (who raise considerable money from selling sweatshirts and hats to sports fans) put their logo on products manufactured under basic fair labor conditions. They created the Worker’s Rights Consortium (WRC) to enforce codes of conduct among factory workers, which several universities agreed to after mass protests in the 1980’s and 90’s. The industry created the Fair Labor Association (FLA) as a looser alternative. But both groups ultimately contributed to the improvement of sweatshop working conditions in places like Cambodia. While famous New York Times columnists like Nicholas Kristof like to say that sweatshops improve economic growth and are better than starvation, further analysis of the historical and economic evidence suggests that this growth really shifts towards workers (rather than their bosses) when accompanied by stronger labor protections and protests, often facilitated by unions. In China, such unionization has begun to strengthen, in spite of dangerous conditions for their development, given government crackdowns.

With the dot-com boom and the resurgence of Apple as a major consumer electronics force, new labor concerns in China and other industrial zones such as the Maquiladora zone in Mexico have been dominated by the production of electronics. Most of us perceive that electronics are produced on quiet, clean assembly lines by robots or special technicians–as depicted in the Intel commercials where workers float around in spacesuits. But the reality is that most components are still assembled by hand, and by migrant workers.

The public health data emerging from studies of workers in this industry suggest that there are three common hazards to the health among these employees: exposure to industrial solvents often used to clean electronics parts; abuses of labor code that lead to overworking and associated stress injuries and mental health problems; and gender-based physical and sexual harassment and abuse on assembly lines supervised by men.

The context of employment

In the context of limited alternative job opportunities, a large unemployed labor pool, and lack of organized labor regulations, there are several accounts that official laws in SEZs and related labor zones do not actually provide wages at the legal minimum and use both child labor and forced short-term contracts to underpay workers. New studies show that this has led to a cycle of food insecurity and further desperation for short-term work, perpetuating the willingness of workers to be employed for substandard wages and excessive overtime. Large manufacturers like Foxconn have found that hiring migrants is particularly advantageous as these workers cannot afford to pay for transportation back home; the company provides them them in dormitories that house up to 400,000 people on any given day. In the setting of daily work, repetitive strain injuries of the hand are thought to be the most common injury. Several factories fail to rotate workers between different types of repetitive duties; rather, they fire workers once their hands are too damaged to continue working, and hire new employees at lower wages, allowing the company to save year-end bonuses and maintain high throughput. Some American corporations like Apple have reported that they inspect these facilities to prevent such abuses, after recent scandals emerged, but the subsequent changes are not clearly disclosed and reports have suggested worsening conditions.

Types of exposures

One of the highest-profile cases involved the use of N-hexane among Apple workers. N-hexane is a solvent that evaporates faster than regular rubbing alcohol, allowing workers to wipe the screens of new gadgets more quickly and speed up the assembly line. However, N-hexane is also a neurotoxin, leading many workers to lose control of their hands and several to report accompanying skin pathologies and leukemias. Over 100 workers were treated and investigative reports indicated that many had been covertly hospitalized while manufacturing iPhones. Other common chemical exposures reported in the public health literature have been inhalation, absorption and ingestion-related diseases and deaths from brominated flame retardants; mercury, lead and other toxic metals; perfluorinated compounds; and acids. In the semiconductor industry, women have also been found to have an unusual rate of low birth weight infants and spontaneous abortions for unclear reasons that may be related to heavy metal and organic solvent exposures. Another common exposure is to the solvent glycol ether, which is used to protect chips from light damage. Overexposure to this solvent appears to have been related to inhalation injuries and birth defects. Needless to say, many workers do not receive appropriate protective equipment.

Electronics waste

In our pursuit to always get the latest new device, we throw also away about 50 million tons of electronics every year. A 2011 report from Ghana found over 200,000 tons of electronic waste was exported there that year in exchange for small amounts of cash from garbage contractors who find it cheaper to ship and dump in developing countries than in New Jersey. Because the United States has not ratified the Basel Convention or its Ban Amendment, and has no domestic laws forbidding the export of toxic waste. The UN has predicted a 500% increase in the rate of electronic dumping over the next decade, much of it expected to affect India. Because most of this material is not recycled, the heavy metals in the equipment cause considerable environmental damage. But they also exert a health toll when processed. Much of the material being dumped is burned, releasing toxic fumes with dioxins and furans that are carcinogenic and neurotoxic. Groundwater contamination in these regions has also been recorded, and lead toxicity also appears particularly high.

New initiatives

After suicides among Foxconn workers generated negative publicity for Apple, the Electronic Industry Citizenship Coalition (EICC) began to publicize its code of conduct, Apple joined the FLA, and inspections and reports about changing factory conditions have proliferated from a number of electronics companies. Some of these events were recently featured in the NPR show “This American Life”, which contains a moving first-person documentary from a reporter who visited Foxconn workers and has his doubts about the scale of improvement (listening to the podcast is worth the time). Electronics recycling may have become more successful, having been mainstreamed into some computer purchase agreements and even appearing into a new campaign from Comedy Central:

Much of the progress that has been possible to document has been facilitated by unions and activist organizations like SACOM (Students and Scholars Against Corporate Misbehavior), who have documented reports from workers and staged direct protests outside factories as well as trying to publicize their efforts in the press (a real challenge in China, where photos of a protest outside an Apple store were re-depicted as a group of consumers pounding on the store glass to get a new phone). In addition to SACOM, other groups that have been facilitating local labor law improvements also include Mexico’s CEREAL (Center for Reflection and Action on Labor Rights), South Korea’s SHARPS (Supporters for the Health and Rights of People in the Semiconductor Industry), China’s Labor Watch, and the International Labor Rights Forum. It is difficult to systematically document how well things have progressed in spite of some local successes caused by protests from these groups against specific manufacturers; Greenpeace has created a ranking system of which electronics manufacturers have been using less chemical toxins and generating less electronic waste (according to this ranking, HP and Dell are better than Apple, which in turn is doing better than RIM); but labor practices and workers health are not considered in the ranking. The systematic collection of data is still collected by corporations and consultants, but generally not publicly available. The MakeITFair campaign has initiated some reporting efforts to document abuses not only in Asia and Latin America, but also in emerging industrial sectors of Africa and Eastern Europe.

In general, the public health research world has yet to systematically study many of the factories in which electronics production takes place. But some notable work conducted in the border zone between health, environment and labor studies includes the edited volume Challenging the Chip: Labor Rights and Environmental Justice in the Global Electronics Industry and Elizabeth Grossman’s book High Tech Trash: Digital Devices, Hidden Toxins and Human Health. As well a studying labor conditions abroad, a growing group of public health advocates have begun to catalog and work for change among migrant laborers in domestic manufacturing and farming sectors, also producing some inspiring changes will hopefully be sustained as further efforts are made towards equitable economic growth policies.

Causes and consequences

(First of all, a disclosure: this review is not unbiased; I have contributed to several chapters of the book, and work closely with David Stuckler, so I naturally agree with many of the book’s conclusions. However, I do not receive any financial compensation from book sales.)

As we discussed in a previous post, chronic diseases have now emerged as a cause of most deaths in the world, with 80% of these deaths occurring in low- and middle-income countries, and increasingly shifting towards the poor. As Stuckler and Siegel review in their book, there is critical data showing that the majority of this burden is coming from tobacco use, diet (sugar, fat and salt intake), increased physical inactivity and alcohol. What is more surprising about their book is the data revealing that a “transition” does not seem to be taking place from infectious to non-communicable diseases—by contrast, many of the same households in poor countries are continuing to face the burden of infectious diseases like tuberculosis and classical diseases of poverty like undernutrition while simultaneously having family members with diabetes and heart disease (a “double burden”, not a “transition”).

Both under and over-nutrition seem to occur in families with poor purchasing options, leaving them with oily and fatty processed foods that are both inadequate in micronutrients and excessive in useless calories. The combination of tobacco and indoor air pollution from “dirty cookstoves” has similarly led to a rise in chronic lung disease while heightening the risk of tuberculosis. Hence, dichotomizing between the infectious and chronic diseases as diseases of the poor and rich, respectively, seems unsupportable by the evidence.

The authors also tackle the common assertion that chronic diseases are a sign of progress or are a “rational slow suicide”—a matter of personal choice. Indeed, the data from both household surveys and econometric assessments suggests that price, availability, marketing and perceived costs and benefits strongly influences the choice of, for example, unhealthy food consumption—which is one reason that chronic diseases are becoming increasingly concentrated among the poor even when they start among the wealthier groups of a country.

The clearest phenomenon occurring in many nations in “dietary dependency”—or the process by which food availability in low- and middle-income countries shifts toward multinational imports from Western nations, increasingly due to market “divergence”, in which products deemed unhealthy in the West are increasingly imported to poor nations. This is particularly apparent in Mexico, where the North American Free Trade Agreement led to the collapse of Mexican farms as these farmers were unable to compete with the heavily-subsidized US corn industry; the farmers migrated to urban centers where less fruits and vegetables were available, and where heavy marketing of soft drinks has now led to a higher consumption of Coca-Cola than of milk (largely thanks to former Mexican President Vicente Fox, a former Coca-Cola President), and a corresponding rise in obesity to over 70% of the adult population.

Beyond the human toll of these diseases, the economic consequences are huge, but the authors delve into some detail about how economic assessments are complicated by a number of assumptions. Many studies measure non-communicable disease burdens based on crude mortality rates, rather than age-standardized rates that could better capture broad consequences of disease on the working-age population; in several cases, non-communicable disease rates are also estimated based on income levels given the absence of actual disease data, then later studies are done claiming that these disease rates did not effect income growth, a circular argument. There is debate within the economic literature about whether chronic diseases are merely a sign of progress, rather than a market failure. Yet Stuckler and Siegel, along with a series of colleagues, present emerging evidence that the costs to governments, firms, households and individuals is substantial no matter how economic externalities are calculated. Furthermore, given inadequate and imperfect information given to individuals about the consequences of their choices, and given the addictive nature of alcohol and tobacco and possibly certain foods, there is substantive evidence of market failures in chronic disease risk factors like the food sales environment.

What to do?

It’s no surprise that systems of chronic disease care are largely uncoordinated and fragmented in poor countries, with a focus on acute care rather than chronic primary care that delivers both preventive medicine and chronic treatment to avoid the costly complications of diseases like diabetes. Market-based systems, as reviewed by the authors, are highly unlikely to remedy this problem, given inadequate incentives for preventive care and numerous incentives for fee-for-service charges once people become ill (and see Nobel Laureate Kenneth Arrow’s old paper on why markets can’t provide efficient healthcare, given asymmetric information). Stuckler and Siegel powerfully argue for a move away from ‘magic bullet’ interventions that focus on education and technology as the essential solutions for emerging diseases, showing instead that these rarely produce a lasting impact. While many efforts at ‘sustainable’ technology are focused on providing extremely low-cost and very limited interventions in poor countries, they tend to suffer from what Paul Farmer has called the circularity problem: if you give shit to the poor, they will never have more than shit. To build a really effective health system, real and meaningful infrastructure has to be developed over time.

The authors look at a number of examples of extending current systems to control chronic diseases: a case of Malawi, for example, which involved training personnel including community health workers, providing access to low-cost medicines, monitoring patients serially, and providing national supervision for quality control and monitoring of outcomes. The critical pharmacological interventions in many countries appear to include improved diabetes supply and medication availability, blood glucose monitoring devices, antihypertensives and anticholesterol medications, and improved screening programs. The possibility of a “polypill” is also reviewed.

But Stuckler and Siegel are appropriately skeptical of whether healthcare services will be adequate to address the rising burden of disease. They turn to population-level public health interventions that have shown an impact, highlighting that these usually affect the social and economic system rather than just introducing educational or technology-focused interventions that have shown poor efficacy on a population level. The interventions will be politically challenging, but particularly involve modifying standards for food production including lower salt, sugar and fat content; discouraging sales of tobacco, alcohol and unhealthy foods through taxes and subsidies; and engaging in participatory community-based approaches to devising community physical activity and risk factor reduction programs, such as the Agita Sao Palo and Isfahan Healthy Heart Programs.

The politics of these programs are debated in the book both by industry representatives invited to contribute (most notably Derek Yach of PepsiCo) and their critics (such as William Wiist). The industry representatives highlight, expectedly, a number of their public relations and educational campaigns, as well as their focus on using food science to invent new alternatives to sugar and high-fructose corn syrup as well as oils like palm oil (devising alternatives like Palmolein). They report being constrained by consumer choice and lack of R&D investment from the public sector. But their critics highlight corporate activities designed to subvert public regulation of their industry and maintain early consumption of unhealthy foods, alcohol and tobacco among youth, as well as the interlocking relationships between these industries and key global health policy-setting groups like major foundations and donor governments.

Progress in delivery

Actually implementing reforms to chronic disease risk factors to produce healthier diets and less consumption of alcohol and tobacco, and reforming health systems to manage chronic disease, has seemed a formidable challenge. But Stuckler and Siegel appropriately highlight key positive efforts from countries facing the greatest burden of these diseases: Brazil, China, India, Mexico and South Africa. Each of these countries has experienced “double burdens” of disease—both chronic and infectious diseases—and each has experienced a growth in heart and lung disease, as well as diabetes, through major trade transformations (such as the GEAR program in South Africa). While the details of each case study won’t be reviewed here, it’s notable that each country’s campaigners have learned from the tobacco control movement—that to generate major changes in the context of limited resources, it’s important to give a high political priority to regulating markets and taking on political challenges from industries invested in the status quo—whether the industry producing the “risky commodity” or the healthcare delivery industry that is incentivized to treat complications rather than prevent them. In China, this has meant addressing the fact that the federal government is itself invested in tobacco production. In India, it has meant addressing the economic system that preserves inequality in slums for cheap labor, leaving only processed and other unhealthy foods affordable for consumption by this vast semi-employed population in slums. In Mexico, it has involved taking-on Coca-Cola.

Stuckler and Siegel end their book by highlighting how major campaigners and public health activists have galvanized support for a changed risk factor environment—building social networks, reframing debates from individual responsibility to collective focus on social inequalities and gaining the resources for healthy living, and supporting regulation and litigation when necessary. Their concluding chapters—showing how successful mobilization happens, and providing resources for global health mobilization around chronic diseases—are essential reading. They provide a clear guide to how solidarity can be built in a public health sector that has often been characterized by internal infighting about “whose disease in more important”, rarely highlighting the common underlying social inequalities of power and wealth that have driven these epidemics. Ultimately this is a subversive book as much as it is a data-driven epidemiology text. And it has become increasingly clear that such discussion of power and politics must play a central role in our epidemiological debates if we are to effect change to the rising burdens of chronic disease.

The rationale

In theory, international banking, just like other forms of international trade, should help spur economic well-being. That would be true if the world of trade followed Ricardo’s “theory of comparative advantage“, which tells us that if two populations each specialize in what they produce (like China producing electronics and the French making wine), then they will both be better off trading between each other (focusing all of their production on their area of specialty) rather than trying to both make electronics and wine domestically. But like any economic theory, Ricardo’s theory–which has been the basis of free trade arguments for decades–is based on a series of assumptions, many of which are violated in today’s modern trading environment. Ricardo assumed that capital did not move between countries (oops), that trade would take place just between companies (not within companies, double oops), and that markets are totally competitive (triple oops in today’s political work of subsidies, trade sanctions and oligopolies).

Perhaps more serious are the inherent limits of the idea that economic prices adequately reflect the value of goods or services for humans. Market prices often fail to reflect the downstream health or environmental costs of a given product or activity, thereby sending misleading signals about the benefits and risks of a particular economic decision. And those companies conducting the activities that damage public health or the environment do not have to pay the consequences of the damage, leaving the cleanup job to the rest of society.

Some of today’s international banking transactions appear particularly problematic to public health. There is increasing evidence, for example, that banks speculating on basic food commodities (like wheat or rice) have sparked another famine in the Horn of Africa and elsewhere. Many people blamed increasing consumption from India and China for the spike in food prices that has taken place, or the subsidization of biofuels (diverting staples like corn into oil production rather than food). Those factors may lead to gradual increases in food prices due to higher of demand, but can’t explain sudden spikes. In addition, both aggregate and per capita consumption of grain have actually fallen in India and China, and the population growth and total demand from those countries can’t numerically explain the food price spikes. Natural disasters related to global warming also don’t seem to fully explain the sudden spikes in prices that have lead to the famine.

Some component of these spikes seems to be the result of a new form of “speculation” from banks. Speculation wasn’t always a negative force (and could be a positive one in some circumstances); here’s how it used to work:  farmers protected themselves against natural disasters or other risks by “hedging”, or agreeing to sell their crop in advance of the harvest to a trader. This guaranteed the farmers a reasonable price for the crop (even if lower than a future price that the trader might get), and allowed the farmer to plan ahead and invest in infrastructure for the future. Some years, farmers get a better profit than they would have otherwise, while in other years, traders get a higher profit. Under tight regulations, this process might even help stabilize the food market, and the process is controlled by real forces of supply and demand.

That form of speculation was dramatically altered in the mid-1990s. After heavy lobbying, numerous regulations on commodity markets were removed. Contracts to buy and sell foods were turned into “derivatives” that could be bought and sold among traders who had nothing to do with agriculture, producing a sort of “unreal” or false market so that food could be sold like corporate stocks and oil, bundled into complex financial packages and traded to make small profit markets off of market volatility (sounds like the mortgage crisis, no?). When the US sub-prime mortgage disaster happened in 2006, the  banks and traders moved billions of dollars from pension funds and equities into “safe” commodities,  especially foods. The resulting spike in demand for these commodities caused prices to skyrocket, precipitating massive suffering among people of the world who could no longer afford wheat or rice or other basic foods. The UN report on speculation and the banks has captured the problem in great detail, as have further reports from The Oakland Institute and others. A global campaign to curb food speculation has begun.

But while the relationship between speculation and food prices remains controversial in some economic circles, it is more clear that banks also affect public health in a number of other ways. There is increasing evidence that numerous people have lost health insurance in the economic recession that was incontrovertibly started by bank misbehavior (in particular, by doing with mortgages what they are now doing with food), and the subsequent unemployment and health insurance losses have led to increases in morbidity and mortality. The corporate tax subsidies and cuts negotiated by the banks also relate to the budgetary shortfalls at the state and country level, resulting in cuts to social welfare and social protection services that are a critical determinant of public health outcomes.

The question is: what can we possibly do about it?

The proposal

The “Robin Hood Tax” is a type of financial transactions tax (FTT) that is  essentially derived from Nobel Prize-winning economist James Tobin, who proposed years ago to tax foreign currency exchanges. The Robin Hood Tax would go further to place a 0.05% tax (that’s right, very small) on the purchase and sale of certain types of stocks, bonds, commodities, unit trusts, mutual funds, and derivatives such as futures and options. It’s been endorsed by everyone from French President Sarkozy to economist Paul Krugman (another Nobel Prize winner, accompanied by 1000 other economists including Joseph Stiglitz, Ha-Joon Chang, Jeff Sachs and Dani Rodrik), but how and why would it work?

The group of non-governmental organizations who collectively proposed and endorsed the Robin Hood Tax idea in 2010 suggested that it be placed on financial commodities that are most related to volatile international transactions–the kind of transactions that banks make money from by buying and selling commodities with each rise or fall of the stock market, but that destabilize the rest of the economy and that don’t usually constitute meaningful long-term investments.  The idea was to split the tax evenly between domestic social welfare program (e.g., food stamps, among countries collecting from their own domestic banking industry) and international aid. The best estimates suggest that around $400 billion would be collected from such a tax each year. The idea is that the small tax would not accumulate among long-term investors who are truly interested in putting their money into real goods and services, but would accumulate upon and therefore discourage the high-frequency traders who are speculating on derivatives and trying to make a quick buck off of market volatility (probably reducing such trades by about 14% according to recent estimates).

Naturally, the idea has garnered criticism–mostly that it will adversely impact the banking industry, negatively impact overall employment by depressing the economy, and be a “stealth tax” that is transferred over to consumers rather than really being paid by the bankers. Few people are terribly concerned about the impact on the banking industry, given that it is 26 times more profitable than the average business and continues to doll out expensive bonuses for its executives. The consequences for other sectors of the economy have been highly controversial, as arguments have been made about whether sectors of the economy that are dependent on high-frequency trading are really producing meaningful goods and services, or simply employing e-traders and i-bankers who have negative consequences on the rest of us. Some calculations suggest that at the 0.05% rate, the tax is unlikely to affect retail banking, which includes savings and mortgages. It will instead introduce a micro-tax on short-term, casino-style trading which employs a small number of highly paid bankers in a few urban centers (New York, London), not the tens of thousands employed in main street financial services. It may be a stealth tax on consumers, but mostly on consumers who are involved in high-frequency trades, which we wish to discourage anyway. And bankers would not receive commissions and other profits from that kind of trading, creating further incentives for more value-driven investments. In fact, studies of who ends up paying transaction taxes have concluded the Robin Hood Tax would in all likelihood be “highly progressive”, meaning it would fall on the richest institutions and individuals in society, in a similar way to capital gains tax (in contrast to VAT, which falls disproportionately on the poorest people).

There are also alternatives to the Robin Hood financial transactions Tax. The two most common are a bank levy and a FAT tax. A bank levy involves a flat-rate charge imposed on large financial institutions (which is currently employed ni the UK, France and Germany).  But at the rates they have used, these levies haven’t raised much money (being commonly offset by reduced income taxes to the banking sector) and don’t seem to discourage the heighest-risk transactions. The financial activities tax, or FAT tax, is equivalent to a VAT tax on the financial sector; it may work but might be easier to circumvent and cheat than a financial transactions tax.

Progress on implementation

Is the tax just a pie-in-the-sky idea, or does it have political promise?  In the middle of this year, the European Commission reversed its earlier opposition to the Robin Hood Tax, proposing an EU financial transaction tax be adopted within the 27 member states of the European Union. In August, French President Sarkozy and German Chancellor Merkel affirmed their support for the proposed European implementation. Great Britain’s prime minister Cameron remains opposed to the tax unless it can be implemented globally, meaning that a European implementation would likely have to be confined to the Eurozone not the whole EU. The White House also remains opposed. But in September, Bill Gates endorsed the tax the 2011 IMF and World Bank meeting and suggested that the tax apply to the entire G20, which would raise between $48 and $250 billion per year. His endorsement was followed by one from the Pope, but we haven’t heard further yet from the White House about whether Obama will reconsider his position. 

While it may seem critical for a financial transactions tax to be implemented globally, as banks could hide in the Cayman Islands. But surprisingly, while the UK hasn’t implemented the full Robin Hood Tax, it has put in place a stamp duty of 0.5% on all share transactions, and this did not precipitate a run from London.  The UK’s major competitors do not have this duty but the UK raises around £5 billion pounds each year from it. There’s further interest into whether this duty was sufficient to discourage riskier transactions, but it certainly seemed that London life was attractive enough for bankers that few would want to live or work elsewhere simply because of a less than 1% tax. Regardless, once the major set of European countries are on board, most commodities will end up having the tax applied to them, as it would be hard to stay in business as a bank while ignoring mainland Europe (as the banks involved in Greece can testify to).

Whatever happens next to the Robin Hood Tax proposal, it’s important to look back on this year and recognize that the campaigners for the Robin Hood Tax proposal have made incredible strides in just 12 months: moving from an idea on paper to a real proposal being debated by world leaders, hitting headlines across the globe, and gaining endorsements from the most powerful economists and public figures. Let’s hope the momentum continues into the New Year…

In this blog post, we describe two common (but not exclusive) ways that researchers estimate the cost of a given risk factor like tobacco smoking or air pollution. By understanding the methods behind these approaches, it’s much easier to get a sense of their advantages and drawbacks.

The costs of a risk factor typically consist of three components:

(1)     direct costs,

(2)     indirect costs of lost productivity from related illness, and

(3)     indirect costs of premature deaths caused by related disease

We will discuss two approaches to estimating costs #1 and #2, then revisit cost #3 after a brief discussion.

Option 1: Using a prevalence-based approach to estimating costs #1 and #2

The more complex of the two common procedures is to estimate the economic cost of a risk factor by adding up all illness and deaths that occurred in a given year that resulted from exposure to that risk factor. An attributable fraction (AF) is estimated and applied to the total measure of interest, e.g., the AF for hospitalization expenditures represents the proportion of hospitalization expenditures that are attributable to the risk factor. The AF is therefore multiplied by the total hospitalization expenditures in a community to obtain hospital expenditures attributable to the risk factor.

Direct costs

Multiple types of health care services can be included in the direct cost calculation, such as hospitalizations, ambulatory care, nursing home care, prescription drugs, and home health care.

Many people use derivatives of the following econometric models to estimate direct costs; these consist of multiple equations describing the effect of exposure to a risk factor S (current exposure, former exposure, and never exposed) on the past history of the related diseases D, on self reported poor health status H, on the probability of having health care expenditures X, and on the magnitude of expenditures given that expenditures took place.

Demographic and socioeconomic status Y (age, income, health insurance coverage, etc.) and other relevant risk behaviors R (obesity, seatbelt use, whatever might apply) should be controlled for in the model. The structural forms of these equations are:

(1)     D* = f1 (S, Y, R)

(2)     H* = f2 (S, Y, R,D*|D)

(3)     Prob(X>0) = f3 (S, Y, R, H*|H)

(4)     Log(X|X>0) = f4 (S, Y, R, H*|H)

D is a binary variable that equals one if the respondent reported having one of the diseases related to the risk factor, and zero otherwise. D* is an unobservable variable for the propensity for having a diseases from the risk factor, and is estimated as a probit model. H is self reported health status (e.g., categorized as excellent, good, fair, or poor). H* is an unobservable variable for the propensity of having poor health and is estimated as an ordered probit model. D*|D denotes the expected propensity for having risk-factor-related disease conditional on self reported disease history. Likewise, H*|H denotes the expected propensity for having poor health conditional on self reported health status. Equation 3 is estimated as a probit model. Equation 4 is the logarithm of the magnitude of expenditures for those individuals with expenditures and is estimated using ordinary least squares regression.

The coefficients are estimated from survey data and calculated based on the observed population, then repeated with a fake population of people who are identical in every way except for having no exposure to the risk factor. The difference in X between the two populations, divided by X for the real population, is the AF.

Indirect costs of lost productivity due to illness

Two indicators of morbidity costs are often considered: risk-factor-attributable work loss days and bed disability days. These are determined as the product of the AF and the total number of days lost. The standard epidemiological formula to calculate AFs for work loss days and bed disability days is: AF = [(pn + pc(RRc) + pf(RRf)] – 1 / [(pn + pc(RRc) + pf(RRf)], where pn, pc, and pf denote the percentage of people who have never been exposed, are currently exposed, and were formerly exposed to the risk factor; RRc (RRf) denotes the relative risk of the outcome measure of interest for currently (formerly) exposed people relative to people never exposed to the risk factor.

To calculate RR, first work loss days or bed disability days are estimated as a function of risk factor status, controlling for geographic region, demographic and socioeconomic variables and other risk modifiers using a Tobit model. Then the relative risk for currently (or formerly) exposed people is calculated as the ratio of predicted days for currently (formerly) exposed to predicted days for a hypothetical group of currently (formerly) exposed people with all the same characteristics except for not having any history of exposure to the given risk factor.

Option 2: Using an incidence-based approach

The morbidity costs of a disease or condition can also be estimated using an incidence-based cost of illness approach. This approach is generally considered to yield a conservative estimate of the true cost of disease.

The Healthcare Cost and Utilization Project has data from which we can derive average length-of-stay and medical costs for hospitalizations from different conditions. The Medical Expenditure Panel Survey provides the average cost per emergency department visit, annual out-of-pocket expenses for hospitalized patients, and outpatient expenses by diagnostic condition category. We can use these two data sources to calculate the direct medical costs associated with disease incidence attributed to a given risk factor of interest. This is easiest to do when there is a discrete risk factor from which incidence can be easily assessed, such as in natural disasters. Then we can add in lost work productivity estimated from hospital stays based on the usual median weekly earnings of full-time employees as reported by the Bureau of Labor Statistics.

Of note, we have to convert reported hospital charges to actual hospital costs, since hospitals tend to bill much more than the actual cost of providing healthcare (a fact worth extensive commentary on its own). The Healthcare Cost and Utilization Project has a web tool that allows researchers to do the conversion easily. The tool uses cost-to-charge ratios based on national and state hospital accounting reports (the cost-to-charge ratio is usually around 0.65).

Adding in indirect costs of lost productivity due to premature death: cost #3

Approaches to estimating the “cost” of premature death are heavily debated. The “value of a statistical life” approach assumes a particular value for a persons’ life (e.g., a typical American’s life, based on court litigation rewards and numerous other indexes, was valued at $7.9 million in 2008 dollars according to the FDA), which is of course an inherently ethically confusing and scientifically debatable concept. Regardless, the use of this number is standard in insurance and related markets. Simply multiply the portion of life lost by this number to estimate the quantified “cost” of years lost per this index of society’s valuation of life. Some people also use a “willingness to pay” approach, in which people are asked through surveys how much they are willing to pay to avoid the costs of pain and suffering associated with illnesses; this of course also comes with many caveats.

The related human capital approach to measure the value of lost productivity accounts for value lost due to risk-factor-attributable diseases. The cost to society of attributable premature death is calculated as the product of attributable deaths and the present value of lifetime earnings (PVLE) for each person. The number of attributable deaths is estimated by multiplying the AFs by total deaths for each underlying cause of death reported as being causally linked to the risk factor. The AF is determined for each age group and sex according to the epidemiological formula above. The relative risk of death from each associated disease is taken from real-world data. Risk factor exposure rates are also estimated from epidemiological survey data. Total deaths for each risk-factor-related diagnosis by sex and age can be obtained from government death registries.

The number of years lost from risk-factor-caused death is estimated by sex and age group as the product of the number of attributable deaths and the average number of years of life expectancy remaining at the age of death. PVLE per person is estimated by age group and sex by estimating the life expectancy for different sex and age groups, varying rates of labor force participation, and changing pattern of earnings at successive ages. This assumes that people will be working and productive during their lifetimes in accordance with the current pattern of earnings and work experience for their sex and age groups.

Important caveats

In studies that use any of these approaches, there are multiple data sources and assumptions being combined into one estimate, as with any mathematical model or economics assessment. Variability is introduced with each illnesses considered, with each direct costs included or excluded, and with numerous inferences about those costs. Where the data presents a range of options, it’s important to conduct a sensitivity analyses to determine how differences in inputs to the estimate can result in different estimates of ultimate cost.

Regardless, in looking at these two methods, it’s clear that a lot of assumptions and loose estimates are needed to arrive at any sense of the economic impact of a risk factor or disease. Nevertheless, these approaches may help us to gain some quantitative sense of a risk’s impact—let alone alert the public to a major public health issue that might otherwise seem diffuse and difficult to understand the importance of.

Relationships between climate change and human health

We’ve transitioned from using the term “global warming” to employing the term “climate change”, in part because increased average global temperature isn’t the only pattern being observed; other effects of climate change include stronger storm systems, increased frequency of heavy precipitation and extended dry periods. The contraction of the Greenland ice sheet is anticipated to cause a rise in sea-levels, and it has been argued that the effects of these phenomena will be inequitably distributed, since most of the actions causing climate change originate from the developed world, but the less developed world is likely to bear the brunt of the public health burden: declines in water quality and quantity, particularly in already dry regions; infectious disease transmission extending to newly-warmed areas; and hurricanes, floods and mudslides affecting the poorest area, leading to stagnant water-borne diseases like cholera.

Climate change is likely to have other specific impacts on food production. By increasing the populations of pests in currently temperate regions, climate change is likely to lower the yield of major crops and induce storms that damage those crops. Changes to the availability of feed, as well as heat stress, are also anticipated to affect animal production, and migration of fish away from coastal regions (affecting food availability) have also been observed.

Back in 800 BC, Homer catalogued the impact of ocean pollution in The Odyssey, writing about the death of sailors from contaminated fish. Today, ocean-induced poisoning is often related to consumption of contaminated seafood, particularly following harmful algal blooms (HABs)—accumulations of algae in the water (sometimes associated with a “red tide”), which have been found to produce neurotoxins and contaminate fish. Over 60,000 cases of poisoning by exposure to HABs are reported in the US each year. Some of the more dramatic cases involve paralytic shellfish poisoning, but chronic liver disease and other more indolent pathologies also result from HABs. Climate change and ocean warming appears to increase eutrophication (nutrient loading) causing phytoplankton growth, increasing the frequencies of HABs and especially the build-up of toxic species of algae—in addition to contributing to higher mercury uptake among fish and accumulation of toxic Vibrio bacterial species.

The data on food safety and climate change

Early evidence for the relationship between climate and food safety came from analyses showing that foodborne and diarrheal diseases exhibited a seasonal incidence pattern, increasing with average temperatures and after severe weather events. More recently, epidemiologists have noted increases in salmonella and campylobacter infections after weeks of elevated ambient temperature. Rates of salmonella in Australia specifically vary with increasing average yearly temperature, and higher humidity has also correlated with decreased hospitalization rates for children diagnosed with food-borne rotavirus (after correcting for other confounding factors), likely because survival of the virus is favoured at lower humidity. El Nino-associated rises in cholera have been documented in both Peru and Bangladesh, as have increases in diarrhoeal disease in Peruvians. HABs and related phytoplankton proliferation seems to increase environmental pH, giving Vibrio cholerae a competitive advantage over other marine bacteria since it thrives at higher pH and promotes attachment of  V. cholerae cells to zooplankton (particularly copepods) which protect V. cholerae cells from external stresses.

Recently, zoonotic diseases—those transferred from animals to humans—have been observed to contaminate food and water sources in the context of climate change. This appears to result from increased susceptibility of animals to disease, increased range or abundance of vectors and animal reservoirs for disease pathogens to hitch-hike on, and prolonged transmission cycles among vectors. Susceptibility of animals to disease seems to be occurring in the context of heat waves; high water temperatures, for example, inhibit the functioning of oysters’ immune systems, which precipitated a series of outbreaks of V. paparahaemolyitic among humans who consumed oysters  from northern waters (e.g., Alaska) between 1997 and 2004. Cycles of drought followed by heavy rainfall have also been found to enhance breeding sites for midge and mosquito vectors, which are associated with outbreaks of livestock diseases (similar patterns have been observed with the expansion of tick breeding territories). In 2000, global temperature changes were associated with strains of Rift Valley Fever (that probably originated in east Africa) escaping from Africa for the first time and infecting the Arabian Peninsula, an area well connected to Europe by a ‘ruminant street’ (rodents hitch-hiking on trade vessels). Changes to climate were thought to facilitate the survival of rodents and the overlap of rodent populations to a degree not previously thought possible.

Finally, a number of concerns have been raised from mycotoxins: a group of highly toxic chemical substances (like aflatoxin) that are produced by toxigenic moulds like Aspergillus that commonly grow on a number of crops when temperatures rise.  In recent years, outbreaks of acute aflatoxicosis have been reported for the first time; 125 deaths occurred in Kenya for example, out of 317 reported cases resulting from consumption on aflatoxin contaminated maize in 2004, followed by repeat events in 2005 and 2006. Benin and Togo have now reported similar occurrences in the context of annual temperature increases. Aspergillus outbreaks in Europe and the United States have now started to occur for the first time, following extended droughts.

Approaches to prevention

Mathematical models have recently been used to predict when HABs or other sudden outbreaks of harmful diseases will occur. Inferences can be made based on patterns from existing data, such as by studying disease trends in the presence and absence of El Nino events. For example, one group recently used time series analysis along with epidemiological data to predict disease outbreaks caused by three foodborne pathogens (Salmonella, Campylobacter, and  E. coli), warning inspectors about the times they needed to be particularly vigilant or have extra human-power available for testing food samples. Another group used remote sensing data to indirectly measure V. cholera behaviour as a function of ocean temperature and surface height, providing a means by which to predict conditions conducive to pandemic disease. This resulted in keeping sensors along coastal areas to detect early HABs and warn fishing regulators.

A second approach to addressing the problem is through molecular ecology, in which nucleic acid sequence comparisons and other genomics-based approaches are used to characterize complex microbial communities, identifying which may be pathogenic or produce important toxins. These methods are applicable to the study of microbial evolution during periods of increasing temperature, including virulence factor acquisition and changes in gene expression (potentially affecting growth of algae or production of toxins) due to environmental exposures. When combined with remote sensing and geographic information systems, it should be possible to use this information to model the distribution and spread of different types of pathogens as a function of temperature or other climate change variables.

Building capacity

Perhaps most important of all is proactive surveillance through direct detection of pathogens in foods and the environment: old school, door-to-door epidemiology. This requires a lot of human effort, but can be aided with molecular methods. One problem is that common molecular laboratory tools like PCR can’t tell between inactive and active pathogens in a food sample, while activity-based assays like enzyme immunoassays aren’t very specific and can produce a lot of false positive results. An ideal method would provide rapid field-based testing of dangerous pathogens in water, crops or animal products. While this technology is not yet available, a surrogate is used such as the detection of fecal organisms and E. coli to predict fecal contamination of water. Unfortunately, these surrogates often do not correspond well to cholera or other pathogenic bacterial contamination levels.

The other problem with developing a strategy to detect environmental contaminants is not technical, but political. As the UN’s Food and Agriculture Organization (FAO) recently specified, building capacity to detect contaminated food is often counter-productive from an economic standpoint: the surveying country would not want to face the economic trouble of declaring that their exported food is potentially hazardous. Furthermore, as food-borne illness or unsafe foods typically affect the poorest groups, building safe food stocks is often not considered by powerful politicians who rarely engage with the lower class.

The FAO and others have therefore engaged in a mutual-risk/mutual-surveillance scheme in which international modelling and monitoring of HABs and other risks to food is publicized widely, just as the WHO publicizes infectious disease outbreaks, in the hope that such attention will force politicians to engage in risk mitigation. Whether or not this “Emergency Prevention System for Transboundary Animals and Plant Pests and Diseases” (EMPRESS) program will work (and whether anyone can remember what the acronym stands for) has yet to be tested in a real-world emergency. But increased attention to the problem from epidemiologists like Epstein is likely to at least maintain active surveillance and build initial capacity.

There is increasing evidence that the quality of our homes and cities is a critical determinant of cardiovascular disease, diabetes and lung conditions. As urbanization and economic change occur globally, whether we live in a house free of dust in a city with open parks and traffic regulations, or in a dusty tenement building next to a major road, seems critically correlated with our likelihood for having shortened life expectancy, poor nutrition, heart disease and lung problems. In this week’s blog post, we look at some of the mechanisms relating the “built environment”—our human-made surroundings of daily living—to the risk of illness. We ask the question: can we do for our hearts and lungs what the Bauhaus movement did for functional design?

Indoor air quality

If Dwell Magazine had a feature edition on designing a healthy home, they’d have to tackle the major issue of indoor air quality. Much research on the built environment’s impact on health was revealed through a series of studies on asthma among children living in low-income public housing units in the United States. Poor indoor air quality resulting from dust and dirt in public housing units was a major cause of emergency room visits during the 1980’s and 90’s among these children, leading to new programs for housing quality checks and maintenance, which we featured in a previous post.

A parallel concern about indoor air quality has been highlighted in the global health realm because of “dirty cookstoves”—the wood-burning stoves that many people in Asia, Africa and Latin America use to cook food indoors. Most people who use these stoves don’t live in an area where it’s easy to cook outside, or don’t have the funds to convert to a gas-burning stove, so wood smoke (just like from a campfire) accumulates in the home, where (usually) a woman is cooking for several hours a day, sometimes with a child strapped to her back. The studies on this cause of indoor air pollution reveal that the wood smoke significantly impairs the immune system; an Indian study found that those exposed are 2.5 times more likely to experience tuberculosis, and infants are 2.2 times more likely to acquire a respiratory tract infection, one of the leading causes of death among children worldwide. Lung cancer and emphysema have been similarly observed to increase in frequency among users of these wood-burning stoves, and the particulate matter from them acts as an eye irritant, leading to a 1.3-fold increase in the risk of cataracts among those exposed.

In part due to the work of Professor Kirk Smith and others at Berkeley, improved stoves have been designed and deployed in a number of countries. See here for some of the representative designs. Costs for construction and installation of improved stoves typically range from $1.20 to $5 per unit. Even developing better ventilation without a new stove can improve outcomes, as evidenced by the use of simple chimneys.

Environments built for activity

More recently, the continued rise of obesity has led to a series of research studies on how urban design, both in individual homes and in larger neighborhoods, can critically determine whether people will engage in physical activity to counteract the impact of living increasingly sedentary lifestyles in front of computer or television screens.

One of the principal challenges to encouraging physical activity has been to create urban spaces that are conducive to, and safe for, walking or biking. A variety of people have created “walkability” scores to assess how easy it is to avoid major roads and access clear walking paths in a neighborhood. The safety of walking and biking seems to be a critical determinant of whether people will engage in these activities, especially in low-income neighborhoods and in areas with heavy road traffic.

In the past, zoning laws were used to prevent mixed use of land, such that living and working and playing in the same spaces was nearly impossible; in fact, public health practitioners supported such zoning to reduce the spread of tuberculosis and separate homes and schools from toxic chemicals spewing out of abattoirs and tanneries. The growth of the suburbs then followed with the invention of the automobile to separate people from work and play even further. But newer studies show that a focus on making urban environments more hospitable for living (rather than focusing on suburban development) can improve physical activity outcomes and subsequent cardiovascular disease; mixed-use neighborhoods (those with shops, homes and businesses in one area) have more destinations worth walking to, and seem to reduce crime while promoting exercise. This may also be because of greater access to fitness centers and changes in social dynamics to encourage healthier eating, according to two studies showing that the significantly lower obesity rates in mixed use areas were not just a result of physical activity improvements.

Living in a neighborhood with higher crime is also associated with less desire to get out of the house to do physical activity and increased coronary heart disease prevalence even after controlling statistically for individual-level income. The ability of children to play safely outside is a critical determinant of whether their parents will let them do so, and subsequently whether they develop obesity. These and other related principles have been captured in the “new urbanism” and “Smart Growth” movements.

Food environments: access is necessary, but maybe not sufficient

Concordant with the research on physical activity is research on nutrition. We discussed food desserts in a previous post, in which we described how limited access to healthy foods and supermarkets makes it difficult for many people to defer junk food at the neighborhood gas station instead of traveling far for a healthy meal.

But new research also suggests that while better access may be necessary for improved nutrition, it may not be sufficient to change people’s consumption patterns. Even as more nutritious food has become a focus of city programs, there is evidence that people have not converted over to it, perhaps because the almost ‘addictive’ nature of junk food. Something else is going on socially that explains why increased access to supermarkets doesn’t seem to be sufficient to improve nutrition. Curtailing neighborhood fast food outlet density, for example, doesn’t have as much impact as we had assumed in the past, and similarly improving supermarket availability doesn’t seem to confer as much benefit as we thought. Actually promoting change in what food people select, after facilitating access, seems to be the next step, but this requires further investigation into variables like what economic and social factors are playing into individual-decision making (like food taxes, which we discussed in a previous post).

Back to basic infrastructure

While the social factors affecting food purchasing are difficult to tackle in urban design, an even more difficult challenge is class politics. In his legendary book Planet of Slums (which we highly recommend as essential reading for anyone concerned with public health), Mike Davis reviews the evidence that rapid and unprecedented urbanization is leading most people to live in high-concentrated living environments. In this context, it’s important not to get carried away in thinking that all healthy design should focus on the needs of sedentary people in high-income countries. About one billion people still live in slums today, and the basic infrastructure needed to support a healthy life in these communities is sorely lacking.

As revealed by the Sinai fire in Nairobi last September, slums are regularly haunted by the fact that they are built as shantytowns—essentially as perpetual occupations of the poor over the only land they’re allowed to live on. As a result of their essentially un-planned design and undesirable land characteristics, dangers lurk around every corner in these communities. Electrical lines and (in the case of the Nairobi disaster) oil pipelines are directly next to living spaces, posing grave dangers for injury. In the Nairobi pipeline disaster, 75 people were killed and 112 badly burned after an oil pipeline leaked and exploded, setting sewage on fire and hurling exploding vapor through makeshift homes that directly abutted the line. Many politicians assumed that pushing people out of this kind of environment—rather than building better infrastructure within the slum—would solve the problem. After Nairobi’s fire, dozens of slum homes were razed by bulldozer in an attempt by the government to claim they were protecting people from further harm; those displaced by the bulldozing occupied an already-settled area, worsening crowding, violence and a provoking a deadly stampede.

But when there’s nowhere else to go, the question is not how to push people off the land, but how to make the land more hospitable. The most common focus for such infrastructure improvement is, unsurprisingly, sewer systems and water quality. One of the more inspiring movements in this realm is “Community Led Total Sanitation” (CLTS). Rather than creating standardized toilet designs, providing hardware subsidies, developing educational modules, and spending money on the other usual development junk, the CLTS approach has been a social movement around…well… shit. The approach is to facilitate communities to analyze their own defecation patterns and problems (sometimes with embarrassed guffaws), present the findings to each other through community forums, and move into the realm of community-driven intervention. Sometimes the work fails, but a lot of times it’s succeeded in overcoming local and regional politics and building some real sanitation infrastructure.

The CLTS successes in Bangladesh, for example, highlight for us that not all design comes from thought experiments among elite architects. Kamal Kar, one of the pioneers of the movement, reminds us that some guts and commitment are needed to design our homes and communities in a participatory manner, and that we might import insights from programs like CLTS into our higher-income countries:

Trends in smoking prevalence

The best available data on tobacco use describes trends in cigarette smoking among adults. Smoking prevalence among adults has markedly diverged among disparate countries over the past decade. Using industry data from 1997 to 2010, it’s clear that a few countries have markedly reduced the proportion of adults who smoke cigarettes, having lowered smoking prevalence by 10% or more: Norway (from 33% of adults smoking in 1997 to 19% smoking in 2010), South Korea (from 35% to 21% during that period), Slovenia (from 36% to 24%), Japan (from 36% to 25%) and Denmark (from 32% to 22%). The latest tobacco control reports from these countries indicate that they implemented tax policies to reduce tobacco consumption (most instituted a combination of a specific tax–like a tax per quantity of cigarettes sold–and ad valorem taxes–which are like sales taxes taking a percentage of the sales price). The countries also confiscated equipment and supplies related to illicit tobacco trading, banned smoking in a number of public places, and enforced various package labeling requirements. Denmark and Slovenia even implemented a comprehensive ban on all tobacco advertising, promotion and sponsorship. It would be interesting to conduct an analysis of which measures may have contributed most to the decline in smoking prevalence among these nations, as compared with less successful countries.

It’s intriguing that a couple of these more successful countries are from Eastern Europe and Southeast Asia, as their next-door neighbors dominate the list of countries where more than one-third of adults still smoke (see the table below). This implies that cultural or regional economic factors are not insurmountable barriers to effective tobacco control. But Eastern Europe and the Middle East do face significant challenges; a few countries have experienced more than a 5% increase in adult smoking prevalence from 1997 to 2010, specifically Romania (from 19% of adults smoking in 1997 to 28% in 2010), Iran (from 11% to 19% during that period) and Saudi Arabia (from 23% to 30%). While all three of these countries signed the FCTC, none of them have turned in their requested reports on FCTC progress, which were due earlier this year.

The highest overall prevalence rates in the world are among Russians (43% of adults smoking in 2010), Greeks (40% in 2010), and Bosnians (39%), followed by Bulgarians (38%) and Macedonians (36%). These countries are also FCTC signatories, but Russia just started its implementation process in 2008. Greece, Macedonia and Bosnia are overdue in reporting its FCTC progress, suggesting that lack of political will to generate regulation may be a common theme among countries with higher or rising smoking rates.

The tobacco industry has arrived at its own projections for where tobacco smoking prevalence is likely to rise and fall in the future. The available projections span five years, and predict the largest declines in adult smoking prevalence in the Ukraine (from 29% smoking prevalence in 2010 to 24% in 2015), Cameroon (from 13% to 9% during that time period), Serbia (30% to 27%), Norway (19% to 15%) and New Zealand (21% to 18%). Of 80 countries for which data are available, 63 are anticipated to experience declines in smoking prevalence and three are expected to have no significant change in prevalence over the next five years. The few countries who are projected by the industry to have a significant rise in future smoking prevalence over the next half decade are in Africa and the Middle East: Morocco (increasing prevalence from 20% in 2010 to 29% in 2015), Kenya (20% to 23%), Tunisia (36% to 38%), Iran (19% to 21%) and Saudi Arabia (30% to 31%). Kenya has implemented FCTC provisions since 2004 and kept up to date with all of its reporting, while the other countries anticipated to face a rise in prevalence have failed to report on their progress in ratifying the treaty’s provisions.

The overall adult prevalence statistics from these countries, however, disguise a striking demographic feature hidden beneath the averaged data. That is, there is a marked difference within countries between how much adult men smoke and how much adult women do. In several countries, male smoking rates are at least twice the rates among women. In Russia, for example, 62% of adult men smoked in 2010, while about 29% of women did. Similarly, 61% of Tunisian men, 48% of Chinese men and 24% of Indian men smoked, while the rates of smoking among women in those countries was 11%, 3% and 4%, respectively. There are a few locations where prevalence rates of smoking among women are strikingly high, but still not as high as among men (33% of Bosnian women, for example, smoke, as compared to 46% of Bosnian men).

To get a better look at tobacco industry data, see the WHO’s online database. To find out what tobacco controls from the FCTC provisions have been implemented in different countries, click here. You can also access individual country reports describing FCTC implementation progress here.

Actual sticks sold

Because prevalence statistics simply reflect the proportion of a country’s population that smokes, the statistics make it hard to visualize how many actual people are affected, which is conditional upon the population size of the country. And even the absolute number of smokers doesn’t give a sense of the full public health impact of smoking, since cigarette smoke exhibits a “dose-response” effect on negative outcomes like heart and lung disease, such that an individual with a longer and heavier smoking history will have a greater likelihood of experiencing tobacco-related disease than someone with a brief or light smoking history. The number of actual cigarette sticks sold can therefore give some sense of the burden of cigarettes around the world, capturing some aspects of both the population size and the dosage of cigarette exposure affecting different countries or regions.

When we look at the number of cigarettes sold by region and by country (figures below), we see the true impact of Chinese tobacco use on the overall global tobacco epidemic. While other countries have a greater absolute smoking prevalence, China’s moderately high prevalence (26% of adults smoking in 2010) translates into an effectively very large market of consumers given the country’s massive population size. Of greater concern is that while the absolute smoking prevalence in the country has remained essentially flat for the last decade (and is projected to remain flat at 26% for the next five years according to the industry’s own projections) the number of sticks sold in China has increased dramatically and continues to rise on an exponential upward trajectory that is about five times faster than the population curve, suggesting heavier smoking over time. Overall, 2.3 million cigarettes were sold in China last year, which is projected to rise to 2.8 million by 2015. When China is removed from the figures, we can see more clearly that cigarette sales have declined in North America, Western Europe and Latin America, plateaued in Eastern Europe to some extent, and rose in the Middle East and Africa. Among the top cigarette consuming nations, the US and Japan have markedly lowered their consumption over the past decade or so, while Russia and Indonesia have increased their cigarette consumption, also beyond their population growth curves, as with China.

Corporate market shares

It’s interesting to look at which key companies own different shares of the global cigarette market. Given the size of the Chinese industry, it’s not surprising that the China National Tobacco Corporation owns the majority share (40% of the global market in 2010), and is increasing its share over time (it owned 32% of the global cigarette market in 2001). Philip Morris (aka Altria) has released its data more recently, and comes in second (at 15% of the global market in 2010), followed by British American Tobacco (12%) then Japan Tobacco (10%) and Imperial Tobacco (5%).

But such aggregate global market share data are not reflective of stark regional differences. In every region outside of Asia, Philip Morris/Altria and British American Tobacco dominate the marketplace (see table below). Morris/Altria is dominant in Eastern Europe (29% share), North America (43%) and Western Europe (37%), while British American Tobacco is dominant in Latin America (54% share) and the Middle East and Africa (22%). Ultimately this means that, worldwide, Marlboros are the most smoked cigarette (consumed by 5.1% of the market), followed by Hongtashan (2.5%) because of the Chinese consumer market.

What’s missing

The data on non-cigarette tobacco products is limited. Smokeless tobacco seems to be increasingly popular in some countries, but the industry rarely advertises these statistics. We also lack clear industry data on tobacco use among youth (probably because they don’t want to advertise that they collect such data), the WHO has kept track of tobacco use estimates among the young as part of its MPOWER initiative. Of course, official industry statistics are usually from large national or multinational corporations, while many cigarettes are sold through smaller-scale and under-the-table initiatives, as with the cottage industry of Indian bidis. These informal tobacco products can sometimes contain higher dangerous carcinogenic content than mass-manufactured cigarettes.

Enforcing the FCTC

There have been extensive reviews of “what works” in tobacco control, so we will not rehash that full literature here. The FCTC website briefly summarizes the critical findings from years of research: that while a minority of smokers know the dangers of smoking, most of those who know about the dangers want to quit but have difficulty doing so; that smoking cessation products can significantly increase the likelihood of quitting; that picture warnings on packages reduce the number of children who begin smoking and increase the number of smokers who quit; that bans on advertising significantly reduce tobacco consumption; and that taxes are the most effective way to reduce tobacco use, especially among the young and among the poor.

What the industry’s data on tobacco sales reveal is additional useful information:

(1) That most countries are expected to reduce their overall adult smoking prevalence over the next several years;

(2) But some North African and Middle Eastern countries may see perverse increases in adult smoking rates, and have exhibited limited reporting to the FCTC Conference of Parties, which is charged with enforcing the treaty. Are there teeth to ensure their participation in the treaty’s mandates? Bad press may be a main avenue for action, along with reporting the significant human and financial costs of maintaining high tobacco prevalence rates;

(3) High smoking rates in Eastern Europe can be tackled effectively by existing strategies, as demonstrated by Slovenia;

(4) But China remains it’s own unique animal, with a massive and politically well-connected tobacco producer expanding its markets, and indications that heavier cigarette consumption is taking place in the country over time, pushing up regional and worldwide tobacco use statistics.

The signing of the FCTC by 174 countries serves as an incredible precedent for global health cooperation and regulation. The question now is whether we can sustain interest and concern in tobacco control to actually enforce the treaty’s contents, given that tobacco has been viewed as a previous generation’s problem in North America and Western Europe, but remains one of the top risk factors for disease in most of the world.

The physician Jeffrey Brenner became famous for piloting a model of healthcare that would attempt to simultaneously improve services while reducing healthcare costs in his city of Camden, New Jersey. His model, recently profiled in Atul Gawande’s popular New Yorker article “The Hot Spotters”, was based on a simple observation: that sick people with poorly-treated diseases tend to be clustered in certain parts of the city. The sickest areas are not always the poorest, surprisingly; some city blocks with a higher rate of poverty have lower hospitalization rates than other areas with a lower rate of poverty, probably due to several other neighborhood factors like social cohesion (no neighbors to help out when you’re sick) and lack of access to a grocery store (leaving you to purchase junk food from gas stations or liquor stores). Brenner started to make maps of his city using local hospital admissions data, trying to identify where people with the highest healthcare costs tended to live. He found “hot spots” of disease in certain nursing homes that were unsafe, or housing projects with poor access to essential nutrition needs. And he reached out to those people who were sickest, to give them special attention as a doctor: more frequent medical visits than a regular patient would get; a “health coach” to ensure they were able to get their medications and take them properly; more social workers to tackle insurance problems or to fill-out Meals on Wheels paperwork; community meetings in the worst-affected blocks, to rally for political change.

This model of “hot spotting” quickly spread to other major urban areas. As part of San Francisco’s public health system, I work in the “Housing and Urban Health” clinic, a primary care “medical home” that reaches out to patients in the Tenderloin district of the city. The clinic provides serial visits to physicians, psychiatrists and nurses for patients who receive services as far ranging as daily dressing changes for chronic wounds to the direct dispensing of medications every morning so that even the most transient people can take their pills on schedule. Case managers come knocking on patients’ doors in the high-rise “single room occupancy” hotels in the area, finding out why patients missed appointments, and even helping them to negotiate “behavioral contracts” to avoid getting evicted. And despite how extensive these services seem, there’s one striking reason that our City Council keeps them around: to save money. By getting people into substance abuse treatment and averting associated crimes and hospitalizations, and by preventing extremely expensive trips to San Francisco General Hospital’s Emergency Room for out-of-control diabetes or high blood pressure, this program and others like it around the country have averted costs for city councils with already-constrained budgets. One of my patients had previously visited the emergency room 56 times in one year (that’s more than once a week) for problems caused by alcoholism (falling down stairs, having a seizure, etc.); not only does every doctor and nurse in the ER know him by name, but they joke that he’s back in his usual “lounge chair” (the gurney), requesting his coffee cup. Since enrolling in the program, he’s only gone twice to the ER (once for pneumonia and a second time after stepping on a nail), and is no longer homeless or chronically drunk. Last week, he got a part-time job in a local restaurant.

But there is something deeply unsettling about this “hot spot” model. If we can look at the data describing ambulance pick-ups by city block, or the addresses of people who end up in the ER, or the rate of drug overdoses at different intersections, and map all of that information out in a way that helps us pinpoint where ill people need services the most, then why can’t we prevent the illnesses in the first place? Can’t we look at the data on what environments are most likely to make people sick, and prevent them from even getting disease—by ringing their doorbells and preemptively offering preventive services?

When I posed the question to a group of physicians recently, I got pummeled by doctors on both the left and the right wings of the political spectrum. The liberals were afraid that if we said that “prevention is better than cure,” we’d play into the hands of Republicans who want to cut-off healthcare from people who are already sick. Besides, prevention is too difficult, they said, because no one takes a doctor’s advice until after they’re already ill. The conservatives thought that we already spent too much money on people in the same neighborhoods, taxing those who work for a living to pay for those who have delved into drugs and fatty foods and made other bad decisions, so mass media education alone would do better than paying for yet another new welfare-style program. In any case, I was told, this was a matter of poverty and politics, not a matter of medicine.

It seems to me that all of these excuses are fairly weak . There is no reason that public health prevention programs should be mutually exclusive with providing medical care; in fact, some of our most effective prevention opportunities exist among people who already need medical care for one condition (e.g., diabetes or HIV) and for whom good medicine for that condition can avert secondary problems that cause suffering and rack-up medical expenses (e.g., kidney failure or infections, respectively).

Furthermore, models of community-based prevention do exist, and do quite well, often in the unlikeliest of places. The group Nyaya Health has been among many to pilot community health worker programs that send teams of roving nurses out to patients’ homes—often in the most inhospitably mountainous regions of Nepal. Armed with a minimal amount of medical equipment in a backpack, it’s possible to deliver prevention door-to-door, whether in the form of vaccines and medications, or health advice and assistance. And this task should be easier on the flat terrain of urban America than in the Himalayas. Such door-to-door prevention is the basis for the “active case finding” approach to tuberculosis control, which involves tracking down family members and friends of tuberculosis patients throughout both urban and rural regions, to treat latent tuberculosis before it becomes a deadly active disease. This model has been used for decades; there’s no reason now to prevent those roving agents from visiting high-risk people with a blood pressure cuff or glucometer once a week, to assist with dietary changes or adjust preventive medications or provide other support to keep them out of illness even if they wouldn’t otherwise come to the doctor’s office until they actually feel symptoms of disease in another five or ten years. What’s most surprising about these programs is that people really do open their homes to folks who act in good faith; some of our patients in the Tenderloin mention that we’re the first people in years to knock on their doors and see how they’re doing. And after entering, we discover new issues to address, like the dust content of a public housing building where future asthma sufferers live.

What about problems like drug use or obesity? Are these problems just a matter of bad decision-making, which can’t be repaired by bringing access to preventive services to someone’s home? An abundance of research suggests that the quality of a neighborhood truly matters for these problems: a recent New England Journal of Medicine study showed that when people are randomized to live in low-income housing in a low-income neighborhood, versus similar housing in a higher-income neighborhood, the latter group reduced their rates of obesity in spite of having the same socio-demographic characteristics as the former group (possibly because of easier access to grocery stores and safe parks to exercise in). The same trends appear to apply to substance abuse as well; a neighborhood’s influence on health includes the likelihood of becoming a drunk or getting addicted to drugs. But since we can’t move everyone out of poor neighborhoods into rich ones, trying to correct for the deficiencies of some neighborhoods through enhanced services—whether by simply providing cooking recipes to make healthier food with what’s locally available and inexpensive, or coming to a person’s home for weekly blood-pressure monitoring—could avert the onset of diseases that leave households with medical bills and the loss of a breadwinner after strokes and heart attacks. There’s early data from community-based prevention programs that even difficult problems like substance abuse can be averted through local, door-to-door action of this kind. Even for the hard-core right-wing folks who don’t want to pay for their community members’ medical expenses, the reality is that their communities will be increasingly blighted by neighbors who descend into sickness, and current data suggest that no amount of segregation can prevent that from affecting the overall economy, even if the white picket fence around their homes are built far away from the problems of the inner city.

The fact that our hospitals are overrun with preventable diseases makes the neighborhood factors affecting health a matter for physicians as much as it’s a matter for anyone else involved in their community. We now have the technology and data to make maps of who is likely to become ill in the future—the predictive models to know what constellation of demographic, social and economic factors are likely to substantially increase the risk of different chronic diseases. We also know, from our hospital databases, which people are unlikely to access doctors or healthcare advice or health resources before they are rushed in an ambulance to the ER a few years from now for complications of undiagnosed or uncontrolled disease. Even as we try to address those underlying factors causing these disease (like poverty and inequality), which will take decades, we have the resources and medical know-how to design good prevention interventions to avert illnesses among people who currently live under the conditions that will cause disease before we rectify the underlying vast social problems. Now, it seems, we just need the willpower to make prevention a practical reality, even if it requires making a few house calls.

In Greece, representative samples of 12 346 and 15 045 respondents were recruited in 2007 and 2009, respectively, by use of consistent methods, of which a total of 26 489 had complete sociodemographic data (see here for more details). We also drew on reports from medical research institutes, health prefectures, and non-governmental organisations (NGOs). These reports include epidemiological indicators, data on hospital admissions, and reports on mental health problems and the status of vulnerable groups.

Compared with 2007—ie, before the crisis—2009 saw a significant increase in people reporting that they did not go to a doctor or dentist despite feeling that it was necessary (odds ratio 1.15, 95% CI 1.02—1.30 for doctors’ visits; 1.14, 1.01—1.28 for dentists’ visits (figure below), after correcting for differences in survey respondents including age, sex, marital status, educational attainment, and urban or rural residence. The main reasons for not seeking medical care did not seem significantly linked to an inability to afford care (0.87, 0.74—1.02), but to long waiting times (1.83, 1.26—2.64), travel distance to care (2.50, 1.35—4.63), waiting to feel better (1.93, 1.26—2.96), and other reasons not captured by the survey (1.54, 1.05—2.27).

Since Greece’s universal public health-care system entitles citizens and those with social insurance to visit general practitioners (GPs) free of charge and to attend outpatient clinics of hospitals for €0—5, these noted reductions in access probably reflect supply-side problems: there were about 40% cuts in hospital budgets, understaffing, reported occasional shortages of medical supplies, and bribes given to medical staff to jump queues in overstretched hospitals.

Although people were less likely to visit GPs and outpatient facilities, there was a rise in admissions to public hospitals of 24% in 2010 compared with 2009, and of 8% in the first half of 2011 compared with the same period of 2010. Major private health providers, although comprising a smaller proportion of care delivery than public providers, were also hit by pressure on personal budgets and registered losses after the onset of the crisis. A 2010 study reported a 25—30% decline in admissions to private hospitals.

There are signs that health outcomes have worsened, especially in vulnerable groups. We noted a significant rise in the prevalence of people reporting that their health was “bad” or “very bad” (1.14, 1.02—1.28). Suicides rose by 17% in 2009 from 2007 and unofficial 2010 data quoted in parliament mention a 25% rise compared with 2009. The Minister of Health reported a 40% rise in the first half of 2011 compared with the same period in 2010. The national suicide helpline reported that 25% of callers faced financial difficulties in 2010 and reports in the media indicate that the inability to repay high levels of personal debt might be a key factor in the increase in suicides. Violence has also risen, and homicide and theft rates nearly doubled between 2007 and 2009. The number of people able to obtain sickness benefits declined (0.61, 0.38—0.98) between 2007 and 2009, probably owing to budget cuts, and further reductions to access and the level of benefits are to be expected once austerity measures are fully implemented.

A significant increase in HIV infections occurred in late 2010. The latest data suggest that new infections will rise by 52% in 2011 compared with 2010 (922 new cases versus 605), with half of the currently observed increases attributable to infections among intravenous drug users. Data for the first 7 months of 2011 show more than a 10-fold rise in new infections in these drug users compared with the same period in 2010. The prevalence of heroin use reportedly rose by 20% in 2009, from 20 200 to 24 100, according to estimates from the Greek Documentation and Monitoring Centre for Drugs.

Budget cuts in 2009 and 2010 have resulted in the loss of a third of the country’s street-work programs; one survey of 275 drug users in Athens in October, 2010, found that 85% were not on a drug-rehabilitation program. Many new HIV infections are also linked to an increase in prostitution (and associated unsafe sex). An authoritative report described accounts of deliberate self-infection by a few individuals to obtain access to benefits of €700 per month and faster admission onto drug substitution programs. These programs offer access to synthetic opioids and can have waiting lists of 3 years or more in urban areas.

Another indicator of the effects of the crisis on vulnerable groups is increased use of street clinics run by NGOs. Until recently, these clinics mainly catered to immigrants, but the Greek chapter of Médecins du Monde estimates that the proportion of Greeks seeking medical attention from their street clinics rose from 3—4% before the crisis to about 30%.

Despite many adverse signs, there are some indications of improvement. There have been marked reductions in alcohol consumption and, according to police data, drink-driving has decreased. These trends were not artifacts of reduced detection owing to budget cuts in the police force, since police checks remained the same and more drivers were screened in 2009 than 2008.

Overall, the picture of health in Greece is concerning. It reminds us that, in an effort to finance debts, ordinary people are paying the ultimate price: losing access to care and preventive services, facing higher risks of HIV and sexually transmitted diseases, and in the worst cases losing their lives. Greater attention to health and health-care access is needed to ensure that the Greek crisis does not undermine the ultimate source of the country’s wealth—its people.

This article first appeared in The Lancet, and is co-authored by Alexander Kentikelenis, Marina Karanikolos, Irene Papanicolas, Martin McKee and David Stuckler.