City leaders across the country face tight budgets, decreasing revenues, and unemployment challenges. And, as Â鶹´«Ã½AV-Healthways Well-Being Index data reveal, they also face another looming problem: high obesity rates that are accompanied with astronomical healthcare costs.
Obesity's healthcare costs aren't distributed equally across the nation, and definitely not across U.S. cities.
According to its 2009 studies of 187 U.S. metro areas, Â鶹´«Ã½AV estimates that the direct costs associated with obesity and related chronic conditions are about $50 million per 100,000 residents annually in cities with the highest rates of obesity. The direct and additional hidden costs of obesity are stifling businesses and organizations that stimulate jobs and growth in U.S. cities.
Losing weight, gaining savings
Obesity's healthcare costs are not distributed equally across the nation, and definitely not across U.S. cities. The majority of cities Â鶹´«Ã½AV studied need to cut their obesity rates by at least a quarter to come close to the national goal of 15% set by the Centers for Disease Control and Prevention. Cities with the highest rates of obesity need to cut their rates by more than half.
From a cost savings perspective, if all 187 cities reduced their obesity rates to 15%, the U.S. could save $32.6 billion in healthcare costs annually. Additionally, if the nation's 10 most obese cities cut their rates to the national 2009 average of 26.5%, they could collectively save nearly $500 million in healthcare costs each year. Cut to 15%, the cost savings would climb to nearly $1.3 billion annually.
Â鶹´«Ã½AV is able to calculate the incremental cost of healthcare per year for each of these cities by multiplying the estimated additional direct annual healthcare costs for an obese person ($1,429 per person per year) by the population, then multiplying that by the obesity rate. A city of 100,000 citizens with a 20% obesity rate, for example, will have an incremental healthcare cost of $28,580,000 ($1,429 X 100,000 X 0.20 =$28,580,000).
Twenty-one metro areas -- led by Montgomery, Alabama and Stockton, California -- earned the unhappy distinction of having obesity rates of 31% or higher in 2009, based on their residents' self-reported height and weight. In the 10 most obese cities, where at least one-third of residents reported a body mass index (BMI) higher than 30, the annual obesity cost per 100,000 residents was about $50 million. This is roughly twice the cost per 100,000 residents in the least obese cities. (See graphic "The 10 Most Obese U.S. Cities and Associated 2009 Healthcare Costs.")
Among the 11 cities with obesity rates lower than 20%, four are in Colorado. One of these cities, Denver, still spent an estimated $704 million in preventable healthcare costs in 2009 because of its 19.3% obesity rate. But its healthcare costs would have been $262 million higher if its obesity rate matched the national average. (See graphic "The 11 Least Obese U.S. Cities and Associated 2009 Healthcare Costs.")
Beyond the dollars and statistics, real people are experiencing physical health problems associated with obesity. In the 10 most obese metro areas, diabetes is 72.5% more common than in the 11 least obese cities, as is a history of heart attacks (64.5%). The incidence of high blood pressure and high cholesterol is also greater in these cities.
These health challenges may help explain why residents in the most obese cities are more likely to report lower energy levels than those in America's least obese cities. Residents in the most obese cities report 4.2% less daily energy, which can result in lost productivity -- one of the hidden costs of obesity. (See graphic "Differences in Health, Healthy Behavior, Basic Access, and Daily Energy.")
What America's cities can do
Â鶹´«Ã½AV's data reveal that America's obesity crisis is hitting its cities hard. The most obese cities have higher than average numbers of residents facing serious health challenges and the healthcare costs to prove it. Even in the least obese metro area in the country, Fort Collins-Loveland, Colorado, there is room for improvement. The obesity rate in this metro area is still higher than the national target of 15%, and the result is obesity-related costs estimated at more than $68 million annually.
Real change starts with greater awareness and a combination of community measurement and education. These efforts may be more likely to succeed if leaders apply behavioral economics to the problem and change social expectations:
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Schools and businesses can help reduce obesity and encourage healthy behaviors by eliminating sugary drinks and foods that are deep-fried or high in sugar, replacing them with healthier alternatives accompanied by nutritional information.
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Schools can initiate "walking school buses" to encourage kids and parents to walk to school safely in large groups.
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Businesses can give their employees an incentive to maintain healthy BMIs by increasing contributions to medical spending accounts when these goals are met.
City governments also can use behavioral economics to promote healthy behaviors among their residents. Some cities are already developing "well-being institutes" that house local health, well-being, and economic data, such as data from sources like the Â鶹´«Ã½AV-Healthways Well-Being Index. Communities also can:
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invest in widening and lengthening walking/biking paths to encourage pedestrian traffic to grocery stores, schools, and mass transit
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denote bike lanes on city roads
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levy additional taxes on foods that are high in sugar to reduce their purchase and consumption
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maintain a security presence in at-risk neighborhoods to ensure that people feel safe when they shop fresh food stands and farmers markets
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certify and promote restaurants that offer smaller portions and a wide selection of heart-healthy fare as "community well-being" establishments
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certify and promote grocery stores that make it easy for shoppers to find healthy, low-fat foods and that provide free recipes for healthy home cooking
Above all, communities can encourage healthcare, business, political, and education leaders to share and discuss common goals and metrics to measure their efforts toward improvement. Bringing leaders together regularly to analyze leading and lagging indicators of their community's Physical Well-Being could drive decisive action and policies to create positive results.
Research MethodologyThe Â鶹´«Ã½AV-Healthways Well-Being Index collects daily random samples of the U.S. 18 and older population, completing approximately 350,000 interviews per year. This robust sample enables Â鶹´«Ã½AV to annually report on many different aspects of cities' well-being. With specific focus and support, it is possible to obtain substantial sample sizes within cities so that the variability in obesity, for instance, can be better measured and understood at a local level, where change is most likely to occur. With local data, leaders can design programs and policies that are relevant to the specific situation of each city ZIP code or neighborhood. Results are based on telephone interviews with more than 353,000 American adults, aged 18 and older, conducted January 2, 2009 to December 30, 2009. At least 300 surveys were completed for each reported city. Metropolitan Statistical Areas (MSAs) are defined by the U.S. Office of Management and Budget. Each respondent is attributed to his or her MSA based on the self-report of his or her ZIP code. In many cases, more than one city is included in the same MSA. The San Jose, California MSA, for example, includes the smaller nearby cities of Sunnyvale and Santa Clara in addition to San Jose, but residents of all three cities would be considered a part of a "large metro area" because the combined population is estimated at more than 1.8 million. Interviews are conducted with respondents on landline telephones (for respondents with a landline telephone) and cellular phones (for respondents who are cell phone only and cell phone mostly). In addition to sampling error, question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of public opinion polls. |
