Get Complete Project Material File(s) Now! »
Impacts of Obesity on Health
Many epidemiological studies have consistently shown that obesity is associated with increased risks of morbidity, disability and mortality (Visscher et al., 2001). A study revealed that the impact of obesity on mortality is nearly as important as that of cigarette smoking (Peeters et al., 2003). Generally, obesity increases the risk of a number of health conditions, the more important of which are several cardiovascular diseases, various types of cancer, adverse lipid concentrations, and type 2 diabetes. In fact, it is the severity and the duration of obesity that contribute to the risk of comorbidities. Although the mortality rate of obese adults may not differ significantly from that of adults with a normal weight, the higher risk of a number of noncommunicable diseases due to obesity eventually contributes significantly to the total health burden and leads to reduced life expectancy (de Lusignan et al., 2006).
Even obese children show raised levels of susceptibility for many of the aforementioned diseases (WHO, 2007). For example Cook et al (2003) showed that 4% of adolescents and approximately 30% of overweight adolescents in the United States met the criteria for the metabolic syndrome, which dramatically increases the possibility to develop type 2 diabetes and get inflicted by cardiovascular diseases in the future.
Economic Consequences of Obesity
According to Thompson and colleagues, obese people (BMI above 30 kg/m2), in comparison with people of normal weight (BMI of 20.0–24.9 kg/m2), had 36% higher annual health care costs, while overweight people (BMI of 25.0–29.9 kg/m2) had 10% higher annual health care costs (Thompson et al., 2001). Numerous studies have attempted to estimate the economic consequences of obesity. However, estimating the total cost of obesity is not a simple task and cost estimates differ among studies, depending mainly on the data and methods employed. Most of the studies describe the medical costs associated with obesity (direct costs), while some also take into account loss of productivity (indirect costs).
Productivity effects may be categorized into at least four different types: absenteeism, presenteeism, disability, and premature mortality. Absenteeism is defined as the productivity costs due to employees being absent from work for obesity-related health reasons and presenteeism as the decreased productivity of employees while at work. Although it is difficult to estimate the total loss of productivity costs, it is assumed to be substantial and probably higher than $66 billion annually for the US (Hammond et al., 2010). Additionally, annual direct medical expenditures attributable to obesity are estimated to be as high as $147 billion per year (Finkelstein et al., 2009). As a result, in the United States the total annual economic costs associated with obesity might eventually exceed $215 billion.
The Case of New York City
According to the New York City (NYC) Community Health Survey of 2011 conducted by the NYC Department Of Health and Mental Hygiene (DOHMH) almost one in four New Yorkers is obese, and more than half of the population is overweight or obese, as can be seen in Figure 2.2.
Among children, obesity rates are even higher – almost 33% of the children in NYC are obese (NYSDOH, 2011). Since 2002, when the NYC DOHMH started releasing the results of NYC community health surveys, obesity rates in NYC have increased steadily, as revealed in Figure 2.3. In 2002, the obesity rate was 18,2% in 2002, but in 10 years it has increased to 23,7%.
On the other hand, the percentage of those who are overweight but not obese has decreased by 1,2. In 2002 it was 35% and in a decade dropped to 33,8%.
The spatial distribution of obesity in NYC as presented in the choropleth map of Figure 2.4 is interesting as it will be discussed later on. It can be easily seen that obesity rates are fairly low in Manhattan and quite high in Bronx.
Benefits of Physical Activity on Health
The spread of the obesity epidemic can be attributed to profound changes in society and in behavioral patterns of communities over the last decades. While genes are important in determining a person’s susceptibility to weight gain, economic growth, modernization, urbanization and globalization of food markets are some of the forces believed to underlie the epidemic. At the same time, considerable changes have been observed especially in developed countries towards less physically demanding activities. The increased use of motorized transport, technology in the home, and more passive leisure pursuits is another fact that also leads to less physical activity (WHO, 2007).
A large number of studies have described the interrelation between physical activity and improved health, providing adequate evidence that moderate but regular physical activity is maybe the best method against obesity and its comorbidities. For example, Bassuk and Manson (2005) have found that physically active persons have a significantly lower risk of developing heart disease and type 2 diabetes. According to the World Health Organization (WHO, 2010) physical inactivity is the fourth-leading risk factor for global mortality and is responsible for 6% of deaths globally – around 3.2 million deaths per year. In another report of WHO it is clearly stated that (WHO, 2006): “…it is recommended that individuals engage in adequate levels [of physical activity] throughout their lives. Different types and amounts of physical activity are required for different health outcomes: at least 30 minutes of regular, moderate-intensity physical activity on most days reduces the risk of cardiovascular disease and diabetes, colon cancer and breast cancer. Muscle strengthening and balance training can reduce falls and increase functional status among older adults. More activity may be required for weight control.”
Walking: Simple But Healthy
Walking is the simplest and most common form of physical activity among adults, regardless of age, sex, ethnic group, education or income level (Saelens et al., 2003). It is a rather inexpensive form of exercise, does not require learning new skills and can also be used for transportation purposes. Besides, walking is the most sustainable form of transportation per se, as it contributes to reductions in air pollution and has the potential to reduce the rates of respiratory diseases associated with air pollution, by reducing reliance on the automated transport at the same time (Frank et al., 2007).
Walking promotes also social life and public participation by providing opportunities for face-to-face contact and casual interaction, all of which subsequently are proven to improve mental health and well-being (Robertson et al., 2012). A vibrant, economically viable and safe community needs people on streets and in public places. A walkable environment can also provide significant health benefits and independence to specific groups such as children and third age people who rely more on their local neighborhoods (Berke et al., 2007).
Walkability and Neighborhood Characteristics
Neighborhood characteristics might significantly affect people’s decision to walk because those characteristics are related to travel patterns basically by impacting directness of travel between destinations and proximity between these destinations. For example, when common destinations such as shops, grocery stores, post offices, schools and daycare stations are situated within the close vicinity of a neighborhood, people are more likely to prefer to reach their destinations on foot or by bicycle, instead of driving or be driven. Besides, a neighborhood characterized by higher population densities tends to support a richer variety of shops and services in the neighborhood, while there is a higher possibility for increased ridership and higher quality transit, encouraging people to walk to and from transit stops. In the same way, a residential area that has a street network of reduced traffic speeds tends to become more walkable as it becomes more pedestrian-friendly. Those are examples of some neighborhood features that alone or in combination can contribute to the walkability of a neighborhood (Tomalty et al., 2009).
Understanding the potential impact of the built environment on walkability requires relevant, easy-to-comprehend, and reliable measurable features (Brownson et. al. 2009). These measurable characteristics can assist in determining how much the built environment affects the people. These measures can also provide indirectly evidence of the state of population health for the area under study.
Over the last 2 decades, there has been considerable progress regarding measuring walkability, various different measurable features of the built environment have been incorporated to models, and different approaches have been developed.
The first method is based on interviews or self-administered questionnaires. Questionnaires can potentially reveal the extent to which individuals perceive various elements of the built environment and how a person experiences a neighborhood. This method is considered as “subjective” because two unique individuals may perceive the same environment differently. The most commonly assessed measurable environmental features of perception are land use, traffic, aesthetics, and neighborhood safety from crime (Tomalty et al., 2009; Brownson et al., 2009).
The second approach uses built environment characteristics obtained by systematic observations or audits that quantify the environmental attributes of an area, including the presence or absence of features hypothesized to affect physical activity.
Audit tools are used for measuring and assessing physical features through direct observation, and include one or more measurable characteristics, such as: land use (e.g. commercial space), streets and traffic (e.g. pedestrian crosswalk), sidewalks (e.g. presence, width, and continuity of sidewalks), public space/amenities (e.g. presence of benches), architecture or building characteristics (e.g. building height), parking/driveways (e.g. presence of parking lot(s)), maintenance (e.g. presence of litter), and indicators related to safety (e.g. presence of graffiti) (Brownson et al., 2009; Pelletier, 2009; Gauvin et al., 2005).
New York City, Obesity and Walkability: Previous Studies and Modeling Attempts
During the last 5 years several studies related to obesity and walkability for NYC have been published. Black et al. attempted to measure the relations between neighborhood food availability, opportunities and barriers for physical activity, income and racial composition with obesity in NYC, controlling for individual level factor. Their study revealed that obesity rates ranged from 6,8% to 31,7%, widely varying between neighborhoods. They also concluded that several neighborhood-level factors significantly affected obesity. In particular, the researchers found that availability of supermarkets and foodstores, fitness facilities, percent of commercial land use area and income are significantly associated with obesity in NYC (Black et al., 2010).
Rundle et al (2009) examined the relation of neighborhood food environments with body mass index (BMI) and obesity. Controlling neighborhood walkability in NYC revealed that differences in the neighborhood food environment contribute significantly to disparities in obesity. In particular, the density of BMI-healthy food outlets (supermarkets, fruit and vegetable markets, and natural food stores) was inversely associated with BMI, meaning that access to the aforementioned stores is linked with lower prevalence of obesity and lower BMI.
Lovasi et al (2013) examined possible relations between modifiable neighborhood characteristics, active transportation and health problems related to insufficient physical activity for the NYC area. They attempted to evaluate whether aesthetically “friendly” neighborhoods with sidewalk cafés, street trees and clean sidewalks, and fewer safety hazards (pedestrian-auto fatalities and homicides) are associated with active transportation. Their findings revealed that those living near sidewalk cafés were 10 % more likely to report active transportation. Besides, higher homicide rates affected physically active transportation negatively. Their conclusion was that measures to prevent homicides and investments in aesthetic amenities are expected to promote active transportation. A very promising project of the last years is the “Walk Score”. The “Walk Score” project calculates walkability for NYC and all major cities of USA, Canada, Australia, and New Zealand. Its main objective is to aid the home-buying process. The “Walk Score” walkability map is based on a set of two different types of data:
Walking routes and distances to amenities.
Road connectivity metrics.
The final product of “Walk Score” is determined by a set of customizable amenity categories, weights, and distance decay functions.
In a nutshell, the algorithm which is used in “Walk Score” creates a score for a group of selected amenities and facilities based on the street network analysis and the given weight of each amenity. In the calculation, a decay function is applied, that dictates the score between the closest zone around an amenity (a radius of 400 m) and the theoretically accepted farthest distance around the amenity (2,4 km). Then the score is penalized by 0-10% according to an estimated pedestrian friendliness that is based on intersection density and average block length (WalkScore, 2010). Figure 2.5 presents a screen dump of WalkScore’s walkability map.
Table of contents :
1.1 Research Objectives
1.2 The Structure of the Thesis
2.1.1 Impacts of Obesity on Health
2.1.2 Economic Consequences of Obesity
2.1.3 The Case of New York City
2.1.4 Benefits of Physical Activity on Health
2.2 Walking: Simple But Healthy
2.2.1 Walkability and Neighborhood Characteristics
2.2.2 Measurable Neighborhood Characteristics
2.3 New York City, Obesity and Walkability: Previous Studies and Modeling Attempts
3. Study Area and Data Description
3.1 Study Area
3.2 Data Description
3.2.1 Points of Interest and Facilities
3.2.2 Zonal Data
3.2.3 Auxiliary Data
3.2.4 Data Created for the Study
3.3 Spatial Units of Analysis
4.1 Preparation of Land-use Map and Street Junction Map
4.2 Density – Household Density
4.6 Environmental Friendliness – Sidewalk Roadbed Ratio (SRR)
4.7 Commercial Density – Retail Floor Area Ratio (FAR)
4.8 Calculating Walkability Index
5. Results and Discussion
5.1 New York City Land-Use Map
5.2 Household Density
5.6 Environmental Friendliness for Walking
5.7 Commercial Density – Retail Floor Area
5.8 Results – Walkability of NYC
5.9 Linking Obesity with Walkability
5.11 Validating the Walkability Index
5.12 Further Development of Walkability Methods