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As such, clinics base operating decisions by assessing the demand curve for their services. The implication of this assumption is that issues of endogeneity arising from estimating a demand curve without also controlling for factors affecting the supply curve for clinic services are irrelevant, since these firms essentially do not face a supply curve for their services.

Given our literature review and data constraints, we postulate a reduced form, linear in coefficients and variables equation to explain the demand for HIV testing, which we estimate using regression analysis techniques. The advantages of this approach are that it is both parsimonious and also allows the signs and significance of each individual coefficient estimate to test each of the hypotheses identified in our literature review.

The Probit Model
A crucial econometric issue is how to specify the dependent variable for our regression analysis, and consequently the regression technique to estimate our equation. We have two dependent variables, both of which express similar information, but in different ways. Our HIVDV variable is a binary indicator of whether or not a clinic offers HIV testing. We examine whether epidemiologic, clinic-specific and socio-economic factors influence the demand for testing by estimating a standard Probit model (Greene, 2000).

One other technical note about the probit model deserves mentioning. Because the model is estimated via maximum likelihood (an inherently non-linear procedure), the coefficient estimates in the probit model cannot be directly interpreted as marginal effects, as is the case in other regression procedures such as ordinary least squares (OLS). However, the probit model does facilitate the construction of marginal effects, which are directly dependent on these coefficient estimates. As such, when interpreting the results of the probit model, we give primary emphasis to interpreting the signs, magnitudes and significance of the marginal effects, as opposed to the coefficient estimates.

The signs and significance of our marginal effects (and their underlying parameter estimates) can be used to test Hypotheses 1 – 3. For example, if the marginal effects for our HIV/AIDS variables are significantly different from zero, then we would reject Hypothesis 1 (in its null form). Moreover, the magnitudes of these marginal effects (if significant) allow us to gain additional information about how epidemiological conditions impact whether clinics perform HIV tests. Similar analysis of the clinic-specific and socio-economic marginal effects can be used to test Hypotheses 2 and 3, respectively.

The Tobit Model
The HIV variable takes this a step further by identifying the number of tests provided, and zero otherwise. Thus, HIV is essentially a variable that is either censored or truncated (on the left side of the distribution) at zero. The crucial issue is how to interpret the values of HIV at the censoring or truncation point. One approach is to assume that the zero values are determined simultaneously with the positive values. Our HIV variable is actually a count variable. This implies that the distribution is censored, and can be estimated with a standard Tobit model (Greene 2000).

As with the probit model, the Tobit model is estimated by defining and subsequently maximizing the (cumulative) log likelihood function for a censored (at HIV = 0) normal distribution. When testing Hypotheses 1 – 3, this also forces us to calculate and interpret marginal effects, as opposed to simply interpreting the model’s coefficient estimates.

The revenue variables indicate that clinics utilize a wide variety of sources to obtain funds. With the exception of managed Medicare, each revenue source is utilized by between one half and two-thirds of the clinics, on average. Clinics appear to be highly reliant on traditional Medicare and Medi-Cal reimbursement, as well as Federal grant and contract funds. Not surprisingly, among clinics receiving positive funds, clinics receive a larger amount of funds from Federal sources than from local or private sources. Reimbursement for traditional Medi-Cal patients is also more generous than Medicare, at the mean.
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The county level demographics also corroborate our earlier assertion that these clinics treat a disproportionate number of disadvantaged individuals within a particular county. Mean yearly income per county is approximately $42,700 ― a value much higher than the poverty level. The proportions of black, Hispanic and female residents in each county are also lower than the mean proportions of patients that an average clinic treats.

Lastly, the descriptive statistics for our epidemiological variables provide some insight into the extent of the epidemic in California. During 2004, an average of 314 individuals in a given county was living with AIDS, and approximately 47 people per county died of AIDS. Approximately 1,234 people per county, on average, were HIV-positive in 2004. Clearly, these statistics imply both a growing epidemic as well as the need for policies designed to curb the spread of the disease and provide palliative care for those already infected.

Empirical Methodology
Our empirical methodology operates under a number of assumptions, which are all consistent with the economic and epidemiological literatures. Perhaps most importantly, we assume clinics are the primary source of care for disadvantaged individuals within their communities. From an economic standpoint this implies clinics have a high degree of monopoly power over this segment of society. At the very least, these firms can be considered as monopolistic competitors, meaning they hold power over a segment of the market for health care in the short run. Given the time frame of our analysis, this less stringent assumption is also sufficient to justify our empirical approach.

As such, clinics base operating decisions by assessing the demand curve for their services. The implication of this assumption is that issues of endogeneity arising from estimating a demand curve without also controlling for factors affecting the supply curve for clinic services are irrelevant, since these firms essentially do not face a supply curve for their services.
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Given our literature review and data constraints, we postulate a reduced form, linear in coefficients and variables equation to explain the demand for HIV testing, which we estimate using regression analysis techniques. The advantages of this approach are that it is both parsimonious and also allows the signs and significance of each individual coefficient estimate to test each of the hypotheses identified in our literature review.

From our literature review, we expect demographic, economic and epidemiological conditions outside of a clinic’s control to impact HIV testing decisions; therefore, county level information was collected on these characteristics. Data on the spread of HIV/AIDS was collected from the California HIV Cumulative Surveillance Reports, which are published monthly (and by county) by the California Department of Health Services, Office of AIDS. These reports are available on the web at CADHS. These reports contain information about the cumulative number of AIDS cases (both currently living and total), AIDS-related deaths, and total HIV cases per county.

We use this data to construct three variables, which we believe adequately measure the current state of the disease in each county between December 31, 2003 and December 31, 2004: the change in the cumulative number of living AIDS cases in a county; the change in the cumulative number of AIDS deaths; and the change in the cumulative number of HIV infections. We use the change in these cumulative data to capture the extent of the epidemic during 2004. Additionally, each of these variables provides information (in a manner consistent with the static, marginal nature of our analysis) about the history, potential growth and potential decline of the epidemic. In particular, the number of new HIV infections represents the potential growth of the epidemic, while the number of AIDS deaths in 2004 represents potential decline. Since there is usually a large incubation period between HIV infection and the onset of AIDS, the number of living AIDS cases also provides some information about the history of the epidemic in a particular county.

As a measure of economic prosperity within a county, we collected data on average weekly wages per county (in 2004) from the California Economic Development Department. This weekly measure was subsequently multiplied by 52 to arrive at an average yearly income variable. Demographic data were obtained from the US Census Bureau. In addition to the total population per county, we collected data on race, Hispanic origin, sex and age. Consistent with our clinic-specific demographics, we express each of our demographic variables as a proportion of the total population to reduce the potential for heteroskedasticity.

Appendix B contains some basic descriptive statistics for non-truncated variables used in our analysis. The following are characteristics of the average (mean) clinic. Approximately half (350 out of 706) of the clinics provide HIV testing. Of these 350 clinics that do provide testing, 378.98 tests are performed annually. These clinics have approximately 5.2 full time staff who provide services to patients. Of these FTEs, thirty-eight percent are physicians and twenty-four percent are nurse practitioners. The majority of the staff are employed on a salary basis, as opposed to a contract or a volunteer basis. Seventy percent of patients are white, while just under fifty percent are Hispanic. In our data, Hispanic is treated as an ethnicity, as opposed to a race. As a result, a large proportion of patients classified as white are also likely to be Hispanic. Nearly sixty percent of patients are below the poverty line, and nearly two-thirds of patients are female.

We construct a number of variables describing the populations served by each clinic. Information was collected on the total number of patients receiving treatment at each clinic. This data is also disaggregated by race (white, black and all other races), ethnicity (Hispanic versus non-Hispanic), age (patients aged 65 and older versus all other ages) and gender. Patient level data are also decomposed by income level (below 100 percent of the poverty level, between 100-200 percent of the poverty level, above 200 percent of the poverty level, and non-reporters). To ease exposition and reduce the possibility of heteroskedasticity in our empirical analysis, we express each patient sub-group as a proportion of the total number of patients.
As noted earlier, financial concerns may influence a clinic’s ability to offer HIV testing. Because these clinics are not-for-profit, and because many clinics treat a disproportionate number of disadvantaged individuals, government and philanthropic sources of revenue are vital to a clinic’s ability to cover its operating expenses. Our data allow us to measure several of these potential revenue sources. First, we collect information on grant and contract monies from Federal, local (state, county and other local agencies) and private sources. We also identified the dollar value of donations to each clinic. Lastly, we measured the average price per patient encounter that Medicare and Medi-Cal (California’s Medicaid program) reimburse clinics. Because both Medicare and Medi-Cal have both traditional and managed care programs, we provide two separate prices for each insurance program. Two comments are in order here. First, a patient encounter is the appropriate measure of output for prices, because a particular patient may visit a clinic multiple times during the year, making the number of patient encounters at least as large as the number of patients. Because price is, by definition, average revenue per unit of use, the number of encounters is the appropriate measure. For a more detailed discussion of this issue, see Rosenman et al. (2000) and Rosenman et al. (2005). Second, all Medicare and Medi-Cal programs (whether managed care or otherwise) reimburse for services in a manner consistent with prospective payment. As a result, clinics do not have the ability to significantly manipulate the average reimbursement per encounter they receive for treating Medicare and Medi-Cal-insured patients (Friesner, 2003).
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Because not all clinics treat all types of Medicare and Medi-Cal patients, nor do all clinics have access to all four grant/contract and donation sources, we also create a series of dummy variables that identify whether a particular clinic has access to a particular revenue source. Finally, to reduce the potential for heteroskedasticity, we take the natural logarithm of our grant/contract and donation variables.

Hypothesis 2: Enhanced clinic resources should lead to an increased demand for HIV testing.

Our second hypothesis implies that endowing the outpatient clinics with additional manpower and/or monetary resources should allow them to be more proactive in providing services to their constituents, and thus induce a greater quantity demanded for their services. The underlying assumption to Hypothesis 2 is that clinics are under-funded and/or understaffed, and do not use their market power to exploit their patients. Thus, if we fail to reject Hypothesis 2 (again, in its null form), researchers and policy-makers may want to re-examine the tactics and practices of these clinics, as they would not be fulfilling their core missions of community service.

Hypothesis 3: The demand for HIV testing should vary by the socio-economic characteristics of the community the clinic serves.

Our literature review implies that clinics serving poorer communities and those with higher minority populations should experience a lower demand for testing. One interesting corollary to this hypothesis (which we control for but, to our knowledge, has not been addressed in the literature) is the importance of distinguishing between the socio-economic characteristics of the entire community in which the clinic resides and those of the sub-population that utilize the clinic’s services. This is especially true for clinics operating in urban and/or geographically dispersed communities, which may cater to specific minority group, or may share the responsibility for serving the greater community with a small number of other clinics.

Data
Our primary source of data comes from the California Office of Statewide Health Planning and Development (OSHPD). Each year, all primary care, outpatient clinics in the State are required to report revenue, expense and utilization data to OSHPD. These data are subsequently packaged and made available on the OSHPD website. Our data come from these reports for the calendar year 2004. The complete set of data contains 804 clinics. After eliminating observations due to missing or mis-measured data, our sample consists of 706 observations. We eliminated 67 clinics because they were either not open for a full calendar year, or had a suspended operating license. Another 25 observations were eliminated because they provided mis-measured or unreliable data. Specifically, seven clinics reported having zero employees, four clinics reported zero operating revenue and five clinics reported negative prices for treating Medicare and Medi-Cal insured patients. We also eliminated nine firms for not treating at least 30 patients in a full year. Lastly, six observations were eliminated because they failed to report any information on the poverty status of their patients. Appendix A contains the names and definitions of all of the variables used in our analysis.

We measure the amount of HIV testing within a clinic with two, related variables. The first, HIV, is a truncated variable that gives a value of zero if the clinic performed no HIV tests and the quantity of HIV tests performed otherwise.

Because approximately half of the clinics did not perform any HIV tests, we also created a dummy variable (HIVDV) that gives a value of one if the clinic performed HIV tests, and zero otherwise.
The data allow for the construction of a number of variables describing the operating characteristics of these clinics. For example, the data contain a dummy variable identifying clinics located in rural areas (as defined by OSHPD). The data also include the total number of full time equivalent employees (FTEs) who have direct patient contact. This data is further disaggregated by the type of FTE (e.g., physician, dentist, physician’s assistant, nurse family practitioner, etc.) as well as the method by which FTEs are compensated (e.g., salary, contract, or volunteer).

Galvan, Bing & Bluthenthal (2000) found that racial and age differences exist once testing has been obtained. Young people and African Americans were less likely to return for results of their HIV tests. Age and race were also found to be a factor in accepting voluntary HIV testing. Hull, Bettinger, Gallaher, Keller, Wilson, & Mertz (1988) found that a higher percentage of African American males did not participate in HIV testing as compared to males from other ethnic groups. Kellerman et al. (2002), comparing the results from the HIV Testing Surveys, HITS1 and HITS2, found a decreasing proportion of people under the age of twenty-five years obtain HIV testing, whereas those over twenty-five years of age showed increased demand.

Two other patterns of HIV testing are of note. People engaging in higher risk behaviors have been found (Fernyak, Page-Shafer, Kellogg, McFarland, & Katz, 2002) to have higher demands for repeated testing. This is further evidence that higher perceptions of risk lead not only to increased HIV testing, but also repeat testing. However, in blind studies of anonymous testing, researchers found that the HIV prevalence rates are higher for people who have not had voluntary testing, regardless of risk group or socio-demographics (Weinstock et al., 2002).

Finally, there have been a number of studies which extend the traditional determinants of the demand for HIV testing to include the concepts of rational risk taking and the fear associated with a the possibility of a positive test. For example, Gritzman (2005) provided evidence indicating that individuals respond rationally to social and economic stimuli when it comes to taking risks. Therefore, viewing AIDS as a rational disease enriches our understanding of the behavioral underpinnings to the spread of AIDS and by extension, the determinants of demand for testing.

Caplin & Eliaz (2003) addressed the fear of a positive test as crucial to understanding the demand for testing. There are obviously strong health-based incentives to test for AIDS. However, the authors postulated that fear may override these incentives. They suggested decreasing the informativeness of a bad test result, thereby mitigating the fear of bad news. This would allow the health-based incentives to once again come to the forefront of the testing decision. The authors developed a model of AIDS transmission that acknowledges this form of fear. A mechanism is designed that not only encourages testing but also shows the spread of the disease through voluntary transmission. The authors showed that their model confirms that psychological interventions may slow the spread of AIDS, but conceded that much more work is needed in this area.

En totem, our literature review suggests three major hypotheses about the determinants of the demand for HIV testing. Stated in alternative form, these hypotheses can be characterized via the following statements:
Hypothesis 1: Higher levels of (perceived) risk should lead to an increased demand for HIV testing.
In this analysis, we measure perceived risk as an actual change in epidemiological conditions and outpatient clinics as the primary source for such tests. This assumes that individuals base their perceptions (at least partially) on some form of fact. Moreover, if people base their perceptions solely on fact, one would expect the demand for testing, on average, would change in direct proportion to changing epidemiological conditions. If fear, information uncertainty or other factors were used in the evaluation of risk perception, then this relationship would not be proportional, and (in extreme cases, for example, if individuals become fatalistic) possible inversely proportional.

The study of socio-demographic determinants of HIV testing has been the subject of a significant amount of research. Several surveys of persons at risk of HIV infection have found that increased perception of risk leads to a greater demand for HIV testing. In a sample of 621 homeless women (Nyamathi, Stein and Swanson, 2000), increased intravenous drug use, unprotected sexual contact and other risky behaviors led to higher HIV testing. Extending the survey to include men, Stein & Nyamathi (2000) examine gender differences for HIV testing. While increased perception of risk increased the demand for HIV testing for both genders, men typically had a lower perception of risk despite reports of more risky behavior.

Boozer & Philipson (2000) address the issue of risk perception from a slightly different perception and find evidence in support of public testing for HIV. In this research, a blood test for HIV was administered as part of a longitudinal survey. This framework for the demand for information on HIV implies that because only individuals who are surprised by the results of the intervention respond to the results (in this case low-risk people who test HIV-positive or high-risk individuals who test HIV-negative) an information-intervention of this type may reach populations who would otherwise not seek testing.

A significant amount of research has also focused on the special considerations of injection drug users (IDU). For example, Harris (2006) examined the efficient allocation of resources to prevent HIV infection among IDUs in the context of the Prevention Point Philadelphia, a multi-site needle exchange program. At the optimal allocation of needles, the estimated cost per case of HIV averted was $2,800. This favorable cost-effectiveness ratio came primarily from the program’s low marginal cost per distributed needle.

In contrast, Heimer, Grau, Curtin, Khoshnood, & Singer (2007) sought to determine the extent of HIV testing among urban IDUs in order to assess the potential effectiveness of additional targeted testing programs for this population. Of the 1,543 IDUs in the sample, 93% had already been tested for HIV. The authors estimated the number of undetected infections among urban IDUs to be less than 40,000 in the United States. As a result, the authors concluded that expending scarce prevention money to expand testing of IDUs is unlikely to be productive. Given the national goals to identify previously undetected infections, the authors concluded that resources should be spent for proven HIV-prevention strategies including syringe exchange, drug treatment, and secondary prevention for those who are HIV positive.

Despite increased demand for HIV testing, most people diagnosed with AIDS were generally tested late in the course of their HIV infections while under acute medical care (Wortley et al., 1995). The demand for HIV testing varied according to risk group, race, and ethnicity. Intravenous drug users and heterosexual contact risk groups obtained the highest level of HIV testing during hospitalization. Additional evidence of late testing is found by Nakashima, Campsmith, Wolfe, Nakamura, Begley, & Teshale (2003). The authors interviewed 5,980 participants (from 16 different states) aged 18 and older with HIV or AIDS between May 2000 and February 2003. Participants were categorized in two groups: early testers (those who had their first positive test HIV test five or more years before the diagnosis of AIDS, or had gone five or more years with a diagnosis of AIDS after the first positive HIV test) or late testers (those who had their first positive HIV test one year or less before the diagnosis of AIDS). Among participants with AIDS, 24% where early testers and 45% were late testers. Late testers where more likely than early testers (1) to be 18-29 years old, (2) to be black or Hispanic, (3) to have acquired HIV through heterosexual contacts, (4) to have high school education or less, (5) to ever have been tested for HIV before the first positive result, (6) to have had confidential testing, (7) or to have received their first positive result from an HIV testing site or an acute or referral care setting. In addition, sixty five percent of late testers were tested for HIV because of illness, while the most common reason for testing among early testers was self-perceived risk (twenty nine percent).

There are over 760 of these clinics in the State, all of which are not-for-profit institutions. While these clinics treat a significant number of middle and high-income patients (who are generally insured by private, third party payers), the majority of patients are both poor and of minority ethnic backgrounds (Appendix B). These clinics generally rely on government-sponsored insurers, grants and donations to cover expenses. Government-sponsored insurers, including Medicare, Medicaid and other local plans, often do not reimburse as generously as private, third party payers. Thus, while these clinics provide an initial point of access to health care, particularly for underserved populations, they may be limited in the extent of care that they can provide.
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From a policy perspective, it is important to understand the role that these primary care outpatient clinics play in the fight against HIV/AIDS. More specifically, two questions appear especially relevant. First, as sociological, economic and epidemiological conditions change, there should be a corresponding change in the demand for HIV testing. If this is the case, what proportion of this demand is met by these primary care outpatient clinics? If there is a strong relationship between changing prevalence rates and the amount of testing in these clinics, then policy makers can reduce the spread of the disease by diverting resources towards primary care outpatient clinics.

Second, if these clinics play a significant role in providing HIV testing, how do financial constraints impact clinics’ abilities to provide these services? Previous studies using California outpatient clinics (Friesner, 2003; Rosenman, Friesner & Stevens, 2005; Rosenman, Li & Friesner, 2000) have found evidence indicating that revenue considerations (particularly the ability to obtain grants and donations, as well as reliance on third-party payers) significantly impact the quantity and quality of services provided. Do specific types of revenue sources (reliance on grants and/or Medicaid funding, for example) have a disproportionate impact on the amount of HIV testing in these clinics? From a policy perspective, this is an important question because it not only identifies some possible methods of increasing HIV testing, but it also gives an indication about how resources should be allocated to maximize their effectiveness.

This paper provides an initial empirical analysis on the impact of changing epidemiological, economic and sociological conditions on the amount of HIV testing in California outpatient clinics. Particular attention is paid to examining whether changes in HIV/AIDS prevalence impact the amount of testing these clinics perform, and also how financial constraints impact this relationship.
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The remainder of this paper proceeds in four steps. First, we present a brief literature review, which we use to develop our empirical methodology. Next, we present and discuss the data used in our analysis. The third section contains our empirical results. We conclude the paper by discussing the policy implications of our findings, as well as providing some suggestions for future research.

This paper provides an initial empirical analysis of the impact of changing epidemiological, economic and sociological conditions on the amount of HIV testing in primary care, outpatient clinics. Particular attention is paid to examining whether changes in HIV/AIDS prevalence impact the amount of testing these clinics perform, and also how financial constraints impact this relationship. Using a sample of California clinics, we find that changing epidemiological conditions do impact the demand for HIV testing. Additionally, certain clinic characteristics, such as the type of practitioners providing care and the socio-economic characteristics of patients treated at each clinic also affect the demand for testing. However, we find little evidence supporting increased government or private grants, contracts and donations as a means of enhancing the demand for HIV testing.

Introduction
While the HIV/AIDS epidemic is taking its largest toll on societies in Southeast Asia and Sub-Saharan Africa, the US has also been significantly impacted by HIV/AIDS. According to the Centers for Disease Control and Prevention (CDC, 2004), in 2003 the cumulative number of AIDS cases in the US surpassed 900,000. Additionally, the cumulative number of HIV infections exceeded 200,000, with over 33,000 new infections during the year.

Within the US, the State of California has been especially hard-hit by the epidemic. As of 2003, California ranks second in the US with over 133,000 cumulative AIDS cases ― nearly 15 percent of the nation’s total. It also ranks second in the number of new AIDS cases with just over 5,900 ― nearly 13 percent of the nation’s total (CDC, 2004). Moreover, among new AIDS cases in 2003, California had the third highest proportion of new cases among the Hispanic population. Thus, not only has California been hard-hit by the disease, but also the impact has disproportionately affected its largest minority population.

Since there is no known cure for the disease, HIV testing is one of the most important actions an individual can take to prevent transmitting the disease or, once acquired, obtain life-prolonging treatment. Given the development of anti-retroviral which prevent rapid health deterioration due to the onset of AIDS, testing is important because it allows infected individuals to start treatment sooner, thus increasing both longevity and quality of life. Despite these facts, California ranks only ninth among US states in the percent of its population ever tested for HIV, and 33rd among states in the percentage of individuals between the ages of 18-64 who were tested for HIV in 2001 (CDC, 2004). Clearly, there is a need to increase both access to, and utilization of, HIV testing in California.

For most individuals in California, routine outpatient health care services (including testing for HIV and other sexually transmitted diseases) are provided by primary care outpatient clinics.

For a variety of reasons – social influences, genetics, and other health factors, to name a few – weight loss is not a viable solution for all obese people. Meanwhile, our culture’s endless obsession with weight loss and unrealistic ideals for thinness are hurting everyone, not just the obese.

A recently released two-year study of obese women who had been chronic dieters found that women who went through a program that focused on self-acceptance and a healthy lifestyle rather than weight loss experienced declines in their cholesterol levels and blood pressure and increased self-esteem, despite the fact that they did not lose weight (Bacon, Stern, Van Loan, & Keim, 2005). In contrast, study participants who followed a regimented weight-loss program regained most of the weight they lost, reported poorer self-esteem, and did not sustain improvements in their cholesterol and blood pressure. These types of findings provide growing evidence for developing weight management programs that fit the characteristics and abilities of the priority population instead of the other way around.

Moving Forward: Creating Healthy Environments
We have tools and prevention concepts to address both poor health and the prejudice that often is encountered by both overweight people and the populace as a whole. One of the lessons of the approximately 40-year-old health promotion and prevention movement in the U.S. and Canada is that individuals and communities do care about their long-term health, and are willing to make environmental and policy changes that promote healthy behaviors. This was particularity noticeable in California where the tobacco movement created smoke-free environments in work, school, and dining settings. It is now even expanding into park and beach ‘play’ settings.

If we want to prevent people from jumping on the bandwagon of the next fashionable weight-loss diet, we need to utilize a social ecological approach that can influence individual behaviors and does not isolate, discriminate, or marginalize the obese and overweight. We have an opportunity to transform our country’s view of health by talking about healthy behaviors and environments in a real-life context instead of as a billboard ad ideal. More importantly, we need to address the external factors that act as barriers, or promoters, of healthy eating and active living. Examples of comprehensive approaches abound, including the Prevention Institute’s Strategic Alliance for Healthy Food and Activity Environments which has created ENACT (Environmental Nutrition and Activity Community Tool), the State of Washington which has crafted a “policy cookbook”, and the City of Chicago which has formed a city-wide collaborative CLOCC – the Consortium for Lowering Obesity In Chicago’s Children. Specific approaches to changing environments which can have an impact on individuals include:

In Philadelphia: the Food Trust’s Food Marketing Task Force which is working with local community civic and government leaders, and the supermarket industry, to address health disparities associated with unhealthy diets by increasing the number of supermarkets in the city’s underserved areas. In low-income neighborhoods, where there are no grocery stores, it is difficult and expensive to eat a nutritious diet. In 2004, they increased the availability of nutritious affordable food, and provided 258 well-paying jobs, by opening its first store. This type of environmental change is supported by the Moorland study (2003), which utilized survey data from over 10,000 households from the mid-west and southeast, and documented that fruit and vegetable intake by African-Americans increased by 32% for each additional supermarket in the neighborhood, while fruit and vegetable intake among White Americans only increased by 11% with the presence of one or more supermarkets.

We are not advancing the health of people who are overweight when going to gym means being silently judged and getting weighed at the doctor’s office feels like receiving a negative report card. Yet, that is exactly the experience many obese individuals say they face when they choose to make medical appointments or start a fitness program.

Conscious and subconscious negative attitudes within the health community (including physicians, nurses, and dieticians) toward the obese have been documented in numerous studies (Hoppe & Ogden, 1997; Maroney & Golub, 1992; Obberrieder, Walker & Monroe, 1995; Price, Desmond, Krol, Snyder, & O’Connell, 1987). More recently, Block, DeSalvo, and Fisher (2003) found that despite solid knowledge of the conditions associated with obesity, medical residents have a poor grasp of the tools necessary to identify obesity. They also have negative opinions about their skills for treating obese patients. They conclude that residency training programs must not only improve knowledge of obesity but also must address physicians’ negative attitudes.

These attitudes can be compounded by the fact that for more than 20 years clinicians have been using the term “good cholesterol” and “bad cholesterol” to describe medical conditions that, may leave people feeling that they are labeled as “bad” because of their elevated LDL cholesterol level. Indeed, even the NIH’s National Heart, Lung, and Blood Institute uses these terms to describe the LDL and HDL lipoproteins. These negative attitudes do not go unnoticed, and they are often cited as a reason for avoiding medical care. Studies have shown that the most important factor in women postponing or canceling medical appointments was the fear of being weighed, and that increased BMI is associated with decreased preventive health services (Adams, Smith, Wilbur, & Grady, 1993; Olson, Schumaker, & Yawn, 1994; Ostbye, Taylor, Yancey, & Krouse, 2005).

Further, when patients are encouraged or referred to exercise, the same issues tend to repeat. A study of exercise science students found that they, too, possessed negative associations with obesity such as laziness and lack of self-control (Chambliss, Finley, & Blair, 2004). It seems that obese individuals are faced with conflicting messages – that they need to exercise to lose weight, but that they should not exercise because they are overweight. Who could blame anyone for avoiding exercise under these circumstances?