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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Patient Educ Couns. Author manuscript; available in PMC 2012 January 1.
Published in final edited form as:
PMCID: PMC2902765

Physician Practice Patterns of Obesity Diagnosis and Weight-Related Counseling



To examine national physician practice patterns of obesity care and the predictors of these practices.


We analyzed cross-sectional clinical encounter data. Obese adults were obtained from the 2005 National Ambulatory Medical Care Survey (N=2458).


A third of obese adults received an obesity diagnosis (28.9%) and approximately a fifth received counseling for weight reduction (17.6%), diet (25.2%), or exercise (20.5%). Women (OR = 1.54; 95%CI: 1.14, 2.09), young adults ages 18 to 29 (OR = 2.61; 95%CI: 1.37, 4.97), and severely/morbidly obese individuals (class II: OR 2.08; 95%CI: 1.53, 2.83; class III: OR 4.36; 95% CI: 3.09, 6.16) were significantly more likely to receive an obesity diagnosis. One of the biggest predictors of weight-related counseling was an obesity diagnosis (weight reduction: OR = 5.72; 95%CI: 4.01, 8.17; diet: OR = 2.89; 95%CI: 2.05, 4.06; exercise: OR = 2.54; 95%CI: 1.67, 3.85). Other predictors of weight-related counseling included seeing a cardiologist/other internal medicine specialist, a preventive visit, or spending more time with the doctor (p < 0.05).


Most obese patients do not receive an obesity diagnosis or weight-related counseling.

Practice Implications

Preventive visits may provide a key opportunity for obese patients to receive weight-related counseling from their physician.

Keywords: obesity, physician practice patterns, weight-related counseling, diagnosis


Clinical guidelines for obesity call for assessment (e.g., diagnosis) and management (e.g., dietary and exercise therapy) [1]. In addition, the U.S. Preventive Task Force recommends that clinicians screen all patients for obesity and offer intensive counseling and behavioral interventions to promote sustained weight loss [2]. Despite these guidelines, physician obesity care is sub-optimal and varies by patient characteristics [3-10]. There is also evidence pointing to low rates of physician screening and advice for behavioral health risk factors related to obesity (e.g., physical inactivity) [11-12].

Low rates of obesity diagnosis and management as well as differential treatment by patient characteristics is concerning. There is a growing body of evidence suggesting that patients who are told by their physician that they are overweight are more likely to lose weight relative to those who are not told [13-14], that patients who are counseled about their weight or weight-related behaviors are more likely to report working on those areas [3, 7, 15], and that patients who are advised by their physician to modify their behavior are generally more confident and motivated to engage in lifestyle modifications (e.g., dietary changes, increased physical activity) [3, 16-17].

The primary purpose of this paper was to examine whether obese patients receive an obesity diagnosis and weight-related counseling from their physician. We additionally identified the sociodemographic, physician, and clinical encounter characteristics associated with receipt of obesity diagnosis and weight-related counseling. In light of the current federal priorities to reduce obesity and eliminate health disparities, identified in the U.S. Department of Health and Human Services’ Healthy People 2000 and Healthy People 2010 initiatives [18-20], there is a need to better understand variations in physician delivery of obesity care and the predictors of these patterns.



The study was a retrospective assessment of cross-sectional clinical encounter data from physician office visits.


The data for this study was obtained from the 2005 National Ambulatory Medical Care Survey (NAMCS) – a national annual survey of patient visits reported by physicians [21]. Participating physicians were randomly selected from the master files of the American Medical Association and the American Osteopathic Association by geographical area and specialty. Only patient visits to the offices of non–federally employed physicians, with visits classified by the American Osteopathic Association and the American Medical Association as office-based, patient care, were included in the 2005 NAMCS. Of selected physicians, the response rate was 61.5%. For each participating physician, one week of the year was randomly selected for systematic sampling of 20% to 100% of patient visits. For each selected patient visit, physicians completed an encounter form which detailed the specific clinical services provided during the visit, patient demographics, International Classification of Diseases, Ninth Revision (ICD-9) diagnostic codes, reason for visit, physician characteristics, visit characteristics, new or continued medications, diagnostic tests and types of counseling provided.

The 2005 NAMCS is the first time actual height and weight measurements were included in the survey instrument. A third of the sample had both height and body weight measurements which allowed BMI to be determined. Measures of patient height and body weight were obtained at the time of the sampled patient visit and recorded by the physician in the clinical encounter form. If patient height and weight were not measured at the visit, the provider reviewed the patient chart for the last time height and weight were recorded [21]. For this study, individuals were considered obese if their body mass index (BMI) ≥ 30 kg/m2. The sample size for the analysis included 2,458 obese individuals (ages 18 and older).


Outcome Variables

Four outcome measures were explored in this analysis. One identified obese patients who received an obesity diagnosis and three examined obese patients who received weight-related counseling (weight reduction, diet or exercise). Individuals were considered to have an obesity diagnosis or to have received diet, exercise, or weight reduction counseling if indicated via check box on the NAMCS encounter form (by the physician). Weight-related counseling did not include receipt of medications or surgeries. Each outcome variable was examined as a separate, dichotomous outcome variable (e.g., 1 = received diet counseling, 0 = did not receive diet counseling).

Independent Variables

The independent variables included: patient sociodemographic characteristics (race/ethnicity, gender, age, region, and health insurance), patient risk (co-morbidity risk status and obesity class), physician characteristics (specialty) and characteristics of the clinical encounter (visit type, seen before by practice and time spent with doctor). The obesity classes included: class I = BMI ≥ 30 & < 35; class II = BMI ≥ 35 & < 40 or class III = BMI ≥ 40. Insurance was defined as private or non-private (Medicare, Medicaid, worker's compensation, self-pay, charity, other). Physicians were categorized into five different specialties: primary care (general internal medicine, general/family practice, pediatrics); cardiology and other internal medicine specialties; obstetrics/gynecology (hereafter referred to as OB/GYN); surgical care specialties and other medical care specialties. We included cardiologists/other internal medicine sub-specialties as a separate category from general internal medicine given evidence suggesting that attitudes towards involvement in obesity management differed significantly between these groups [22]. The type of clinical visit was defined as preventive (e.g., routine or general exam), ongoing chronic problem or acute problem.

Risk Status

We categorized patients according to their overall co-morbidity risk status using the clinical guidelines for the identification, evaluation and treatment of obesity in adults [23]. International Classification of Diseases, Ninth Revision (ICD-9) diagnostic codes were used to identify patient co-morbidities. Patient were classified as having a very high co-morbidity risk if they had ischemic heart disease (410-414), diabetes (250), sleep apnea (780.57) or other atherosclerotic diseases (peripheral arterial disease, 443.9; abdominal aortic aneurysm, 441; symptomatic carotid artery disease, 433.1); high risk if they had any one of the following: osteoarthritis (715), gallstones (574), stress incontinence (female 625.6; male 788.32), hyperlipidemia (272), hypertension (401-405), smoked cigarettes or were men aged 45 and older, or women aged 55 years or older; and lower risk if they were obese but had no other obesity-related risk factors. Ideally, we would have included other measures in our risk status variable, such as physical inactivity, but those data were not available in the 2005 NAMCS.

Statistical Analyses

Multivariate logistic regression with binary outcomes was used to model the outcome variables of interest. All identified variables were included in the models based on the literature regardless of statistical significance. For all analyses, we used the patient visit survey weight (PATWT) provided in the NAMCS dataset to adjust the sample of visits to represent the total national population of ambulatory visits in the survey year. We also adjusted all analyses to account for the clustering of patients among physicians Patient data was moderately clustered by provider (ICC = 0.29); the 2,458 patients included in the analysis were clustered among 439 physicians. Statistical analyses were performed using the STATA, version 9.2 software package (StataCorp LP, College Station, TX).


Characteristics of the sample

Table 1 reports the characteristics of the study sample. Three-fourths of the study sample was White (75.3%), more than half were female (57.6%), two-thirds were age 45 and older (68.4%) and more than half had private insurance (57.0%). The majority of patients had a high or very high co-morbidity risk status (59.7%) and about half were class I obese (54.3%). About half of the patient visits were characterized as preventive or chronic (preventive, 14.8%; chronic 36.6%) and most patients had been seen before by someone in the practice (86.7%). Physicians in the sample represented the following specialties: primary care (62.6%), surgical care (14.4%), cardiology and other internal medicine specialists (10.4%), OB/GYN (5.2%) and other (7.3%). Primary care specialists included general internal medicine, general/family practice and pediatrics.

Table 1
Characteristics of the study population, obese adults (N = 2,458)

Characteristics of the outcome variables

Frequencies for the outcome variables of interest are also presented in Table 1. A third of our study sample (28.9%) received an obesity diagnosis, approximately a fifth received weight reduction counseling (17.6%) or exercise counseling (20.5%), and a quarter received diet counseling (25.2%).

Obesity prevalence and obesity diagnosis

Figure 1 illustrates rates of obesity prevalence in the 2005 NAMCS sample and the fraction of obese patients that received and obesity diagnosis from their physician. For both sexes, all race/ethnicity groups, age groups and each region of the country, rates of obesity diagnosis were considerably lower than the prevalence of obesity. In addition, rates of obesity diagnosis differed by patient characteristics. For example, the rate of obesity was higher among men as compared to women (38.7% vs. 34.9%). Yet, our observed rate of obesity diagnosis was higher among women (9.0% vs. 11.5%).

Figure 1
Obesity Prevalence and Physician Diagnosis of Obesity among Obese U.S. Adults

Predictors of an obesity diagnosis among obese adults

Table 2 (column 1) shows the adjusted predictors of an obesity diagnosis among obese adults. Patients who were female (OR = 1.54; 95% CI: 1.14, 2.09), younger (age 18-29: OR = 2.61; 95% CI: 1.37, 4.97), with class II or class III obesity (class II: OR = 2.08; 95% CI: 1.53, 2.83; class III: OR = 4.36; 95% CI: 3.09, 6.16), living in the Midwest (OR = 1.78; 95% CI: 1.08, 2. 93) or being seen by a cardiologist or other internal medicine specialist (OR = 2.22; 95% CI: 1.09, 4.56) had a greater likelihood of receiving an obesity diagnosis than their respective counterparts. We did not observe a relationship between patient race and receipt of an obesity diagnosis.

Table 2
Adjusted predictors of obesity diagnosis and weight-related counseling among obese adults

Predictors of weight-related counseling among obese adults

The frequency of weight related counseling differed significantly between obese adults with an obesity diagnosis and obese adults without and obesity diagnosis (weight reduction: 37.1% vs. 9.6%; diet: 42.6% vs. 18.1%; and exercise: 33.3% vs. 15.3%; p < 0.000). It also differed significantly by risk status; obese patients at very high risk were significantly more likely to receive weight related counseling as compared to obese patient at lower risk status (weight reduction: 25.7% vs. 12.9%; diet: 38.0% vs. 18.6%; and exercise: 29.0% vs. 14.0% p < 0.05). Table 2 also examines the predictors of each type of weight-related counseling among obese adults: weight reduction (second column), diet (third column) and exercise (fourth column).

One of the largest predictors of each type of weight-related counseling was receipt of an obesity diagnosis (weight-reduction: OR = 5.72; 95% CI: 4.01, 8.17; diet: OR = 2.89; 95% CI: 2.05, 4.06; exercise: OR = 2.54; 95% CI: 1.67, 3.85). With respect to patient demographic characteristics, we observed significant relationships between weight reduction counseling and patient age (age 30-44: OR = 2.19; 95% CI: 1.08, 4.41) as well as all three types of weight-related counseling and living in the Northeast (weight-reduction: OR = 2.81; 95% CI: 1.38, 5.74; diet: OR = 2.13; 95% CI: 1.17, 3.91; exercise: OR = 2.03; 95% CI: 1.04, 3.97). A higher comorbidity risk was also associated with each type of weight-related counseling (weight-reduction – very high: OR = 2.91; 95% CI: 1.43, 5.92, high: OR = 2.26; 95% CI: 1.36, 3.77; diet – very high: OR = 2.84; 95% CI: 1.59, 5.06, high: OR = 1.98; 95% CI: 1.28, 3.06; exercise – high: OR = 1.94; 95% CI: 1.21, 3.09. For both weight-reduction and diet counseling, patients who saw a cardiologist or other internal medicine specialist were significantly more likely to receive counseling (weight-reduction: OR = 4.01; 95% CI: 1.49, 10.74; diet: OR = 4.17; 95% CI: 1.57, 11.08). A preventive visit was also significantly associated with each type of counseling (weight reduction: OR = 1.79; 95% CI: 1.12, 2.85; diet OR = 1.61; 95% CI: 1.09, 2.39; exercise: OR = 2.00; 95% CI: 1.27, 3.15). A chronic, ongoing visit was significantly associated with patient receive of exercise counseling (OR = 1.58; 95% CI: 1.06, 2.35). Patients who spent more time with the doctor were significantly more likely to receive each type of counseling (weight reduction – more than 24 minutes: OR = 2.92; 95% CI: 1.40, 6.09; diet – more than 24 minutes: OR = 2.84; 95% CI: 1.62, 4.96, 13-24 minutes: OR = 1.76; 95% CI: 1.02, 3.04; exercise – more than 24 minutes: OR = 3.68; 95% CI: 1.99, 6.82, 13-24 minutes: OR = 2.14; 95% CI: 1.16, 3.95.


This paper examined whether obese patients receive an obesity diagnosis and weight-related counseling from their physician. Our secondary aim was to identify sociodemographic characteristics, physician characteristics, or characteristics of the clinical encounter associated with obesity diagnosis and weight-related counseling.

Our findings indicate that rates of obesity diagnosis and weight-related counseling were low in 2005, despite clinical guidelines suggesting that clinicians screen all adult patients for obesity and offer intensive counseling to promote sustained weight loss for obese adults [2]. We also found that receipt of an obesity diagnosis was one of the largest predictors of weight-related counseling. For our model looking at predictors of an obesity diagnosis, our results suggest that female gender, young age, living in the Midwest, class II or class III obesity, and being seen by a cardiologist or other internal medicine specialties were positively and significantly associated with receipt of an obesity diagnosis. For our models looking at the predictors of weight-related counseling, receipt of an obesity diagnosis was the largest predictor in the weight reduction model and one of the largest in the diet and exercise counseling models. Consistent across all three counseling models was the significant relationship between receipt of weight-related counseling and the following covariates: high co-morbidity risk status, preventive visit (as compared to an acute visit), time spent with the doctor, and geography (obese patients living in the Northeast as compared to the South).

There are both similarities and differences between these findings and previous research. Like earlier studies, we observed a low rate of obesity diagnosis among obese individuals (28.9%) [6, 8, 24]. Also similar to prior research, we found a higher likelihood of obesity diagnosis among women as compared to men [25], among younger age individuals as compared to older individuals [24], and among individuals with higher co-morbidity risk as compared to individuals with lower co-morbidity risk [8, 24]. Our findings related to the predictors of weight-related counseling also align well with previous work. We found that a high co-morbidity risk status [26], living in the Northeast region of the country [8], and having an obesity diagnosis [24] were positively associated with weight-related counseling. Similar to research using the NAMCS data from a prior year (1995) [8], we did observe an association between cardiologists/other internal medicine specialties and obesity diagnosis/weight-reduction counseling. In contrast to national estimates [27], higher rates of obesity were observed in men rather than women. Unlike previous studies [5], we did not observe a relationship between race/ethnicity and receipt of weight-related counseling. This suggests that minority patients, who are disproportionately impacted by obesity, are not more likely than Whites to be identified as obese or receive weight-reduction counseling. Our lack of a race effect suggests that physicians may lack sensitivity to underlying levels of obesity risk in the adult population given that the sub-groups who are at higher obesity risk are not more likely to receive obesity care. While lower-risk groups will likely benefit from receiving an obesity diagnosis or weight-related counseling, physician practices related to obesity care may be more effective if they focus more on sub-populations at higher risk for obesity.

This paper contributes to the literature by updating prior (nationally representative) estimates with data including measured height and body weight, allowing for a more precise estimation of the obese population. We additionally identify the sociodemographic, physician, and clinical encounter characteristics associated with receipt of obesity diagnosis and weight-related counseling.

Future Research

The results from this study suggest the need for more research. In all models included in these analyses, the variance explained was below twenty percent. This suggests that there are factors, other than those we measured in this paper, which are also associated with our outcome variables of interest. Previous research suggests that low rates of physician diagnosis and weight-related counseling may also be related to inadequate training [16, 28-31], the belief that advice would have little effect on patient behavior [30, 32], the belief that patients are not interested or ready for treatment [28, 30, 33-34], negative attitudes towards obese patients [35-37], the belief that obesity is the responsibility of the patient [38], or the belief that obesity is hard to handle [39]. Health system barriers to effective obesity care, which have been previously identified, include lack of: payment by insurance companies for weight-related counseling and care [30, 40-41], available teaching materials for patients [28, 30], infrastructure support/places to refer patients [42], a reminder system [43], or sufficient staff or consultant support [43]. To what extent these physician attitudes and health system factors are associated with physician practice patterns of obesity care is unknown.

Another important area of future research is the variation in the frequency and predictors of the three weight-related counseling activities explored in this analysis. Our results suggested that physicians were more comfortable providing diet counseling (as compared to weight-reduction or exercise counseling), and that the magnitude of the effect of an obesity diagnosis on receipt of weight-related counseling was not consistent across the three counseling strategies. A better understanding of these relationships may help facilitate more effective delivery of obesity-related services.

The Dartmouth Atlas Project, which has documented considerable variation in the delivery of healthcare by geographic variation, also serves as a useful guide for continued research in this area [44-45]. The Atlas Project has shown, for example, that the most significant factor associated with Medicare spending in a given region is the availability of medical resources. Therefore, future research should consider the availability of supply-side factors when estimating variations of obesity care, particularly among smaller geographic units (e.g., counties, zip codes). Future research should also examine why certain physician specialties (i.e. cardiology and other internal medicine specialties) are more likely to provide obesity care as well as whether additional physician characteristics, such as knowledge or body weight, influence physician practice patterns of obesity diagnosis and weight-related counseling.


There are several limitations to this analysis worth noting. First, this analysis is cross-sectional which limits our ability to make causal inferences. Second, the data rely on physician reports (which cannot be objectively verified), and which may lead to an underestimation of weight-related counseling given evidence suggesting that physicians under report behavioral counseling [46]. However, recent research validating the NAMCS indicates that it is highly specific, suggesting that if physicians report having delivered a service, there is a high likelihood that it was given [47]. Third, patient race/ethnicity is based on physician report. A physician's perception of a patient's race/ethnicity may differ from the patient's own perception; however, it is likely that physicians’ perceptions of patients’ race/ethnicity influence their own communication behaviors and decision-making. Fourth, because the NAMCS is a visit-based survey, obese patients who see their physician more often may have received an obesity diagnosis or weight-reduction counseling at a clinical encounter not captured by the survey. To account for this limitation, we do control for whether the patient was seen previously by a health professional in the practice. Fifth, patients were considered to have received weight-related counseling if it was “ordered or provided” at the clinical encounter. In the case of ordered services, it was not possible to know whether the patient actually received them. It is also not possible to know the quality of counseling services provided or ordered. Sixth, we are unable to determine from the data whether patients had health insurance coverage for weight-reduction counseling. However, we controlled for respondents’ health insurance status as a crude proxy for access to obesity-related services. Seventh, missing height and body weight information likely excludes some obese patients. Finally, physicians may be more likely to provide weight-related counseling to morbidly obese patients (as compared to obese patients) since they are at higher risk for metabolic abnormalities and other adverse health conditions. To account for this possible limitation, we controlled for obesity class in the models.

Practice Implications

Physicians and other health professionals are uniquely positioned to impact obesity care; in 2005 an estimated 963.6 million visits were made to doctors’ offices [48]. Given the penetration of the obesity epidemic, even modest reductions in body weight at the individual-level can lead to significant health benefits and reduced costs at the population-level [49-50]. Efforts to improve the care of obese patients in clinical practice will need to consider barriers which may undermine obesity care previously identified in the literature [16, 28-30, 33-34, 40, 42-43, 51]. The results from this analysis suggest that preventive visits may provide a key opportunity for obese patients to receive weight-related counseling from their physician.

There is a need for better systems to appropriately diagnose obese patients, particularly those at highest risk, and subsequently provide necessary weight-related counseling to help patients lose or maintain their weight. One possible system that could facilitate improvement of obesity care is the chronic care model which supports delivery of effective and efficient clinical care and self-management support, data systems that monitor the performance of the care system and provide reminders for both providers and patients, and decision support that is consistent with scientific evidence and patient preferences, among other objectives [52].


In summary, our findings suggest considerable missed opportunities in the diagnosis and management of adult obesity. Moreover, most obese patients with a higher underlying risk of excess weight (e.g., racial/ethnic minorities) do not have a higher likelihood of receiving obesity care after controlling for demographic characteristics, risk status, physician characteristics and characteristics of the clinical encounter.


Funding: This work was supported by two grants from the National Heart, Lung, and Blood Institute (1K01HL096409 and K24HL083113).


Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Competing interests: None declared


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