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
]. 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
]. 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.
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
], the belief that advice would have little effect on patient behavior [30
], the belief that patients are not interested or ready for treatment [28
], negative attitudes towards obese patients [35
], 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
], available teaching materials for patients [28
], 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
]. 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.
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
]. 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
]. 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