Characterizing Obesity in a Large Health Care Delivery System

ASBTRACT

Objectives: To characterize the prevalence of obesity and associated health care use within an integrated health care system in California.

Study Design: Cross-sectional study using electronic health records.

Methods: Primary care patients 18 years and older receiving care at Sutter Health between 2015 and 2020 were included in the study. Obesity was classified and health care utilization was ascertained at index and during the follow-up periods. Differences in prevalence by demographic and clinical characteristics among patients with and without obesity were assessed. Logistic regression was used to estimate the relationship between obesity class and health care utilization (outpatient encounters).

Results: Of the 1,094,790 primary care patients included in the analysis, 35% were classified as having obesity, defined as a body mass index of 30 kg/m² or more or 25 kg/m² or more for Asian individuals. Obesity prevalence was greater in Hispanic patients (46%) than in non-Hispanic White patients (30%). Patients without obesity had fewer outpatient visits (mean [SD], 3.7 [3.8]) than those with class 1 (4.1 [4.0]), class 2 (4.6 [4.4]), and class 3 (5.2 [4.8]) obesity. In the fully adjusted regression model, the odds of being a high utilizer among patients with obesity were 1.1 (class 1), 1.2 (class 2), and 1.3 (class 3) times that of patients without obesity (P < .001).

Conclusion: Obesity prevalence is high among patients in the Sutter Health system, varying by race/ethnicity, and was associated with increased outpatient visit utilization. There is a need for greater awareness of the impact of obesity and the specific patient populations affected by the disease.

Am J Manag Care. 2023;29(11):558-564. https://doi.org/10.37765/ajmc.2023.89451

Takeaway Points

Obesity is a chronic disease associated with many comorbidities. We conducted a study of patients receiving primary care at the Sutter Health system between 2015 and 2020 to assess the prevalence of obesity and health care utilization in patients with obesity.

• More than one-third of primary care patients had obesity, with variations in prevalence by geographic area, race/ethnicity, and health insurance type.
• Patients with obesity, particularly those with severe obesity and certain racial/ethnic backgrounds, had higher outpatient health care utilization.
• Greater awareness and resources are needed for health care professionals to help manage obesity and improve patient outcomes.

Obesity is a chronic disease associated with multiple comorbidities or complications, including cardiovascular disease, stroke, type 2 diabetes, certain types of cancers, osteoarthritis, mental health disorders, and sleeping disorders,^1-9 as well as an increased risk of mortality.¹⁰ Obesity has also been shown to increase the risk for COVID-19 complications, mortality, and hospitalizations.^11-13 Obesity poses a substantial burden on the health care system due to its association with increased health care utilization and medical costs.^5,14-17

Health care professionals, especially primary care providers, play an important role in helping patients manage obesity, which requires a multifactorial approach.^18-20 However, obesity is often not managed effectively in primary care settings^21-24 due to a variety of factors.^22,25-27 Clinicians often lack the training or the appropriate tools or resources to educate patients about potential treatment options or the risks associated with obesity.^28-30 Understanding the prevalence of obesity and its impact within a health care system, as well as the specific demographic and clinical characteristics of the population, can help inform the design of obesity management tools and approaches. Resources tailored to the needs of a specific population that can be efficiently introduced at the point of care may help clinicians better support patients with obesity and potentially improve their health outcomes.

The goal of this descriptive study was to better understand the prevalence of obesity within a racially diverse integrated health care delivery system, with a primary aim to quantify the prevalence of obesity and its variation across the system. A secondary aim was to describe patterns in health care utilization among patients with and without obesity.

METHODS

Study Design and Data Source

We conducted a cross-sectional study to examine obesity prevalence and health care utilization for patients receiving care at Sutter Health, a community-based, not-for-profit health care delivery network in Northern California. Sutter Health is an integrated, provider-based network comprising more than 100 ambulatory clinic sites and 24 acute-care hospitals across diverse urban and rural communities within 22 counties. Sutter Health is organized into 5 regions: Central Valley, Sacramento Sierra, East Bay, San Francisco/West Bay, and South Bay/Peninsula (Palo Alto). Data were extracted from Sutter Health’s electronic health record (EHR) system (Epic Systems) between January 1, 2015, and December 31, 2020. The study was reviewed and approved by the Sutter Health Institutional Review Board, which granted a waiver of Health Insurance Portability and Accountability Act authorization and informed consent to access the medical records for research purposes.

Study Population

The study population included all patients 18 years and older receiving care in the Sutter Health system who had at least 1 primary care visit between January 1, 2015, and December 31, 2019, and at least 12 to 24 months of activity prior to the patient’s first in-person encounter (index date) during the study period. Patients with a pregnancy diagnosis or a documented childbirth 6 months prior to, or any time during, the study period, were excluded.

To classify patients’ obesity status, clinical characteristics, and health care utilization patterns, we defined an index date and 3 time periods or “windows” relative to the index date (eAppendix Figure [eAppendix available at ajmc.com]). The baseline window was defined as the 12-month period prior to and including a patient’s index date. This time period was used to identify all comorbid conditions using the problem list and/or encounter diagnoses and ensured patients’ comorbid conditions were current. The activity window was defined as the 12-month period prior to the baseline window, ensuring that patients had any type of recorded activity in the EHR associated with the Sutter Health system. The follow-up window was defined as the 12-month period following the index date during which health care utilization was assessed.

Outcome Measures

The prevalence of obesity, comorbidities, and health care utilization (outpatient office encounters, emergency department [ED] visits, inpatient hospitalizations) were measured. Baseline demographic characteristics (eg, age, sex, health insurance, and self-identified race and ethnicity) and clinical characteristics (eg, body mass index [BMI], comorbidities, Charlson Comorbidity Index [CCI] score,³¹ and smoking status) were extracted from the EHR. BMI, which was assessed at the index encounter, is a structured field in the EHR (ie, it does not need to be calculated from height and weight measures). Obesity classifications were modified from the World Health Organization definition to adjust for the Asian-Pacific population (eAppendix Table 1).³² Obesity classes for the patients of Asian-Pacific descent were defined as class 1: BMI between 25 kg/m² and less than 30 kg/m²; class 2: BMI between 30 kg/m² and less than 40 kg/m²; and class 3: BMI of 40 kg/m² or more. For all other patients, obesity classes were defined as class 1: BMI between 30 kg/m² and less than 35 kg/m²; class 2: BMI between 35 kg/m² and less than 40 kg/m²; and class 3: BMI of 40 kg/m² or more.

A combined race/ethnicity field was defined by self-identification of Hispanic identity, followed by racial group. Patients who did not self-identify as Hispanic were classified based on their race. Extracted health care utilization data included office visits, ED visits, and inpatient hospitalizations.

Statistical Analysis

We examined differences in the prevalence of obesity by geographic region and differences in demographic and clinical characteristics among patients with and without obesity. Summary statistics were calculated on categorical variables (percentages) and continuous variables (means, medians, SDs). When continuous variables were not normally distributed, they were categorized. Outliers and missing variables were set to missing and no imputation was conducted.

We assessed each patient’s health care utilization during the 12-month follow-up window for outpatient encounters, ED visits, and inpatient hospitalizations. For each measure, we calculated the mean, median, percentile, and number of encounters of each type; this revealed a strong left-skewed distribution (data not shown), meaning most patients had 0 encounters during the follow-up window. Ambulatory outpatient encounters, while still left skewed, were a median of 3 per patient. Accordingly, we used outpatient encounters as our basis for modelling utilization. We used a logistic regression model to estimate the odds of outpatient encounters (fewer than 3 encounters = 0; 3 or more encounters = 1) based on obesity class. We then adjusted for demographic and clinical variables (eAppendix Table 2). Models were constructed sequentially, starting with obesity class and building to a final model that included all comorbidities (eAppendix Table 2).

All hypothesis tests were 2-sided with an α of 0.05. Point estimates and 95% CIs were generated for all end points. We used Tukey multiple comparison tests to estimate differences in means for continuous variables and χ² tests to estimate difference for categorical variables. Data analyses were performed in RStudio (Posit PBC).

RESULTS

Patient Characteristics

A total of 1,094,790 patients met eligibility criteria; 380,113 (35%) were classified as having obesity (BMI ≥ 30 kg/m² or ≥ 25 kg/m² for Asian individuals). Table 1 and Table 2 present the demographic and clinical characteristics of the study sample by obesity status, respectively. Less than 1% of patients with or without obesity were reported to have metabolic syndrome. The proportion of patients with hypertension was more than 1.5 times higher than that of patients without obesity; the proportion of patients with obesity and prediabetes or type 2 diabetes were 2 and 3 times higher, respectively.

eAppendix Table 3 shows the proportion of patients with obesity by each characteristic. Prevalence of obesity was high among Hispanics (46%), non-Hispanic Blacks (49%), non-Hispanic Native Americans (47%), non-Hispanic Asians (44%), and non-Hispanic Native Hawaiians/Other Pacific Islanders (47%) compared with non-Hispanic Whites (31%; all P < .001) and among those of unknown race (27%; P < .001). Forty-two percent of patients with Medicaid had obesity, more than patients with Medicare (34%; P < .001) or commercial insurance (34%; P < .001). Patients with a CCI score of 3 or more were more likely to have obesity (61%-78%) compared with those with a CCI score of 2 or less (24%-31%; all P < .001).

The proportion of patients by each characteristic within each obesity class is shown in eAppendix Table 4. Sixty-two percent of patients with obesity had class 1 obesity, 25% had class 2, and 13% had class 3. Females with obesity were more likely than males to have class 3 obesity (16% vs 9%; P < .001). Among patients covered by Medicaid, 21% had class 3 obesity. Class 3 obesity also was prevalent in 13% of patients covered by Medicare and 13% of those who had commercial insurance. Patients with obesity who had a CCI score of 5 to 6 or more were more likely to have class 3 obesity (47% and 37%, respectively) than those with a CCI score of 5 or less (0%-19%; all P < .001).

eAppendix Table 5 presents the patient characteristics by race/ethnicity. Across broad race/ethnicity categories, the non-Hispanic Black group had the highest proportion of patients in class 3 (24%), followed by non-Hispanic White (16%), Hispanic (14%), and non-Hispanic Other (14%). Non-Hispanic Asian patients had the lowest prevalence of class 3 obesity at 2%. These patients comprised the only racial subgroup with a higher proportion of males with obesity (55%, vs 35%-48% in all other racial/ethnic subgroups). Both Hispanic and non-Hispanic Black patients with obesity had higher proportions on Medicaid (both 4%), whereas non-Hispanic White patients had the lowest proportion with commercial insurance (58%) and the highest proportion on Medicare (26%).

Among patients with obesity, the rural areas of the Central Valley and Sacramento Sierra region had the highest proportion of patients with class 3 obesity (Figure 1). The East Bay region, which tends to be more suburban, had the highest proportion of female patients with obesity (65% vs 49%-56%; P < .001 in all other regions). eAppendix Table 6 presents the patient characteristics by region. The highest proportions of patients with obesity self-identifying as Hispanic were in the Central Valley (30%) and East Bay (19%) regions. The proportion of self-identified non-Hispanic Black patients with obesity was highest in the East Bay region (20%); all other regions ranged from 2% to 6%. The Central Valley region had the lower proportion of patients with commercial insurance (56% vs 60%-70% in all other regions) and, along with the urban San Francisco/West Bay region, the highest proportion with Medicaid (4% for both vs 2% in the other regions). Being a current smoker was more common in the Central Valley (9%) and Sacramento Sierra (9%) regions than in the other regions (5%-7%).

Health Care Resource Utilization

Office visits, ED visits, and inpatient admissions were more common among patients with obesity than those without obesity and were progressively more common with each higher obesity class (Figure 2). Patients without obesity had a mean of 3.7 office visits, lower than that seen for patients with class 1 (4.1 visits), class 2 (4.6 visits), and class 3 (5.2 visits) obesity (eAppendix Table 7).

In the base (unadjusted) logistic regression model, there was a strong relationship between obesity class and high health care utilization (3 or more outpatient encounters). Compared with patients who did not have obesity, patients with class 1, 2, and 3 obesity had 1.21 (95% CI, 1.19-1.22), 1.50 (95% CI, 1.47-1.52), and 1.94 (95% CI, 1.90-1.99) times higher odds of being high utilizers, respectively (eAppendix Table 8). In the fully specified model (Figure 3), this relationship was consistent, although the odds ratios (ORs) were attenuated (OR, 1.11 for class 1, 1.22 for class 2, and 1.32 for class 3; all P < .001). Patients who were Hispanic (OR, 1.18; 95% CI, 1.16-1.19), non-Hispanic Black (OR, 1.17; 95% CI, 1.14-1.20), and non-Hispanic Native American (OR, 1.4; 95% CI, 1.24-1.57) had greater odds of being high utilizers than non-Hispanic White patients. Non-Hispanic Asian patients were at significantly lower odds (OR, 0.76; 95% CI, 0.75-0.77) of being high utilizers than non-Hispanic White patients. Patients with obesity covered by Medicaid (OR, 1.51; 95% CI, 1.47-1.55), Medicare (OR, 1.46; 95% CI, 1.44-1.48), or other insurance (OR, 1.38; 95% CI, 1.26-1.51) were at significantly higher odds of being high utilizers than those covered by commercial insurance, whereas patients with self-pay or unknown coverage were at significantly lower odds (OR, 0.78; 95% CI, 0.74-0.82).

DISCUSSION

This study describes the prevalence of obesity and associated health care utilization in a population of patients within a large health system in California. Of the primary care patients in the Sutter Health system included in our analysis, 35% were classified as having obesity; this total is slightly lower than that of the most recent national estimate of 42.4%,³³ but higher than the current overall obesity prevalence in California (27.6%).³⁴ One possible explanation is that the proportion of non-Hispanic Asian patients is 18% in the Sutter Health population, substantially higher than that of the US population (6%).³⁵ However, we used a lower threshold of obesity for patients of Asian descent, from a BMI of 25 kg/m² or more to a BMI of 30 kg/m² or more, which should more accurately reflect the prevalence of obesity in this group. Another possible explanation for the differences was our requirement of receiving primary health care. The US and California estimates likely include individuals who do not utilize the health care system as it was derived from a survey (the National Health and Nutrition Examination Survey).³⁵

Obesity prevalence in our study varied by race and ethnicity, very similar to that seen in the United States generally, except for non-Hispanic White individuals, for whom it was lower (31% vs 42%).³³ Almost a quarter of non-Hispanic Black patients in the Sutter Health sample had class 3 obesity, substantially more than any other racial/ethnic group. This is concerning as severe obesity is associated with increased morbidity³⁶ and is estimated to shorten life expectancy by as much as 14 years.³⁷ These findings highlight the importance of capturing race/ethnicity and other socioeconomic information in EHRs, which is often lacking and can lead to an incomplete picture of the health and health care utilization patterns in patient populations.^38-41

Our study found that 42% of patients with Medicaid insurance had obesity in the Sutter Health system compared with 34% of those with commercial insurance, potentially indicating a higher obesity rate among those with lower income. We also found differences in demographic and clinical characteristics across Sutter Health’s broad geography. Among patients with obesity, the more rural regions of Sacramento Sierra and Central Valley with lower socioeconomic status had the highest proportion of individuals with class 3 obesity than the more urban regions with higher socioeconomic status.⁴² Patients in the Central Valley region were least likely to have commercial insurance. Resources for health care professionals in the region should consider the differences in socioeconomic status and the unique barriers they pose to obesity management in these patient populations.

We found a very low proportion of patients in our study had metabolic syndrome, much lower than the 2012 estimation of 34% in the US adult population.⁴³ This could be due to documentation patterns in the health system, or it could suggest infrequent screening for the condition, likely due to a lack of consensus regarding the clinical definition.^43,44 We did find higher levels of prediabetes, type 2 diabetes, and hypertension among patients with obesity, indicating that clinicians may be more likely to identify individual risk factors among their patients. Because metabolic syndrome is related to a higher risk of developing cardiovascular diseases,^45,46 it is important to identify the condition, including in younger populations.^44,47

In our study, more patients with obesity had office visits than patients without obesity, but there was less variability in health care utilization of ED visits and inpatient admissions, likely due to low counts in each of these categories. In multivariable models using outpatient utilization as a binary measure (ie, at or above vs below the median of 3 outpatient encounters), we found that high utilization was significantly associated with obesity, and the association got stronger with higher obesity class (OR, 1.3 for class 3 obesity vs patients without obesity). Our findings are similar to retrospective cohort studies examining the association between obesity and health care utilization in populations of patients with commercial insurance and Medicare, with similar odds of having an outpatient encounter increasing by obesity class.^15,16 Compared with non-Hispanic White patients, patients who were Hispanic, non-Hispanic Black, and non-Hispanic Native American were at increased odds of being high utilizers. A large observational study in Italy showed that patients with obesity who did not receive treatment for their obesity had greater health care utilization and costs than patients of normal weight.⁴⁸ In a US population, a retrospective cohort study of adults with obesity found that nonsurgical weight loss was associated with health care cost savings.⁴⁹ Although we did not study costs or weight changes, nor did we assess the use of obesity treatments, we did find that higher classes of obesity were associated with greater odds of being a high utilizer. If the Italian cohort findings apply to our cohort,⁴⁸ we can expect an even greater utilization burden for our patients who do not receive treatment for their obesity.

Limitations

As this is an observational study, potential confounding factors cannot be ruled out. EHR data were collected as part of routine clinical practice rather than as mandatory assessments at prespecified time points, which is dependent on the documentation practices within the health system; this could have an impact on the amount of available data and its interpretation. Additional limitations include missing data and incomplete coding of diagnoses. Sutter Health is an open system and therefore, patients can receive their care outside the system, and some of that utilization will be invisible in the Sutter Health EHR. Finally, our findings represent the results at a single health care system. Although Sutter Health is large and diverse, the findings may not be generalizable for all health care systems.

CONCLUSIONS

Although the prevalence of obesity within the Sutter Health population is slightly lower than national US estimates, there are important differences by clinical and demographic characteristics in this population. Obesity rates can be different across a large health system with multiple locations, potentially requiring different approaches for managing the disease within the health system. Additionally, obesity is associated with higher utilization of outpatient visits, particularly for certain racial/ethnic subgroups and patients with Medicare and Medicaid, highlighting the importance of obesity management in these vulnerable populations. Further research is needed to understand the obesity management practices within the Sutter Health system to identify knowledge and practice gaps, ultimately with a goal to develop tools and resources to help health care professionals better support their patients with obesity.

Acknowledgments

The authors thank Rebecca Hahn, MPH, and Elizabeth Tanner, PhD, of KJT Group, Inc, Rochester, New York, for providing medical writing support, which was funded by Novo Nordisk, Inc, Plainsboro, New Jersey, in accordance with Good Publication Practice (GPP 2022) guidelines.

Author Affiliations: Center for Health Systems Research, Sutter Health (AP, JBJ, XX, AS), Sacramento, CA; Novo Nordisk Inc (SA, SW, ED), Plainsboro, NJ.

Source of Funding: Novo Nordisk Inc funded the study and writing support, and had a role in the study design, data collection, analysis, interpretation of data, and review/approval of the manuscript.

Author Disclosures: Dr Pressman reports former employment by PRECISIONheor, a company that has previously conducted research in obesity. Ms Scott, Dr Jones, and Ms Xu report employment by Sutter Health, which received funds from Novo Nordisk to conduct the study. Ms Alvarez reports being an employee of Novo Nordisk and owning stock in Novo Nordisk. Dr Watkins and Dr Durden report being employees of Novo Nordisk at the time this study was conducted.

Authorship Information: Concept and design (AP, JBJ, SA, SW, ED); acquisition of data (JBJ, XX); analysis and interpretation of data (AP, JBJ, XX, AS, SA, SW); drafting of the manuscript (AP, JBJ, XX, AS, SA, SW); critical revision of the manuscript for important intellectual content (AP, JBJ, XX, SA, SW, ED); statistical analysis (AP, XX); provision of patients or study materials (XX); obtaining funding (AP, JBJ); administrative, technical, or logistic support (AS, SA); and supervision (JBJ, ED).

Address Correspondence to: Alexandra Scott, MA, Sutter Health, 2121 North California Blvd, Ste 310, Walnut Creek, CA 94596. Email: alex.scott@sutterhealth.org.

REFERENCES

1. Avgerinos KI, Spyrou N, Mantzoros CS, Dalamaga M. Obesity and cancer risk: emerging biological mechanisms and perspectives. Metabolism. 2019;92:121-135. doi:10.1016/j.metabol.2018.11.001

2. Cancer and obesity. CDC. Updated October 1, 2019. Accessed November 17, 2020. https://www.cdc.gov/vitalsigns/obesity-cancer/index.html

3. Garg SK, Maurer H, Reed K, Selagamsetty R. Diabetes and cancer: two diseases with obesity as a common risk factor. Diabetes Obes Metab. 2014;16(2):97-110. doi:10.1111/dom.12124

4. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health. 2009;9:88. doi:10.1186/1471-2458-9-88

5. Li Q, Blume SW, Huang JC, Hammer M, Ganz ML. Prevalence and healthcare costs of obesity-related comorbidities: evidence from an electronic medical records system in the United States. J Med Econ. 2015;18(12):1020-1028. doi:10.3111/13696998.2015.1067623

6. Massetti GM, Dietz WH, Richardson LC. Excessive weight gain, obesity, and cancer: opportunities for clinical intervention. JAMA. 2017;318(20):1975-1976. doi:10.1001/jama.2017.15519

7. Meurling IJ, Shea DO, Garvey JF. Obesity and sleep: a growing concern. Curr Opin Pulm Med. 2019;25(6):602-608. doi:10.1097/mcp.0000000000000627

8. Pereira-Miranda E, Costa PRF, Queiroz VAO, Pereira-Santos M, Santana MLP. Overweight and obesity associated with higher depression prevalence in adults: a systematic review and meta-analysis. J Am Coll Nutr. 2017;36(3):223-233. doi:10.1080/07315724.2016.1261053

9. Rajan TM, Menon V. Psychiatric disorders and obesity: a review of association studies. J Postgrad Med. 2017;63(3):182-190. doi:10.4103/jpgm.JPGM_712_16

10. Xu H, Cupples LA, Stokes A, Liu CT. Association of obesity with mortality over 24 years of weight history: findings from the Framingham Heart Study. JAMA Netw Open. 2018;1(7):e184587. doi:10.1001/jamanetworkopen.2018.4587

11. Földi M, Farkas N, Kiss S, et al. Obesity is a risk factor for developing critical condition in COVID-19 patients: a systematic review and meta-analysis. Obes Rev. 2020;21(10):e13095. doi:10.1111/obr.13095

12. Klang E, Kassim G, Soffer S, Freeman R, Levin MA, Reich DL. Severe obesity as an independent risk factor for COVID-19 mortality in hospitalized patients younger than 50. Obesity (Silver Spring). 2020;28(9):1595-1599. doi:10.1002/oby.22913

13. Sattar N, Valabhji J. Obesity as a risk factor for severe COVID-19: summary of the best evidence and implications for health care. Curr Obes Rep. 2021;10(3):282-289. doi:10.1007/s13679-021-00448-8

14. Cawley J, Biener A, Meyerhoefer C, et al. Direct medical costs of obesity in the United States and the most populous states. J Manag Care Spec Pharm. 2021;27(3):354-366. doi:10.18553/jmcp.2021.20410

15. Kamble PS, Hayden J, Collins J, et al. Association of obesity with healthcare resource utilization and costs in a commercial population. Curr Med Res Opin. 2018;34(7):1335-1343. doi:10.1080/03007995.2018.1464435

16. Suehs BT, Kamble P, Huang J, et al. Association of obesity with healthcare utilization and costs in a Medicare population. Curr Med Res Opin. 2017;33(12):2173-2180. doi:10.1080/03007995.2017.1361915

17. Waters H, Graf M. The costs of chronic disease in the U.S. 2018. August, 2018. Accessed October 20, 2021. https://milkeninstitute.org/sites/default/files/reports-pdf/ChronicDiseases-HighRes-FINAL.pdf

18. Garvey WT, Mechanick JI, Brett EM, et al; Reviewers of the AACE/ACE Obesity Clinical Practice Guidelines. American Association of Clinical Endocrinologists and American College of Endocrinology comprehensive clinical practice guidelines for medical care of patients with obesity. Endocr Pract. 2016;22(suppl 3):1-203. doi:10.4158/EP161365.GL

19. Jensen MD, Ryan DH, Apovian CM, et al; American College of Cardiology/American heart Association Task Force on Practice Guidelines; Obesity Society. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation. 2014;129(25 Suppl 2):S102-38. doi:10.1161/01.cir.0000437739.71477.ee

20. Semlitsch T, Stigler FL, Jeitler K, Horvath K, Siebenhofer A. Management of overweight and obesity in primary care—a systematic overview of international evidence-based guidelines. Obes Rev. 2019;20(9):1218-1230. doi:10.1111/obr.12889

21. Bardia A, Holtan SG, Slezak JM, Thompson WG. Diagnosis of obesity by primary care physicians and impact on obesity management. Mayo Clin Proc. 2007;82(8):927-932. doi:10.4065/82.8.927

22. Ferrante JM, Piasecki AK, Ohman-Strickland PA, Crabtree BF. Family physicians’ practices and attitudes regarding care of extremely obese patients. Obesity (Silver Spring). Sep 2009;17(9):1710-1716. doi:10.1038/oby.2009.62

23. Smith AW, Borowski LA, Liu B, et al. U.S. primary care physicians’ diet, physical activity, and weight-related care of adult patients. Am J Prev Med. 2011;41(1):33-42. doi:10.1016/j.amepre.2011.03.017

24. Kahan SI. Practical strategies for engaging individuals with obesity in primary care. Mayo Clin Proc. 2018;93(3):351-359. doi:10.1016/j.mayocp.2018.01.006

25. Sabin JA, Marini M, Nosek BA. Implicit and explicit anti-fat bias among a large sample of medical doctors by BMI, race/ethnicity and gender. PLoS One. 2012;7(11):e48448. doi:10.1371/journal.pone.0048448

26. Croghan IT, Ebbert JO, Njeru JW, et al. Identifying Opportunities for Advancing Weight Management in Primary Care. J Prim Care Community Health. 2019;10:2150132719870879. doi:10.1177/2150132719870879

27. Simon R, Lahiri SW. Provider practice habits and barriers to care in obesity management in a large multicenter health system. Endocr Pract. 2018;24(4):321-328. doi:10.4158/ep-2017-0221

28. Butsch WS, Kushner RF, Alford S, Smolarz BG. Low priority of obesity education leads to lack of medical students’ preparedness to effectively treat patients with obesity: results from the U.S. medical school obesity education curriculum benchmark study. BMC Med Educ. 2020;20(1):23. doi:10.1186/s12909-020-1925-z

29. Butsch WS, Robison K, Sharma R, Knecht J, Smolarz BG. Medicine residents are unprepared to effectively treat patients with obesity: results from a U.S. internal medicine residency survey. J Med Educ Curric Dev. 2020;7:2382120520973206. doi:10.1177/2382120520973206

30. Orjuela-Grimm M, Butsch WS, Bhatt-Carreño S, Smolarz BG, Rao G. Benchmarking of provider competencies and current training for prevention and management of obesity among family medicine residency programs: a cross-sectional survey. BMC Fam Pract. 2021;22(1):132. doi:10.1186/s12875-021-01484-y

31. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. doi:10.1016/0021-9681(87)90171-8

32. Romanelli RJ, Huang HC, Chopra V, et al. Longitudinal weight outcomes from a behavioral lifestyle intervention in clinical practice. Diabetes Educ. 2019;45(5):529-543. doi:10.1177/0145721719872553

33. Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity and severe obesity among adults: United States, 2017-2018. NCHS Data Brief. Feb 2020;(360):1-8.

34. Adult Obesity Prevalence Maps. CDC. Updated March 17, 2023. Accessed October 17, 2022. https://www.cdc.gov/obesity/data/prevalence-maps.html

35. QuickFacts United States. United States Census Bureau. Accessed August 2, 2022. https://www.census.gov/quickfacts/fact/table/US/PST045221

36. Khan SS, Krefman AE, Zhao L, et al. Association of body mass index in midlife with morbidity burden in older adulthood and longevity. JAMA Netw Open. 2022;5(3):e222318. doi:10.1001/jamanetworkopen.2022.2318

37. Kitahara CM, Flint AJ, Berrington de Gonzalez A, et al. Association between class III obesity (BMI of 40-59 kg/m2) and mortality: a pooled analysis of 20 prospective studies. PLoS Med. 2014;11(7):e1001673. doi:10.1371/journal.pmed.1001673

38. Casey JA, Pollak J, Glymour MM, Mayeda ER, Hirsch AG, Schwartz BS. Measures of SES for electronic health record-based research. Am J Prev Med. 2018;54(3):430-439. doi:10.1016/j.amepre.2017.10.004

39. Yee K, Hoopes M, Giebultowicz S, Elliott MN, McConnell KJ. Implications of missingness in self-reported data for estimating racial and ethnic disparities in Medicaid quality measures. Health Serv Res. 2022;57(6):1370-1378. doi:10.1111/1475-6773.14025

40. Cook L, Espinoza J, Weiskopf NG, et al; N3C Consortium. Issues with variability in electronic health record data about race and ethnicity: descriptive analysis of the national covid cohort collaborative data enclave. JMIR Med Inform. 2022;10(9):e39235. doi:10.2196/39235

41. Polubriaginof FCG, Ryan P, Salmasian H, et al. Challenges with quality of race and ethnicity data in observational databases. J Am Med Inform Assoc. 2019;26(8-9):730-736. doi:10.1093/jamia/ocz113

42. Azar KMJ, Nasrallah C, Szwerinski NK, et al. Implementation of a group-based diabetes prevention program within a healthcare delivery system. BMC Health Serv Res. 2019;19(1):694. doi:10.1186/s12913-019-4569-0

43. Moore JX, Chaudhary N, Akinyemiju T. Metabolic syndrome prevalence by race/ethnicity and sex in the United States, National Health and Nutrition Examination Survey, 1988-2012. Prev Chronic Dis. 2017;14:E24. doi:10.5888/pcd14.160287

44. Boisvenue JJ, Oliva CU, Manca DP, Johnson JA, Yeung RO. Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis. CMAJ Open. 2020;8(4):E779-E787. doi:10.9778/cmajo.20200007

45. Li X, Zhai Y, Zhao J, et al. Impact of metabolic syndrome and it’s components on prognosis in patients with cardiovascular diseases: a meta-analysis. Front Cardiovasc Med. 2021;8:704145. doi:10.3389/fcvm.2021.704145

46. Shi TH, Wang B, Natarajan S. The influence of metabolic syndrome in predicting mortality risk among US adults: importance of metabolic syndrome even in adults with normal weight. Prev Chronic Dis. 2020;17:E36. doi:10.5888/pcd17.200020

47. Kim I, Kim MC, Sim DS, et al. Effect of the metabolic syndrome on outcomes in patients aged <50 years versus >50 years with acute myocardial infarction. Am J Cardiology. 2018;122(2):192-198. doi:10.1016/j.amjcard.2018.03.366

48. Colao A, Lucchese M, D’Adamo M, et al. Healthcare usage and economic impact of non-treated obesity in Italy: findings from a retrospective administrative and clinical database analysis. BMJ Open. 2017;7(2):e013899. doi:10.1136/bmjopen-2016-013899

49. Ding Y, Fan X, Blanchette CM, Smolarz BG, Weng W, Ramasamy A. Economic value of nonsurgical weight loss in adults with obesity. J Manag Care Spec Pharm. 2021;27(1):37-50. doi:10.18553/jmcp.2020.20036