Weight Loss for Patients With Obesity

Liu, Natalie; Birstler, Jen; Venkatesh, Manasa; Hanrahan, Lawrence P.; Chen, Guanhua; Funk, Luke M.

doi:10.1097/mlr.0000000000001277

Cited by 10 publications

(8 citation statements)

References 38 publications

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“…We propose that the monitoring of population weight data by public health agencies should involve incorporating information on intra-individual weight changes from EHRs, which are becoming increasingly available. 19 , 20 …”

Section: Discussionmentioning

confidence: 99%

Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records

Katsoulis

Lai

Díaz-Ordaz

et al. 2021

The Lancet Diabetes & Endocrinology

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Background Targeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR). MethodsIn this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions. Findings We included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4•22 [95% CI 3•86-4•62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4•60 (4•06-5•22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5•87 (5•23-6•59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1•23 (1•18-1•27), for men versus women was 1•12 (1•08-1•16), and for Black individuals versus White individuals was 1•13 (1•04-1•24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period.Interpretation A radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care.

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Section: Discussionmentioning

confidence: 99%

Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records

Katsoulis

Lai

Díaz-Ordaz

et al. 2021

The Lancet Diabetes & Endocrinology

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“…Length of follow-up was defined as the time between surgery and the most recent clinical encounter within the EHR. Using a modified version of an algorithm proposed by Cheng et al 29 and used by our group previously, 30 height and weight data from the EHR were “cleaned” to minimize inclusion of incorrect heights and weights due to data entry errors. All weights below 55 pounds or above 1,000 pounds were removed.…”

Section: Methodsmentioning

confidence: 99%

“…Biologically implausible BMIs (BMIs <7.5 kg/m 2 or >108.8 kg/m 2 ) were excluded. 29 , 30 No patients were excluded from the study cohort during data imputation and cleaning.…”

Section: Methodsmentioning

confidence: 99%

Association Between Medicaid Status, Social Determinants of Health, and Bariatric Surgery Outcomes

Liu

Venkatesh

Hanlon

et al. 2021

Annals of Surgery Open

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Objective: To compare outcomes after bariatric surgery between Medicaid and non-Medicaid patients and assess whether differences in social determinants of health were associated with postoperative weight loss. Background: The literature remains mixed on weight loss outcomes and healthcare utilization for Medicaid patients after bariatric surgery. It is unclear if social determinants of health geocoded at the neighborhood level are associated with outcomes. Methods: Patients who underwent laparoscopic sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) from 2008 to 2017 and had ≥1 year of follow-up within a large health system were included. Baseline characteristics, 90-day and 1-year outcomes, and weight loss were compared between Medicaid and non-Medicaid patients. Area deprivation index (ADI), urbanicity, and walkability were analyzed at the neighborhood level. Median regression with percent total body weight (TBW) loss as the outcome was used to assess predictors of weight loss after surgery. Results: Six hundred forty-seven patients met study criteria (191 Medicaid and 456 non-Medicaid). Medicaid patients had a higher 90-day readmission rate compared to non-Medicaid patients (19.9% vs 12.3%, P < 0.016). Weight loss was similar between Medicaid and non-Medicaid patients (23.1% vs 21.9% TBW loss, respectively; P = 0.266) at a median follow-up of 3.1 years. In adjusted analyses, Medicaid status, ADI, urbanicity, and walkability were not associated with weight loss outcomes. Conclusions: Medicaid status and social determinants of health at the neighborhood level were not associated with weight loss outcomes after bariatric surgery. These findings suggest that if Medicaid patients are appropriately selected for bariatric surgery, they can achieve equivalent outcomes as non-Medicaid patients.

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“…All recorded heights and weights in the EHR were cleaned to reduce the inclusion of incorrect heights and weights because of errors in data entry. Similar to our previous study using EHR data, we used the methodology proposed by Cheng et al [ 14 ] to remove biologically implausible heights and weights [ 15 ]. All heights >90 inches, <44 inches, and >1 SD from the mean height when SD was >2.5% of the mean were removed.…”

Section: Methodsmentioning

confidence: 99%

Obesity and BMI Cut Points for Associated Comorbidities: Electronic Health Record Study

et al. 2021

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Background Studies have found associations between increasing BMIs and the development of various chronic health conditions. The BMI cut points, or thresholds beyond which comorbidity incidence can be accurately detected, are unknown. Objective The aim of this study is to identify whether BMI cut points exist for 11 obesity-related comorbidities. Methods US adults aged 18-75 years who had ≥3 health care visits at an academic medical center from 2008 to 2016 were identified from eHealth records. Pregnant patients, patients with cancer, and patients who had undergone bariatric surgery were excluded. Quantile regression, with BMI as the outcome, was used to evaluate the associations between BMI and disease incidence. A comorbidity was determined to have a cut point if the area under the receiver operating curve was >0.6. The cut point was defined as the BMI value that maximized the Youden index. Results We included 243,332 patients in the study cohort. The mean age and BMI were 46.8 (SD 15.3) years and 29.1 kg/m2, respectively. We found statistically significant associations between increasing BMIs and the incidence of all comorbidities except anxiety and cerebrovascular disease. Cut points were identified for hyperlipidemia (27.1 kg/m2), coronary artery disease (27.7 kg/m2), hypertension (28.4 kg/m2), osteoarthritis (28.7 kg/m2), obstructive sleep apnea (30.1 kg/m2), and type 2 diabetes (30.9 kg/m2). Conclusions The BMI cut points that accurately predicted the risks of developing 6 obesity-related comorbidities occurred when patients were overweight or barely met the criteria for class 1 obesity. Further studies using national, longitudinal data are needed to determine whether screening guidelines for appropriate comorbidities may need to be revised.

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Weight Loss for Patients With Obesity

Cited by 10 publications

References 38 publications

Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records

Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records

Association Between Medicaid Status, Social Determinants of Health, and Bariatric Surgery Outcomes

Obesity and BMI Cut Points for Associated Comorbidities: Electronic Health Record Study

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