Objective: To determine if distinct financial trajectories exist and if they are associated with quality-of-life outcomes. Summary of Background Data: Financial hardship after injury measurably impacts Health-Related Quality of Life outcomes. Financial hardship, encompassing material losses, financial worry, and poor coping mechanisms, is associated with lower quality of life and increased psychological distress. However, recovery is dynamic and financial hardship may change over time. Methods: This is a secondary analysis of a cohort of 500 moderate-tosevere nonneurologic injured patients in which financial hardship and Health-related Quality of Life outcomes were measured at 1, 2, 4, and 12 months after injury using survey instruments . Enrollment occurred at an urban, academic, Level 1 trauma center in Memphis, Tennessee during January 2009 to December 2011 and followup completed by December 2012. Results: Four hundred seventy-four patients had sufficient data for Group-Based Trajectory Analysis. Four distinct financial hardship trajectories were identified: Financially Secure patients (8.6%) had consistently low hardship over time; Financially Devastated patients had a high degree of hardship immediately after injury and never recovered (51.6%); Financially Frail patients had increasing hardship over time (33.6%); and Financially Resilient patients started with a high degree of hardship but recovered by year end (6.2%). At 12-months, all trajectories had poor Short Form-36 physical component scores and the Financial Frail and Financially Devastated trajectories had poor mental health scores compared to US population norms. Conclusions and Relevance: The Financially Resilient trajectory demonstrates financial hardship after injury can be overcome. Further research into understanding why and how this occurs is needed.

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.

No abstract

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.

Background: Numerous studies have reported that losing as little as 5% of one’s total body weight (TBW) can improve health, but no studies have used electronic health record data to examine long-term changes in weight, particularly for adults with severe obesity [body mass index (BMI) ≥35 kg/m2]. Objective: To measure long-term weight changes and examine their predictors for adults in a large academic health care system. Research Design: Observational study. Subjects: We included 59,816 patients aged 18–70 years who had at least 2 BMI measurements 5 years apart. Patients who were underweight, pregnant, diagnosed with cancer, or had undergone bariatric surgery were excluded. Measures: Over a 5-year period: (1) ≥5% TBW loss; (2) weight loss into a nonobese BMI category (BMI <30 kg/m2); and (3) predictors of %TBW change via quantile regression. Results: Of those with class 2 or 3 obesity, 24.2% and 27.8%, respectively, lost at least 5% TBW. Only 3.2% and 0.2% of patients with class 2 and 3 obesity, respectively, lost enough weight to attain a BMI <30 kg/m2. In quantile regression, the median weight change for the population was a net gain of 2.5% TBW. Conclusions: Although adults with severe obesity were more likely to lose at least 5% TBW compared with overweight patients and patients with class 1 obesity, sufficient weight loss to attain a nonobese weight class was very uncommon. The pattern of ongoing weight gain found in our study population requires solutions at societal and health systems levels.