Association of obesity with healthcare resource utilization and costs in a commercial population

Kamble, Pravin; Hayden, Jennifer; Collins, Jenna; Harvey, Raymond A.; Suehs, Brandon T.; Renda, Andrew; Hammer, Mette; Huang, Jie; Bouchard, Jonathan

doi:10.1080/03007995.2018.1464435

Cited by 19 publications

(5 citation statements)

References 20 publications

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“…Among Weight Loss Maintainers, those with the greatest weight loss had lower overall healthcare utilization, except for hospitalizations. Healthcare resource utilization did not demonstrate expected trends as it has been shown that obesity is associated with higher healthcare utilization [ 48 , 49 ]. Future studies will need to explore the relationship between healthcare utilization and obesity.…”

Section: Discussionmentioning

confidence: 99%

Effects of sustained weight loss on outcomes associated with obesity comorbidities and healthcare resource utilization

et al. 2021

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Objective Determine the impact of long-term non-surgical weight loss maintenance on clinical relevance for osteoarthritis, cancer, opioid use, and depression/anxiety and healthcare resource utilization. Methods A cohort of adults receiving primary care within Geisinger Health System between 2001–2017 was retrospectively studied. Patients with ≥3 weight measurements in the two-year index period and obesity at baseline (BMI ≥30 kg/m2) were categorized: Obesity Maintainers (reference group) maintained weight within +/-3%; Weight Loss Rebounders lost ≥5% body weight in year one, regaining ≥20% of weight loss in year two; Weight Loss Maintainers lost ≥5% body weight in year one, maintaining ≥80% of weight loss. Association with development of osteoarthritis, cancer, opioid use, and depression/anxiety, was assessed; healthcare resource utilization was quantified. Magnitude of weight loss among maintainers was evaluated for impact on health outcomes. Results In total, 63,567 patients were analyzed including 67% Obesity Maintainers, 19% Weight Loss Rebounders, and 14% Weight Loss Maintainers; median follow-up was 9.7 years. Time until osteoarthritis onset was delayed for Weight Loss Maintainers compared to Obesity Maintainers (Logrank test p <0.0001). Female Weight Loss Maintainers had a 19% and 24% lower risk of developing any cancer (p = 0.0022) or obesity-related cancer (p = 0.0021), respectively. No significant trends were observed for opioid use. Weight loss Rebounders and Maintainers had increased risk (14% and 25%) of future treatment for anxiety/depression (both <0.0001). Weight loss maintenance of >15% weight loss was associated with the greatest decrease in incident osteoarthritis. Healthcare resource utilization was significantly higher for Weight Loss Rebounders and Maintainers compared to Obesity Maintainers. Increased weight loss among Weight Loss Maintainers trended with lower overall healthcare resource utilization, except for hospitalizations. Conclusions In people with obesity, sustained weight loss was associated with greater clinical benefits than regained short-term weight loss and obesity maintenance. Higher weight loss magnitudes were associated with delayed onset of osteoarthritis and led to decreased healthcare utilization.

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Section: Discussionmentioning

confidence: 99%

Effects of sustained weight loss on outcomes associated with obesity comorbidities and healthcare resource utilization

et al. 2021

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“…[8][9][10] Higher medical care costs among individuals with obesity have been found for a variety of U.S. subgroups across various data samples using several different research methods. [11][12][13][14][15] Previous research has shown that medical expenditures associated with obesity vary by state, both overall and by type of payer. [16][17][18][19][20] For example, Biener et al (2018) reported that the proportion of medical expenditures associated with obesity ranged from approximately 4% in California to 14% in North Carolina in 2015, based on estimates from the Medical Expenditure Panel Survey (MEPS).…”

Section: What This Study Addsmentioning

confidence: 99%

Direct medical costs of obesity in the United States and the most populous states

Cawley

Biener

Meyerhoefer

et al. 2021

JMCP

191

122

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BACKGROUND: After a dramatic increase in prevalence over several decades, obesity has become a major public health crisis in the United States. Research to date has consistently demonstrated a correlation between obesity and higher medical costs for a variety of U.S. subpopulations and specific categories of care. However, by examining associations rather than causal effects, previous studies likely underestimated the effect of obesity on medical expenditures. OBJECTIVE:To estimate the causal effect of obesity on direct medical care costs at the national and state levels.

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“…Together with the multitude of comorbidities (diabetes, cardiovascular disease, cancer, etc.) associated with obesity and increased healthcare costs corresponding with higher BMI classifications [4][5][6][7][8][9], it is important to use big data sources to understand at the population-level variations in health outcomes of those with obesity; the stratification by BMI classifications may also provide a more in-depth understanding of the impact of obesity severity on health outcomes. The utility of this RWE generation, particularly when combined with other big data technologies (e.g., genomics, metabolomics, information collected by personal monitoring devices, GPS, Fitbit), can be explored under the infrastructures of health system disease management and public health surveillance and interventions [24,25].…”

Section: Discussionmentioning

confidence: 99%

“…The BMI is predictive of greater risk for multiple disease conditions, including metabolic syndrome, type II diabetes, cardiovascular disease, some cancers, liver and kidney disease, arthritis, asthma, and depression, as well as a greater risk for all-cause mortality [4][5][6]. Additionally, variations in BMI are predictive of healthcare resource utilization and costs [7][8][9]. The health risks associated with obesity and its high prevalence in the US [4] necessitates the study of populations with obesity on several inter-related facets, such as population sociodemographic and clinical characteristics, current and emerging health outcomes and costs, value of therapeutic interventions, patient-drug/procedure interactions, etc.…”

Section: Introductionmentioning

confidence: 99%

Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses

Chow

Sakthivel³

et al. 2021

Adv Ther

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Introduction Administrative claims data provide an important source for real-world evidence (RWE) generation, but incomplete reporting, such as for body mass index (BMI), limits the sample sizes that can be analyzed to address certain research questions. The objective of this study was to construct models by implementing machine-learning (ML) algorithms to predict BMI classifications (≥ 30, ≥ 35, and ≥ 40 kg/m 2 ) in administrative healthcare claims databases, and then internally and externally validate them. Methods Five advanced ML algorithms were implemented for each BMI classification on a random sampling of BMI readings from the Optum PanTher Electronic Health Record database (2%) and the Optum Clinformatics Date of Death (20%) database, while incorporating baseline demographic and clinical characteristics. Sensitivity analyses with oversampling ratios were conducted. Model performance was validated internally and externally. Results Models trained on the Super Learner ML algorithm (SLA) yielded the best BMI classification predictive performance. SLA model 1 utilized sociodemographic and clinical characteristics, including baseline BMI values; the area under the receiver operating characteristic curve (ROC AUC) was approximately 88% for the prediction of BMI classifications of ≥ 30, ≥ 35, and ≥ 40 kg/m 2 (internal validation), while accuracy ranged from 87.9% to 92.8% and specificity ranged from 91.8% to 94.7%. SLA model 2 utilized sociodemographic information and clinical characteristics, excluding baseline BMI values; ROC AUC was approximately 73% for the prediction of BMI classifications of ≥ 30, ≥ 35, and ≥ 40 kg/m 2 (internal validation), while accuracy ranged from 73.6% to 80.0% and specificity ranged from 71.6% to 85.9%. The external validation on the MarketScan Commercial Claims and Encounters database yielded relatively consistent results with slightly diminished performance. Conclusion This study demonstrated the feasibility and validity of using ML algorithms to predict BMI classifications in administrative healthcare claims data to expand the utility for RWE generation. Supplementary Information The online version contains supplementary material available at 10.1007/s12325-020-01605-6.

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Association of obesity with healthcare resource utilization and costs in a commercial population

Abstract: Increasing BMI was associated with higher prevalence of cardiometabolic conditions and higher HRU and costs. There is an urgent need to address the epidemic of obesity and its clinical and economic impacts.

Cited by 19 publications

References 20 publications

Effects of sustained weight loss on outcomes associated with obesity comorbidities and healthcare resource utilization

Effects of sustained weight loss on outcomes associated with obesity comorbidities and healthcare resource utilization

Direct medical costs of obesity in the United States and the most populous states

Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses

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