A Patient-Centered PaTH to Address Diabetes: Protocol for a Study on the Impact of Obesity Counseling
Bibliographic record
Abstract
BACKGROUND: Overweight and obesity are America's number one health concern. The prevalence of obesity in the United States is greater than 36%, a rate that has doubled since 1970. As the second most preventable cause of death, obesity is a risk factor for diabetes, cardiovascular disease, stroke, and cancer, all major causes of death. Primary care clinics may be an ideal setting for weight control interventions to help manage and prevent diabetes. For this reason, the Centers for Medicare and Medicaid Services (CMS) implemented a health care procedure coding system code for intensive behavioral therapy (IBT) for obesity within primary care in 2012 to facilitate payment for addressing obesity, which was followed by broader coverage by most insurers for IBT for adults in 2013. However, the impact of this coverage on patient-centered outcomes is largely unknown. OBJECTIVE: The overarching goal of this study is to understand the comparative effectiveness of obesity counseling as covered by CMS and other insurers in improving weight loss for adults either with or at increased risk for type 2 diabetes. METHODS: This study leverages the novel infrastructure of the Patient-Centered Outcomes Research Institute-funded PaTH Clinical Data Research Network. The PaTH network is comprised of Geisinger Health System, Johns Hopkins University, Johns Hopkins Health System, Lewis Katz School of Medicine at Temple University, Temple Health System, Penn State College of Medicine, Penn State Milton S Hershey Medical Center, University of Pittsburgh, UPMC and UPMC Health Plan, and the University of Utah. Electronic health record (EHR) data will originate from the 6 PaTH health systems. Specifically, we will (1) evaluate the impact of broader preventive service coverage for obesity screening and counseling on weight loss, diabetes incidence, and diabetes outcomes in patients with diabetes or at increased risk for diabetes (defined by body mass index [BMI] ≥25). We will determine how the annual probability of receiving obesity and/or nutritional counseling changed pre- and postpolicy across all insurers in a cohort of patients with diabetes and at high risk for diabetes. We will (2) compare patient weight loss and diabetes-related outcomes among those who receive obesity screening and counseling with those who do not, following implementation of preventive service coverage. We will examine postpolicy impact of obesity screening and counseling in a cohort of patients with diabetes and at increased risk for diabetes. Specific outcomes to be examined include weight loss, diabetes incidence, and diabetes outcomes. Exploratory outcomes will include patient-reported outcomes. Furthermore, we will determine patient characteristics, including demographics, and practice characteristics, including provider type. RESULTS: Our PCORI-funded study is underway. To date, we have obtained our second data extraction from the PaTH CDRN and are performing data editing and cleaning. Next steps include analysis of early policy change. CONCLUSIONS: Given patients who are overweight are at highest risk for diabetes, improved weight management services could prevent diabetes and its negative health outcomes. Comparing weight and diabetes outcomes in 3 states using EHRs and claims data before and after this policy was implemented using the PaTH Network will allow important insight into policy effectiveness. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12054.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.180 | 0.035 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".