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Prompt Reduction in Use of Medications for Comorbid Conditions After Bariatric Surgery

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Abstract

Background

Bariatric surgery leads to weight loss, but it is unclear whether surgery reduces conditions associated with obesity. We explored this by assessing the change in use of medications to treat diabetes mellitus, hypertension, and hyperlipidemia in the year following surgery.

Methods

This is a cohort study using administrative data from 2002 to 2005 from seven Blue Cross/Blue Shield Plans. We compared the mean number of medications at the time of surgery and in the subsequent year. Medication usage by surgical patients was also compared to usage by matched enrollees without surgery but with a propensity score suggesting obesity. With Poisson and logistic regression, we tested for statistical differences in usage, accounting for repeated measures and controlling for age, sex, and diabetes. We also evaluated medications expected to be less influenced by surgery (antidepressants, thyroid replacement, and antihistamines).

Results

Our cohort included 6,235 enrollees with bariatric surgery. Their mean age was 44 years with 82% women; 34% had diabetes. Medication use declined significantly by 3 months. By 12 months after surgery, medication use for diabetes, hypertension, and hyperlipidemia had declined by 76%, 51%, and 59%, respectively. In contrast, thyroid hormone, antihistamine, and antidepressant use decreased by only 6%, 15%, and 9%, respectively. Enrollees without surgery had a modest increase in medications for diabetes, hypertension, and hyperlipidemia of 4%, 8%, and 20%, respectively.

Conclusions

Medication use for three serious obesity-associated conditions decreased promptly following surgery. The clinical and economic benefits of reduced medication requirements should be considered when making decisions about the effects of bariatric surgery.

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Acknowledgement

This research was conducted by the Johns Hopkins University DEcIDE Center under contract to the Agency for Health Care Research and Quality (contract # HHSA290-05-0034-1-TO2-WA2, project I.D. # 20-EHC-2), Rockville, MD, USA. The dataset used in this current study was originally created for a different research project on patterns of obesity care within selected Blue Cross/Blue Shield (BCBS) plans. The previous research project (but not the current study) was funded by unrestricted research grants from Ethicon Endo-Surgery, Inc. (a Johnson & Johnson company), Pfizer, Inc., and GlaxoSmithKline. The data and database development support and guidance were provided by the BCBS Association, BCBS of Tennessee, BCBS of Hawaii, BCBS of Michigan, BCBS of North Carolina, Highmark, Inc. (of Pennsylvania), Independence Blue Cross (of Pennsylvania), Wellmark BCBS of Iowa, and Wellmark BCBS of South Dakota. All investigators had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All listed authors contributed to the design, analysis, or writing of this study, and none has conflicts of interest. The authors of this abstract are responsible for its content. No statement may be construed as the official position of the Agency for Health Care Research and Quality of the US Department of Health and Human Services.

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Authors and Affiliations

  1. Johns Hopkins University School of Medicine, 1830 E. Monument St. Room 8047, Baltimore, MD, 21287, USA

    Jodi B. Segal, Jeanne M. Clark, Andrew D. Shore, Francesca Dominici, Thomas Magnuson, Thomas M. Richards, Jonathan P. Weiner, Eric B. Bass, Albert W. Wu & Martin A. Makary

Authors
  1. Jodi B. Segal
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  2. Jeanne M. Clark
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  3. Andrew D. Shore
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  4. Francesca Dominici
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  5. Thomas Magnuson
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  6. Thomas M. Richards
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  7. Jonathan P. Weiner
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  8. Eric B. Bass
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  9. Albert W. Wu
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  10. Martin A. Makary
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Corresponding author

Correspondence to Jodi B. Segal.

Appendices

Appendix A. Bariatric operations

Table 6 Bariatric operations
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Appendix B. Medications of interest

Table 7 Medications of interest
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Appendix C. Description of the development and validation of a propensity score for obesity

Introduction

Obesity is associated with many comorbidities and disability. Obesity is typically undercoded by practicing physicians, hampering efforts for disease management or research on obesity using administrative data [26–29]. Our objective was to develop a propensity score model based on clinical data found in health plan claim files. The ultimate goal was to identify patients with class II or III obesity (BMI ≥ 35 kg/m2). For this project, this tool was used to identify a nonsurgical cohort to serve as a comparison group for a cohort of patients undergoing bariatric surgery.

Methods

We used data from “health risk appraisal” (HRA) surveys from three participating BCBS plans, which included self-reported height and weight, and linked it to claims data from 2002 to 2005 (N = 115,495). We then excluded records with any of the following:

  • <6 months coverage in the year in which the HRA was completed (N = 16,810)

  • Missing data regarding age or age < 18 years (N = 135)

  • Had a bariatric surgery claim during the study period (N = 171)

  • Had a pregnancy claim during the study period (N = 3,493)

  • BMI unable to be calculated or BMI <10 or >100 kg/m2 (N = 625)

Our final sample (N = 71,057) was randomly split into two subsamples, one for development (N = 35,529) and one for validation (N = 35,528).

Our dependent outcome was class II or III obesity, defined by a BMI of ≥35 (from self-reported height and weight). In addition to age and gender, we used ICD-9-CM diagnosis codes that we categorized using the Expanded Diagnosis Clusters (EDC) clustering system as our predictors. We also used prescription drug claims information (NDC codes) to identify additional persons under treatment for disease who may not have been identified using ICD diagnosis codes). This system for categorizing NDC codes based on the likely condition being treated is known as the Rx Morbidity Group (RxMG) system. Both of these disease markers methodologies are part of the widely used and validated Johns Hopkins ACG case-mix/predictive risk methodology (see https://doi.org/www.acg.jhsph.edu) [30,31].

We conducted bivariate logistic regression analyses to determine which covariates were associated with obesity. We then conducted multivariate logistic regression analyses in several phases using: (1) all variables, (2) stepwise regression to select variables with p < 0.10, (3) variables with odds ratios >2.0 or <0.5, and (4) variables anticipated to be associated (+ or −) based on clinical expertise. We reviewed and compared all models and selected a final model. We then tested the model in the second half of the sample. We examined the model by applying it to a large sample of enrollees in five participating BCBS plans using data from the same time period.

Results

The comparison of the performance of different predictive (propensity) models is shown in Table 8. We present the “C” statistic (based on “receiver operating characteristics” (ROC), also known as area under the curve).

Table 8 Comparison of the performance of different models in the validation sample (N = 35,528)
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In our model, the ICD-9-CM-based “obesity EDC” had a very significant and sizable predictive coefficient. (That is, this code significantly contributed information to the prediction that a person’s BMI was greater than 35 kg/m2). For case-finding purposes, in general populations, this model would be quite useful. Every person receiving bariatric surgery had this EDC code because all persons receiving a bariatric procedure required a hospital diagnosis of obesity for payment of the claim. However, we found that only about 15% of those persons with a known BMI greater than 35 kg/m2 (but without bariatric surgery) were coded as having an obesity diagnosis by their providers. Thus, the use of this diagnosis code differed among obese persons in our two study cohorts (i.e., those obese persons undergoing surgery versus those not receiving surgery). Therefore, we opted to exclude this single EDC from the final obesity propensity model.

As noted on Table 8, the final “parsimonious” model included a selection of EDC and RxMG categories while excluding the obesity EDC code. The final model had an ROC of 0.714 in the validation sample.

Table 9 presents the sensitivity, specificity, and positive predictive value for different levels of the propensity score for the final model, within the validation half of the HRA survey population. For those persons whose claims-based propensity score fell into the highest 5% percentile, the full 96% reported BMIs greater than 35 kg/m2. This very high specificity suggests that the propensity score can effectively be applied to claims data files to identify a cohort that is extremely likely to be obese.

Table 9 Screening characteristics of the selected propensity model in the validation sample
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Segal, J.B., Clark, J.M., Shore, A.D. et al. Prompt Reduction in Use of Medications for Comorbid Conditions After Bariatric Surgery. OBES SURG 19, 1646–1656 (2009). https://doi.org/10.1007/s11695-009-9960-1

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