Association of buprenorphine retention and subsequent adverse outcomes following non-fatal overdose: An analysis using statewide linked Maryland databases.

INTRODUCTION: Patients receiving buprenorphine after a non-fatal overdose have lower risk of future nonfatal or fatal overdose, but less is known about the relationship between buprenorphine retention and the risk of adverse outcomes in the post-overdose year. OBJECTIVE: To examine the relationship between the total number of months with an active buprenorphine prescription (retention) and the odds of an adverse outcome within the 12 months following an index non-fatal overdose. MATERIALS AND METHODS: We studied a cohort of people with an index non-fatal opioid overdose in Maryland between July 2016 and December 2020 and at least one filled buprenorphine prescription in the 12-month post-overdose observation period. We used individually linked Maryland prescription drug and hospital admissions data. Multivariable logistic regression models were used to examine buprenorphine retention and associated odds of experiencing a second non-fatal overdose, all-cause emergency department visits, and all-cause hospitalizations. RESULTS: Of 5439 people, 25% (n=1360) experienced a second non-fatal overdose, 78% had an (n=4225) emergency department visit, and 37% (n=2032) were hospitalized. With each additional month of buprenorphine, the odds of experiencing another non-fatal overdose decreased by 4.7%, all-cause emergency department visits by 5.3%, and all-cause hospitalization decreased by 3.9% (p<.0001, respectively). Buprenorphine retention for at least nine months was a critical threshold for reducing overdose risk versus shorter buprenorphine retention. CONCLUSIONS: Buprenorphine retention following an index non-fatal overdose event significantly decreases the risk of future overdose, emergency department use, and hospitalization even among people already on buprenorphine.

Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study.

BACKGROUND: Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations. OBJECTIVE: We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs. METHODS: We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient. RESULTS: The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs. CONCLUSIONS: Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.

Comparing the Predictive Effects of Patient Medication Adherence Indices in Electronic Health Record and Claims-Based Risk Stratification Models.

Multiple indices are available to measure medication adherence behaviors. Medication adherence measures, however, have rarely been extracted from electronic health records (EHRs) for population-level risk predictions. This study assessed the value of medication adherence indices in improving predictive models of cost and hospitalization. This study included a 2-year retrospective cohort of patients younger than age 65 years with linked EHR and insurance claims data. Three medication adherence measures were calculated: medication regimen complexity index (MRCI), medication possession ratio (MPR), and prescription fill rate (PFR). The authors examined the effects of adding these measures to 3 predictive models of utilization: a demographics model, a conventional model (Charlson index), and an advanced diagnosis-based model. Models were trained using EHR and claims data. The study population had an overall MRCI, MPR, and PFR of 14.6 ± 17.8, .624 ± .310, and .810 ± .270, respectively. Adding MRCI and MPR to the demographic and the morbidity models using claims data improved forecasting of next-year hospitalization substantially (eg, AUC of the demographic model increased from .605 to .656 using MRCI). Nonetheless, such boosting effects were attenuated for the advanced diagnosis-based models. Although EHR models performed inferior to claims models, adding adherence indices improved EHR model performances at a larger scale (eg, adding MRCI increased AUC by 4.4% for the Charlson model using EHR data compared to 3.8% using claims). This study shows that medication adherence measures can modestly improve EHR- and claims-derived predictive models of cost and hospitalization in non-elderly patients; however, the improvements are minimal for advanced diagnosis-based models.

The Impact of Various Risk Assessment Time Frames on the Performance of Opioid Overdose Forecasting Models.

BACKGROUND: An individual's risk for future opioid overdoses is usually assessed using a 12-month "lookback" period. Given the potential urgency of acting rapidly, we compared the performance of alternative predictive models with risk information from the past 3, 6, 9, and 12 months. METHODS: We included 1,014,033 Maryland residents aged 18-80 with at least 1 opioid prescription and no recorded death in 2015. We used 2015 Maryland prescription drug monitoring data to identify risk factors for nonfatal opioid overdoses from hospital discharge records and investigated fatal opioid overdose from medical examiner data in 2016. Prescription drug monitoring program-derived predictors included demographics, payment sources for opioid prescriptions, count of unique opioid prescribers and pharmacies, and quantity and types of opioids and benzodiazepines filled. We estimated a series of logistic regression models that included 3, 6, 9, and 12 months of prescription drug monitoring program data and compared model performance, using bootstrapped C-statistics and associated 95% confidence intervals. RESULTS: For hospital-treated nonfatal overdose, the C-statistic increased from 0.73 for a model including only the fourth quarter to 0.77 for a model with 4 quarters of data. For fatal overdose, the area under the curve increased from 0.80 to 0.83 over the same models. The strongest predictors of overdose were prescription fills for buprenorphine and Medicaid and Medicare as sources of payment. CONCLUSIONS: Models predicting opioid overdose using 1 quarter of data were nearly as accurate as models using all 4 quarters. Models with a single quarter may be more timely and easier to identify persons at risk of an opioid overdose.

Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims.

BACKGROUND: Nonadherence to medication regimens can lead to adverse health care outcomes and increasing costs. OBJECTIVES: To (a) assess the level of medication complexity at an outpatient setting using population-level electronic health record (EHR) data and (b) evaluate its association with medication adherence measures derived from medication-dispensing claims. METHODS: We linked EHR data with insurance claims of 70,054 patients who had an encounter with a U.S. midwestern health system between 2012 and 2013. We constructed 3 medication-derived indices: medication regimen complexity index (MRCI) using EHR data; medication possession ratio (MPR) using insurance pharmacy claims; and prescription fill rates (PFR; 7 and 30 days) using both data sources. We estimated the partial correlation between indices using Spearman's coefficient (SC) after adjusting for age and sex. RESULTS: The mean age (SD) of 70,054 patients was 37.9 (18.0) years, with an average Charlson Comorbidity Index of 0.308 (0.778). The 2012 data showed mean (SD) MRCI, MPR, and 30-day PFR of 14.6 (17.8), 0.624 (0.310), and 81.0 (27.0), respectively. Patients with previous inpatient stays were likely to have high MRCI scores (36.3 [37.9], P < 0.001) and were less adherent to outpatient prescriptions (MPR = 50.3 [27.6%], P < 0.001; 30-day PFR = 75.7 [23.6%], P < 0.001). However, MRCI did not show a negative correlation with MPR (SC = -0.31, P < 0.001) or with 30-day PFR (SC = -0.17, P < 0.001) at significant levels. CONCLUSIONS: Medication complexity and adherence indices can be calculated on a population level using linked EHR and claims data. Regimen complexity affects patient adherence to outpatient medication, and strength of correlations vary modestly across populations. Future studies should assess the added values of MRCI, MPR, and PFR to population health management efforts. DISCLOSURES: No outside funding supported this study. The authors have nothing to disclose. The abstract of this work was presented at INFORMS Healthcare Conference, held on July 27-29, 2019, in Cambridge, MA.

Comorbidity Characterization Among eMERGE Institutions: A Pilot Evaluation with the Johns Hopkins Adjusted Clinical Groups® System.

Electronic health records (EHR) are valuable to define phenotype selection algorithms used to identify cohorts ofpatients for sequencing or genome wide association studies (GWAS). To date, the electronic medical records and genomics (eMERGE) network institutions have developed and applied such algorithms to identify cohorts with associated DNA samples used to discover new genetic associations. For complex diseases, there are benefits to stratifying cohorts using comorbidities in order to identify their genetic determinants. The objective of this study was to: (a) characterize comorbidities in a range of phenotype-selected cohorts using the Johns Hopkins Adjusted Clinical Groups® (ACG®) System, (b) assess the frequency of important comorbidities in three commonly studied GWAS phenotypes, and (c) compare the comorbidity characterization of cases and controls. Our analysis demonstrates a framework to characterize comorbidities using the ACG system and identified differences in mean chronic condition count among GWAS cases and controls. Thus, we believe there is great potential to use the ACG system to characterize comorbidities among genetic cohorts selected based on EHR phenotypes.

Evaluating the Impact of Prescription Fill Rates on Risk Stratification Model Performance.

BACKGROUND: Risk adjustment models are traditionally derived from administrative claims. Prescription fill rates-extracted by comparing electronic health record prescriptions and pharmacy claims fills-represent a novel measure of medication adherence and may improve the performance of risk adjustment models. OBJECTIVE: We evaluated the impact of prescription fill rates on claims-based risk adjustment models in predicting both concurrent and prospective costs and utilization. METHODS: We conducted a retrospective cohort study of 43,097 primary care patients from HealthPartners network between 2011 and 2012. Diagnosis and/or pharmacy claims of 2011 were used to build 3 base models using the Johns Hopkins ACG system, in addition to demographics. Model performances were compared before and after adding 3 types of prescription fill rates: primary 0-7 days, primary 0-30 days, and overall. Overall fill rates utilized all ordered prescriptions from electronic health record while primary fill rates excluded refill orders. RESULTS: The overall, primary 0-7, and 0-30 days fill rates were 72.30%, 59.82%, and 67.33%. The fill rates were similar between sexes but varied across different medication classifications, whereas the youngest had the highest rate. Adding fill rates modestly improved the performance of all models in explaining medical costs (improving concurrent R by 1.15% to 2.07%), followed by total costs (0.58% to 1.43%), and pharmacy costs (0.07% to 0.65%). The impact was greater for concurrent costs compared with prospective costs. Base models without diagnosis information showed the highest improvement using prescription fill rates. CONCLUSIONS: Prescription fill rates can modestly enhance claims-based risk prediction models; however, population-level improvements in predicting utilization are limited.

Comparing Population-based Risk-stratification Model Performance Using Demographic, Diagnosis and Medication Data Extracted From Outpatient Electronic Health Records Versus Administrative Claims.

BACKGROUND: There is an increasing demand for electronic health record (EHR)-based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification. METHODS: This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined. RESULTS: EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1% utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims. DISCUSSION: The results show a promising performance of models predicting cost and hospitalization using outpatient EHR's diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.

Association of antibiotics in infancy with early childhood obesity.

IMPORTANCE: Obesity in children and adults is associated with significant health burdens, making prevention a public health imperative. Infancy may be a critical period when environmental factors exert a lasting effect on the risk for obesity; identifying modifiable factors may help to reduce this risk. OBJECTIVE: To assess the impact of antibiotics prescribed in infancy (ages 0-23 months) on obesity in early childhood (ages 24-59 months). DESIGN, SETTING, AND PARTICIPANTS: We conducted a cohort study spanning 2001-2013 using electronic health records. Cox proportional hazard models were used to adjust for demographic, practice, and clinical covariates. The study spanned a network of primary care practices affiliated with the Children's Hospital of Philadelphia including both teaching clinics and private practices in urban Philadelphia, Pennsylvania, and the surrounding region. All children with annual visits at ages 0 to 23 months, as well 1 or more visits at ages 24 to 59 months, were enrolled. The cohort comprised 64,580 children. EXPOSURES: Treatment episodes for prescribed antibiotics were ascertained up to 23 months of age. MAIN OUTCOMES AND MEASURES: Obesity outcomes were determined directly from anthropometric measurements using National Health and Nutrition Examination Survey 2000 body mass index norms. RESULTS: Sixty-nine percent of children were exposed to antibiotics before age 24 months, with a mean (SD) of 2.3 (1.5) episodes per child. Cumulative exposure to antibiotics was associated with later obesity (rate ratio [RR], 1.11; 95% CI, 1.02-1.21 for ≥ 4 episodes); this effect was stronger for broad-spectrum antibiotics (RR, 1.16; 95% CI, 1.06-1.29). Early exposure to broad-spectrum antibiotics was also associated with obesity (RR, 1.11; 95% CI, 1.03-1.19 at 0-5 months of age and RR, 1.09; 95% CI, 1.04-1.14 at 6-11 months of age) but narrow-spectrum drugs were not at any age or frequency. Steroid use, male sex, urban practice, public insurance, Hispanic ethnicity, and diagnosed asthma or wheezing were also predictors of obesity; common infectious diagnoses and antireflux medications were not. CONCLUSIONS AND RELEVANCE: Repeated exposure to broad-spectrum antibiotics at ages 0 to 23 months is associated with early childhood obesity. Because common childhood infections were the most frequent diagnoses co-occurring with broad-spectrum antibiotic prescription, narrowing antibiotic selection is potentially a modifiable risk factor for childhood obesity.

Final long-term results of a phase I/II study of dose-escalated intensity-modulated radiotherapy for locally advanced laryngo-hypopharyngeal cancers.

OBJECTIVES: We previously described dose-escalated intensity-modulated radiotherapy (IMRT) in squamous cell cancer of the larynx/hypopharynx (SCCL/H) to offer improved locoregional control with a low incidence of toxicity at 2 years. We now present outcome and safety data at 5 years. MATERIALS AND METHODS: A sequential cohort Phase I/II trial design was used. Patients with SCCL/H received IMRT at two dose levels (DL): DL1, 63 Gy/28 fractions to planning target volume 1 (PTV1) and 51.8 Gy/28 Fx to PTV2; DL2, 67.2 Gy/28 Fx and 56 Gy/28 Fx to PTV1 and PTV2, respectively. Patients received induction cisplatin/5-fluorouracil and concomitant cisplatin. RESULTS: Between 09/2002 and 01/2008, 60 patients (29 DL1, 31 DL2) with stage III (41% DL1, 52% DL2) and stage IV (52% DL1, 48% DL2) disease were recruited. Median (range) follow-up for DL1 was 5.7 (1.0-10.2) years and for DL2 was 6.0 (0.3-8.4) years. Five-year local control rates (95% confidence interval) for DL1 and DL2, respectively, were 68% (50.6-85.4%) and 75% (58.9-91.1%), locoregional progression-free survival rates were 54% (35.6-72.4%) and 62.6% (44.8-80.4%), and overall survival was 61.9% (44.1-79.7) and 67.6 (51.1-84.1%). Five-year laryngeal preservation rates were 66.7% (37.4-87.9%) and 71.4% (44.4-85.8%), respectively. Cumulative toxicities reported were: one patient in DL1 and 2 in DL2 developed benign pharyngeal strictures. No other G3/4 toxicities were reported. CONCLUSIONS: Dose-escalated IMRT at DL2 achieves higher 5-year local control, larynx preservation and survival rates with acceptable late toxicity. Recruitment into a Cancer Research UK Phase III study (ART-DECO), with DL2 as the experimental arm, is ongoing.

Laboratory testing for and diagnosis of nutritional deficiencies in pregnancy before and after bariatric surgery.

OBJECTIVE: Bariatric surgery can reduce the risk of obesity-related complications of pregnancy, but may cause essential nutrient deficiencies. To assess adherence to laboratory testing guidelines, we examined frequency of testing for and diagnosis of deficiency during preconception and pregnancy using claims data in women with a delivery and bariatric surgery. METHODS: Retrospective analysis of claims from seven Blue Cross/Blue Shield plans between 2002 and 2008. We included women with a delivery and bariatric surgery within the study period. We used common procedural terminology (CPT) and ICD-9 codes to define laboratory testing and deficiencies for iron, folate, vitamin B12, vitamin D, and thiamine. Using Student's t-test and chi-square testing, we compared frequency of laboratory tests and diagnoses during 12 months preconception and 280 days of pregnancy between women with pregnancy before versus after surgery. We used multivariate logistic regression to evaluate for predictors of laboratory testing. RESULTS: We identified 456 women with pregnancy after bariatric surgery and 338 before surgery. The frequency of testing for any deficiency was low (9%-51%), but higher in those with pregnancy after surgery (p<0.003). The most common deficiency was vitamin B12 (12%-13%) with pregnancy after surgery (p<0.006). Anemia and number of health provider visits were independent predictors of laboratory testing. CONCLUSION: Women with pregnancy after bariatric surgery were tested for and diagnosed with micronutrient deficiencies more frequently than those with pregnancy before surgery. However, most laboratory testing occurred in less than half the women and was triggered by anemia. Increased testing may help identify nutrient deficiencies and prevent consequences for maternal and child health.

Change in claims-based diabetes medications is a diabetes improvement indicator.

BACKGROUND: Because laboratory test results are less available to researchers than claims data, a claims-based indicator of diabetes improvement would be valuable. OBJECTIVES: To determine whether a decrease in medication use for diabetes parallels clinical improvement in glycemic control. STUDY DESIGN: This was a retrospective cohort study using up to 3.5 years of pharmacy and laboratory data from 1 private insurer. Data included 104 patients with diabetes who underwent bariatric surgery and had at least 1 glycated hemoglobin (A1C) test before and after surgery. METHODS: We assigned each A1C test to a 90-day interval before or after surgery. Medication availability was noted for the midpoint of the interval (on insulin, on oral medications, count of medications). Each subject could contribute 1 presurgery and up to 3 postsurgery observations. We recorded the changes in A1C test results and medication use from the presurgery to the postsurgery period. Using the A1C test as the reference standard, positive and negative predictive values of medication-based indicators were calculated. RESULTS: After bariatric surgery, A1C test values decreased by more than 1% and the count of unique medications decreased by 0.6. All 3 medication-based indicators had high positive predictive values (0.85) and low negative predictive values (0.20), and count of medications had better performance than the other indicators. CONCLUSIONS: Without clinical information, a decrease in use of medications can serve as a proxy for clinical improvement. Validation of results in other settings is needed.

Doctor shopping by overweight and obese patients is associated with increased healthcare utilization.

OBJECTIVE: Negative interactions with healthcare providers may lead patients to switch physicians or "doctor shop." We hypothesized that overweight and obese patients would be more likely to doctor shop, and as a result, have increased rates of emergency department (ED) visits and hospitalizations as compared to normal weight nonshoppers. DESIGN AND METHODS: We combined claims data from a health plan in one state with information from beneficiaries' health risk assessments. The primary outcome was "doctor shopping," which we defined as having outpatient claims with ≥5 different primary care physicians (PCPs) during a 24-month period. The independent variable was standard NIH categories of weight by BMI. We performed multivariate logistic regression to evaluate the association between weight categories and doctor shopping. We conducted multivariate zero-inflated negative binominal regression to evaluate the association between weight-doctor shopping categories with counts of ED visits and hospitalizations. RESULTS: Of the 20,726 beneficiaries, the mean BMI was 26.3 kg m(-2) (SD 5.1), mean age was 44.4 years (SD 11.1) and 53% were female. As compared to normal weight beneficiaries, overweight beneficiaries had 23% greater adjusted odds of doctor shopping (OR 1.23, 95%CI 1.04-1.46) and obese beneficiaries had 52% greater adjusted odds of doctor shopping (OR 1.52, 95%CI 1.26-1.82). As compared to normal weight non-shoppers, overweight and obese shoppers had higher rates of ED visits (IRR 1.85, 95%CI 1.37-2.45; IRR 1.83, 95%CI 1.34-2.50, respectively), which persisted during within weight group comparisons (Overweight IRR 1.50, 95%CI 1.10-2.03; Obese IRR 1.54, 95%CI 1.12-2.11). CONCLUSION: Frequently changing PCPs may impair continuity and result in increased healthcare utilization.

Impact of bariatric surgery on health care costs of obese persons: a 6-year follow-up of surgical and comparison cohorts using health plan data.

IMPORTANCE: Bariatric surgery is a well-documented treatment for obesity, but there are uncertainties about the degree to which such surgery is associated with health care cost reductions that are sustained over time. OBJECTIVE: To provide a comprehensive, multiyear analysis of health care costs by type of procedure within a large cohort of privately insured persons who underwent bariatric surgery compared with a matched nonsurgical cohort. DESIGN: Longitudinal analysis of 2002-2008 claims data comparing a bariatric surgery cohort with a matched nonsurgical cohort. SETTING: Seven BlueCross BlueShield health insurance plans with a total enrollment of more than 18 million persons. PARTICIPANTS: A total of 29 820 plan members who underwent bariatric surgery between January 1, 2002, and December 31, 2008, and a 1:1 matched comparison group of persons not undergoing surgery but with diagnoses closely associated with obesity. MAIN OUTCOME MEASURES: Standardized costs (overall and by type of care) and adjusted ratios of the surgical group's costs relative to those of the comparison group. RESULTS: Total costs were greater in the bariatric surgery group during the second and third years following surgery but were similar in the later years. However, the bariatric group's prescription and office visit costs were lower and their inpatient costs were higher. Those undergoing laparoscopic surgery had lower costs in the first few years after surgery, but these differences did not persist. CONCLUSIONS AND RELEVANCE: Bariatric surgery does not reduce overall health care costs in the long term. Also, there is no evidence that any one type of surgery is more likely to reduce long-term health care costs. To assess the value of bariatric surgery, future studies should focus on the potential benefit of improved health and well-being of persons undergoing the procedure rather than on cost savings.

Glucagonlike peptide 1-based therapies and risk of hospitalization for acute pancreatitis in type 2 diabetes mellitus: a population-based matched case-control study.

IMPORTANCE: Acute pancreatitis has significant morbidity and mortality. Previous studies have raised the possibility that glucagonlike peptide 1 (GLP-1)-based therapies, including a GLP-1 mimetic (exenatide) and a dipeptidyl peptidase 4 inhibitor (sitagliptin phosphate), may increase the risk of acute pancreatitis. OBJECTIVE: To test whether GLP-1-based therapies such as exenatide and sitagliptin are associated with an increased risk of acute pancreatitis. We used conditional logistic regression to analyze the data. DESIGN: Population-based case-control study. SETTING: A large administrative database in the United States from February 1, 2005, through December 31, 2008. PARTICIPANTS: Adults with type 2 diabetes mellitus aged 18 to 64 years. We identified 1269 hospitalized cases with acute pancreatitis using a validated algorithm and 1269 control subjects matched for age category, sex, enrollment pattern, and diabetes complications. MAIN OUTCOME MEASURE: Hospitalization for acute pancreatitis. RESULTS: The mean age of included individuals was 52 years, and 57.45% were male. Cases were significantly more likely than controls to have hypertriglyceridemia (12.92% vs 8.35%), alcohol use (3.23% vs 0.24%), gallstones (9.06% vs 1.34), tobacco abuse (16.39% vs 5.52%), obesity (19.62% vs 9.77%), biliary and pancreatic cancer (2.84% vs 0%), cystic fibrosis (0.79% vs 0%), and any neoplasm (29.94% vs 18.05%). After adjusting for available confounders and metformin hydrochloride use, current use of GLP-1-based therapies within 30 days (adjusted odds ratio, 2.24 [95% CI, 1.36-3.68]) and recent use past 30 days and less than 2 years (2.01 [1.37-3.18]) were associated with significantly increased odds of acute pancreatitis relative to the odds in nonusers. CONCLUSIONS AND RELEVANCE: In this administrative database study of US adults with type 2 diabetes mellitus, treatment with the GLP-1-based therapies sitagliptin and exenatide was associated with increased odds of hospitalization for acute pancreatitis.

Validating the adapted Diabetes Complications Severity Index in claims data.

OBJECTIVES: To test the validity of the adapted Diabetes Complications Severity Index (aDCSI), which does not include laboratory test results, as an indicator of diabetes severity. STUDY DESIGN: Retrospective cohort study using 4 years of claims data from 7 health insurance plans. METHODS: Individuals with diabetes mellitus and continuous enrollment were study subjects (N = 138,615). The 2 independent variables--the aDCSI score (sum of 7 diabetes complications graded by severity as 0, 1, or 2; range 0-13) and the aDCSI diabetes complication count (sum of 7 diabetes complications without severity grading; range 0-7)--were generated using only claims data. We evaluated the numbers of hospitalizations attributable to the aDCSI with Poisson regression models, both categorically and linearly. RESULTS: The aDCSI score (risk ratio 1.39 to 6.10 categorically and 1.41 linearly) and diabetes complication count (risk ratio 1.67 to 9.11 categorically and 1.65 linearly) were both significantly positively associated with the number of hospitalizations over a 4-year period. Risk ratios from the aDCSI score were very similar to the risk ratios previously reported for the Diabetes Complications Severity Index (DCSI); the absolute difference between risk ratios ranged from 0.01 to 1.6 categorically and was 0.05 linearly. CONCLUSIONS: The aDCSI is a good measure of diabetes severity, given its ability to explain hospitalizations and its similar performance to the DCSI.

Predicting costs with diabetes complications severity index in claims data.

OBJECTIVES: To test the usefulness of the Diabetes Complications Severity Index (DCSI) without laboratory test results in predicting healthcare costs, for potential use in disease management programs. STUDY DESIGN: Retrospective cohort study using up to 2 years of claims data from 7 health insurance plans. METHODS: Individuals with diabetes mellitus and continuous enrollment were study subjects. The DCSI (sum of 7 diabetes complications graded by severity as 0, 1, or 2; range 0-13) and count of diabetes complications (sum of 7 diabetes complications without severity grading; range 0-7) were the main independent variables and were generated using only diagnostic codes. We analyzed 5 types of healthcare costs (ie, total costs, inpatient costs, hospital other costs, pharmacy costs, and professional costs) attributable to the DCSI and the complication count with linear regression models, both concurrently and prospectively. RESULTS: The DCSI without laboratory data was a better predictor of costs than was complication count (adjusted R2 of total costs: 0.095 vs 0.080). The DCSI explained concurrent costs better than future costs (adjusted R2 of total costs: 0.095 vs 0.019). There were important differences in healthcare utilization among people stratifi ed by DCSI scores: 5-fold and 3-fold differences in concurrent and prospective total costs, respectively, across 4 DCSI groups. CONCLUSIONS: The DCSI without laboratory data may be useful for stratifying individuals with diabetes into morbidity groups, which can be used for selection into disease management programs or for matching in observational research.

Clinical outcomes after bariatric surgery: a five-year matched cohort analysis in seven US states.

BACKGROUND: Bariatric surgery is the most effective weight loss treatment, yet few studies have reported on short- and long-term outcomes postsurgery. METHODS: Using claims data from seven Blue Cross/Blue Shield health plans serving seven states, we conducted a non-concurrent, matched cohort study. We followed 22,693 persons who underwent bariatric surgery during 2003-2007 and were enrolled at least 6 months before and after surgery. Using logistic regression, we compared serious and less serious adverse clinical outcomes, hospitalizations, planned procedures, and obesity-related co-morbidities between groups for up to 5 years. RESULTS: Relative to controls, surgery patients were more likely to experience a serious [odds ratio (OR) 1.9; 95% confidence interval (CI) 1.8-2.0] or less serious (OR 2.5, CI 2.4-2.7) adverse clinical outcome or hospitalization (OR 1.3, CI 1.3-1.4) at 1 year postsurgery. The risk remained elevated until 4 years postsurgery for serious events and 5 years for less serious outcomes and hospitalizations. Some complication rates were lower for patients undergoing laparoscopic surgery. Planned procedures, such as skin reduction, peaked in postsurgery year 2 but remained elevated through year 5. Surgery patients had a 55% decreased risk of obesity-related co-morbidities, such as type 2 diabetes, in the first year postsurgery, which remained low throughout the study (year 5: OR 0.4, CI 0.4-0.5). CONCLUSIONS: While bariatric surgery is associated with a higher risk of adverse clinical outcomes compared to controls, it also substantially decreased obesity-related co-morbidities during the 5-year follow-up.

Trends in and patterns of obesity reduction medication use in an insured cohort.

Several prescription medications are approved to treat obesity, yet little is known about their use in the United States. Our objective was to describe recent trends and patterns of obesity reduction medication use in an insured US population. From among ~4.2 million persons enrolled in two Blue Cross and Blue Shield plans, we obtained all medical and pharmacy claims for 86,804 persons who took an obesity reduction medication anytime during 2002-2005. Overall, obesity reduction medication use decreased significantly over time from 1% in 2002 to 0.7% in 2005 (P for trend <0.001), which was most notable for the newer medications (orlistat and sibutramine). Few (range: 11-18%) used these medications longer than 3 months regardless of whether they were Federal Drug Administration (FDA)-approved for long-term use or not. More than half (57%) of obesity reduction medication users also took narcotics and 38% took antidepressants. Few sympathomimetic users had potential serious contraindications prior to medication initiation, including cardiovascular diseases (2.4%), schizophrenia (2.5%), and age >65 (1.2%). Despite the high prevalence of obesity, obesity reduction medication use was low and decreased significantly from 2002 through 2005. Prescribers of these agents should be aware of approved durations, potential contraindications, and consider screening for depression and substance abuse.

Quantification of a methadone metabolite (EDDP) in urine: assessment of compliance.

OBJECTIVE: To investigate the possibility of utilizing the ratio of the methadone metabolite, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), to urine creatinine to develop a regression model that would predict drug adherence in patients prescribed methadone for either pain management or drug addiction. DESIGN: Retrospective study. SETTING: Marshfield Clinic-Lakeland Center, one of 41 regional centers that make up Marshfield Clinic, a large, private, multi-specialty healthcare institution in central Wisconsin. PARTICIPANTS: Patients receiving methadone treatment for substance abuse or chronic pain. Group 1 was an initial pilot group consisting of 7 patients who were followed for a 4-month period. Group 2 consisted of 33 patients who were followed over a 28-month period. METHODS: Age, gender, weight, height, methadone dosage, quantitative urine creatinine and EDDP levels, reported compliance/non-compliance, and relevant clinical cofactors were retrospectively abstracted from the patients' medical records. Log-log regression analyses were used to model EDDP and the EDDP/creatinine ratio from urine screening results as functions of methadone dose, and in the larger cohort (group 2), body size, gender and age. The coefficient of determination adjusted for the number of predictor terms (R(adj)(2)) was reported as a measure of model fit. RESULTS: For group 1 data, there was a significant positive relation (P<0.001) but also substantial variability (R(adj)(2) = 0.49). Adjustment for creatinine through the EDDP/creatinine ratio provided a tighter relation (R(adj)(2) = 0.95). Similarly, for group 2 data, there was a significant positive relation (P=0.001) and substantial variability (R(adj)(2) = 0.53). Adjustment for creatinine through EDDP/creatinine ratios provided a substantially stronger relation (R(adj)(2) = 0.73). Gender and age showed no evidence of association with the EDDP/creatinine ratio (P=0.60 and P=0.51, respectively). Body size was significant in the model, both when measured by body surface area and by lean body weight, and improved the prediction when added to our model (R(adj)(2) = 0.80). CONCLUSION: For the first time, urine analyses may be used to monitor methadone over- or under-use in a clinical setting, regardless of the state of patient hydration or the manipulation of a sample by addition of another substance, such as bleach, soap, or even methadone, which could render an appropriate sample inappropriate or an inappropriate sample appropriate. A similar approach may prove useful for other drug treatments, allowing for more accurate monitoring of commonly abused prescription medications.