Reduced medical spending associated with integrated pharmacy benefits.

OBJECTIVES: Although pharmacy benefit carve-outs are promoted as a cost-containment tool, their impact on medical spending is not well understood. We compare the health care spending of Blue Cross and Blue Shield of Louisiana (BCBSLA) members covered by an integrated ("carved-in") pharmacy benefit with that of members covered under a pharmacy benefit carve-out. STUDY DESIGN: Matched, longitudinal cohort study. METHODS: We identified members with coverage through an employer contracting for administrative services only (ie, self-insured) and determined whether they received a pharmacy benefit through BCBSLA. We matched members with and without integrated benefits using a baseline year and compared their medical spending trajectories in 3 subsequent years. These comparisons were repeated in the subset of patients with chronic comorbidities. RESULTS: Among patients with chronic illnesses, relative growth in per-member per-month (PMPM) medical spending was significantly lower in the integrated benefit group by the second and third follow-up years. Neither the level nor the growth of PMPM medical spending significantly differed in the full population sample, although point estimates suggest that the integrated benefit members may be on a lower cost growth trajectory over time. CONCLUSIONS: Members with chronic illnesses receiving an integrated pharmacy benefit experienced slower medical cost growth compared with members covered by a pharmacy carve-out. Group leaders and brokers should consider the additional cost savings achieved by integrated pharmacy benefits when comparing the total costs of carve-in vs carve-out prescription drug programs.

Association of co-pay elimination with medication adherence and total cost.

OBJECTIVES: To determine whether elimination of co-pays for prescription drugs affects medication adherence and total health care spending. STUDY DESIGN: Retrospective comparative study. METHODS: We conducted a difference-in-differences comparison in the year before and after expansion of a Zero Dollar Co-pay (ZDC) prescription drug benefit in commercially insured Louisiana residents. Blue Cross and Blue Shield of Louisiana members with continuous disease management program enrollment were analyzed, of whom 6463 were enrolled in the ZDC program and 1821 were controls who were ineligible because their employers did not opt in. RESULTS: After ZDC expansion, medication adherence fell in the control group and rose in the ZDC group, with a relative increase of 2.1 percentage points (P = .002). Medical spending fell by $71 per member per month (PMPM) (P = .027) in the ZDC group relative to controls. Overall, there was no significant increase in the cost of drugs between treatment and controls. However, when drugs were further categorized, there was a significant increase of $8 PMPM for generic drugs and no significant difference for brand name drugs. Comparisons of medication adherence rates by household income showed the largest relative increase post ZDC expansion among low-income members. CONCLUSIONS: Elimination of co-pays for drugs indicated to treat chronic illnesses was associated with increases in medication adherence and reductions in overall spending of $63. Benefit designs that eliminate co-pays for patients with chronic illnesses may improve adherence and reduce the total cost of care.

Delayed emergencies: The composition and magnitude of non-respiratory emergency department visits during the COVID-19 pandemic.

IMPORTANCE: COVID-19 has been associated with excess mortality among patients not diagnosed with COVID-19, suggesting disruption of acute health care provision may play a role. OBJECTIVE: To determine the degree of declines in emergency department (ED) visits attributable to COVID-19 and determine whether these declines were concentrated among patients with fewer comorbidities and lower severity visits. DESIGN: We conducted a differences-in-differences analysis of all commercial health insurance claims for ED visits in the first 20 weeks of 2018, 2019, and 2020. The intervention period began March 9 (week 11) of 2020, following state stay-at-home orders. SETTING: We analyzed claims from Blue Cross Blue Shield of Louisiana (BCBSLA), located in a state with an early US COVID-19 outbreak. Visit and patient risk was assessed through comorbidities previously described as increasing the risk of COVID-19 decompensation, the hospital location's COVID-19 outbreak status, and the Ambulatory Care Sensitive Condition algorithm. PARTICIPANTS: The study population comprised all ED visits from all BCBSLA members, whether admitted or discharged. There were 332,917 ED visits over the study period. The study population spanned member demographics including sex, age, and geography. Uninsured adults were not included due to data limitations. EXPOSURES: The COVID-19 outbreak beginning March 9, 2020 in Louisiana. MAIN OUTCOMES AND MEASURES: The main outcome of interest for this analysis is the difference (percent change) in all ED visits, categorized as either respiratory or non-respiratory, from week 1-20 in 2019 and week 1-10 in 2020, compared to week 11-20 in 2020. RESULTS: In this differences-in-differences study using data from a commercial health insurer, we found that non-respiratory ED visits declined by 39%, whereas respiratory visits did not experience a significant decline. Visits that were potentially deferrable or from lower risk patient populations showed greater declines, but even high-risk patients and non-avoidable visits experienced large declines in non-respiratory ED visits. Non-respiratory ED visits declined by only 18% in areas experiencing COVID outbreak. CONCLUSIONS AND RELEVANCE: COVID-19 has resulted in significant avoidance of ED care, comprising a mix of deferrable and high severity care. Hospital and public health pronouncements should emphasize appropriate care seeking.

Association between visit-to-visit HbA1c variability and the risk of cardiovascular disease in patients with type 2 diabetes.

AIM: To investigate the association between visit-to-visit HbA1c variability and the risk of cardiovascular disease in patients with type 2 diabetes. MATERIALS AND METHODS: We performed a retrospective cohort study of 29 260 patients with at least four HbA1c measurements obtained within 2 years of their first diagnosis of type 2 diabetes. Different HbA1c variability markers were calculated, including the standard deviation (SD), coefficient of variation (CV) and adjusted SD. Cox proportional hazards regression models were used to estimate the association of these HbA1c variability markers with incident cardiovascular disease. RESULTS: During a mean follow-up of 4.18 years, a total of 3746 incident cardiovascular disease cases were diagnosed. Multivariate-adjusted hazard ratios for cardiovascular disease across the first, second, third and fourth quartiles of HbA1c SD values were 1.00, 1.30 (95% confidence interval [CI] 1.18-1.42), 1.40 (95% CI 1.26-1.55) and 1.59 (95% CI 1.41-1.77) (P for trend <.001), respectively. When we utilized HbA1c CV and adjusted HbA1c SD values as exposures, similar positive associations were observed. HbA1c variability was also associated with the risk of first and recurrent severe hypoglycaemic events. A mediating effect of severe hypoglycaemia was observed between HbA1c variability and incident cardiovascular disease. CONCLUSIONS: Large visit-to-visit HbA1c variability is associated with an increased risk of cardiovascular disease in patients with type 2 diabetes. Severe hypoglycaemia may mediate the association between HbA1c variability and incident cardiovascular disease.

Value-based insurance design in Louisiana: Blue Cross Blue Shield's Zero Dollar Co-pay program.

OBJECTIVES: To determine whether a program that eliminated pharmacy co-pays, the Blue Cross Blue Shield of Louisiana (BCBSLA) Zero Dollar Co-pay (ZDC) program, decreased health care spending. Previous studies have found that value-based insurance designs like the ZDC program have little or no impact on total health care spending. ZDC included an expansive set of medications related to 4 chronic diseases rather than a limited set of medications for 1 or 2 chronic diseases. Additionally, ZDC focused on the most at-risk patients. STUDY DESIGN: ZDC began in 2014 and enrolled patients over time based on (1) when a patient answered a call from a nurse care manager and (2) when a patient or their employer changed the benefit structure to meet the program criteria. During 2015 and 2016, 265 patients with at least 1 chronic condition (asthma, diabetes, hypertension, mental illness) enrolled in ZDC. METHODS: Observational study using within-patient variation and variation in patient enrollment month to identify the impact of the ZDC program on health spending measures. We used 100% BCBSLA claims data from January 2015 to June 2018. Monthly level event studies were used to test for differential spending patterns prior to ZDC enrollment. RESULTS: We found that total spending decreased by $205.9 (P = .049) per member per month, or approximately 18%. We saw a decrease in medical spending ($195.0; P = .023) but did not detect a change in pharmacy spending ($7.59; P = .752). We found no evidence of changes in spending patterns prior to ZDC enrollment. CONCLUSIONS: The ZDC program provides evidence that value-based insurance designs that incorporate a comprehensive set of medications and focus on populations with chronic disease can reduce spending.

Effectiveness of sodium-glucose co-transporter-2 inhibitors on ischaemic heart disease.

AIM: To compare the cardiovascular risks between users and non-users of sodium-glucose co-transporter-2 (SGLT2) inhibitors based on electronic medical record data from a large integrated healthcare system in South Louisiana. MATERIALS AND METHODS: Demographic, anthropometric, laboratory and medication prescription information for patients with type 2 diabetes who were new users of SGLT2 inhibitors, either as initial treatments or as add-on treatments, were obtained from electronic health records. Mediation analysis was performed to evaluate the association of use of SGLT2 inhibitors and changes of metabolic risk factors with the risk of incident ischaemic heart disease. RESULTS: A total of 5338 new users of SGLT2 inhibitors were matched with 13 821 non-users. During a mean follow-up of 3.26 years, 2302 incident cases of ischaemic heart disease were defined. After adjusting for multiple confounding factors, patients using SGLT2 inhibitors had a lower risk of incident ischaemic heart disease compared to patients not using SGLT2 inhibitors (hazard ratio [HR] 0.63, 95% confidence interval [CI] 0.54-0.73). Patients using SGLT2 inhibitors also had a lower risk of incident ischaemic heart disease within 6 months (HR 0.36, 95% CI 0.25-0.44), 12 months (HR 0.40, 95% CI 0.32-0.49), 24 months (HR 0.53, 95% CI 0.43-0.60) and 36 months (HR 0.65, 95% CI 0.54-0.73), respectively. Reductions in systolic blood pressure partly mediated lowering risk of ischaemic heart disease among patients using SGLT2 inhibitors. CONCLUSIONS: The real-world data in the present study show the contribution of SGLT2 inhibitors to reducing risk of ischaemic heart disease, and their benefits beyond glucose-lowering.

Association between Hemoglobin A1c and Stroke Risk in Patients with Type 2 Diabetes.

BACKGROUND AND PURPOSE: The association between hemoglobin A1c (HbA1c) and stroke risk along with its subtypes is rarely reported. We aimed to investigate the association between HbA1c and the risk of incident stroke in patients with type 2 diabetes based on real world data from three healthcare systems. METHODS: We performed a retrospective cohort study of 27,113 African Americans and 40,431 whites with type 2 diabetes. Demographic, anthropometric, laboratory, and medication information were abstracted from the National Patient-Centered Clinical Research Network common data model. Incident stroke events including both ischemic and hemorrhagic stroke were defined. RESULTS: During a mean follow-up period of 3.79±1.68 years, 7,735 patients developed stroke (6,862 ischemic and 873 hemorrhagic). Multivariable-adjusted hazard ratios across levels of HbA1c at baseline (<6.0%, 6.0% to 6.9% [reference group], 7.0% to 7.9%, 8.0% to 8.9%, 9.0% to 9.9%, and ≥10%) were 1.07, 1.00, 1.13, 1.23, 1.27, and 1.37 (Ptrend <0.001) for total stroke, 1.02, 1.00, 1.13, 1.20, 1.24, and 1.35 (Ptrend <0.001) for ischemic stroke, and 1.40, 1.00, 1.14, 1.47, 1.47, and 1.51 (Ptrend=0.002) for hemorrhagic stroke. When we used an updated mean value of HbA1c, the U-shaped association of HbA1c with stroke risk did not change. This U-shaped association was consistent among patients of different subgroups. The U-shaped association was more pronounced among patients taking antidiabetic, lipid-lowering, and antihypertensive medications compared with those without these medications. CONCLUSIONS: These data suggest that diabetes management may have to be individualized according to the guideline recommendations rather than intensively attempting to lower HbA1c.

Evaluating the performance of a predictive modeling approach to identifying members at high-risk of hospitalization.

Aims: To evaluate the risk-of-hospitalization (ROH) models developed at Blue Cross Blue Shield of Louisiana (BCBSLA) and compare this approach to the DxCG risk-score algorithms utilized by many health plans.Materials and Methods: Time zero for this study was December 31, 2016. BCBSLA members were eligible for study inclusion if they were fully insured; aged 80 years or younger; and had continuous enrollment starting on or before June 1, 2016, through time zero. Up to 2 years of historical claims data from time zero per patient was included for model development. Members were excluded if they had cancer, renal failure, or were admitted for hospice. The Blue Cross ROH models were developed using (1) regularized logistic regression and (2) random decision forests (a tree ensemble learning classification method). All models were generated using Scikit-learn: Machine Learning in Python. Prognostic capabilities of DxCG risk-score algorithms were compared to those of the Blue Cross models.Results: When stratifying by the top 0.1% of members with the highest ROH, the Blue Cross logistic regression model had the highest area under the receiving operator characteristics curve (0.862) based on the result of 10-fold cross-validation. The Blue Cross random decision forests model had the highest positive predictive value (49.0%) and positive likelihood ratio (61.4), but sensitivity, specificity, negative predictive values, and negative likelihood ratios were similar across all four models.Limitations: The Blue Cross ROH models were developed and evaluated using BCBSLA data, and predictive power may fluctuate if applied to other databases.Conclusions: The predictability of the Blue Cross models show how member-specific, regional data can be used to accurately identify patients with a high ROH, which may allow healthcare workers to intervene earlier and subsequently reduce the healthcare burden for patients and providers.

Association between Body Mass Index and Stroke Risk Among Patients with Type 2 Diabetes.

CONTEXT: Very few studies focused on the association between body mass index (BMI) and stroke risk among patients with diabetes. OBJECTIVE: We aimed to investigate the association between BMI and stroke risk in patients with type 2 diabetes. DESIGN: Demographic, anthropometric, laboratory, and medication information were extracted from the National Patient-Centered Clinical Research Network common data model. PARTICIPANTS: We performed a retrospective cohort study of 67 086 patients with type 2 diabetes. MAIN OUTCOME MEASURES: Incident stroke including both ischemic and hemorrhagic stroke were defined. RESULTS: During a mean follow up of 3.74 years. 8918 incident stroke events occurred. Multivariable-adjusted hazard ratios across different categories of BMI at baseline (18.5-24.9 [reference group], 25.0-29.9, 30.0-34.9, 35.0-39.9, and ≥40 kg/m2) were 1.00, 0.92, 0.85, 0.74, and 0.63 (Ptrend <0.001) for total stroke; 1.00, 0.93, 0.88, 0.77, and 0.65 (Ptrend <0.001) for ischemic stroke; and 1.00, 0.79, 0.50, 0.50, and 0.41 (Ptrend <0.001) for hemorrhagic stroke, respectively. When we used an updated mean value of BMI, the graded inverse association of body mass index with stroke risk did not change. This linear association was consistent among patients of different subgroups. Further sensitivity analysis excluding patients who were diagnosed stroke within 6 months after first diagnosis of type 2 diabetes or including non-smokers only also confirmed our findings. CONCLUSION: The present study found an inverse association between BMI and the risk of total, ischemic, and hemorrhagic stroke among patients with type 2 diabetes. More clinical and molecular insights are still needed in explaining these findings.

Inverse Association Between HDL (High-Density Lipoprotein) Cholesterol and Stroke Risk Among Patients With Type 2 Diabetes Mellitus.

Background and Purpose- Few studies have assessed the association of HDL (high-density lipoprotein) cholesterol with stroke risk among patients with type 2 diabetes mellitus. We aimed to investigate the association of HDL cholesterol with total and type-specific stroke risk in patients with type 2 diabetes mellitus. Methods- We performed a retrospective cohort study of 27 113 blacks and 40 431 whites with type 2 diabetes mellitus. Cox proportional hazards regression models were used to estimate the association of different levels of HDL cholesterol with stroke risk. Results- During a mean follow-up period of 3.0 years, 8496 patients developed stroke (8048 ischemic and 448 hemorrhagic). Multivariable-adjusted hazard ratios across levels of HDL at baseline (<30 [reference group], 30-39.9, 40-49.9, 50-59.9, 60-69.9, 70-79.9, and ≥80 mg/dL) were 1.00, 0.86, 0.77, 0.71, 0.71, 0.77, and 0.69 ( Ptrend <0.001) for total stroke, 1.00, 0.89, 0.82, 0.75, 0.78, 0.76, and 0.75 ( Ptrend <0.001) for ischemic stroke, and 1.00, 0.89, 0.69, 0.66, 0.47, and 0.94 ( Ptrend =0.021) for hemorrhagic stroke, respectively. When we used an updated mean value of HDL cholesterol, the inverse association of HDL cholesterol with stroke risk did not change. This inverse association was consistent among patients of different ages, races, sexes, body mass index, hemoglobin A1c levels, never and past or current smokers, and patients with and without using glucose-lowering, cholesterol-lowering, or antihypertensive agents. Conclusions- The present study found consistent inverse associations between HDL cholesterol and the risk of total, ischemic and hemorrhagic stroke among patients with type 2 diabetes mellitus.

Race and sex differences in rates of diabetic complications.

BACKGROUND: Studies on racial differences in diabetic complications are very limited. The aim of this study was to investigate the race and sex differences in diabetic complications between African Americans and Whites with type 2 diabetes (T2D) in Louisiana. METHODS: A prospective cohort study was performed of 27 113 African Americans and 40 431 Whites with T2D who were 35 to 95 years of age from three healthcare systems located in south Louisiana. Four major diabetic complications were assessed: coronary heart disease (CHD), heart failure, stroke, and end-stage renal disease (ESRD). RESULTS: The age- and sex-adjusted incident rates per 1000 person-years and 95% confidence intervals (CI) for CHD, heart failure, stroke, and ESRD for African Americans with diabetes were 43.1 (95% CI 41.6-44.6), 36.6 (95% CI 35.2-37.9), 29.6 (95% CI 28.4-30.8), and 38.3 (95% CI 36.9-39.7), respectively. Cox regression models showed that African American women had a higher risk than White women for heart failure (hazard ratio (HR) 1.26; 95% CI 1.18-1.34), stroke (HR 1.15; 95% CI 1.08-1.22), and ESRD (HR 1.32; 95% CI 1.24-1.40), whereas African American men had higher risks than White men for heart failure (HR 1.33; 95% CI 1.25-1.43) and ESRD (HR 1.47; 95% CI 1.37-1.57) but a lower risk of CHD (HR 0.88; 95% CI 0.83-0.94). CONCLUSIONS: The incidence of major diabetic complications varied among difference race and sex groups. More race- or sex-specific studies on complications in patients with diabetes are needed to see whether incident rates are changing over time.

Medical homes: cost effects of utilization by chronically ill patients.

OBJECTIVES: The impact of primary care practices adopting the patient-centered medical home (PCMH) model is analyzed by comparing per member per month (PMPM) costs and utilization among commercial HMO members with chronic illnesses in PCMH and non-PCHM practices in the Philadelphia area. Transforming primary care practices to conform to the PCMH model has shown early promise in reducing costs and improving outcomes, and chronically ill patients' frequent contact with the healthcare system and costly care make them ideal targets for such health system reforms. STUDY DESIGN AND METHODS: The impact of the PCMH model on PMPM costs was analyzed using a generalized linear regression model to adjust for age, gender, and baseline cost. The impact of the PCMH model on utilization per 1000 rates was analyzed with the Poisson regression model, adjusting for baseline differences in age, gender, and risk score. RESULTS: After accounting for differences at baseline, PCMH practices achieved lower total, inpatient, and specialist PMPM costs, as well as lower relative utilization of hospital admissions and specialist visits. CONCLUSIONS: These findings suggest that policy makers should maintain or expand incentives to adopt PCMH reforms and that targeting chronically ill patients may be the most effective way to leverage the benefits of PCMH adoption.

Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.

We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion, population-level risk prediction for type 2 diabetes using readily available administrative data is feasible and has better prediction performance than classical diabetes risk prediction algorithms on very large populations with missing data. The new model enables intervention allocation at national scale quickly and accurately and recovers potentially novel risk factors at different stages before the disease onset.

Do patient-centered medical homes reduce emergency department visits?

OBJECTIVE: To assess whether adoption of the patient-centered medical home (PCMH) reduces emergency department (ED) utilization among patients with and without chronic illness. DATA SOURCES: Data from approximately 460,000 Independence Blue Cross patients enrolled in 280 primary care practices, all converting to PCMH status between 2008 and 2012. RESEARCH DESIGN: We estimate the effect of a practice becoming PCMH-certified on ED visits and costs using a difference-in-differences approach which exploits variation in the timing of PCMH certification, employing either practice or patient fixed effects. We analyzed patients with and without chronic illness across six chronic illness categories. PRINCIPAL FINDINGS: Among chronically ill patients, transition to PCMH status was associated with 5-8 percent reductions in ED utilization. This finding was robust to a number of specifications, including analyzing avoidable and weekend ED visits alone. The largest reductions in ED visits are concentrated among chronic patients with diabetes and hypertension. CONCLUSIONS: Adoption of the PCMH model was associated with lower ED utilization for chronically ill patients, but not for those without chronic illness. The effectiveness of the PCMH model varies by chronic condition. Analysis of weekend and avoidable ED visits suggests that reductions in ED utilization stem from better management of chronic illness rather than expanding access to primary care clinics.

Clinical and economic burden of surgical site infection in hysterectomy.

BACKGROUND: Owing to a lack of current understanding of outcomes and costs by type of hysterectomy procedure, we attempt to quantify the incidence and impact of surgical site infection (SSI) in laparoscopic and non-laparoscopic approaches to abdominal and vaginal hysterectomy. METHODS: Patients whose data were contained in the Premier Perspectives Database of 600 hospitals in the United States were selected on the basis of a post-operative diagnosis of SSI and treatment with antibiotics. The incidence of SSI and associated hospital length of stay (LOS) and costs were estimated. The effect of SSI on readmission was also analyzed. RESULTS: Of 210,916 hysterectomies included in the study, 55% were open abdominal procedures. Although the overall incidence of SSI in hysterectomy was low, its incidence was greater in open abdominal hysterectomy than in other approaches to hysterectomy. Patients with an SSI experienced a three- to five-fold greater LOS, two-fold greater cost, and three-fold greater risk of hospital readmission than those without an SSI. CONCLUSIONS: This study provides clinical evidence in support of less invasive approaches to hysterectomy. In addition to other documented benefits of such less invasive procedures, the lower incidence of SSIs and lower rates of associated complications and costs with these procedures than with open abdominal hysterectomy should be taken into account when weighing the risks and benefits of a surgical approach for patients whose condition warrants hysterectomy.

Medical homes and cost and utilization among high-risk patients.

OBJECTIVES: Evaluate the effects of the patient-centered medical home (PCMH) model on medical costs and utilization in the nonpediatric population, particularly among high-risk patients. STUDY DESIGN: Longitudinal case-control design, comparing per member per month (PMPM) cost and utilization per 1000 patients for members enrolled in PCMH and non-PCMH practices from 2009 to 2011. METHODS: Commercial health maintenance organization members in nonpediatric practices that adopted the PCMH model in 2009 were matched to patients in nonpediatric practices that did not adopt the model until 2011 or later. Propensity score matching was used to identify a pool of similar controls, and difference-in-differences regression analysis was used to compare PCMH and non-PCMH patients relative to baseline. Analysis was conducted using the complete pool of matched patients (N = 6940 cases and 6940 controls), then using the 10% of patients with the highest DxCG risk scores (N = 654 cases and 734 controls). RESULTS: There were no significant cost or utilization differences for the overall population. Total cost decreased significantly more for the PCMH group than for controls in the high-risk group in years 1 and 2 (reductions of $107 and $75 PMPM), driven by lower inpatient costs. The PCMH group experienced a significantly greater reduction in inpatient admissions in all 3 years (61, 48, and 94 hospitalizations per 1000). CONCLUSIONS: PCMH practices had significantly reduced costs and utilization for the highest risk patients, particularly with respect to inpatient care. As high-risk members represent a high-cost group, the most benefit can be gained by targeting these members.

Patient-centered medical home impact on health plan members with diabetes.

OBJECTIVE: To compare costs and utilization for patients with diabetes enrolled in patient-centered medical home (PCMH) practices and non-PCMH practices. DESIGN: Commercial Health Maintenance Organization members with diabetes who enrolled between 2008 and 2011 in 26 Pennsylvania-based PCMH practices that were recognized by the National Committee for Quality Assurance in 2009 were compared with similar patients in 97 non-PCMH primary care practices. A difference-in-differences longitudinal research design was used to analyze differences between both groups on per-member, per-month costs and utilization. The statistical models controlled for baseline practice and patient-level characteristics through 2-step propensity score matching. The regression analysis on program effect further controlled for within-practice variation. Sensitivity analyses were also conducted on patients with type 1 and type 2 diabetes separately, and a third analysis was limited to diabetic patients enrolled in practices within Philadelphia. RESULTS: Adoption of the PCMH reduced overall medical costs for diabetic patients by 21% in year 1. This reduction was driven largely by inpatient costs, which fell by 44%. Reductions in emergency department visits, outpatient costs, and specialist visits were also seen in subsequent years among patients enrolled in PCMH practices. Additional sensitivity analyses indicated that adoption of the PCMH model yielded similar results when analyzing patients with type 2 diabetes as well as for diabetic patients enrolled in PCMH practices located within the city of Philadelphia. CONCLUSIONS: The cost of care for patients with diabetes can be reduced by securing care at a PCMH practice. Immediate results were seen in reduction of inpatient costs, which indicate that these patients enrolled in PCMH practices were using less costly inpatient services.

The association of self-monitoring of blood glucose use with medication adherence and glycemic control in patients with type 2 diabetes initiating non-insulin treatment.

BACKGROUND: The value of self-monitoring of blood glucose (SMBG) for persons with type 2 diabetes who do not use insulin remains controversial. This observational study compares the likelihood of medication adherence and change in glycated hemoglobin (A1C) for non-insulin-using patients using SMBG versus those not using SMBG. The study also assessed the association between diabetes medication adherence and SMBG use. PATIENTS AND METHODS: Data were extracted on 5,172 patients who began non-insulin diabetes medication between October 1, 2006, and March 31, 2009. The study assessed change in A1C associated with SMBG use and testing frequency at different categorical levels of baseline A1C. The likelihood of medication adherence for SMBG users was compared with that for non-SMBG users at different categorical levels of baseline A1C. The study further explored the interactions between SMBG use and medication adherence on change in A1C. RESULTS: SMBG users had greater reductions in A1C compared with nonusers when the baseline A1C was ≥ 7%. Increasing SMBG frequency was associated with greater reductions in A1C. The study also examined the associations among SMBG use, medication adherence, and change in A1C. SMBG users had greater decreases in A1C for both medication-adherent and -nonadherent patients. As expected, medication adherence was associated with greater reductions in A1C for both SMBG nonusers and users. It is interesting that medication-nonadherent SMBG users had similar reductions in A1C compared with medication-adherent non-SMBG users. CONCLUSIONS: Both SMBG use and medication adherence were associated with similar degrees of A1C reduction after controlling for baseline A1C, suggesting that both factors may be important for attaining glycemic control.

Association between sitagliptin adherence and self-monitoring of blood glucose.

BACKGROUND: We evaluated the association between self-monitoring of blood glucose (SMBG) use and sitagliptin or sitagliptin/metformin (SSMT) adherence. SSMT was chosen as these medications have little risk of hypoglycemia and are believed to not require SMBG data for titration. METHODS: This was an observational study using data extracted from a large United States insurance claims database (i3 InVision™ Data Mart, Ingenix, Inc.). Data were extracted on noninsulin-using patients initiating SSMT for each 12-month period pre- and post-SSMT initiation. Logistic regression was used to assess the relationship between SMBG use and the likelihood of being medication adherent (defined as a medication possession ratio of ≥75%) while controlling for covariates. RESULTS: This analysis included 7,306 patients (57.6% male; mean age 54.2 years). Mean pre-SSMT hemoglobin A1c (HbA1c) was 8.0%. In the post-SSMT initiation period, 58% of patients were adherent with SSMT. Older age, male gender, prior use of oral diabetes medication, and lower HbA1c were associated with improved SSMT adherence. SMBG use was associated with improved adherence [odds ratio (OR) ranged from 1.198 to 1.338; p < .05] compared with patients with no SMBG use pre- or post-SSMT initiation. For patients who began SMBG after starting SSMT, greater SMBG use was associated with better adherence (OR 1.449 for higher vs 1.246 for lower strip use; p < .05). CONCLUSIONS: This study demonstrated that SMBG is associated with improved SSMT adherence. This relationship is strengthened with greater SMBG use.

Rates of venous thromboembolism among patients with major surgery for cancer.

BACKGROUND: Previous studies have reported VTE rates during surgical stays in hospitals or by diagnoses over extended periods without being linked to specific surgical events. The purpose of this project was to assess the potential rate of venous thromboembolism in patients with cancer after a surgical procedure within the immediate posthospital admission period of 30 days, with special emphasis in increased sensitivity of detection. METHODS: Patients with cancer who had a major surgery were identified in a large commercial (non-Medicare) database containing data from more than 22 million patients in the United States. Those with a new diagnosis of VTE within 30 days postadmission for surgery were identified. Additional drug-based criteria were used to vary the VTE definition in a sensitivity analysis. VTE rates are reported for each of the surgical procedure group and overall. RESULTS: The overall 30-day VTE rate was 3.5% with a diagnosis based definition, with rates ranging by procedure from 1.8 to 13.2%. Esophageal resection patients have a VTE rate of 13.2% (95% confidence interval (CI), 8.8-18.9%), whereas prostatectomy patients have a VTE rate of 1.8% (95% CI, 1.5-2.1%). Of the 3.5% of patients with a VTE diagnosis on or before postoperative day 30, 73% of those have the VTE diagnosis by day 14. Another 1.15% is added to the overall VTE rate as the definition sensitivity is increased with outpatient pharmacy claims data. CONCLUSIONS: Using administrative data from large populations provides valuable insight into the potential VTE rates that occur within the 30-day post period after various cancer-related surgeries. The information can be used by surgeons as one component of the benefit-risk decision regarding postoperative VTE prophylaxis in surgical patients.