Andrea DeVries, PhD

 To evaluate the relationship between cost sharing for blood glucose testing strips and glycemic control rates.

 A retrospective observational study using medical and pharmacy claims data integrated with laboratory glycated hemoglobin (A1C) values for patients using insulin and testing strips. A new user study design was utilized to identify individuals from 14 commercial US health plans who filled testing strips with assumed intention to monitor blood glucose.

Patients were divided into low (<20% of annual testing strip cost; n = 3575) and high (≥20%; n = 3580) cost-sharing categories. We compared the likelihood of patients in low and high cost-sharing categories achieving glycemic control (A1C <8.0%) through modified Poisson regression models.

Patients with low cost sharing for testing strips had higher rates of control than those with high cost sharing (58.1% vs 50.3%; 

<.001). Low cost sharing was associated with greater probability of glycemic control (adjusted risk ratio [aRR], 1.14; 95% CI, 1.09-1.20; 

<.0001). Glycemic control was more likely for patients in areas with median household income greater than $60,000 versus less than $40,000 (aRR, 1.16; 95% CI, 1.07-1.25; 

<.01) and greater than $80,000 versus less than $40,000 (aRR, 1.18; 95% CI, 1.06-1.32; 

 We found a statistically significant correlation between cost sharing for testing strips and better A1C control for patients using insulin medication. Lower cost sharing for testing strips can remove a barrier to diabetes self-management and may lead to improved glycemic control at the population level. Future efforts should study the potential benefits of reducing diabetic complications and associated cost savings.

Lower cost sharing for blood glucose testing strips can remove a barrier to self-management of diabetes and potentially improve glycemic control.

Patients with low cost sharing were likely to achieve glycemic control 58.1% of the time versus 50.3% for those with high cost sharing.

Despite filling more testing strips than patients with high cost sharing, those with low cost sharing had lower out-of-pocket costs.

Given these results and the benefits of glycemic control in patients with diabetes, payers may want to consider placing glucose testing strips in lower cost-sharing tiers and including monitoring of testing strip fills as part of the care management process.

Diabetes management aims to maintain glycated hemoglobin (A1C) levels as close to the normal range as possible, and self-monitoring of blood glucose (SMBG) levels is an important tool to help patients achieve this goal.

 Based on existing evidence describing the effectiveness of SMBG for managing glucose levels in patients with insulin-dependent diabetes,

 the American Diabetes Association and others recommend SMBG at least 3 times daily.

 However, up to 23% of patients with insulin-dependent diabetes do not regularly monitor their glucose levels.

A recent claims analysis found that glucose test utilization varied according to patients’ type of health insurance, suggesting that differences in coverage and reimbursement affected SMBG.

 Out-of-pocket (OOP) costs for testing supplies, as an additional financial burden for patients with diabetes who may be taking multiple medications that are each subject to cost sharing, have been shown to be an important barrier to regular SMBG practices.

 However, a large cross-sectional study in Canada reported that implementing quantity limits for reimbursement of testing strips was not related to worsened short-term clinical outcomes.

 That study did not separately analyze the impact on insulin users.

Other than this study, few studies have quantified the relationship between cost sharing for testing strips and A1C values, most likely due to a lack of available data sources that include cost sharing information and A1C levels. Published cost analyses have focused on utilization of testing supplies and associated costs.

 The few studies that have focused on A1C levels were based on small patient samples.

 Our study evaluated the relationship between cost sharing for diabetic testing supplies and A1C levels among patients using insulin, because patients on insulin are more likely to benefit from SMBG than patients using oral antidiabetic drugs (OADs).

 We hypothesized that patients with low cost sharing for testing strips would be more likely to attain glycemic control.

Administrative medical and pharmacy claims data integrated with laboratory test results were queried from the HealthCore Integrated Research Environment, a repository of fully adjudicated claims data for approximately 25 million Blue Cross and Blue Shield health plan members across the United States. The A1C laboratory values were obtained from national reference laboratories. All study data were kept de-identified to safeguard patient confidentiality; researchers only accessed a limited dataset, devoid of individual patient identifiers, which exempted this study from investigational review board review per 45 CFR 164.514(e), Other Requirements Relating to Uses and Disclosures of Protected Health Information.

This retrospective cohort analysis focused on patients with diabetes using insulin. The study population consisted of commercially insured patients with 2 years of continuous medical and pharmacy eligibility during the study period (January 1, 2009, through December 31, 2013). Patients with diabetes were identified if they had at least 1 medical claim for 

International Classification of Diseases, Ninth Revision, Clinical Modification 

Patients were identified as new users of diabetic testing strips if they had at least 1 testing strip prescription filled in the intake period from January 1, 2010, through December 31, 2012, with no prior fill within the previous year. As our study aim was to examine the impact on glycemic control of low versus high cost sharing for testing strips vis-à-vis initiation of testing strip use, we employed the new user study design for 2 reasons.

 First, prevalent users of testing strips are “survivors” of early SMBG and their inclusion could introduce bias, as risk for outcome (glycemic control) varies with time. Second, covariates for testing strip users at study entry could plausibly be affected by the use of the testing strips themselves. Because observational studies cannot exhaustively identify and adjust for all possible covariates, inclusion of prevalent users of testing strips could introduce confounding. Patients were required to have at least 1 year of continuous medical and pharmacy eligibility both before (baseline) and after (follow-up) the initial testing strip fill. Additional age- (18-75 years) and condition-specific inclusion and exclusion criteria were adapted from the Comprehensive Diabetes Care guidelines from the National Committee for Quality Assurance (NCQA) (n = 164,456).

Among these patients, those with at least 1 pharmacy claim with an insulin medication fill during the study period and an insulin fill within 30 days from the start date of testing strip fill (index date) were identified (n = 30,445). Patients were required to have at least 1 A1C test result within the 1-year follow-up (postindex) period as the primary measure of glycemic control. A total of 7155 (23.5% of 30,445) patients met these criteria, forming the final study sample.

Patient cost sharing for blood glucose testing strips was calculated as the OOP cost percentage of total testing strip costs, created by dividing OOP costs by total testing strip costs and multiplying by 100%. The OOP cost was the sum of co-pays, coinsurances, and deductibles paid by the patient over the 1-year period from the first testing strip fill. The total testing strip cost was the sum of OOP and health plan—paid amounts over the same period. We chose to use cost-sharing percentages rather than dollar amounts because the latter would reflect more on adherence differences instead of benefit design differences, as patients with more testing fills (better adherence) are likely to pay more out of pocket than those with fewer fills (lower adherence).

Using the median cost-sharing percentage (20%) in our final sample, we generated 2 study groups: the low cost-sharing group (OOP cost percentage below the median) and the high cost-sharing group (OOP cost percentage at or above the median).

Existing comorbid conditions and resource utilization were assessed during the baseline period; A1C outcomes, utilization, and cost measures were assessed during the follow-up period. The primary clinical outcome was the most recent A1C laboratory value during the follow-up period. The number and percentage of patients achieving glycemic control per NCQA guidelines (ie, A1C <8.0%) were analyzed. For the subset of patients whose baseline lab results were available, we also reported their baseline A1C lab values.

Descriptive statistics, including means (± SD) and frequencies, were reported for continuous and categorical data, respectively. Differences in descriptive characteristics between the low versus high cost-sharing groups were assessed with Pearson’s χ

 tests or nonparametric tests for numeric data. All statistical analyses were conducted with SAS 9.4 software (SAS Institute; Cary, North Carolina). Alpha was set at 0.05.

The association between cost sharing and glycemic control was first examined in the aforementioned descriptive (bivariate) analysis, followed by multivariable analysis in all patients and then a sensitivity analysis in a subset of patients. In the multivariable analysis in all patients, the likelihood of patients achieving glycemic control in the low cost-sharing group was compared with that in the high cost-sharing group through relative risk ratios, estimated from the modified Poisson model with the associated standard errors obtained from the sandwich method.

 Patient characteristics, such as age, gender, and area median income, were adjusted for in the multivariable model. In the sensitivity analysis, among the subset of patients with available baseline A1C values, we modeled the change in the proportion of patients achieving glycemic control before and after initiation of testing strip use with a difference-in-differences (DID) analysis through generalized estimating equations.

As an exploration of the impact of testing strip use on glycemic results, we examined the proportions of patients achieving glycemic control in groups with different numbers of testing strip fills. This was conducted in the subset of patients with available baseline A1C values.

A total of 7155 patients were included in the analysis: 3575 in the low cost-sharing group and 3580 in the high cost-sharing group (

). The mean age of patients in the 2 groups was similar (49 years and 50 years, respectively; 

<.01), although a higher proportion of those in the high cost-sharing group were aged 45 to 64 years (67.3% of high cost-sharing vs 58.7% of low cost-sharing). The low cost-sharing group resided in areas with a higher median household income (based on the zip codes for the areas in which the patients resided) compared with the high cost-sharing group (mean = $56,473 vs $53,025, respectively; 

Baseline use of medications for metabolic disorders was high in both groups (74.9% high and 72.1% low cost-sharing; 

<.05). A greater proportion of patients with high cost sharing had baseline OAD use compared with those with low cost sharing (57.4% high vs 48.6% low cost sharing; 

Testing Strip Utilization and Other Postindex Characteristics

Testing strip utilization, as measured by the mean number of glucose testing strip fills during follow-up, was higher in the low cost-sharing group than in the high cost-sharing group (5.7 fills vs 5.0 fills, respectively; 

). More patients with low cost sharing had 4 or more testing strip fills compared with those with high cost sharing (65.2% vs 55.1%, respectively; 

<.001). Not surprisingly, patients in the low cost-sharing group also had more days’ supply of testing strips (mean = 200 days in low vs 152 in high cost-sharing group; 

<.001) and more testing strips dispensed (mean = 1054 in low vs 604 in high; 

<.001). Although the low cost-sharing group used more testing strips than the high cost-sharing group, their mean OOP costs were significantly lower ($89 vs $225, respectively; 

<.001). During follow-up, the mean number of insulin fills was similar between the groups (9.1 vs 8.9 fills, respectively; 

From descriptive (bivariate) analysis, in the year after initial testing strip use, patients in the low cost-sharing group achieved a higher rate of glycemic control compared with those in the high cost-sharing group (58.1% vs 50.3%, respectively; 

In the multivariable analysis, after adjusting for patient characteristics, such as age, comorbidities, and area median income, low cost sharing was associated with a greater probability of achieving glycemic control (adjusted risk ratio [aRR], 1.14; 95% CI, 1.09-1.20; 

<.0001), as was older age (65-75 years vs 18-44 years: aRR, 1.17; 95% CI, 1.08-1.27; 

). Additionally, glycemic control was more likely among patients residing in areas with a median household income higher than both $60,000 (aRR, 1.16; 95% CI, 1.07-1.25; 

<.01) and $80,000 (aRR, 1.18; 95% CI, 1.06-1.32; 

<.01) compared with those residing in areas with a median income lower than $40,000.

In the sensitivity analysis among patients who had baseline A1C values, both cost-sharing groups exhibited declines in mean A1C levels from baseline to follow-up periods, although the difference was slightly greater for those with high cost sharing (19.1% vs 18.9%, respectively) (

). The multivariable DID regression model showed that cost sharing for testing strips had a statistically significant association with changes in glycemic control when comparing the baseline period to the follow-up period across low versus high cost-sharing groups (

). However, the magnitude of change as a proportion of patients attaining glycemic control was modest (DID estimate, 0.91; 95% CI, 0.85-0.98) (eAppendix Table 3).

Testing Strip Utilization and A1C Results

In the subgroup of patients with pre-index A1C values (n = 4592), the number of testing strip fills correlated with glycemic control. The proportion of patients achieving glycemic control rose with increasing numbers of testing strip fills both in the baseline and follow-up periods (

). When examining the changes in percentages of patients achieving glycemic control from baseline to follow-up, greater improvement was found among patients who had 3 or more testing strip fills (20% absolute increase in percentage achieving glycemic control) than among those with 2 fills or 1 fill (17% and 14%, respectively).

In this real-world analysis, patients treated with insulin who had low cost sharing for testing strips were likely to achieve A1C control 58.1% of the time compared with 50.3% among patients with high cost sharing. Those in the low cost-sharing group filled more testing strips while paying lower OOP costs than those in the high cost-sharing group. After adjusting for patient characteristics, the relationship held: those with low cost sharing were 14% more likely to achieve A1C control than those with high cost sharing. In the sensitivity analysis on the subset of patients with both baseline and follow-up A1C tests, the relationship between low cost sharing and A1C control was still statistically significant.

A previous cross-sectional analysis of the effect of co-pay changes found that testing supply utilization decreased when co-pays were increased by a California healthcare organization.

 However, when co-pays were eliminated and testing strips were offered free of charge, utilization rates unexpectedly were unaffected, regardless of income level or poverty status.

 When lower co-pays were reintroduced after the free period, utilization decreased marginally while adherence to SMBG recommendations remained unchanged.

 Notably, the relationship between testing strip usage and A1C levels was not examined in that study, which is a gap filled by the current analysis.

In addition to the cost-sharing relationship, older age was related to a higher likelihood of glycemic control. This is consistent with literature indicating that the effect of age remained even after adjustment for other factors, such as duration of diabetes.

 Another striking relationship we observed was the association between A1C control rates and area median income. The regression analysis showed that patients in areas where the median income was higher than both $60,000 and $80,000 were 16% and 18% more likely, respectively, to achieve A1C control relative to individuals in areas with a median income lower than $40,000. Prior published studies were conducted in a pediatric population

 and all relied on self-reported income levels.

 Other investigations into a relationship between area median income and glycemic control were conducted in low-income populations, which presupposes an effect of income on achieving A1C goals.

 By quantifying the relationship between area income and glycemic control, our results are unique and provide more specific information to decision makers.

The benefits of A1C control to patients with diabetes are well established. Individuals who are able to achieve A1C levels near to those of the population without diabetes are at reduced risk for diabetes complications.

 In addition to the clinical benefits for patients, there is a short-term (business-focused) justification for increasing A1C control rates within a population with diabetes. Increasingly, provider groups are being measured (and rewarded accordingly) on quality metrics via accountable care organization arrangements and patient-centered medical care models. Health plans are also rated by NCQA guidelines, which identify A1C control as a key metric for diabetes care. Lastly, Health Insurance Marketplace plans are also being scored and rated according to quality measures, including A1C control. Identifying benefit design—related strategies to improve adherence to diabetes self-management is highly important across numerous stakeholders.

A number of possible actions could be taken based on these findings. First, payers may consider including diabetes testing strips in lower cost-sharing tiers or communicating to members the preferred testing strips that are in the lower cost-sharing tiers. Meanwhile, healthcare providers should ensure that the glucose meters prescribed to patients are compatible with the testing strips covered under the lowest tier formulary of the patient’s health plan. Patients may also be encouraged to use glucose monitor kits that work for the lower-cost testing strips.

Another action may be for providers or payers to include monitoring of testing strip fills as part of the care management processes for people taking insulin medication. In this study, 10.7% of those in the low cost-sharing group and 17.7% in the high cost-sharing group did not follow through with a second testing strip fill after their first testing strip fill. In cases such as these, an outreach effort, such as refill reminders, could be triggered to highlight the importance of frequent testing.

As with all studies relying on administrative and clinical lab data, this study had several limitations. A filled prescription for diabetic testing strips does not mean that the patient used all of the strips. We may be overestimating the relationship between cost sharing and actual testing; however, this presumably would not mean an over&shy;estimation of the relationship between cost sharing and A1C control.

Additionally, patients may have obtained testing strips through sources other than their health plan (eg, by purchasing them over the counter or with cash). In this case, we may be underestimating actual strip use. We mitigated this by excluding individuals with no fills, as we were unable to determine whether they were nonadherent or had procured testing strips through an alternate source.

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Patients with diabetes receiving insulin treatment with lower cost sharing for blood glucose testing strips were more likely to achieve glycemic control than those with higher cost sharing.

Leveraging Benefit Design for Better Diabetes Self-Management and A1C Control

To assess baseline quality metrics, healthcare utilization, and costs of commercially insured patients treated at practices participating in a patient-centered medical home (PCMH) pilot.

Observational cohort study utilizing claims data for patients treated at PCMH and non-PCMH practices.

Data from Empire Blue Cross and Blue Shield, 1 of 14 plans in the HealthCore Integrated Research Database, were queried for patients identified based on visits to PCMH and non-PCMH practices during 2007-2008; outcome metrics were formulated from the baseline calendar year, 2009. Differences in healthcare utilization were determined with

tests. Regression models were used to test differences in adjusted emergency department (ED) use, inpatient services, and costs.

The study included 31,032 PCMH and 350,015 non-PCMH patients. Among PCMH-treated patients, diabetics had higher rates of glycated hemoglobin testing; cardiovascular disease patients had higher rates of testing and better low-density lipoprotein cholesterol control; imaging rates for low back pain were lower; among pediatric patients, inappropriate antibiotic use for nonspecific or viral respiratory infections was lower. PCMH-treated adults and children had 12% and 23% lower odds of hospitalization, and required 11% and 17% fewer ED services, respectively, than non-PCMH patients. Risk-adjusted total per member per month costs were 8.6% and 14.5% lower for PCMH-treated pediatric and adult patients, respectively (

PCMH practices in this pilot were associated with better preventive health, higher levels of disease management, and lower resource utilization and costs in 2009 compared with practices not pursuing PCMH status.

Baseline quality metrics, healthcare utilization, and costs for commercially insured patients treated at practices participating in a patient-centered medical home (PCMH) pilot were assessed.

		Compared with patients treated in practices not pursuing PCMH status, patients treated within PCMH practices had equal or better care management; lower rates of high-cost diagnostic imaging procedures, emergency department visits, and hospitalizations; and lower resource utilization and costs.

		Providers using the PCMH model may have an opportunity to offer enhanced patient care even during early transformation.

Amidst dwindling economic resources and shrinking healthcare budgets, patient-centered medical homes (PCMHs) are gaining traction as an innovative approach for improving healthcare quality while reducing costs.

 the PCMH model seeks to improve the continuum of patient care8 and drive efficiency by reconfiguring the primary care system.

 First introduced for pediatric patients in the 1960s,

 the PCMH concept now enjoys more support because of its broader potential in chronic disease management

and for the enhancement of the vital triple outcomes: quality, cost, and the experiences of patients, their families, and providers.

The PCMH approach seeks to replace volume-based financial incentives for providers (eg, reimbursements tied to numbers of visits, laboratory tests, and procedures) with more coordinated care that targets better patient outcomes.

 Practices typically seek recognition from accredited entities such as the National Committee for Quality Assurance (NCQA),

 which requires the satisfaction of a number of well-defined “must-pass” requirements (

 Two prominent practice models have emerged: consultative, in which practices engage external consultants to help them navigate the transformation period; and enhanced chronic care models, which pursue PCMH recognition by enhancing their core capabilities in caring for chronic illnesses.

In a study of 7 PCMH demonstrations, Fields et al showed annual reductions in hospitalizations (6% to 40%) and emergency department (ED) visits (7.3% to 29%), suggesting improvements in quality, and total annual savings ranging from $71 to $640 per patient.

 The practices did not focus on chronic care exclusively, and one, the Colorado Medical Homes for Children, reported total mean annual savings of $169 for all patients versus $530 for patients with chronic conditions.

 Transformation to PCMH status, however, could be a long and complicated process, as Nutting et al observed in a study of the first national PCMH demonstration (36 practices) that spanned from June 2006 to May 2008.

 Apart from payment reforms, PCMH transformation entails numerous adjustments such as learning new or redesigned service delivery models, changed perceptions about patient-provider relationships, and the need for extended periods of external guidance, among others.

Increasing evidence suggests that the PCMH model offers greater advantages to stakeholders than non-PCMH practices in treatment delivery, especially for chronic care. To date, however, much of the available data have been derived from pre-PCMH versus post-PCMH comparisons, which provide a limited picture of changes from baseline to full-fledged PCMH status.

Only a few more than a third of the demonstration projects nationwide used non-PCMH controls in their comparisons,

 and in 7 studies evaluated by Fields et al, only 1 study used a control.

 A control population enhances the robustness and reliability of the results in comparison studies.

The main objective of this study was to compare PCMH practices during their prerecognition phase with non-PCMH practices to assess important quality differences in healthcare delivery and costs that may already be evident during the transformative baseline period. This study, the first of its kind in a large commercially insured population, compared key elements of the vital triple outcomes for PCMH practices, with particular emphasis on quality, including appropriate prevention, screening, and care of common chronic conditions; laboratory evaluations; and cost improvements.

This study compared patients treated within primary care practices classified as PCMH and non-PCMH, and located within the same geographic region. All practices (both PCMH [10] and non-PCMH [202]) were located within the 5 boroughs of New York City and its suburbs in Nassau County, Suffolk County, and Westchester County, were part of the Empire Blue Cross and Blue Shield (BCBS) network, and received payment according to existing negotiated fee schedules. The 10 practices in the PCMH category achieved NCQA recognition in 2010. The PCMH practices employed 247 physicians at 86 different locations. The 202 non-PCMH practices operated from 898 different sites and employed a total of 4048 physicians; non- PCMH practice sizes ranged from 1 to 250 providers.

Data on the patients treated by the practices were drawn from the HealthCore Integrated Research Database, which contains medical and pharmacy claims from 14 commercial healthcare plans in the northeastern, southeastern, mid-Atlantic, Midwestern, and western regions of the United States. Data for 2007 through 2009 were selected for 1 northeastern managed care plan, Empire BCBS, relative to its PCMH initiative that started in August 2010. Data handling in this observational retrospective study complied with the Health Insurance Portability and Accountability Act of 1996.

This study included administrative claims data from 2 periods: (1) the identification period of 2007 to 2008 and (2) the measurement period of calendar year 2009. Administrative claims for calendar years 2007 and 2008 were used for patient-provider attribution; a patient was attributed to the single provider with whom the patient had the highest number of office visits based on the patient’s medical records in 2007 and 2008. Patients attributable to multiple providers were assigned to the provider visited most recently. Patients were then classified as PCMH or non-PCMH according to the PCMH recognition status as of 2010 for the provider to whom they were attributed. Members who had at least 2 visits to a practice that had not achieved PCMH recognition in 2010 were assigned to the non-PCMH control group. Data in the second period, calendar year 2009, were used to examine patients’ outcome metrics.

The study’s inclusion criteria required that the patients be treated at primary care practices located within specific zip codes and that the practices participate in the BCBS network. There were no age restrictions on patients, but members who were older than 65 years at baseline (January 1, 2009) were excluded from the analysis to avoid any service complications or potential missing expenditures associated with Medicare eligibility. Members without continuous eligibility during calendar year 2009 were also excluded from the analysis to avoid any incompleteness in the data set.

The processes and clinical metrics in this analysis focused on appropriate prevention of and care for chronic conditions, derived from a selection of measures in the Healthcare Effectiveness Data and Information Set (HEDIS).

We measured appropriate markers for disease control, where applicable (eg, laboratory tests for glycated hemoglobin [A1C] and low-denlipoprotein cholesterol [LDL-C]), which were adapted from the HEDIS measures (

). Also compared were rates of eye examinations (retinal) and medical attention for nephropathy for diabetic patients, imaging procedures for low back pain that were not supported by appropriate diagnoses, and appropriate testing of children with pharyngitis. Appropriate medication usage was assessed, including antibiotic use among children with nonspecific or viral upper respiratory tract infections and among adults with acute bronchitis, as well as the use of long-term controller medications among patients with persistent asthma. This study also evaluated the rates of inpatient hospitalization and use of ED services, and costs for PCMH and non-PCMH patients.

This analysis assessed 2 types of costs: medical costs only and total costs on a per member per month (PMPM) basis. Allowed medical costs included plan-paid and patient out-of-pocket costs (deductibles, copayments, and coinsurance) associated with medical claims only, which allowed for the inclusion of members who did not receive their pharmacy benefits through BCBS. Total costs represented the aggregate of allowed costs associated with both medical and pharmacy claims, and applied to members who received both their medical and pharmacy benefits from BCBS only. Members with zero PMPM costs were excluded from the cost analyses as these cases could potentially distort the analysis.

 tests were used to assess the statistical significance of any differences in preventive services and care management between the PCMH and non-PCMH cohorts. Statistical models were fitted individually to the pediatric and adult groups. Logistic regression models were used to test differences in adjusted rates of inpatient hospitalizatioin and use of ED services, with covariates including age, sex, health plan type, and Deyo-Charlson comorbidity index (DCI) scores that were calculated from claims between July 1, 2008, and December 31, 2008, depending on their clinical merit and potential importance.

 Differences in costs were analyzed with multivariate generalized linear regression modeling, using gamma distribution and log link function because of its applicability to continuous variables with highly skewed distribution. To risk-adjust costs, multivariate generalized linear regression models were fitted for costs based on non-PCMH cohorts’ data. The cost models included age, sex, health plan type, and DCI score as covariates because of their clinical importance to healthcare utilization.

 Female patients, older patients, and patients with greater comorbidity burdens were likelier to utilize healthcare services. We also adjusted for health plan types (health maintenance organizations, preferred provider organizations, point-of-service plans) to account for potential differences due to benefit design or variations in insurance products. Cost models were used to predict the costs for each patient in the PCMH cohort given an individual’s age, sex, health plan type, and DCI score, and predicted costs were presented as risk-adjusted costs.

 To address outlier issues in the costs data, PMPM caps were applied in accordance with actuarial standards ($8333.33 for pediatrics and $20,833.33 for adults). Statistical analyses were conducted with SAS 9.1 software (SAS Institute Inc, Cary, North Carolina). The alpha level was set at .05 for each test.

Patients’ Clinical and Demographic Characteristics at Baseline

This study included 31,032 patients in the PCMH cohort and 350,015 patients in the non-PCMH cohort, each of which had a greater proportion of females (

The pediatric population (<18 years old) had 14,434 members in the PCMH cohort and 77,810 in the non-PCMH group; mean (±SD) age and sex distributions were similar (

). A greater proportion of the PCMH-treated pediatric patients (51.0%) had pharmacy benefits through BCBS versus 43.5% in the non-PCMH group (

 <.001). Asthma (7.3% and 8.0%), bronchitis (5.7% and 6.0%), and strep throat (10.3% and 7.4%) were among the most common ailments in the PCMH and non-PCMH groups, respectively. In the PCMH cohort, 7.2% had a DCI score of 1 to 2 compared with 6.2% in the non-PCMH cohort; higher DCI scores indicate greater comorbidity burden.

There were 16,598 and 272,205 adults aged 18 to 64 years in the PCMH and non-PCMH cohorts, with a mean (±SD) age of 41.1 (±13.3) and 42.4 (±12.5) years, respectively (

). HMOs and preferred provider organizations were the dominant payers, and more PCMH members had pharmacy benefits compared with the non-PCMH members (

<.001). The most prevalent conditions were hypertension (17.8% and 22.3%), hypercholesterolemia (19.1% and 23.0%), and diabetes (7.9% and 8.3%) in the PCMH and non-PCMH groups, respectively. A DCI severity score of 1 to 2 was reported for 11.9% of the PCMH and 13.1% of the non-PCMH groups (

Patient-centered medical home practices provided better preventive care and disease management with less resource utilization than practices not pursuing PCMH status.

Andrea DeVries, PhD

Articles

Leveraging Benefit Design for Better Diabetes Self-Management and A1C Control

Impact of Medical Homes on Quality, Healthcare Utilization, and Costs