Kit N. Simpson, DrPH

 The asthma medication ratio (AMR) (number of controller medications / [number of controller medications + number of rescue medications]) can be calculated using claims data. This measure has not previously been studied longitudinally. Our objective is to conduct a longitudinal examination of the AMR in a large national cohort of children with asthma.

 Retrospective analysis of pharmacy and medical claims data.

 Using 2013-2014 TruvenHealth MarketScan data, we identified children with asthma. Beginning with the month of first controller claim, we calculated an AMR for each rolling 3-month period and each rolling 6-month period and examined the proportion who had AMRs classified as low-risk (≥0.5), high-risk (<0.5), and missing for each period. Using logistic regression, we tested how a rolling AMR predicted a child’s hospitalization or emergency department (ED) visit for asthma.

 We identified 197,316 patients aged 2 to 17 years with a claim for a controller. AMRs were relatively stable over time, with the majority of patients remaining in the same AMR category through a 12-month period. Using both the rolling 3-month and 6-month AMRs, a higher proportion of patients with high-risk AMRs (9.6% and 9.5%,

respectively) had an ED visit or hospitalization compared with patients with low-risk (5.0% and 5.7%) and missing (3.5% and 3.2%) AMRs (

<.0001). Using logistic regression, the 3-month AMR is more strongly associated with subsequent ED visit or hospitalization than the 6-month AMR.

AMR-based risk assignment is relatively stable over time. Three-month AMR calculation periods appear to provide the most accurate assessment of risk. Children with missing AMRs likely have inactive asthma and are at the lowest risk for emergent asthma visits.

The asthma medication ratio (AMR) can be calculated using pharmacy claims data and used to identify patients with asthma who are at highest risk for exacerbation in the coming months.

AMR-based risk assignment is relatively stable over time.

In any given time period, 5% to 8% of children with asthma will be at high risk.

Children with no pharmacy claims for either rescue or controller medications are at lowest risk for exacerbation.

Asthma remains a frequent cause of emergency department (ED) visits and hospitalizations among children.

 Controller medications, particularly inhaled corticosteroids (ICSs), are effective at reducing the incidence of these acute care visits for asthma.

 However, these medications continue to be underutilized.

 Using pharmacy claims data to identify patterns of poor controller medication adherence is a potential way to target medication adherence interventions to high-risk children. With this in mind, the asthma medication ratio (AMR; number of controller medication claims / [number of controller medication claims + number of rescue medication claims]) has been developed to measure adherence and assign risk for exacerbation.

 Findings from previous studies have shown that the AMR predicts risk for future exacerbation on the patient level.

 The AMR has the potential to risk stratify large populations of children with asthma in real time, thereby accurately identifying the patients at highest risk for exacerbation and allowing for intervention before exacerbation occurs. This could ultimately prevent costly ED visits and hospitalizations, driving down healthcare costs and improving quality of life attributed to this common pediatric chronic disease.

Despite its potential for risk assessment and risk communication to prevent exacerbations, the AMR has not yet been translated to a point-of-care, real-time monitoring tool. All previous AMR studies have utilized a fixed cross-sectional AMR assessment period, capturing adherence behaviors for 1 specific moment. Before designing and testing an AMR-based intervention, we must better understand the longitudinal behavior of the AMR using rolling periods. This represents the most practical way to calculate the AMR in real time and will allow risk assessment using the most recent claims data available.

Traditionally, studies have relied on the Healthcare Effectiveness Data and Information Set (HEDIS) criteria for persistent asthma to determine who is eligible for AMR measurement. HEDIS is a quality tool and was designed to measure systems, not individuals. HEDIS criteria work well for reporting on the performance of health systems through tools such as the Quality Compass. Previous studies' results have shown that patients often do not meet HEDIS criteria for persistent asthma in consecutive years and that the number of consecutive years of HEDIS qualification was strongly associated with ICS use.

 HEDIS requires up to 1 year of claims monitoring in order to classify a patient as persistent asthmatic. This would lead to missed opportunities for intervention during the measurement year and potential inappropriate interventions the following year. Instead, we propose that the AMR can be measured in all children with a pharmacy claim for an ICS. According to the National Heart, Lung, and Blood Institute (NHLBI) Guidelines for the Diagnosis and Management of Asthma, ICSs are recommended only for children with persistent asthma.

 Therefore, it is unlikely that children with intermittent asthma would be identified using this criterion.

The objective of this study was to examine the longitudinal behavior of the AMR among a large national cohort of privately insured children with asthma, including a comparison of the predictive accuracy of a 3-month rolling AMR with that of a 6-month rolling AMR, as well as determining the proportion of patients in each risk category at any given time.

In order to identify all children for whom the AMR would be a potentially valid risk assessment tool, we first identified all asthma medication claims from the Truven Health MarketScan pharmacy claims databases for 2013 and 2014 using National Drug Code numbers. Medications were then categorized as rescue medications or controller medications. Patients aged 2 to 17 years with any claim for an ICS-containing medication were eligible for inclusion in our study cohort. Because we took an alternative approach to identifying the cohort of children eligible for AMR measurement, we assessed the proportion of our cohort that met HEDIS persistent asthma criteria and determined in any given AMR measurement period how many patients would qualify as at high risk for exacerbation but not as persistent asthmatic according to HEDIS criteria.

We defined each patient’s study index date as the date of his or her first ICS-containing medication claim in the study period. Patients with fewer than 360 (12 × 30) days of continuous insurance enrollment after their index date and patients with a diagnosis of cystic fibrosis (

International Classification of Diseases, Ninth Revision

] code 277.XX) at any time during the study period were excluded. We then identified all inpatient and ED visit claims with a primary diagnosis of asthma (

 code 493.XX) for each patient in the cohort. Only those claims representing visits that occurred after the patient’s index date were retained in the final analytical data set. Covariates used in this analysis include patient age (defined as age at index date, operationalized continuously and in age categories), sex, geographic region, and season of index date (winter, January-March; spring, April-June; summer, July-September; fall, October-December). MarketScan does not include a race variable.

Using the formula of number of controller medication claims/(number of controller medication claims + number of rescue medication claims), an AMR was calculated for each patient in each month from the index date to study month 12. AMRs can range from 0 (only rescue medication claims) to 1 (only controller medication claims) or be “missing” (no rescue or controller medication claims) for any given period. Leukotriene-receptor modifiers were included as controllers in the AMR calculation if there was not also an ICS-containing medication claim that month, but they were not included as index controllers. This decision was made to ensure that our cohort represented children with persistent asthma who received first-line guideline-recommended therapy for that diagnosis. Oral albuterol was not included as a rescue medication.

Similar to previous studies, we first calculated fixed 12-month, 6-month, and 3-month AMRs for each patient for the 12 months after the index date. Therefore, each patient has one 12-month AMR, 2 fixed 6-month AMRs, and 4 fixed 3-month AMRs. Next, we calculated rolling AMRs for each patient. Rolling 3-month AMRs were calculated using months 1 to 3, 2 to 4, 3 to 5, and so on. Rolling 6-month AMRs were calculated using months 1 to 6, 2 to 7, 3 to 8, and so on.

Based on previous study results, AMRs were classified as high-risk (<0.5), low-risk (≥0.5), or missing for each calculation period. Because of our definition of index date, the AMR values for the first period (those that include month 1) are artificially inflated. Identifying this phenomenon influenced further analytical decisions.

Population-level AMR distribution through 12 study months was plotted using stacked bar charts. Patients in each category (high-risk, low-risk, and missing) for the first analyzable rolling 6-month and 3-month period were tracked at the population level over the course of the year. We also determined the proportion of patients who remained in the same risk category from one month to the next.

To identify differences by patient characteristics,

AMR classification changes over time were also assessed by age group, season of index date, and category of index controller (ICS vs ICS/long-acting β agonist [LABA]).

ED visits and hospitalizations with a primary diagnosis of asthma (

 code 493.XX) were identified. Having any ED visit or hospitalization for asthma in a given period was the primary dichotomous outcome for this study. Future studies should include more in-depth analyses of the rolling AMR’s relationship to this outcome as well as individual outcomes of ED visits, hospitalizations, and oral steroid dispensing events.

To begin to understand how patients with missing AMRs should be classified in future interventional studies, we compared proportions of patients with any ED visit or hospitalization for asthma among those classified as having high-risk, low-risk, and missing AMRs in the first analyzable rolling 3-month and 6-month periods (months 2-4 and months 2-7, respectively). Chi-square tests were used to identify any statistically significant differences.

Determining the Relative Strength of Association of 3-Month Versus 6-Month Rolling AMRs With Events

Simple logistic regression models with the outcome of ED visit or hospitalization in 3-month and 6-month outcome windows were built for rolling 3-month AMRs and rolling 6-month AMRs. Comparisons of odds ratios (ORs) between the 3-month AMRs and 6-month AMRs were used to quantify strength of association to help inform the decision of which calculation strategy to use in subsequent analyses.

Of the 9.5 million children aged 2 to 17 years present in the 2013 MarketScan data, 197,316 patients had least 1 claim for an ICS or ICS/LABA and at least 360 days of continuous enrollment after their index date. Of these patients, 60% were male and the mean age was 8.8 years; 36% were aged 2 to 6 years, 41% aged 7 to 12, and 23% aged 13 to 17. Forty-seven percent of assigned index dates were in the winter (January-March). Eighty-three percent of children had an ICS as their index controller. Ultimately, 4.5% of patients had at least 1 ED visit or hospitalization with a primary diagnosis of asthma within 18 months of their study index date (

HEDIS Categorization of Patients in Cohort

Forty-two percent of the patients in our cohort qualified as persistent asthmatic using the HEDIS criteria. The majority of the 58% who would not qualify as such under HEDIS criteria (“non-HEDIS”) are represented in the missing AMR category in any given AMR measurement period. For example, in months 2 to 4, 85,152 of the 114,320 non-HEDIS patients had a missing AMR; 26,569 had a low-risk AMR; and 2599 had a high-risk AMR (

Rolling 3-Month Versus Rolling 6-Month AMR

Using a rolling 3-month AMR calculation period and excluding the month 1 values due to the issue of AMR inflation, an average of 5% of the cohort were identified as high-risk in each calculation period, while 59% had a missing AMR (no asthma prescriptions filled during the calculation period) (

). Using a rolling 6-month AMR calculation period and excluding the month 1 values, an average of 8% of the population were identified as high-risk in each calculation period and an average of 45% had a missing AMR (

). Moving from a 3- to a 6-month calculation period significantly reduced the proportion of patients with missing AMRs in each given AMR calculation period. In regression analysis with the outcome variable of any ED visit or hospitalization for asthma, the 3-month AMRs had a stronger predictive ability than the 6-month AMRs (OR for 3-month AMR in months 2-4 with 3-month outcome window, 2.5; 95% CI, 2.1-2.9; OR for 6-month AMR in months 2-7 with 3-month outcome window, 1.8; 95% CI, 1.6-2.1). 

 presents complete results through the entire study period.

The issue of AMR inflation with our index date assignment is demonstrated graphically (

). Because time is not accounted for in the AMR, even 1 month with a controller fill can significantly inflate a patient’s AMR for the following 11 months.

There was a slightly higher proportion of children 13 years and older in the high-risk AMR category compared with younger children (

). A higher proportion of children with a winter index date maintained low-risk AMRs throughout the year (

). Children whose index controller was an ICS/LABA rather than an ICS were more likely to have a low-risk AMR throughout the year, indicating better adherence to controller medication therapy (

Do Children Stay in the Same Category Throughout the Year? 

To determine if early risk-category assignment held throughout the year or if children frequently bounced in and out of categories, we followed children in each group (low-risk, high-risk, and missing, based on their assignment in the first analyzable period) through the year. The majority of children with a missing AMR in the first analyzable period continued to have missing AMRs through month 12. Similarly, the majority who were initially low-risk remained low-risk through month 12. The biggest departures from original classification occurred in the high-risk group, with only 20% (in the 3-month approach) and 39% (in the 6-month approach) remaining high-risk through month 12 (

). Analyzed another way, of patients who had a high-risk AMR in any given calculation period, an average of 68% had a high-risk AMR in the following calculation period. Of patients with a low-risk AMR in any given period, an average of 84% had a low-risk AMR in the following calculation period. Finally, of patients with a missing AMR in any given period, an average of 90% had a missing AMR in the following calculation period.

With such a large proportion of the population having missing AMRs, it was important to begin to understand how to handle these patients. The fact that they had no albuterol claims suggests that they are at low risk for exacerbation, but could they actually be at high risk and either using albuterol from previous months or not recognizing their symptoms? To help answer this question, we used each patient’s classification from the first analyzable period and calculated the proportion of patients in each category with any emergent event in study months 1 through 18. The patients with missing AMRs had the lowest proportion of events in both approaches (3.5% based on 3-month AMR classification and 3.2% based on 6-month AMR classification). This was significantly lower than the proportion with events in both the high-risk and low-risk AMR categories (

<.0001). This finding suggests that those children with missing AMRs are likely to be children with inactive asthma at low risk for exacerbation (

This longitudinal analysis of the AMR in a large cohort of privately insured children with asthma supports the rolling AMR calculation as a practical approach to risk assessment. This approach is superior to a cross-sectional fixed-AMR calculation period approach as it allows for risk category assignment based on the most recent available claims data. When applied to this cohort of privately insured children with asthma, the rolling 3-month calculation approach identified approximately 5% of the population as being at high risk for exacerbation in any given period.

As the AMR calculation period decreases (from 12 to 6 to 3 months), a larger proportion of patients have missing AMRs in any given period. To have a missing AMR, the patient must not have any claims for rescue or controller medications in the AMR calculation period. The higher proportion of patients with missing AMRs in the 3-month approach suggests that the 6-month AMR calculation approach might be superior to the 3-month approach. However, further investigation supports the 3-month approach. First, we demonstrated that children with missing AMRs are less likely to have an ED visit or hospitalization for asthma compared with children with both low-risk and high-risk AMRs. These children appear to have relatively inactive asthma, illustrated by their absence of asthma medication claims and low rate of emergent care visits. This raises the idea that some children do not necessarily remain persistent asthmatics for long periods, reflecting variation in their asthma control. They may require controller medications most of the time, but they may also have periods of disease inactivity where they do fine without controller medications. Recognizing these patients clinically will be a challenge, however. We suggest that children with missing AMRs can be treated like children with low-risk AMRs in future interventional studies. Second, our regression analysis illustrates that the 3-month AMR has a stronger relationship to the outcome of ED visit or hospitalization for asthma compared with the 6-month AMR. However, both are statistically significant, suggesting that a 6-month AMR approach would be valid and acceptable.

Differences in adherence patterns between patients with ICS versus those with ICS/LABA raise the question of whether or not these 2 populations can be handled the same way in an AMR-based intervention study. There are several potential reasons that the latter group has better controller medication adherence than the ICS group. The NHLBI Guidelines for the Diagnosis and Management of Asthma recommend subspecialist consultation for any child requiring “Step 4” therapy, which includes ICS/LABAs.

 Therefore, these children are likely to have more severe baseline disease and to have been seen by a subspecialist than are children on ICS therapy alone. The cumulative medication adherence messaging that ICS/LABA patients receive might be more extensive and more effective than the messaging that ICS patients receive.

Because of our method of index date assignment, our study highlights the issue of AMR inflation. In a rolling approach with shorter AMR calculation periods, an initial month with a controller claim would be dropped as soon as the next month’s claims data are available, allowing for a more accurate AMR-based risk assessment. There is less potential for inflation with shorter rolling calculation periods.

Articles

This longitudinal examination of the asthma medication ratio in a national sample of children has determined the predictive accuracy of a rolling 3-month ratio.

A Longitudinal Examination of the Asthma Medication Ratio in Children

: To evaluate whether attending diabetes group visits (GVs) leads to lower medical care charges for inadequately insured patients with type 2 diabetes mellitus (DM). 

: Data were abstracted from financial records for 186 patients with uncontrolled type 2 DM randomized to receive care in GVs or usual care for 12 months. Mann-Whitney tests for differences of means for outpatient visits (primary and specialty care), emergency department (ED) visits, and inpatient stays were performed. Separate charge models were developed for primary and specialty outpatient visits. Because GV adherence is potentially dependent on unobserved patient characteristics, treatment effect models of outpatient charges and specialty care visits were estimated using maximum likelihood methods.

: Mann-Whitney test results indicated that GV patients had reduced ED and total charges but more outpatient charges than usual care patients. Ordinary least squares estimations confirmed that GVs increased outpatient visit charges; however, controlling for endogeneity by estimating a treatment effect model of outpatient visit charges showed that GVs statistically significantly reduced outpatient charges (P <.001). Estimation of a separate treatment effect model of specialty care visits confirmed that GV effects on outpatient visit charges occurred via a reduction in specialty care visits.

: After controlling for endogeneity via estimation of a treatment effect model, GVs statistically significantly reduced outpatient visit charges. Estimation of a separate treatment effect model of specialty care visits indicated that GVs likely substitute for more expensive specialty care visits.

Various organizations are using group visits for their patients with chronic diseases. The literature to date has not shown this type of healthcare delivery model to lower the cost of care.

In this study, by estimating a treatment effect model of charges and specialty group visits, we found a statistically significant savings in outpatient charges due to reduction in the use of more expensive specialty visits among a group visit population with diabetes mellitus. 

This information should be taken into account when considering group visits for an organization.

During these times of tightly controlled resources, healthcare organizations endeavor to deliver efficient and effective care to patients with type 2 diabetes mellitus (DM) consistent with American Diabetes Association (ADA) standards of care.

 The group visit (GV) model, initially developed in a managed care setting to improve efficiency and throughput of patients by increasing access and decreasing backlogs of patients awaiting appointments, is promising. Previous studies

 in managed care have shown GVs to be less costly and at least as effective as usual care in terms of quality. However, a recent review of the literature did not find that GVs substantially reduced costs for individuals with DM.

Research on GVs remains in its infancy. Although more than a dozen articles

 describing GVs have been published, only 6 randomized controlled trials

have been reported on to date. None of the literature regarding GVs for DM has shown notable cost results. Herein, we evaluate whether GVs can lower healthcare utilization outpatient charges for patients with DM. We also investigate whether the lack of statistically significant findings in previous studies could have been due to potential endogeneity of the GV variable in cost models.

This study was conducted at the Adult Primary Care Center, Medical University of South Carolina, serving approximately 6000 inadequately insured patients (predominantly of minority races/ethnicities) in Charleston. Using previously described procedures,

 patients with uncontrolled type 2 DM, defined as having a glycosylated hemoglobin (A1C) level of greater than 8.0%, were identified and were invited to participate. Willing patients were enrolled in the study after signing institutional review board&#8211;approved consent documents.

Research assistants blinded to study assignment contacted patients by mail and telephone for baseline, 6-month, and 12-month data collection, assisting those who needed help. Although patients received modest compensation for transportation and for their time at each data collection point, no patients received compensation for medical care. Visit deposit fees for uninsured intervention patients were decreased such that their annual onsite costs were commensurate with those of uninsured control patients ($180 per year,representing $15 per month for intervention patients and $45 per quarter for ADA guideline&#8211;recommended quarterly visits for control patients).

Group visits were modeled after those of the Cooperative Health Care Clinic developed by Beck et al.

 Participating physicians and nurses received onsite training by a senior internist who previously conducted GV training, and all participating staff members received a 3-hour educational session from the coordinator/trainer for Cooperative Health Care Clinic providers and staff.

Patients randomized to the intervention condition met monthly for 12 months in groups of 14 to 17 patients in the same building as the clinic, with the visits functioning as the patients&#8217; primary source of medical care. Physicians provided care for medical needs not amenable to GVs (Papanicolaou smears and rectal examinations) and any care needed between scheduled GVs in one-on-one visits scheduled separately from the GVs.

As with the Cooperative Health Care Clinic model, the schedule provided for each visit to last 2 hours, with 10 to 15 minutes for &#8220;warm-up&#8221; and socialization, 30 to 45 minutes for presentation of a health-related topic (facilitated by the physician or another team member with special expertise), and 60 minutes for one-on-one consultations with the physician. Physicians performed key preventive measures (eg, pneumonia and influenza vaccinations and foot examinations) at the GVs; mammograms and Papanicolaou smears were performed separately from the GVs. Although study participants guided the GV educational topics, the content included core topics appropriate for patients with DM such as nutrition, exercise, and foot care.

Charges for patients attending GVs and those for patients receiving usual care were identical. Because the institution in our study charges a visit deposit fee for patients without insurance, and because group patients were to come monthly as opposed to quarterly for usual care patients (the minimum recommended by the ADA for patients with type 2 DM), the institution decreased the visit deposit fee for uninsured intervention patients such that it was comparable to what uninsured usual care patients would pay during 1 year. Therefore, neither group paid more in visit deposit fees than the other. Otherwise, charges for the patients were the same, and both groups had to reconcile their accounts accordingly; Medicare, Medicaid, and insured patients did not have to pay the visit deposit fees. All patient charges were based on the time that patients spent with the physician one-on-one, following evaluation and management guidelines. Other healthcare utilization and charges (outpatient visits, ED visits, and inpatient stays) were collected from billing records in the Medical University of South Carolina system.

Charges (portioned into outpatient visits, ED visits, and inpatient stays) were collected at the end of the study period and included all resources used in the clinic, associated ED, and hospital. Mann-Whitney tests were performed to analyze differences in the mean charges by service (outpatient visits, ED visits, and inpatient stays). If 2 groups tested have the same distribution, the Mann-Whitney test determines the differences in the means and the medians.

 Separate charge models were then estimated for outpatient visits; ED visits and inpatient stays contained too few nonzero observations to reliably estimate models. Outpatient visit models include controls for payer, Charlson score, and distance to provider, as well as a binary indicator of GV treatment. We included distance to provider in the charge models as binary indicators of whether patients lived within 10, 20, or 30 miles of the provider (with the omitted category being >30 miles) because patients living farther away from the provider may have been more likely to use outpatient services from other providers whose charges were not captured in our data. The Charlson score, a widely accepted measure of illness severity based on International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis and procedure codes in claims data, should capture the effects of differences in the severity of illness among patients with DM.

While first estimating the outpatient charge model using ordinary least squares, we also hypothesized that, when unobserved characteristics of the patients correlate with both the intervention (GVs) and the outcome (healthcare costs), a potential problem known as endogeneity can bias ordinary least squares results. In many randomized control trials, treatment assignment, assessment, and termination and dosage are controlled for by the researcher (exogenous). This is necessary for statistical tests of between-group differences in patient outcomes to be unbiased.

 Patients in our study were randomized to treatment modality (GVs or usual care), but the intensity (dosage) of their treatment is determined largely by the patient, who may choose not to attend all of the GVs or not to stay for the entire duration of the GV. This choice was likely to be affected by the treatment arm to which the patient was randomized, creating endogeneity of the GV variable in the charge model. When endogeneity exists, it can be controlled for by estimating a treatment effect model based on Heckman control function.

 Endogeneity arises in this case because the treatment (GVs) is correlated with the error term in the outcome (charges) equation. The treatment effect model simultaneously estimates equations for the likelihood of treatment (participation in GVs) and the outcome of the treatment (charges for various health service categories). This simultaneous estimation allows the elimination of endogeneity, although with the trade-off of making the assumption that the error terms are jointly normally distributed. Maximum likelihood techniques were used to estimate the model using STATA 9.0 (StataCorp LP, College Station, Tex), which provides a command (

To control for potential endogeneity, we reestimated the outpatient charge model using the treatment effect model that simultaneously estimates the likelihood of GV participation and outpatient charges. We initially ran a probit model to determine which factors were likely to affect GV participation and should be included in the GV participation likelihood equation in the treatment effect model. We ran ordinary least squares for the outpatient charge model to do the same for the charge equation in the treatment effect model. We included drive time as a continuous measure in the GV participation part of the model and included payer, Charlson score, and binary indicators for distance to provider in the outpatient charge models. This is a logical specification because patients are likely to be sensitive to distance when choosing an outpatient provider. To further explore our findings relative to outpatient charges in the treatment effect model, we estimated a treatment effect model for specialty care visits.

Mann-Whitney test results show that GV patients had 34.7% higher outpatient expenditures, 49.1% lower ED expenditures, and 30.2% lower total expenditures compared with those of the control group (

). Our initial ordinary least squares with robust standard errors estimates of the unadjusted outpatient charge model showed a statistically significant effect of GV treatment in outpatient care, with a positive marginal effect of $699.52 (

). Based on these initial estimates, it seemed that GV treatment increased outpatient costs by $699.52 per patient per year. Although these findings were consistent with previous literature on GVs for patients with DM,

 we questioned the unbiasedness of the results because of the potential of unobserved patient characteristics to affect adherence to the intervention.

The results for the treatment effect model of outpatient charges are given in Table 2. Although we found a statistically significant and marginally positive effect on GVs in the outpatient cost model that did not correct for endogeneity, the treatment effect model showed a statistically significant marginally negative effect of GV treatment on outpatient charges of $3065.47. To understand how GV treatment reduced outpatient charges, we estimated a treatment effect model of specialty care visits; the results are summarized in 

. We found that GV treatment leads to a reduction of 4.15 specialty care visits.

In our study of GVs for inadequately insured patients with type 2 DM, we demonstrate that, after controlling for endogeneity of the GV variable, GV treatment statistically significantly lowers outpatient charges by decreasing specialty care visits. This may be because the longer duration of GVs compared with that of a typical primary care encounter gives the provider more time to address process-of-care indicators and screening guidelines than the mean 16.5-minute primary care encounter in the United States.

 Being seen on a monthly basis provides patients with more frequent contact with their physicians and gives them more opportunities to ask questions, while giving providers more opportunities to address process-of-care indicators in a systematic fashion. In addition, when providers care for patients in groups, they are delivering consistent messages to multiple patients at once rather than giving the same message multiple times.

The results we obtained were all under the control of the provider team. On the other hand, charge outcomes are much more under the control of the patients and depend on their following lifestyle guidelines and adhering to medication regimens. Therefore, it is necessary to control for unobserved patient characteristics in estimating the charge model. One of the strengths of this study is that we had 6 different provider teams conducting GVs for 12 months. Because our findings are not the result of a single provider, we can be certain that the effect of GV treatment is not provider dependent.

One possible weakness of our study design is that providers participated in both arms of the study; therefore, there may have been contamination in that providers may have adopted some of the GV strategies (eg, GV educational content and methods for fulfilling process-of-care indicators) for usual care patients. We did not randomize providers (which could have been an alternative study design to prevent contamination) because it was unrealistic and unlikely in an office practice that any provider would only deliver care in GVs. Furthermore, we considered it a fair comparison that the same providers treated patients in both arms. It is noteworthy that, after the 12-month formal study period, all of the GV providers and their patients have chosen to continue with GVs.

Another potential weakness of our study is that only local inpatient outpatient and ED charges were captured in our data. If patients sought providers outside of the system, these were not captured. However, we believe that by controlling for distance to provider in the utilization and charge models, this effect has been reduced. 

This cost study of GVs among inadequately insured patients with type 2 DM showed statistically significant reductions in outpatient charges after controlling for endogeneity of the GV variable in the charge model via a treatment effect model. Because the GV model of care is an intervention that depends on patient adherence, we hypothesized and found evidence of endogeneity of the GV variable. Therefore, we believe that future studies on GVs should consider the potential for endogeneity in estimating the effect of GV treatment on healthcare utilization and charges.

: From the Departments of Medicine (DEC, DB), Health Administration and Policy (CED, KNS), and Psychiatry and Behavioral Sciences (KMM), Medical University of South Carolina, Charleston. 

: The authors (DEC, CED, KMM, KNS, DB) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

: Concept and design (DEC, CED, KMM); acquisition of data (DEC, KMM, KNM); analysis and interpretation of data (DEC, CED, KMM, KNS, DB); drafting of the manuscript (DEC, CED, KMM); critical revision of the manuscript (DEC, CED, KMM); statistical analysis (CED, KNS); and obtaining funding (DEC, KMM).

: Dawn E. Clancy, MD, MSCR, Maybank Internal Medicine, 3312 Maybank Hwy, Ste A, Johns Island, SC 29455. E-mail: 

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Among a diabetes group visit population, this study found substantial savings in outpatient charges due to reduction in the use of more expensive specialty visits.

Kit N. Simpson, DrPH

Articles

A Longitudinal Examination of the Asthma Medication Ratio in Children

Do Diabetes Group Visits Lead to Lower Medical Care Charges?